I never thought about it like this because in my head civil rights goes had and hand with human right. Yet I can see that to X that the black man doesn't want enough, doesn't see that they deserve more then just civil rights. Just barely becoming "equal" will never be enough for the 400 years of suffering, we as black people need true reparations.

Napoleon removed Ferdinand from office suddenly and forcefully. He made a government where a monarch was the head, however they were limited by a constitution and democratically elected government.

Washington wants his race to work hard and prosper among the white man, that if they unite they can be beneficial just like in the times they nursed their sons and daughters as slaves and worked endlessly, now as partners, can have a social economic impact

The idea of long term patency in diplomacy is brought up with this article which also stresses how important it is to separate between rhetorical and real policy conduct. It was important because it suggested that leaders may miss genuine engagement chances because of their emotional responses to hostile words. Treisman gives normative recommendations for Western policymakers while giving his viewpoint in a subjective manner. The main conclusions are that effective diplomacy requires an understanding of difference between rhetoric and reality and that judgment should be given importance over response in foreign affairs. This also explains why the way the West reacts to negative comments which either increase tension or create partnership.

Analogy?

Poetic way to describe owning a dog

Getting out of our own head and into the head, or psychology, of the target audience adds depth of writing and appeal.

eLife Assessment

This important study reports that higher genetically predicted BMI is associated with a modestly increased risk of head and neck cancer. The convincing evidence is supported by rigorous Mendelian Randomization approaches, using multiple genetic instruments and models that reduce sensitivity to pleiotropy. However, results from pleiotropy-robust analyses were less consistent, which limits the strength of causal inference. The work will be of interest to researchers studying cancer risk factors and genetic epidemiology.

Reviewer #1 (Public review):

Summary:

The authors have conducted the largest to date Mendelian Randomization (MR) analysis of the association between genetically predicted measures of adiposity and risk of head and neck cancer (HNC) overall and by subsites within HNC. MR uses genetic predictors of an exposure, such as gene variants associated with high BMI or tobacco use, rather than data from individual physical exams or questionnaires and if it can be done in its idealized state, there should be no problems with confounding. Traditional epidemiologic studies have reported a variety of associations between BMI (and a few other measures of adiposity) and risk of HNC that typically differs by the smoking status of the subjects. Those findings are controversial given the complex relationship between tobacco and both BMI and HNC risk. Tobacco smokers are often thinner than no-smokers so this could create an artificial ('confounded') association that may not be fully adjusted away in risk models. The findings of a BMI-HNC association are often attributed to residual confounding and this seems ripe for an MR approach if suitable genetic instrumental variables can be created. Here the authors built a variety of genetic instrumental variables for BMI and other measures of adiposity as well as two instrumental variables for smoking habits and then tested their hypotheses in a large case-controls set of HNC and controls with genetic data.

The authors found that the genetic model for BMI was associated with HNC risk in simple models, but this association disappeared when using models that better accounted for pleiotropy, the condition when genetic variants are associated with more than one trait such as both BMI and tobacco use. When they used both adiposity and tobacco use genetic instruments in a single model, there was a strong association with genetically predicted tobacco use (as is expected) but there was no remaining association with genetic predictors of adiposity. They conclude that high BMI/adiposity is not a risk factor for HNC.

Strengths:

The primary strength was the expansive use of a variety of different genetic instruments for BMI/adiposity/body size along with employing a variety of MR model types, several of which are known to be less sensitive to pleiotropy. They also used the largest case-control sample size to date.

Weaknesses:

The lack of pleiotropy is an unconfirmable assumption of MR and the addition of those models is therefore quite important as this is a primary weakness of the MR approach. Given that concern, I read the sensitivity analyses using pleiotropy-robust models as the main result and in that case, they are more limited in their ability to test their hypothesis as these models do not show a robust BMI instrumental variable association.

Comments on the revised manuscript:

After the first round of review, the authors have improved the manuscript by (1) adding the requested power calculations and adding text to help the reader integrate that additional information; (2) adding the main effects for the tobacco instruments; (3) updating the comparison of their results to the prior literature; (4) and some other edits to the text. They have declined to include the smoking stratified estimates and provide a rationale for this decision that references the potential for collider bias. While true that yet another bias might be introduced, that gets added to the list and the careful reader would know that. Many important questions in cancer etiology can only be addressed via observational approaches and each observational approach has the potential for a long list of biases. The best inference comes from integrating the totality of the data and realizing that most conclusions are subject to updating as we conduct more work and learn more.

Author response:

The following is the authors’ response to the original reviews.

Joint Public Review:

Weaknesses:

The lack of pleiotropy is an unconfirmable assumption of MR, and the addition of those models is therefore quite important, as this is a primary weakness of the MR approach. Given that concern, I read the sensitivity analyses using pleiotropy-robust models as the main result, and in that case, they can't test their hypotheses as these models do not show a BMI instrumental variable association. The other weakness, which might be remedied, is that the power of the tests here is not described. When a hypothesis is tested with an under-powered model, the apparent lack of association could be due to inadequate sample size rather than a true null. Typically, when a statistically significant association is reported, power concerns are discounted as long as the study is not so small as to create spurious findings. That is the case with their primary BMI instrumental variable model - they find an association so we can presume it was adequately powered. But the primary models they share are not the pleiotropy-robust methods MR-Egger, weighted median, and weighted mode. The tests for these models are null, and that could mean a couple of things: (1) the original primary significant association between the BMI genetic instrument was due to pleiotropy, and they therefore don't have a robust model to explore the effects of the tobacco genetic instrument. (2) The power for the sensitivity analysis models (the pleiotropy-robust methods) is inadequate, and the authors share no discussion about the relative power of the different MR approaches. If they do have adequate power, then again, there is no need to explore the tobacco instrument.

Reviewing Editor Comments:

We suggest that the authors add power estimates to assess whether the sample size is sufficient, given the strength and variability of the genetic instruments. It would also be helpful to present effect estimates for the tobacco instruments alone, to clarify their independent contribution and improve the interpretation of the joint models. In addition, the role of pleiotropy should be addressed more clearly, including which model is considered primary. Stratified analyses by smoking status are encouraged, as prior studies indicate that BMI-HNC associations may differ between smokers and non-smokers. Finally, the comparison with previous studies should be revised, as most reported null findings without accounting for tobacco instruments. If this study finds an association, it should not be framed as a replication

We would like to highlight that post-hoc power calculations are often considered redundant since the statistical power estimated for an observed association is directly related to its p-value[1]. In other words, the uncertainty of the association is already reflected in its 95% confidence interval. However, we understand power calculations may still be of interest to the reader, so we have incorporated them in the revised manuscript. We have edited the text as follows (lines 151-155):“Consequently, we used the total R² values to examine the statistical power in our study[42]. However, we acknowledge that the value of post-hoc power calculations is limited, since the statistical power estimated for an observed association is already reflected in the 95% confidence interval presented alongside the point estimate[43].” We have also added supplementary figures 1 and 2.

We can see that when using the latest HEADSpAcE data we were able to detect BMI-HNC ORs as small as 1.16 with 80% power, while the GAME-ON dataset only permitted the detection of ORs as small as 1.26 using the same BMI instruments (Figure B). We have explained these figures in the results section as follows (lines 257-263): “Using the BMI genetic instruments (total R²= 4.8%) and an α of 0.05, we had 80% statistical power to detect an OR as small as 1.16 for HNC risk (Supplementary Figure 1). For WHR (total R²= 3.1%) and WC (total R²= 4.4%), we could detect odds ratios (ORs) as small as 1.20 and 1.17, respectively. This is an improvement in terms of statistical power compared to the GAME-ON analysis published by Gormley et al.[28], for which there was 80% power to detect an OR as small as 1.26 using the same BMI genetic instruments (Supplementary Figure 2).”

The reason we use inverse variance weighted (IVW) Mendelian randomization (MR) to obtain our main results rather than the pleiotropy-robust methods mentioned by the reviewer/editors (i.e., MR-Egger, weighted median and weighted mode) is that the former has greater statistical power than the latter[2]. Hence, instead of focussing on the statistical significance of the pleiotropy-robust analyses, we consider it is of more value to compare the consistency of the effect sizes and direction of the effect estimates across methods. Any evidence of such consistency increases our confidence in our main findings, since each method relies on different assumptions. As we cannot be sure about the presence and nature of horizontal pleiotropy, it is useful to compare results across methods even though they are not equally powered. It is true that our results for the genetically predicted effects of body mass index (BMI) on the risk of head and neck cancer (HNC) differ across methods. This is precisely what led us to question the validity of our main finding (suggesting a positive effect of BMI on HNC risk). We have now clarified this in the methods section of the revised manuscript as advised. Lines 165-171:

“Because the IVW method assumes all genetic variants are valid instruments[44], which is unlikely the case, three pleiotropy-robust two-sample MR methods (i.e., MR-Egger[45], weighted median[46] and weighted mode[47]) were used in sensitivity analyses. When the magnitude and direction of effect estimates are consistent across methods that rely on different assumptions, the main findings are more convincing. As we cannot be sure about the presence and nature of horizontal pleiotropy, it is useful to compare results across methods even if they are not equally powered.”

We understand that the reviewer/editors are concerned that we do not have a robust model to explore the role of tobacco consumption in the link between BMI and HNC. However, we have a different perspective on the matter. If indeed, the main IVW finding for BMI and HNC is due to pleiotropy (since some of the pleiotropy-robust methods suggest conflicting results), then the IVW multivariable MR method is a way to explore the potential source of this bias[3]. We were particularly interested in exploring the role of smoking in the observed association because smoking and adiposity are known to influence each other [4-9] and share a genetic basis[10, 11].

We agree that it would be useful to present the univariable MR effect estimates for smoking behaviour and HNC risk along those obtained using multivariable MR. We have now included the univariable MR estimates for both smoking behaviour variables as a note under Supplementary Table 11 and in the manuscript (lines 316-318): “In univariable IVW MR, both CSI and SI were linked to an increased risk of HNC (CSI OR=4.47 per 1-SD higher CSI, 95%CI 3.31–6.03, p<0.001; SI OR=2.07 per 1-SD higher SI 95%CI 1.60–2.68, p<0.001) (Additional File 2: note in Supplementary Table 11).”

We understand the appeal of conducting stratified MR analyses by smoking status. However, we anticipate such analyses would hinder the interpretation of our findings as they can induce collider bias which could spuriously lead to different effect estimates across strata[12, 13].

We thank the reviewer/editors for their comment regarding the way we frame of our findings. We have now edited the discussion section to highlight our study results are different to those obtained in studies that do not account for smoking behaviour. Lines 398-401: “With a much larger sample (N=31,523, including 12,264 cases), our IVW MR analysis suggested BMI may play a role in HNC risk, in contrast to previous studies. However, our sensitivity analyses implied that causality was uncertain.”

Reviewer #1 (Recommendations for the authors):

The authors do share a table of the percent variance explained of the different genetic instruments, which vary widely, and that table is very welcome because we can get some sense of their utility. The problem is that they don't translate that into a power estimate for the case-control study size that they use. They say that it is the biggest to date, which is good, but without some formal power estimate, it is not particularly reassuring. A framework for MR study power estimates was reported in PMID: 19174578, but that was using very simple MR constructs in use in 2009, and it isn't clear to me if that framework can be used here. That power paper suggests that weak genetic instruments need very large sample sizes, far larger than what is used in the current manuscript. I am unable to estimate the true strength of the instruments used here, and so I am unsure of whether power is an issue or not.

We have now included power calculations in our manuscript to address the reviewer’s concerns. Nevertheless, as mentioned above, post-hoc power calculations are of limited value, as statistical power is already reflected in the uncertainty around the point estimates (the 95% confidence intervals). Hence, it is important to avoid drawing conclusions regarding the likelihood of true effects or false negatives based on these calculations.

Although the hypothesis here is that smoking accounts for the apparent BMI association previously reported for HNC, it would have been preferable to see the estimates for their 2 genetic instruments for tobacco alone. The current results only show the BMI instruments alone and then with the tobacco instruments. I would like to see what the risk estimates are for the tobacco instrument alone, so that I can judge for myself what happens in the joint models. As presented, one can only do that for the BMI instruments.

We thank the reviewer for this comment. The univariable IVW MR estimate of smoking initiation was OR=2.07 (95%CI 1.60 to 2.68, p<0.001), while the one for comprehensive smoking index was OR=4.47 (95%CI 3.31 to 6.03, p<0.001). We have included this information in the manuscript as requested (please see response to reviewing editor above).

On line 319, they write that "We did not find evidence against bias due to correlated pleiotropy..." I find this difficult to parse, but I think it means that they should believe that correlated pleiotropy remains a problem. So again, they seem to see their primary model as compromised, and so do I. This limitation is again stated by the authors on lines 351-352.

We apologise if the wording of the sentence was not easy to understand. When using the CAUSE method, we did not find evidence to reject the null hypothesis that the sharing (correlated pleiotropy) model fits the data at least as well as the causal model. In other words, our CAUSE finding and the inconsistencies observed across our other sensitivity analyses led us to believe that our main IVW MR estimate for BMI-HNC was likely biased by correlated pleiotropy. We believe it is important to explore the source of this bias, which is why we used multivariable MR to investigate the direct effect of BMI on HNC risk while accounting for smoking behaviour.

In the following paragraphs (lines 358-369), the authors state that their findings are consistent with prior reports, but that doesn't seem to be the case if we take their primary BMI instrument as representing the outcome of this manuscript. Here, they find an association between the BMI instrument and HNC risk, but in each of the other papers they present the primary finding was null without the extensive model changes or the aim of accounting for tobacco with another instrument. I don't see that as replication.

This is a good point. We have now edited the discussion of our manuscript to avoid giving the impression that our findings replicate those from studies that do not account for smoking behaviour in their analyses. We have edited lines 384-401 as follows:

“Previous MR studies suggest adiposity does not influence HNC risk[27-29]. Gormley et al.[28] did not find a genetically predicted effect of adiposity on combined oral and oropharyngeal cancer when investigating either BMI (OR=0.89 per 1-SD, 95% CI 0.72–1.09, p=0.26), WHR (OR=0.98 per 1-SD, 95% CI 0.74–1.29, p=0.88) or waist circumference (OR=0.73 per 1-SD, 95% CI 0.52–1.02, p=0.07) as risk factors. Similarly, a large two-sample MR study by Vithayathil et al.[29] including 367,561 UK Biobank participants (of which 1,983 were HNC cases) found no link between BMI and HNC risk (OR=0.98 per 1-SD higher BMI, 95% CI 0.93–1.02, p=0.35). Larsson et al.[27] meta-analysed Vithayathil et al.’s[29] findings with results obtained using FinnGen data to increase the sample size even further (N=586,353, including 2,109 cases), but still did not find a genetically predicted effect of BMI on HNC risk (OR=0.96 per 1-SD higher BMI, 95% CI 0.77–1.19, p=0.69). With a much larger sample (N=31,523, including 12,264 cases), our IVW MR analysis suggested BMI may play a role in HNC risk, in contrast to previous studies. However, our sensitivity analyses implied that causality was uncertain.”

We also deleted part of a sentence in the discussion section, so lines 416-418 now look as follows: “An important strength of our study was that the HEADSpAcE consortium GWAS used had a large sample size which conferred more statistical power to detect effects of adiposity on HNC risk compared to previous MR analyses[27-29].”

On lines 384-386 they note a strength is that this is the largest study to date, but I would reiterate that larger and more powerful does not equate to adequately powered.

This is true. We have included power calculations in the manuscript as requested.

It's well known that different HNC subsites have different etiologies, as they mention on lines 391-392, and it is implicit in their use of data on HPV positive and negative oropharyngeal cancer. They say that they did not find evidence for heterogeneity in this study, but that would only be true for the null BMI instrument. The effect sizes for their smoking instruments are strikingly different between the subsites.

We agree and are sorry for the confusion we may have caused by the way we worded our findings. We have edited the text to clarify that the lack of subsite heterogeneity only applied to our results for BMI/WHC/WC-HNC risk. Lines 418-424 now read as follows:

“Furthermore, the availability of data on more HNC subsites, including oropharyngeal cancers by HPV status, allowed us to investigate the relationship between adiposity and HNC risk in more detail than previous MR studies which limited their subsite analyses to oral cavity and overall oropharyngeal cancers[28, 68]. This is relevant because distinct HNC subsites are known to have different aetiologies[69], although we did not find evidence of heterogeneity across subsites in our analyses investigating the genetically predicted effects of BMI, WHR and WC on HNC risk.”

Finally, the literature on mutational patterns gives us strong reason to believe that HNC caused by tobacco are biologically distinct from tumors not caused by tobacco. The authors report in the introduction that traditional observational studies of BMI and HNC have reported different findings in smokers versus never smokers, so I would assume there is a possibility that the BMI instrument could have different associations with tumors of the tobacco-induced phenotype and tumors with a non-tobacco induced phenotype. I would assume that authors have access to the data on self-reported tobacco use behavior, even if they can't separate these tumors by molecular types. Stratifying their analysis by tobacco users or not might reveal different results with the BMI instrument.

We appreciate the reviewer’s comment. We agree that it would have been interesting to present stratified analyses by smoking status along our main findings. However, we decided against this because of the risk of inducing collider bias in our MR analyses i.e., where stratifying on smoking status may induce spurious associations between the adiposity instruments and confounding factors. Multivariable MR is considered a better way of investigating the direct effects of an exposure (adiposity) on an outcome (HNC) accounting for a third variable (smoking)[14], which is why we opted for this method instead.

References:

(1) Heinsberg LW, Weeks DE: Post hoc power is not informative. Genet Epidemiol 2022, 46(7):390-394.

(2) Burgess S, Butterworth A, Thompson SG: Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013, 37(7):658-665.

(3) Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C et al: Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2019, 4:186.

(4) Morris RW, Taylor AE, Fluharty ME, Bjorngaard JH, Asvold BO, Elvestad Gabrielsen M, Campbell A, Marioni R, Kumari M, Korhonen T et al: Heavier smoking may lead to a relative increase in waist circumference: evidence for a causal relationship from a Mendelian randomisation meta-analysis. The CARTA consortium. BMJ Open 2015, 5(8):e008808.

(5) Taylor AE, Morris RW, Fluharty ME, Bjorngaard JH, Asvold BO, Gabrielsen ME, Campbell A, Marioni R, Kumari M, Hallfors J et al: Stratification by smoking status reveals an association of CHRNA5-A3-B4 genotype with body mass index in never smokers. PLoS Genet 2014, 10(12):e1004799.

(6) Taylor AE, Richmond RC, Palviainen T, Loukola A, Wootton RE, Kaprio J, Relton CL, Davey Smith G, Munafo MR: The effect of body mass index on smoking behaviour and nicotine metabolism: a Mendelian randomization study. Hum Mol Genet 2019, 28(8):1322-1330.

(7) Asvold BO, Bjorngaard JH, Carslake D, Gabrielsen ME, Skorpen F, Smith GD, Romundstad PR: Causal associations of tobacco smoking with cardiovascular risk factors: a Mendelian randomization analysis of the HUNT Study in Norway. Int J Epidemiol 2014, 43(5):1458-1470.

(8) Carreras-Torres R, Johansson M, Haycock PC, Relton CL, Davey Smith G, Brennan P, Martin RM: Role of obesity in smoking behaviour: Mendelian randomisation study in UK Biobank. BMJ 2018, 361:k1767.

(9) Freathy RM, Kazeem GR, Morris RW, Johnson PC, Paternoster L, Ebrahim S, Hattersley AT, Hill A, Hingorani AD, Holst C et al: Genetic variation at CHRNA5-CHRNA3-CHRNB4 interacts with smoking status to influence body mass index. Int J Epidemiol 2011, 40(6):1617-1628.

(10) Thorgeirsson TE, Gudbjartsson DF, Sulem P, Besenbacher S, Styrkarsdottir U, Thorleifsson G, Walters GB, Consortium TAG, Oxford GSKC, consortium E et al: A common biological basis of obesity and nicotine addiction. Transl Psychiatry 2013, 3(10):e308.

(11) Wills AG, Hopfer C: Phenotypic and genetic relationship between BMI and cigarette smoking in a sample of UK adults. Addict Behav 2019, 89:98-103.

(12) Coscia C, Gill D, Benitez R, Perez T, Malats N, Burgess S: Avoiding collider bias in Mendelian randomization when performing stratified analyses. Eur J Epidemiol 2022, 37(7):671-682.

(13) Hamilton FW, Hughes DA, Lu T, Kutalik Z, Gkatzionis A, Tilling K, Hartwig FP, Davey Smith G: Non-linear Mendelian randomization: evaluation of effect modification in the residual and doubly-ranked methods with simulated and empirical examples. Eur J Epidemiol 2025.

(14) Sanderson E, Davey Smith G, Windmeijer F, Bowden J: An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol 2019, 48(3):713-727.

Author response:

Notes to Editors

We previously received comments from three reviewers at Biological Psychiatry, which we have addressed in detail below. The following is a summary of the reviewers’ comments along with our responses.

Reviewers 1 and 2 sought clearer justification for studying the cognition-mental health overlap (covariation) and its neuroimaging correlates. In the revised manuscripts, we expanded the Introduction and Discussion to explicitly outline the theoretical implications of investigating this overlap with machine learning. We also added nuance to the interpretation of the observed associations.

Reviewer 1 raised concerns about the accessibility of the machine learning methodology for readers without expertise in this field. We revised the Methods section to provide a clearer, step-by-step explanation of our machine learning approach, particularly the two-level machine learning through stacking. We also enhanced the description of the overall machine learning design, including model training, validation, and testing.

In response to Reviewer 2’s request for deeper interpretation of our findings and stronger theoretical grounding, we have expanded our discussion by incorporating a thorough interpretation of how mental health indices relate to cognition, material that was previously included only in supplementary materials due to word limit constraints. We have further strengthened the theoretical justification for our study design, with particular emphasis on the importance of examining shared variance between cognition and mental health through the derivation of neural markers of cognition. Additionally, to enhance the biological interpretation of our results, we included new analyses of feature importance across neuroimaging modalities, providing clearer insights into which neural features contribute most to the observed relationships.

Notably, Reviewer 3 acknowledged the strength of our study, including multimodal design, robust analytical approach, and clear visualization and interpretation of results. Their comments were exclusively methodological, underscoring the manuscript’s quality.

Reviewer 1:

The authors try to bridge mental health characteristics, global cognition and various MRI-derived (structural, diffusion and resting state fMRI) measures using the large dataset of UK Biobank. Each MRI modality alone explained max 25% of the cognitionmental health covariance, and when combined together 48% of the variance could be explained. As a peer-reviewer not familiar with the used methods (machine learning, although familiar with imaging), the manuscript is hard to read and I wonder what the message for the field might be. In the end of the discussion the authors state '... we provide potential targets for behavioural and physiological interventions that may affect cognition', the real relevance (and impact) of the findings is unclear to me.

Thank you for your thorough review and practical recommendations. We appreciate your constructive comments and suggestions and hope our revisions adequately address your concerns.

Major questions

(1) The methods are hard to follow for people not in this specific subfield, and therefore, I expect that for readers it is hard to understand how valid and how useful the approach is.

Thank you for your comment. To enhance accessibility for readers without a machine learning background, we revised the Methods section to clarify our analyses while retaining important technical details needed to understand our approach. Recognizing that some concepts may require prior knowledge, we provide detailed explanations of each analysis step, including the machine learning pipeline in the Supplementary Methods.

Line 188: “We employed nested cross-validation to predict cognition from mental health indices and 72 neuroimaging phenotypes (Fig. 1). Nested cross-validation is a robust method for evaluating machine-learning models while tuning their hyperparameters, ensuring that performance estimates are both accurate and unbiased. Here, we used a nested cross-validation scheme with five outer folds and ten inner folds.

We started by dividing the entire dataset into five outer folds. Each fold took a turn being held out as the outerfold test set (20% of the data), while the remaining four folds (80% of the data) were used as an outer-fold training set. Within each outer-fold training set, we performed a second layer of cross-validation – this time splitting the data into ten inner folds. These inner folds were used exclusively for hyperparameter tuning: models were trained on nine of the inner folds and validated on the remaining one, cycling through all ten combinations.

We then selected the hyperparameter configuration that performed best across the inner-fold validation sets, as determined by the minimal mean squared error (MSE). The model was then retrained on the full outer-fold training set using this hyperparameter configuration and evaluated on the outer-fold test set, using four performance metrics: Pearson r, the coefficient of determination ( R²), the mean absolute error (MAE), and the MSE. This entire process was repeated for each of the five outer folds, ensuring that every data point is used for both training and testing, but never at the same time. We opted for five outer folds instead of ten to reduce computational demands, particularly memory and processing time, given the substantial volume of neuroimaging data involved in model training. Five outer folds led to an outer-fold test set at least n = 4 000, which should be sufficient for model evaluation. In contrast, we retained ten inner folds to ensure robust and stable hyperparameter tuning, maximising the reliability of model selection.

To model the relationship between mental health and cognition, we employed Partial Least Squares Regression (PLSR) to predict the g-factor from 133 mental health variables. To model the relationship between neuroimaging data and cognition, we used a two-step stacking approach [15–17,61] to integrate information from 72 neuroimaging phenotypes across three MRI modalities. In the first step, we trained 72 base (first-level) PLSR models, each predicting the g-factor from a single neuroimaging phenotype. In the second step, we used the predicted values from these base models as input features for stacked models, which again predicted the g-factor. We constructed four stacked models based on the source of the base predictions: one each for dwMRI, rsMRI, sMRI, and a combined model incorporating all modalities (“dwMRI Stacked”, “rsMRI Stacked”, “sMRI Stacked”, and “All MRI Stacked”, respectively). Each stacked model was trained using one of four machine learning algorithms – ElasticNet, Random Forest, XGBoost, or Support Vector Regression – selected individually for each model (see Supplementary Materials, S6).

For rsMRI phenotypes, we treated the choice of functional connectivity quantification method – full correlation, partial correlation, or tangent space parametrization – as a hyperparameter. The method yielding the highest performance on the outer-fold training set was selected for predicting the g-factor (see Supplementary Materials, S5).

To prevent data leakage, we standardized the data using the mean and standard deviation derived from the training set and applied these parameters to the corresponding test set within each outer fold. This standardization was performed at three key stages: before g-factor derivation, before regressing out modality-specific confounds from the MRI data, and before stacking. Similarly, to maintain strict separation between training and testing data, both base and stacked models were trained exclusively on participants from the outer-fold training set and subsequently applied to the corresponding outer-fold test set.

To evaluate model performance and assess statistical significance, we aggregated the predicted and observed g_factor values from each outer-fold test set. We then computed a bootstrap distribution of Pearson’s correlation coefficient (_r) by resampling with replacement 5 000 times, generating 95% confidence intervals (CIs) (Fig. 1). Model performance was considered statistically significant if the 95% CI did not include zero, indicating that the observed associations were unlikely to have occurred by chance.”

(2) If only 40% of the cognition-mental health covariation can be explained by the MRI variables, how to explain the other 60% of the variance? And related to this %: why do the author think that 'this provides us confidence in using MRI to derive quantitative neuromarkers of cognition'?

Thank you for this insightful observation. Using the MRI modalities available in the UK Biobank, we were able to account for 48% of the covariation between cognition and mental health. The remaining 52% of unexplained variance may arise from several sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research from our group and others has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank.

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the Research Domain Criteria (RDoC) framework, brain circuits represent only one level of neurobiological analysis relevant to cognition. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. We have now incorporated these considerations into the Discussion section.

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Regarding our confidence in using MRI to derive neural markers for cognition, we base this on the predictive performance of MRI-based models. As we note in the Discussion (Line 554: “Consistent with previous studies, we show that MRI data predict individual differences in cognition with a medium-size performance (r ≈ 0.4) [15–17, 28, 61, 67, 68].”), the medium effect size we observed (r ≈ 0.4) agrees with existing literature on brain-cognition relationships, confirming that machine learning leads to replicable results. This effect size represents a moderate yet meaningful association in neuroimaging studies of aging, consistent with reports linking brain to behaviour in adults (Krämer et al., 2024; Tetereva et al., 2022). For example, a recent meta-analysis by Vieira and colleagues (2022) reported a similar effect size (r = 0.42, 95% CI [0.35;0.50]). Our study includes over 15000 participants, comparable to or more than typical meta-analyses, allowing us to characterise our work as a “mega-analysis”. And on top of this predictive performance, we found our neural markers for cognition to capture half of the cognition-mental health covariation, boosting our confidence in our approach.

Krämer C, Stumme J, da Costa Campos L, Dellani P, Rubbert C, Caspers J, et al. Prediction of cognitive performance differences in older age from multimodal neuroimaging data. GeroScience. 2024;46:283–308.

Tetereva A, Li J, Deng JD, Stringaris A, Pat N. Capturing brain cognition relationship: Integrating task‐based fMRI across tasks markedly boosts prediction and test‐retest reliability. NeuroImage. 2022;263:119588.

(3) Imagine that we can increase the explained variance using multimodal MRI measures, why is it useful? What does it learn us? What might be the implications?

We assume that by variance, Reviewer 1 referred to the cognition-mental health covariation mentioned in point 2) above.

If we can increase the explained cognition-mental health covariation using multimodal MRI measures, it would mean that we have developed a reasonable neuromarker that is close to RDoC’s neurobiological unit of analysis for cognition. RDoC treats cognition as one of the main basic functional domains that transdiagnostically underly mental health. According to RDoC, mental health should be studied in relation to cognition, alongside other domains such as negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. RDoC further emphasizes that each domain, including cognition, should be investigated not only at the behavioural level but also through its neurobiological correlates. This means RDoC aims to discover neural markers of cognition that explain the covariation between cognition and mental health. For us, we approach the development of such neural markers using multimodal neuroimaging. We have now explained the motivation of our study in the first paragraph of the Introduction.

Line 43: “Cognition and mental health are closely intertwined [1]. Cognitive dysfunction is present in various mental illnesses, including anxiety [2, 3], depression [4–6], and psychotic disorders [7–12]. National Institute of Mental Health’s Research Domain Criteria (RDoC) [13,14] treats cognition as one of the main basic functional domains that transdiagnostically underly mental health. According to RDoC, mental health should be studied in relation to cognition, alongside other domains such as negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. RDoC further emphasizes that each domain, including cognition, should be investigated not only at the behavioural level but also through its neurobiological correlates. In this study, we aim to examine how the covariation between cognition and mental health is reflected in neural markers of cognition, as measured through multimodal neuroimaging.”

More specific issues:

Introduction

(4) In the intro the sentence 'in some cases, altered cognitive functioning is directly related to psychiatric symptom severity' is in contrast to the next sentence '... are often stable and persist upon alleviation of psychiatric symptoms'.

Thank you for pointing this out. The first sentence refers to cases where cognitive deficits fluctuate with symptom severity, while the second emphasizes that core cognitive impairments often remain stable even during symptom remission. To avoid this confusion, we have removed these sentences.

(5) In the intro the text on the methods (various MRI modalities) is not needed for the Biol Psych readers audience.

We appreciate your comment. While some members of our target audience may have backgrounds in neuroimaging, machine learning, or psychiatry, we recognize that not all readers will be familiar with all three areas. To ensure accessibility for those who are not familiar with neuroimaging, we included a brief overview of the MRI modalities and quantification methods used in our study to provide context for the specific neuroimaging phenotypes. Additionally, we provided background information on the machine learning techniques employed, so that readers without a strong background in machine learning can still follow our methodology.

(6) Regarding age of the study sample: I understand that at recruitment the subjects' age ranges from 40 to 69 years. At MRI scanning the age ranges between about 46 to 82. How is that possible? And related to the age of the population: how did the authors deal with age in the analyses, since age is affecting both cognition as the brain measures?

Thank you for noticing this. In the Methods section, we first outline the characteristics of the UK Biobank cohort, including the age at first recruitment (40-69 years). Table 1 then shows the characteristics of participant subsamples included in each analysis. Since our study used data from Instance 2 (the second in-person visit), participants were approximately 5-13 years older at scanning, resulting in the age range of 46 to 82 years. We clarified the Table 1 caption as follows:

Line 113: “Table 1. Demographics for each subsample analysed: number, age, and sex of participants who completed all cognitive tests, mental health questionnaires, and MRI scanning”

We acknowledge that age may influence cognitive and neuroimaging measures. In our analyses, we intentionally preserved age-related variance in brain-cognition relationships across mid and late adulthood, as regressing out age completely would artificially remove biologically meaningful associations. At the same time, we rigorously addressed the effects of age and sex through additional commonality analyses quantifying age and sex contributions to the relationship between cognition and mental health.

As noted by Reviewer 1 and illustrated in Figure 8, age and sex shared substantial overlapping variance with both mental health and neuroimaging phenotypes in explaining cognitive outcomes. For example, in Figure 8i, age and sex together accounted for 43% of the variance in the cognition-mental health relationship:

(2.76 + 1.03) / (2.76 + 1.03 + 3.52 + 1.45) ≈ 0.43

Furthermore, neuromarkers from the all-MRI stacked model explained 72% of this age/sexrelated variance:

2.76 / (2.76 + 1.03) ≈ 0.72

This indicates that our neuromarkers captured a substantial portion of the cognition-mental health covariation that varied with age and sex, highlighting their relevance in age/sex-sensitive cognitive modeling.

In the Methods, Results, and Discussion, we say:

Methods

Line 263: “To understand how demographic factors, including age and sex, contribute to this relationship, we also conducted a separate set of commonality analyses treating age, sex, age2, age×sex, and age2×sex as an additional set of explanatory variables (Fig. 1).”

Results

Line 445: “Age and sex shared substantial overlapping variance with both mental health and neuroimaging in explaining cognition, accounting for 43% of the variance in the cognition-mental health relationship. Multimodal neural marker of cognition based on three MRI modalities (“All MRI Stacked”) explained 72% of this age and sex-related variance (Fig. 8i–l and Table S21).”

Discussion

Line 660: “We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.”

(7) Regarding the mental health variables: where characteristics with positive value (e.g. happiness and subjective wellbeing) reversely scored (compared to the negative items, such as anxiety, addition, etc)?

We appreciate you noting this. These composite scores primarily represent standard clinical measures such as the GAD-7 anxiety scale and N-12 neuroticism scale. We did not reverse the scores to keep their directionality, therefore making interpretability consistent with the original studies the scores were derived from (e.g., Davis et al., 2020; Dutt et al., 2022). Complete descriptive statistics for all mental health indices and detailed derivation procedures are provided in the Supplementary Materials (S2). On Page 6, Supplementary Methods, we say:

Line 92: “Composite mental health scores included the Generalized Anxiety Disorder (GAD-7), the Posttraumatic Stress Disorder (PTSD) Checklist (PCL-6), the Alcohol Use Disorders Identification Test (AUDIT), the Patient Health Questionnaire (PHQ-9) [12], the Eysenck Neuroticism (N-12), Probable Depression Status (PDS), and the Recent Depressive Symptoms (RDS-4) scores [13, 14]. To calculate the GAD-7, PCL-6, AUDIT, and PHQ-9, we used questions introduced at the online follow-up [12]. To obtain the N-12, PDS, and RDS-4 scores [14], we used data collected during the baseline assessment [13, 14].

We subcategorized depression and GAD based on frequency, current status (ever had depression or anxiety and current status of depression or anxiety), severity, and clinical diagnosis (depression or anxiety confirmed by a healthcare practitioner). Additionally, we differentiated between different depression statuses, such as recurrent depression, depression triggered by loss, etc. Variables related to self-harm were subdivided based on whether a person has ever self-harmed with the intent to die.

To make response scales more intuitive, we recorded responses within the well-being domain such that the lower score corresponded to a lesser extent of satisfaction (“Extremely unhappy”) and the higher score indicated a higher level of happiness (“Extremely happy”). For all questions, we assigned the median values to “Prefer not to answer” (-818 for in-person assessment and -3 for online questionnaire) and “Do not know” (-121 for in-person assessment and -1 for online questionnaire) responses. We excluded the “Work/job satisfaction” question from the mental health derivatives list because it included a “Not employed” response option, which could not be reasonably coded.

To calculate the risk of PTSD, we used questions from the PCL-6 questionnaire. Following Davis and colleagues [12], PCL-6 scores ranged from 6 to 29. A PCL-6 score of 12 or below corresponds to a low risk of meeting the Clinician-Administered PTSD Scale diagnostic criteria. PCL-6 scores between 13 and 16 and between 17 and 25 are indicative of an increased risk and high risk of PTSD, respectively. A score of above 26 is interpreted as a very high risk of PTSD [12, 15]. PTSD status was set to positive if the PCL-6 score exceeded or was equal to 14 and encompassed stressful events instead of catastrophic trauma alone [12].

To assess alcohol consumption, alcohol dependence, and harm associated with drinking, we calculated the sum of the ten questions from the AUDIT questionnaire [16]. We additionally subdivided the AUDIT score into the alcohol consumption score (questions 1-3, AUDIT-C) and the score reflecting problems caused by alcohol (questions 4-10, AUDIT-P) [17]. In questions 2-10 that followed the first trigger question (“Frequency of drinking alcohol”), we replaced missing values with 0 as they would correspond to a “Never” response to the first question.

An AUDIT score cut-off of 8 suggests moderate or low-risk alcohol consumption, and scores of 8 to 15 and above 15 indicate severe/harmful and hazardous (alcohol dependence or moderate-severe alcohol use disorder) drinking, respectively [16, 18]. Subsequently, hazardous alcohol use and alcohol dependence status correspond to AUDIT scores of ≥ 8 and ≥ 15, respectively. The “Alcohol dependence ever” status was set to positive if a participant had ever been physically dependent on alcohol. To reduce skewness, we logx+1-transformed the AUDIT, AUDIT-C, and AUDIT-P scores [17].”

Davis KAS, Coleman JRI, Adams M, Allen N, Breen G, Cullen B, et al. Mental health in UK Biobank – development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych Open. 2020;6:e18.

Dutt RK, Hannon K, Easley TO, Griffis JC, Zhang W, Bijsterbosch JD. Mental health in the UK Biobank: A roadmap to selfreport measures and neuroimaging correlates. Hum Brain Mapp. 2022;43:816–832.  

(8) In the discussion section (page 23, line 416-421), the authors refer to specific findings that are not described in the results section > I would add these findings to the main manuscript (including the discussion / interpretation).

We appreciate your careful reading. We agree that our original Results section did not explicitly describe the factor loadings for mental health in the PLSR model, despite discussing their implications later in the paper. We needed to include this part of the discussion in the Supplementary Materials to meet the word limit of the original submission. However, in response to your suggestion, we have now added the results regarding factor loadings to the Results section. We also moved the discussion of the association between mental health features and general cognition from the Supplementary Material to the manuscript’s Discussion.

Results

Line 298: “On average, information about mental health predicted the g-factor at  R²_mean = 0.10 and r_mean \= 0.31 (95% CI [0.291, 0.315]; Fig. 2b and 2c and Supplementary Materials, S9, Table S12). The magnitude and direction of factor loadings for mental health in the PLSR model allowed us to quantify the contribution of individual mental health indices to cognition. Overall, the scores for mental distress, alcohol and cannabis use, and self-harm behaviours relate positively, and the scores for anxiety, neurological and mental health diagnoses, unusual or psychotic experiences, happiness and subjective well-being, and negative traumatic events relate negatively to cognition.”

Discussion

Line 492: “Factor loadings derived from the PLSR model showed that the scores for mental distress, alcohol and cannabis use, and self-harm behaviours related positively, and the scores for anxiety, neurological and mental health diagnoses, unusual or psychotic experiences, happiness and subjective well-being, and negative traumatic events related negatively to the g-factor. Positive PLSR loadings of features related to mental distress may indicate greater susceptibility to or exaggerated perception of stressful events, psychological overexcitability, and predisposition to rumination in people with higher cognition [72]. On the other hand, these findings may be specific to the UK Biobank cohort and the way the questions for this mental health category were constructed. In particular, to evaluate mental distress, the UK Biobank questionnaire asked whether an individual sought or received medical help for or suffered from mental distress. In this regard, the estimate for mental distress may be more indicative of whether an individual experiencing mental distress had an opportunity or aspiration to visit a doctor and seek professional help [73]. Thus, people with better cognitive abilities and also with a higher socioeconomic status may indeed be more likely to seek professional help.

Limited evidence supports a positive association between self-harm behaviours and cognitive abilities, with some studies indicating higher cognitive performance as a risk factor for non-suicidal self-harm. Research shows an inverse relationship between cognitive control of emotion and suicidal behaviours that weakens over the life course [73,74]. Some studies have found a positive correlation between cognitive abilities and the risk of nonsuicidal self-harm, suicidal thoughts, and suicidal plans that may be independent of or, conversely, affected by socioeconomic status [75,76]. In our study, the magnitude of the association between self-harm behaviours and cognition was low (Fig. 2), indicating a weak relationship.

Positive PLSR loadings of features related to alcohol and cannabis may also indicate the influence of other factors. Overall, this relationship is believed to be largely affected by age, income, education, social status, social equality, social norms, and quality of life [79–80]. For example, education level and income correlate with cognitive ability and alcohol consumption [79,81–83]. Research also links a higher probability of having tried alcohol or recreational drugs, including cannabis, to a tendency of more intelligent individuals to approach evolutionary novel stimuli [84,85]. This hypothesis is supported by studies showing that cannabis users perform better on some cognitive tasks [86]. Alternatively, frequent drinking can indicate higher social engagement, which is positively associated with cognition [87]. Young adults often drink alcohol as a social ritual in university settings to build connections with peers [88]. In older adults, drinking may accompany friends or family visits [89,90]. Mixed evidence on the link between alcohol and drug use and cognition makes it difficult to draw definite conclusions, leaving an open question about the nature of this relationship.

Consistent with previous studies, we showed that anxiety and negative traumatic experiences were inversely associated with cognitive abilities [90–93]. Anxiety may be linked to poorer cognitive performance via reduced working memory capacity, increased focus on negative thoughts, and attentional bias to threatening stimuli that hinder the allocation of cognitive resources to a current task [94–96]. Individuals with PTSD consistently showed impaired verbal and working memory, visual attention, inhibitory function, task switching, cognitive flexibility, and cognitive control [97–100]. Exposure to traumatic events that did not reach the PTSD threshold was also linked to impaired cognition. For example, childhood trauma is associated with worse performance in processing speed, attention, and executive function tasks in adulthood, and age at a first traumatic event is predictive of the rate of executive function decline in midlife [101,102]. In the UK Biobank cohort, adverse life events have been linked to lower cognitive flexibility, partially via depression level [103].

In agreement with our findings, cognitive deficits are often found in psychotic disorders [104,105]. We treated neurological and mental health symptoms as predictor variables and did not stratify or exclude people based on psychiatric status or symptom severity. Since no prior studies have examined isolated psychotic symptoms (e.g., recent unusual experiences, hearing unreal voices, or seeing unreal visions), we avoid speculating on how these symptoms relate to cognition in our sample.

Finally, negative PLSR loadings of the features related to happiness and subjective well-being may be specific to the study cohort, as these findings do not agree with some previous research [107–109]. On the other hand, our results agree with the study linking excessive optimism or optimistic thinking to lower cognitive performance in memory, verbal fluency, fluid intelligence, and numerical reasoning tasks, and suggesting that pessimism or realism indicates better cognition [110]. The concept of realism/optimism as indicators of cognition is a plausible explanation for a negative association between the g-factor and friendship satisfaction, as well as a negative PLSR loading of feelings that life is meaningful, especially in older adults who tend to reflect more on the meaning of life [111]. The latter is supported by the study showing a negative association between cognitive function and the search for the meaning of life and a change in the pattern of this relationship after the age of 60 [112]. Finally, a UK Biobank study found a positive association of happiness with speed and visuospatial memory but a negative relationship with reasoning ability [113].”

(9) In the discussion section (page 24, line 440-449), the authors give an explanation on why the diffusion measure have limited utility, but the arguments put forward also concern structural and rsfMRI measures.

Thank you for this important observation. Indeed, the argument about voxel-averaged diffusion components (“… these metrics are less specific to the properties of individual white matter axons or bundles, and instead represent a composite of multiple diffusion components averaged within a voxel and across major fibre pathways”) could theoretically apply across other MRI modalities. We have therefore removed this point from the discussion to avoid overgeneralization. However, we maintain our central argument about the biological specificity of conventional tractography-derived diffusion metrics as their particular sensitivity to white matter microstructure (e.g., axonal integrity, myelin content) may make them better suited for detecting neuropathological changes than dynamic cognitive processes. This interpretation aligns with the mixed evidence linking these metrics to cognitive performance, despite their established utility in detecting white matter abnormalities in clinical populations (e.g., Bergamino et al., 2021; Silk et al., 2009). We clarify this distinction in the manuscript.

Line 572: “The somewhat limited utility of diffusion metrics derived specifically from probabilistic tractography in serving as robust quantitative neuromarkers of cognition and its shared variance with mental health may stem from their greater sensitivity and specificity to neuronal integrity and white matter microstructure rather than to dynamic cognitive processes. Critically, probabilistic tractography may be less effective at capturing relationships between white matter microstructure and behavioural scores cross-sectionally, as this method is more sensitive to pathological changes or dynamic microstructural alterations like those occurring during maturation. While these indices can capture abnormal white matter microstructure in clinical populations such as Alzheimer’s disease, schizophrenia, or attention deficit hyperactivity disorder (ADHD) [117–119], the empirical evidence on their associations with cognitive performance is controversial [114, 120–126].”

Bergamino M, Walsh RR, Stokes AM. Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer’s disease. Sci Rep. 2021;11:6990.

Silk TJ, Vance A, Rinehart N, Bradshaw JL, Cunnington R. White-matter abnormalities in attention deficit hyperactivity disorder: a diffusion tensor imaging study. Hum Brain Mapp. 2009;30:2757–2765.

Reviewer 2:

This is an interesting study combining a lot of data to investigate the link between cognition and mental health. The description of the study is very clear, it's easy to read for someone like me who does not have a lot of expertise in machine learning.

We thank you for your thorough review and constructive feedback. Your insightful comments have helped us identify conceptual and methodological aspects that required improvement in the manuscript. We have incorporated relevant changes throughout the paper, and below, we address each of your points in detail.

Comment 1: My main concern with this manuscript is that it is not yet clear to me what it exactly means to look at the overlap between cognition and mental health. This relation is r=0.3 which is not that high, so why is it then necessary to explain this overlap with neuroimaging measures? And, could it be that the relation between cognition and mental health is explained by third variables (environment? opportunities?). In the introduction I miss an explanation of why it is important to study this and what it will tell us, and in the discussion I would like to read some kind of 'answer' to these questions.

Thank you. It’s important to clarify why we investigated the relationship between cognition and mental health, and what we found using data from the UK Biobank.

Conceptually, our work is grounded in the Research Domain Criteria (RDoC; Insel et al., 2010) framework. RDoC conceptualizes mental health not through traditional diagnostic categories, but through core functional domains that span the full spectrum from normal to abnormal functioning. These domains include cognition, negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. Within this framework, cognition is considered a fundamental domain that contributes to mental health across diagnostic boundaries. Meta-analytic evidence supports a link between cognitive functioning and mental health (Abramovitch, et al., 2021; East-Richard, et al., 2020). In the context of a large, population-based dataset like the UK Biobank, this implies that cognitive performance – as measured by various cognitive tasks – should be meaningfully associated with available mental health indicators.

However, because cognition is only one of several functional domains implicated in mental health, we do not expect the covariation between cognition and mental health to be very high. Other domains, such as negative and positive valence systems, arousal and regulatory systems, or social processing, may also play significant roles. Theoretically, this places an upper bound on the strength of the cognition-mental health relationship, especially in normative, nonclinical samples.

Our current findings from the UK Biobank reflect this. Most of the 133 mental health variables showed relatively weak individual correlations with cognition (mean r \= 0.01, SD = 0.05, min r \= –0.08, max r \= 0.17; see Figure 2). However, using a PLS-based machine learning approach, we were able to integrate information across all mental-health variables to predict cognition, yielding an out-of-sample correlation of r = 0.31 [95% CI: 0.29, 0.32].  

We believe this estimate approximates the true strength of the cognition-mental health relationship in normative samples, consistent with both theoretical expectations and prior empirical findings. Theoretically, this aligns with the RDoC view that cognition is one of several contributing domains. Empirically, our results are consistent with findings from our previous mega-analysis in children (Wang et al., 2025). Moreover, in the field of gerontology, an effect size of r = 0.31 is not considered small. According to Brydges (2019), it falls around the 70th percentile of effect sizes reported in gerontological studies and approaches the threshold for a large effect (r \= 0.32). Given that most studies report within-sample associations, our out-of-sample results are likely more robust and generalizable (Yarkoni & Westfall, 2017).

To answer, “why is it then necessary to explain this overlap with neuroimaging measures”, we again draw on the conceptual foundation of the RDoC framework. RDoC emphasizes that each functional domain, such as cognition, should be studied not only at the behavioural level but also across multiple neurobiological units of analysis, including genes, molecules, cells, circuits, physiology, and behaviour.

MRI-based neural markers represent one such level of analysis. While other biological systems (e.g., genetic, molecular, or physiological) also contribute to the cognition-mental health relationship, neuroimaging provides unique insights into the brain mechanisms underlying this association – insights that cannot be obtained from behavioural data alone.

In response to the related question, “Could the relationship between cognition and mental health be explained by third variables (e.g., environment, opportunities)?”, we note that developing a neural marker of cognition capable of capturing its relationship with mental health is the central aim of this study. Using the MRI modalities available in the UK Biobank, we were able to account for 48% of the covariation between cognition and mental health.

The remaining 52% of unexplained variance may stem from several sources. According to the RDoC framework, neuromarkers could be further refined by incorporating additional neuroimaging modalities (e.g., task-based fMRI, PET, ASL, MEG/EEG, fNIRS) and integrating other units of analysis such as genetic, molecular, cellular, and physiological data.

Once more comprehensive neuromarkers are developed, capturing a greater proportion of the cognition-mental health covariation, they may also lead to new research direction – to investigate how environmental factors and life opportunities influence these markers. However, exploring those environmental contributions lies beyond the scope of the current study.

We discuss these considerations and explain the motivation of our study in the revised Introduction and Discussion.

Line 481: “Our analysis confirmed the validity of the g-factor [31] as a quantitative measure of cognition [31], demonstrating that it captures nearly half (39%) of the variance across twelve cognitive performance scores, consistent with prior studies [63–68]. Furthermore, we were able to predict cognition from 133 mental health indices, showing a medium-sized relationship that aligns with existing literature [69,70]. Although the observed mental health-cognition association is lower than within-sample estimates in conventional regression models, it aligns with our prior mega-analysis in children [69]. Notably, this effect size is not considered small in gerontology. In fact, it falls around the 70th percentile of reported effects and approaches the threshold for a large effect at r = 0.32 [71]. While we focused specifically on cognition as an RDoC core domain, the strength of its relationship with mental health may be bounded by the influence of other functional domains, particularly in normative, non-clinical samples – a promising direction for future research.”

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Introduction

Line 43: “Cognition and mental health are closely intertwined [1]. Cognitive dysfunction is present in various mental illnesses, including anxiety [2, 3], depression [4–6], and psychotic disorders [7–12]. National Institute of Mental Health’s Research Domain Criteria (RDoC) [13,14] treats cognition as one of the main basic functional domains that transdiagnostically underly mental health. According to RDoC, mental health should be studied in relation to cognition, alongside other domains such as negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. RDoC further emphasizes that each domain, including cognition, should be investigated not only at the behavioural level but also through its neurobiological correlates. In this study, we aim to examine how the covariation between cognition and mental health is reflected in neural markers of cognition, as measured through multimodal neuroimaging.”

Discussion

Line 481: “Our analysis confirmed the validity of the g-factor [31] as a quantitative measure of cognition [31], demonstrating that it captures nearly half (39%) of the variance across twelve cognitive performance scores, consistent with prior studies [63–68]. Furthermore, we were able to predict cognition from 133 mental health indices, showing a medium-sized relationship that aligns with existing literature [69,70]. Although the observed mental health-cognition association is lower than within-sample estimates in conventional regression models, it aligns with our prior mega-analysis in children [69]. Notably, this effect size is not considered small in gerontology. In fact, it falls around the 70th percentile of reported effects and approaches the threshold for a large effect at r = 0.32 [71]. While we focused specifically on cognition as an RDoC core domain, the strength of its relationship with mental health may be bounded by the influence of other functional domains, particularly in normative, non-clinical samples – a promising direction for future research.”

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. AJP. 2010;167:748–751.

Abramovitch, A., Short, T., & Schweiger, A. (2021). The C Factor: Cognitive dysfunction as a transdiagnostic dimension in psychopathology. Clinical Psychology Review, 86, 102007.

East-Richard, C., R. -Mercier, A., Nadeau, D., & Cellard, C. (2020). Transdiagnostic neurocognitive deficits in psychiatry: A review of meta-analyses. Canadian Psychology / Psychologie Canadienne, 61(3), 190–214.

Wang Y, Anney R, Pat N. The relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis. eLife. 2025.14:RP105537.

Brydges CR. Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology. Innovation in Aging. 2019;3(4):igz036.

Yarkoni T, Westfall J. Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning. Perspect Psychol Sci. 2017;12(6):1100-1122.

Comment 2 Title: - Shouldn't it be "MRI markers" (plural)?

We used the singular form (“marker”) intentionally, as it refers to the composite neuroimaging marker derived from all three MRI modalities in our stacked model. This multimodal marker represents the combined predictive power of all modalities and captures the highest proportion of the mental health-cognition relationship in our analyses.

Comment 3: Introduction - I miss an explanation of why it is useful to look at cognition-mental health covariation

We believe we have sufficiently addressed this comment in our response to Reviewer 2, comment 1 above.

Comment 4: - "Demonstrating that MRI-based neural indicators of cognition capture the covariation between cognition and mental health will thereby support the utility of such indicators for understanding the etiology of mental health" (page 4, line 56-58) - how/why?

Previous research has largely focused on developing MRI-based neural indicators that accurately predict cognitive performance (Marek et al., 2022; Vieira et al., 2020). Building on this foundation, our findings further demonstrate that the predictive performance of a neural indicator for cognition is closely tied to its ability to explain the covariation between cognition and mental health. In other words, the robustness of a neural indicator – its capacity to capture individual differences in cognition – is strongly associated with how well it reflects the shared variance between cognition and mental health.

This insight is particularly important within the context of the RDoC framework, which seeks to understand the etiology of mental health through functional domains (such as cognition) and their underlying neurobiological units of analysis (Insel et al., 2010). According to RDoC, for a neural indicator of cognition to be informative for mental health research, it must not only predict cognitive performance but also capture its relationship with mental health.

Furthermore, RDoC emphasizes the integration of neurobiological measures to investigate the influence of environmental and developmental factors on mental health. In line with this, our neural indicators of cognition may serve as valuable tools in future research aimed at understanding how environmental exposures and developmental trajectories shape mental health outcomes. We discuss this in more detail in the revised Discussion.

Line 481: “Our analysis confirmed the validity of the g-factor [31] as a quantitative measure of cognition [31], demonstrating that it captures nearly half (39%) of the variance across twelve cognitive performance scores, consistent with prior studies [63–68]. Furthermore, we were able to predict cognition from 133 mental health indices, showing a medium-sized relationship that aligns with existing literature [69,70]. Although the observed mental health-cognition association is lower than within-sample estimates in conventional regression models, it aligns with our prior mega-analysis in children [69]. Notably, this effect size is not considered small in gerontology. In fact, it falls around the 70th percentile of reported effects and approaches the threshold for a large effect at r = 0.32 [71]. While we focused specifically on cognition as an RDoC core domain, the strength of its relationship with mental health may be bounded by the influence of other functional domains, particularly in normative, non-clinical samples – a promising direction for future research.”

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, et al. Reproducible brain-wide association studies require thousands of individuals. Nature. 2022;603:654–660.

Vieira S, Gong QY, Pinaya WHL, et al. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull. 2020;46(1):17-26.

Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. AJP. 2010;167:748–751.

Comment 5: - The explanation about the stacking approach is not yet completely clear to me. I don't understand how the target variable can be the dependent variable in both step one and two. Or are those different variables? It would be helpful to also give an example of the target variable in line 88 on page 5

Thank you for this excellent question. In our stacking approach, the same target variable, the g-factor, is indeed used across both modeling stages, but with a key distinction in how predictions are generated and integrated.

In the first-level models, we trained separate Partial Least Squares Regression (PLSR) models for each of the 72 neuroimaging phenotypes, each predicting the g-factor independently. The predicted values from these 72 models were then used as input features for the second-level stacked model, which combined them to generate a final prediction of the g-factor. This twostage framework enables us to integrate information across multiple imaging modalities while maintaining a consistent prediction target.

To avoid data leakage, both modeling stages were conducted entirely within the training set for each cross-validation fold. Only after the second-level model was trained was it applied to the outer-fold test participants who were not involved in any part of the model training process.

To improve accessibility, we have revised the Methods section (see Page 10) to clarify this approach, ensuring that the description remains technically accurate while being easier to follow.

Line 188: “We employed nested cross-validation to predict cognition from mental health indices and 72 neuroimaging phenotypes (Fig. 1). Nested cross-validation is a robust method for evaluating machine-learning models while tuning their hyperparameters, ensuring that performance estimates are both accurate and unbiased. Here, we used a nested cross-validation scheme with five outer folds and ten inner folds.

We started by dividing the entire dataset into five outer folds. Each fold took a turn being held out as the outerfold test set (20% of the data), while the remaining four folds (80% of the data) were used as an outer-fold training set. Within each outer-fold training set, we performed a second layer of cross-validation – this time splitting the data into ten inner folds. These inner folds were used exclusively for hyperparameter tuning: models were trained on nine of the inner folds and validated on the remaining one, cycling through all ten combinations.

We then selected the hyperparameter configuration that performed best across the inner-fold validation sets, as determined by the minimal mean squared error (MSE). The model was then retrained on the full outer-fold training set using this hyperparameter configuration and evaluated on the outer-fold test set, using four performance metrics: Pearson r, the coefficient of determination ( R²), the mean absolute error (MAE), and the MSE. This entire process was repeated for each of the five outer folds, ensuring that every data point is used for both training and testing, but never at the same time. We opted for five outer folds instead of ten to reduce computational demands, particularly memory and processing time, given the substantial volume of neuroimaging data involved in model training. Five outer folds led to an outer-fold test set at least n = 4 000, which should be sufficient for model evaluation. In contrast, we retained ten inner folds to ensure robust and stable hyperparameter tuning, maximising the reliability of model selection.

To model the relationship between mental health and cognition, we employed Partial Least Squares Regression (PLSR) to predict the g-factor from 133 mental health variables. To model the relationship between neuroimaging data and cognition, we used a two-step stacking approach [15–17,61] to integrate information from 72 neuroimaging phenotypes across three MRI modalities. In the first step, we trained 72 base (first-level) PLSR models, each predicting the g-factor from a single neuroimaging phenotype. In the second step, we used the predicted values from these base models as input features for stacked models, which again predicted the g-factor. We constructed four stacked models based on the source of the base predictions: one each for dwMRI, rsMRI, sMRI, and a combined model incorporating all modalities (“dwMRI Stacked”, “rsMRI Stacked”, “sMRI Stacked”, and “All MRI Stacked”, respectively). Each stacked model was trained using one of four machine learning algorithms – ElasticNet, Random Forest, XGBoost, or Support Vector Regression – selected individually for each model (see Supplementary Materials, S6).

For rsMRI phenotypes, we treated the choice of functional connectivity quantification method – full correlation, partial correlation, or tangent space parametrization – as a hyperparameter. The method yielding the highest performance on the outer-fold training set was selected for predicting the g-factor (see Supplementary Materials, S5).

To prevent data leakage, we standardized the data using the mean and standard deviation derived from the training set and applied these parameters to the corresponding test set within each outer fold. This standardization was performed at three key stages: before g-factor derivation, before regressing out modality-specific confounds from the MRI data, and before stacking. Similarly, to maintain strict separation between training and testing data, both base and stacked models were trained exclusively on participants from the outer-fold training set and subsequently applied to the corresponding outer-fold test set.

To evaluate model performance and assess statistical significance, we aggregated the predicted and observed gfactor values from each outer-fold test set. We then computed a bootstrap distribution of Pearson’s correlation coefficient (r) by resampling with replacement 5 000 times, generating 95% confidence intervals (CIs) (Fig. 1). Model performance was considered statistically significant if the 95% CI did not include zero, indicating that the observed associations were unlikely to have occurred by chance.”

Comment 6: Methods - It's not clear from the text and Figure 1 which 12 scores from 11 tests are being used to derive the g-factor. Figure 1 shows only 8 bullet points with 10 scores in A and 13 tests under 'Cognitive tests' in B. Moreover, Supplement S1 describes 12 tests and 14 measures (Prospective Memory test is in the text but not in Supplementary Table 1).

Thank you for identifying this discrepancy. In the original Figure 1b and in the Supplementary Methods (S1), the “Prospective Memory” test was accidentally duplicated, while it was present in the Supplementary Table 1 (Line 53, Supplementary Table 1). We have now corrected both figures for consistency. To clarify: Figure 1a presents the global mental health and cognitive domains studied, while Figure 1b now accurately lists 1) the 12 cognitive scores from 11 tests used to derive the g-factor (with the Trail Making Test contributing two measures – numeric and alphabetic trails) and 2) the three main categories of mental health indices used as machine learning features.

We also corrected the Supplementary Materials to remove the duplicate test from the first paragraph. In Supplementary Table 1, there were 11 tests listed, and for the Trail Making test, we specified in the “Core measures” column that this test had 2 derivative scores: duration to complete the numeric path (Trail 1) and duration to complete the alphabetic path (Trail 2).

Supplementary Materials, Line 46: “We used twelve scores from the eleven cognitive tests that represented the following cognitive domains: reaction time and processing speed (Reaction Time test), working memory (Numeric Memory test), verbal and numerical reasoning (Fluid Intelligence test), executive function (Trail Making Test), non-verbal fluid reasoning (Matrix Pattern Completion test), processing speed (Symbol Digit Substitution test), vocabulary (Picture Vocabulary test), planning abilities (Tower Rearranging test), verbal declarative memory (Paired Associate Learning test), prospective memory (Prospective Memory test), and visual memory (Pairs Matching test) [1].”

Comment 7: - For the mental health measures: If I understand correctly, the questionnaire items were used individually, but also to create composite scores. This seems counterintuitive, because I would assume that if the raw data is used, the composite scores would not add additional information to that. When reading the Supplement, it seems like I'm not correct… It would be helpful to clarify the text on page 7 in the main text.

You raise an excellent observation regarding the use of both individual questionnaire items and composite scores. This dual approach was methodologically justified by the properties of Partial Least Squares Regression (PLSR), our chosen first-level machine learning algorithm, which benefits from rich feature sets and can handle multicollinearity through dimensionality reduction. PLSR transforms correlated features into latent variables, meaning both individual items and composite scores can contribute unique information to the model. We elaborate on PLSR's mathematical principles in Supplementary Materials (S5).

To directly address this concern, we conducted comparative analyses showing that the PLSR model (a single 80/20% training/test split), incorporating all 133 mental health features (both items and composites), outperformed models using either type alone. The full model achieved superior performance (MSE = 0.458, MAE = 0.537, R² \= 0.112, Pearson r = 0.336, p-value = 6.936e-112) compared to using only composite scores (93 features; MSE = 0.461, MAE = 0.538, R² = 0.107, Pearson r = 0.328, p-value = 5.8e-106) or only questionnaire items (40 features; MSE = 0.499, MAE = 0.561, R² = 0.033, Pearson r = 0.184, p-value = 2.53e-33). These results confirm that including both data types provide complementary predictive value. We expand on these considerations in the revised Methods section.

Line 123: “Mental health measures encompassed 133 variables from twelve groups: mental distress, depression, clinical diagnoses related to the nervous system and mental health, mania (including bipolar disorder), neuroticism, anxiety, addictions, alcohol and cannabis use, unusual/psychotic experiences, traumatic events, selfharm behaviours, and happiness and subjective well-being (Fig. 1 and Tables S4 and S5). We included both selfreport questionnaire items from all participants and composite diagnostic scores computed following Davis et al. and Dutt et al. [35,36] as features in our first-level (for explanation, see Data analysis section) Partial Least Squares Regression (PLSR) model. This approach leverages PLSR’s ability to handle multicollinearity through dimensionality reduction, enabling simultaneous use of granular symptom-level information and robust composite measures (for mental health scoring details, see Supplementary Materials, S2). We assess the contribution of each mental health index to general cognition by examining the direction and magnitude of its PLSR-derived loadings on the identified latent variables”

Comment 8: - Results - The colors in Figure 4 B are a bit hard to differentiate.

We have updated Figure 4 to enhance colour differentiation by adjusting saturation and brightness levels, improving visual distinction. For further clarity, we split the original figure into two separate figures.

Comment 9: - Discussion - "Overall, the scores for mental distress, alcohol and cannabis use, and self-harm behaviours relate positively, and the scores for anxiety, neurological and mental health diagnoses, unusual or psychotic experiences, happiness and subjective well-being, and negative traumatic events relate negatively to cognition," - this seems counterintuitive, that some symptoms relate to better cognition and others relate to worse cognition. Could you elaborate on this finding and what it could mean?

We appreciate you highlighting this important observation. While some associations between mental health indices and cognition may appear counterintuitive at first glance, these patterns are robust (emerging consistently across both univariate correlations and PLSR loadings) and align with previous literature (e.g., Karpinski et al., 2018; Ogueji et al., 2022). For instance, the positive relationship between cognitive ability and certain mental health indicators like help-seeking behaviour has been documented in other population studies (Karpinski et al., 2018; Ogueji et al., 2022), potentially reflecting greater health literacy and access to care among cognitively advantaged individuals. Conversely, the negative associations with conditions like psychotic experiences mirror established neurocognitive deficits in these domains.

As was initially detailed in Supplementary Materials (S12) and now expanded in our Discussion, these findings likely reflect complex multidimensional interactions. The positive loadings for mental distress indicators may capture: (1) greater help-seeking behaviour among those with higher cognition and socioeconomic resources, and/or (2) psychological overexcitability and rumination tendencies in high-functioning individuals. These interpretations are particularly relevant to the UK Biobank's assessment methods, where mental distress items focused on medical help-seeking rather than symptom severity per se (e.g., as a measure of mental distress, the UK Biobank questionnaire asked whether an individual sought or received medical help for or suffered from mental distress).

Line 492: “Factor loadings derived from the PLSR model showed that the scores for mental distress, alcohol and cannabis use, and self-harm behaviours related positively, and the scores for anxiety, neurological and mental health diagnoses, unusual or psychotic experiences, happiness and subjective well-being, and negative traumatic events related negatively to the g-factor. Positive PLSR loadings of features related to mental distress may indicate greater susceptibility to or exaggerated perception of stressful events, psychological overexcitability, and predisposition to rumination in people with higher cognition [72]. On the other hand, these findings may be specific to the UK Biobank cohort and the way the questions for this mental health category were constructed. In particular, to evaluate mental distress, the UK Biobank questionnaire asked whether an individual sought or received medical help for or suffered from mental distress. In this regard, the estimate for mental distress may be more indicative of whether an individual experiencing mental distress had an opportunity or aspiration to visit a doctor and seek professional help [73]. Thus, people with better cognitive abilities and also with a higher socioeconomic status may indeed be more likely to seek professional help.

Limited evidence supports a positive association between self-harm behaviours and cognitive abilities, with some studies indicating higher cognitive performance as a risk factor for non-suicidal self-harm. Research shows an inverse relationship between cognitive control of emotion and suicidal behaviours that weakens over the life course [73,74]. Some studies have found a positive correlation between cognitive abilities and the risk of nonsuicidal self-harm, suicidal thoughts, and suicidal plans that may be independent of or, conversely, affected by socioeconomic status [75,76]. In our study, the magnitude of the association between self-harm behaviours and cognition was low (Fig. 2), indicating a weak relationship.

Positive PLSR loadings of features related to alcohol and cannabis may also indicate the influence of other factors. Overall, this relationship is believed to be largely affected by age, income, education, social status, social equality, social norms, and quality of life [79–80]. For example, education level and income correlate with cognitive ability and alcohol consumption [79,81–83]. Research also links a higher probability of having tried alcohol or recreational drugs, including cannabis, to a tendency of more intelligent individuals to approach evolutionary novel stimuli [84,85]. This hypothesis is supported by studies showing that cannabis users perform better on some cognitive tasks [86]. Alternatively, frequent drinking can indicate higher social engagement, which is positively associated with cognition [87]. Young adults often drink alcohol as a social ritual in university settings to build connections with peers [88]. In older adults, drinking may accompany friends or family visits [89,90]. Mixed evidence on the link between alcohol and drug use and cognition makes it difficult to draw definite conclusions, leaving an open question about the nature of this relationship.

Consistent with previous studies, we showed that anxiety and negative traumatic experiences were inversely associated with cognitive abilities [90–93]. Anxiety may be linked to poorer cognitive performance via reduced working memory capacity, increased focus on negative thoughts, and attentional bias to threatening stimuli that hinder the allocation of cognitive resources to a current task [94–96]. Individuals with PTSD consistently showed impaired verbal and working memory, visual attention, inhibitory function, task switching, cognitive flexibility, and cognitive control [97–100]. Exposure to traumatic events that did not reach the PTSD threshold was also linked to impaired cognition. For example, childhood trauma is associated with worse performance in processing speed, attention, and executive function tasks in adulthood, and age at a first traumatic event is predictive of the rate of executive function decline in midlife [101,102]. In the UK Biobank cohort, adverse life events have been linked to lower cognitive flexibility, partially via depression level [103].

In agreement with our findings, cognitive deficits are often found in psychotic disorders [104,105]. We treated neurological and mental health symptoms as predictor variables and did not stratify or exclude people based on psychiatric status or symptom severity. Since no prior studies have examined isolated psychotic symptoms (e.g., recent unusual experiences, hearing unreal voices, or seeing unreal visions), we avoid speculating on how these symptoms relate to cognition in our sample.

Finally, negative PLSR loadings of the features related to happiness and subjective well-being may be specific to the study cohort, as these findings do not agree with some previous research [107–109]. On the other hand, our results agree with the study linking excessive optimism or optimistic thinking to lower cognitive performance in memory, verbal fluency, fluid intelligence, and numerical reasoning tasks, and suggesting that pessimism or realism indicates better cognition [110]. The concept of realism/optimism as indicators of cognition is a plausible explanation for a negative association between the g-factor and friendship satisfaction, as well as a negative PLSR loading of feelings that life is meaningful, especially in older adults who tend to reflect more on the meaning of life [111]. The latter is supported by the study showing a negative association between cognitive function and the search for the meaning of life and a change in the pattern of this relationship after the age of 60 [112]. Finally, a UK Biobank study found a positive association of happiness with speed and visuospatial memory but a negative relationship with reasoning ability [113].”

Karpinski RI, Kinase Kolb AM, Tetreault NA, Borowski TB. High intelligence: A risk factor for psychological and physiological overexcitabilities. Intelligence. 2018;66:8–23.

Ogueji IA, Okoloba MM. Seeking Professional Help for Mental Illness: A Mixed-Methods Study of Black Family Members in the UK and Nigeria. Psychol Stud. 2022;67:164–177.

Comment 10: - All neuroimaging factors together explain 48% of the variance in the cognition-mental health relationship. However, this relationship is only r=0.3 - so then the effect of neuroimaging factors seems a lot smaller… What does it mean?

Thank you for raising this critical point. We have addressed this point in our response to Reviewer 1, comment 2, Reviewer 1, comment 3 and Reviewer 2, comment 1.

Briefly, cognition is related to mental health at around r = 0.3 and to neuroimaging phenotypes at around r = 0.4. These levels of relationship strength are consistent to what has been shown in the literature (e.g., Wang et al., 2025 and Vieira et al., 2020). We discussed the relationship between cognition and mental health in our response to Reviewer 2, comment 1 above. In short, this relationship reflects just one functional domain – mental health may also be associated with other domains such as negative and positive valence systems, arousal and regulatory systems, social processes, and sensorimotor functions. Moreover, in the context of gerontology research, this effect size is considered relatively large (Brydges et al., 2019).

We conducted a commonality analysis to investigate the unique and shared variance of mental health and neuroimaging phenotypes in explaining cognition.  As we discussed in our response to Reviewer 1, comment 2, we were able to account for 48% of the covariation between cognition and mental health using the MRI modalities available in the UK Biobank. The remaining 52% of unexplained variance may arise from several sources.

One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research from our group and others has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank (Tetereva et al., 2025).

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

We have now incorporated these considerations into the Discussion section.

Line 481: “Our analysis confirmed the validity of the g-factor [31] as a quantitative measure of cognition [31], demonstrating that it captures nearly half (39%) of the variance across twelve cognitive performance scores, consistent with prior studies [63–68]. Furthermore, we were able to predict cognition from 133 mental health indices, showing a medium-sized relationship that aligns with existing literature [69,70]. Although the observed mental health-cognition association is lower than within-sample estimates in conventional regression models, it aligns with our prior mega-analysis in children [69]. Notably, this effect size is not considered small in gerontology. In fact, it falls around the 70th percentile of reported effects and approaches the threshold for a large effect at r = 0.32 [71]. While we focused specifically on cognition as an RDoC core domain, the strength of its relationship with mental health may be bounded by the influence of other functional domains, particularly in normative, non-clinical samples – a promising direction for future research.”

Line 658: “Although recent debates [18] have challenged the predictive utility of MRI for cognition, our multimodal marker integrating 72 neuroimaging phenotypes captures nearly half of the mental health-explained variance in cognition. We demonstrate that neural markers with greater predictive accuracy for cognition also better explain cognition-mental health covariation, showing that multimodal MRI can capture both a substantial cognitive variance and nearly half of its shared variance with mental health. Finally, we show that our neuromarkers explain a substantial portion of the age- and sex-related variance in the cognition-mental health relationship, highlighting their relevance in modeling cognition across demographic strata.

The remaining unexplained variance in the relationship between cognition and mental health likely stems from multiple sources. One possibility is the absence of certain neuroimaging modalities in the UK Biobank dataset, such as task-based fMRI contrasts, positron emission tomography, arterial spin labeling, and magnetoencephalography/electroencephalography. Prior research has consistently demonstrated strong predictive performance from specific task-based fMRI contrasts, particularly those derived from tasks like the n-Back working memory task and the face-name episodic memory task, none of which is available in the UK Biobank [15,17,61,69,114,142,151].

Moreover, there are inherent limitations in using MRI as a proxy for brain structure and function. Measurement error and intra-individual variability, such as differences in a cognitive state between cognitive assessments and MRI acquisition, may also contribute to the unexplained variance. According to the RDoC framework, brain circuits represent only one level of neurobiological analysis relevant to cognition [14]. Other levels, including genes, molecules, cells, and physiological processes, may also play a role in the cognition-mental health relationship.

Nonetheless, neuroimaging provides a valuable window into the biological mechanisms underlying this overlap – insights that cannot be gleaned from behavioural data alone. Ultimately, our findings validate brain-based neural markers as a fundamental neurobiological unit of analysis, advancing our understanding of mental health through the lens of cognition.”

Wang Y, Anney R, Pat N. The relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis. eLife. 2025.14:RP105537.

Vieira S, Gong QY, Pinaya WHL, et al. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull. 2020;46(1):17-26.

Brydges CR. Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology. Innovation in Aging. 2019;3(4):igz036.

Tetereva A, Knodt AR, Melzer TR, et al. Improving Predictability, Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking. Preprint. bioRxiv. 2025;2024.05.03.589404.

Reviewer 3:

Buianova et al. present a comprehensive analysis examining the predictive value of multimodal neuroimaging data for general cognitive ability, operationalized as a derived g-factor. The study demonstrates that functional MRI holds the strongest predictive power among the modalities, while integrating multiple MRI modalities through stacking further enhances prediction performance. The inclusion of a commonality analysis provides valuable insight into the extent to which shared and unique variance across mental health features and neuroimaging modalities contributes to the observed associations with cognition. The results are clearly presented and supported by highquality visualizations. Limitations of the sample are stated clearly.

Thank you once more for your constructive and encouraging feedback. We appreciate your careful reading and valuable methodological insights. Your expertise has helped us clarify key methodological concepts and improve the overall rigour of our study.

Suggestions for improvement:

(1) The manuscript would benefit from the inclusion of permutation testing to evaluate the statistical significance of the predictive models. This is particularly important given that some of the reported performance metrics are relatively modest, and permutation testing could help ensure that results are not driven by chance.

Thank you, this is an excellent point. We agree that evaluating the statistical significance of our predictive models is essential.

In our original analysis, we assessed model performance by generating a bootstrap distribution of Pearson’s r, resampling the data with replacement 5,000 times (see Figure 3b). In response to your feedback, we have made the following updates:

(1) Improved Figure 3b to explicitly display the 95% confidence intervals.

(2) Supplemented the results by reporting the exact confidence interval values.

(3) Clarified our significance testing procedure in the Methods section.

We considered model performance statistically significant when the 95% confidence interval did not include zero, indicating that the observed associations are unlikely to have occurred by chance.

We chose bootstrapping over permutation testing because, while both can assess statistical significance, bootstrapping additionally provides uncertainty estimates in the form of confidence intervals. Given the large sample size in our study, significance testing can be less informative, as even small effects may reach statistical significance. Bootstrapping offers a more nuanced understanding of model uncertainty.

Line 233: “To evaluate model performance and assess statistical significance, we aggregated the predicted and observed g-factor values from each outer-fold test set. We then computed a bootstrap distribution of Pearson’s correlation coefficient (r) by resampling with replacement 5 000 times, generating 95% confidence intervals (CIs) (Fig. 1). Model performance was considered statistically significant if the 95% CI did not include zero, indicating that the observed associations were unlikely to have occurred by chance.”

(2) Applying and testing the trained models on an external validation set would increase confidence in generalisability of the model.

We appreciate this excellent suggestion. While we considered this approach, implementing it would require identifying an appropriate external dataset with comparable neuroimaging and behavioural measures, along with careful matching of acquisition protocols and variable definitions across sites. These challenges extend beyond the scope of the current study, though we fully agree that this represents an important direction for future research.

Our findings, obtained from one of the largest neuroimaging datasets to date with training and test samples exceeding most previous studies, align closely with existing literature: the predictive accuracy of each neuroimaging phenotype and modality for cognition matches the effect size reported in meta-analyses (r ≈ 0.4; e.g., Vieira et al., 2020). The ability of dwMRI, rsMRI and sMRI to capture the cognition-mental health relationship is, in turn, consistent with our previous work in pediatric populations (Wang et al., 2025; Pat et al., 2022).

Vieira S, Gong QY, Pinaya WHL, et al. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull. 2020;46(1):17-26.

Wang Y, Anney R, Pat N. The relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis. eLife. 2025.14:RP105537.

Pat N, Wang Y, Anney R, Riglin L, Thapar A, Stringaris A. Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors. Hum Brain Mapp. 2022;43:5520–5542.

(3) The rationale for selecting a 5-by-10-fold cross-validation scheme is not clearly explained. Clarifying why this structure was preferred over more commonly used alternatives, such as 10-by-10 or 5-by-5 cross-validation, would strengthen the methodological transparency.

Thank you for this important methodological question. Our choice of a 5-by-10-fold crossvalidation scheme was motivated by the need to balance robust hyperparameter tuning with computational efficiency, particularly memory and processing time. Retaining five outer folds allowed us to rigorously assess model performance across multiple data partitions, leading to an outer-fold test set at least n = 4 000 and providing a substantial amount of neuroimaging data involved in model training. In contrast, employing ten inner folds ensured robust and stable hyperparameter tuning that maximizes the reliability of model selection. Thus, the 5-outer-fold with our large sample provided sufficient out-of-sample test set size for reliable model evaluation and efficient computation, while 10 inner folds enabled robust hyperparameter tuning. We now provide additional rationale for this design decision on Page 10.

Line 188: “We employed nested cross-validation to predict cognition from mental health indices and 72 neuroimaging phenotypes (Fig. 1). Nested cross-validation is a robust method for evaluating machine-learning models while tuning their hyperparameters, ensuring that performance estimates are both accurate and unbiased. Here, we used a nested cross-validation scheme with five outer folds and ten inner folds.

We started by dividing the entire dataset into five outer folds. Each fold took a turn being held out as the outerfold test set (20% of the data), while the remaining four folds (80% of the data) were used as an outer-fold training set. Within each outer-fold training set, we performed a second layer of cross-validation – this time splitting the data into ten inner folds. These inner folds were used exclusively for hyperparameter tuning: models were trained on nine of the inner folds and validated on the remaining one, cycling through all ten combinations.

We then selected the hyperparameter configuration that performed best across the inner-fold validation sets, as determined by the minimal mean squared error (MSE). The model was then retrained on the full outer-fold training set using this hyperparameter configuration and evaluated on the outer-fold test set, using four performance metrics: Pearson r, the coefficient of determination ( R²), the mean absolute error (MAE), and the MSE. This entire process was repeated for each of the five outer folds, ensuring that every data point is used for both training and testing, but never at the same time. We opted for five outer folds instead of ten to reduce computational demands, particularly memory and processing time, given the substantial volume of neuroimaging data involved in model training. Five outer folds led to an outer-fold test set at least n = 4 000, which should be sufficient for model evaluation. In contrast, we retained ten inner folds to ensure robust and stable hyperparameter tuning, maximising the reliability of model selection.”

(4) A more detailed discussion of which specific brain regions or features within each neuroimaging modality contributed most strongly to the prediction of cognition would enhance neurobiological relevance of the findings.

Thank you for this thoughtful suggestion. To address this point, we have included feature importance plots for the top-performing neuroimaging phenotypes within each modality (Figure 5 and Figures S2–S4), demonstrating the relative contributions of individual features to the predictive models. While we maintain our primary focus on cross-modality performance comparisons in the main text, as this aligns with our central aim of evaluating multimodal MRI markers at the integrated level, we outline the contribution of neuroimaging features with the highest predictive performance for cognition in the revised Results and Discussion.

Methods

Line 255: “To determine which neuroimaging features contribute most to the predictive performance of topperforming phenotypes within each modality, while accounting for the potential latent components derived from neuroimaging, we assessed feature importance using the Haufe transformation [62]. Specifically, we calculated Pearson correlations between the predicted g-factor and scaled and centred neuroimaging features across five outer-fold test sets. We also examined whether the performance of neuroimaging phenotypes in predicting cognition per se is related to their ability to explain the link between cognition and mental health. Here, we computed the correlation between the predictive performance of each neuroimaging phenotype and the proportion of the cognition-mental health relationship it captures. To understand how demographic factors, including age and sex, contribute to this relationship, we also conducted a separate set of commonality analyses treating age, sex, age², age×sex, and age²×sex as an additional set of explanatory variables (Fig. 1).”

Results

dwMRI

Line 331: “Overall, models based on structural connectivity metrics performed better than TBSS and probabilistic tractography (Fig. 3). TBSS, in turn, performed better than probabilistic tractography (Fig. 3 and Table S13). The number of streamlines connecting brain areas parcellated with aparc MSA-I had the best predictive performance among all dwMRI neuroimaging phenotypes (R²_mean = 0.052, r_mean = 0.227, 95% CI [0.212, 0.235]). To identify features driving predictions, we correlated streamline counts in aparc MSA-I parcellation with the predicted g_factor values from the PLSR model. Positive associations with the predicted _g-factor were strongest for left superior parietal-left caudal anterior cingulate, left caudate-right amygdala, and left putamen-left hippocampus connections. The most marked negative correlations involved left putamen-right posterior thalamus and right pars opercularis-right caudal anterior cingulate pathways (Fig. 5 and Supplementary Fig. S2).”

rsMRI

Line 353: “Among RSFC metrics for 55 and 21 ICs, tangent parameterization matrices yielded the highest performance in the training set compared to full and partial correlation, as indicated by the cross-validation score. Functional connections between the limbic (IC10) and dorsal attention (IC18) networks, as well as between the ventral attention (IC15) and default mode (IC11) networks, displayed the highest positive association with cognition. In contrast, functional connectivity between the limbic (IC43, the highest activation within network) and default mode (IC11) and limbic (IC45) and frontoparietal (IC40) networks, between the dorsal attention (IC18) and frontoparietal (IC25) networks, and between the ventral attention (IC15) and frontoparietal (IC40) networks, showed the highest negative association with cognition (Fig. 5 and Supplementary Fig. S3 and S4)”

sMRI

Line 373: “FreeSurfer subcortical volumetric subsegmentation and ASEG had the highest performance among all sMRI neuroimaging phenotypes (R²_mean = 0.068, r_mean = 0.244, 95% CI [0.237, 0.259] and R²_mean = 0.059, r_mean = 0.235, 95% CI [0.221, 0.243], respectively). In FreeSurfer subcortical volumetric subsegmentation, volumes of all subcortical structures, except for left and right hippocampal fissures, showed positive associations with cognition. The strongest relations were observed for the volumes of bilateral whole hippocampal head and whole hippocampus (Fig. 5 and Supplementary Fig. S5 for feature importance maps). Grey matter morphological characteristics from ex vivo Brodmann Area Maps showed the lowest predictive performance (R²_mean = 0.008, r_mean = 0.089, 95% CI [0.075, 0.098]; Fig. 3 and Table S15).”

Discussion

dwMRI

Line 562: “Among dwMRI-derived neuroimaging phenotypes, models based on structural connectivity between brain areas parcellated with aparc MSA-I (streamline count), particularly connections with bilateral caudal anterior cingulate (left superior parietal-left caudal anterior cingulate, right pars opercularis-right caudal anterior cingulate), left putamen (left putamen-left hippocampus, left putamen-right posterior thalamus), and amygdala (left caudate-right amygdala), result in a neural indicator that best reflects microstructural resources associated with cognition, as indicated by predictive modeling, and more importantly, shares the highest proportion of the variance with mental health-g, as indicated by commonality analysis.”

rsMRI

Line 583: “We extend findings on the superior performance of rsMRI in predicting cognition, which aligns with the literature [15, 28], by showing that it also explains almost a third of the variance in cognition that mental health captures. At the rsMRI neuroimaging phenotype level, this performance is mostly driven by RSFC patterns among 55 ICA-derived networks quantified using tangent space parameterization. At a feature level, these associations are best captured by the strength of functional connections among limbic, dorsal attention and ventral attention, frontoparietal and default mode networks. These functional networks have been consistently linked to cognitive processes in prior research [127–130].”

sMRI

Line 608: “Integrating information about brain anatomy by stacking sMRI neuroimaging phenotypes allowed us to explain a third of the link between cognition and mental health. Among all sMRI neuroimaging phenotypes, those that quantified the morphology of subcortical structures, particularly volumes of bilateral hippocampus and hippocampal head, explain the highest portion of the variance in cognition captured by mental health. Our findings show that, at least in older adults, volumetric properties of subcortical structures are not only more predictive of individual variations in cognition but also explain a greater portion of cognitive variance shared with mental health than structural characteristics of more distributed cortical grey and white matter. This aligns with the Scaffolding Theory that proposes stronger compensatory engagement of subcortical structures in cognitive processing in older adults [138–140].”

(5) The formatting of some figure legends could be improved for clarity - for example, some subheadings were not formatted in bold (e.g., Figure 2 c)

Thank you for noticing this. We have updated the figures to enhance clarity, keeping subheadings plain while bolding figure numbers and MRI modality names.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

Evidence, reproducibility and clarity

The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

(1) Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).

(2) Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

(1) The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.

Thanks for your suggestion to avoid confusion. We used the phrase "nodal spacing" instead of "nodal distribution" throughout the revised manuscript.

(2) In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?

As a key distinction, our study focuses specifically on the main trunk of the contralateral projection of NM axons. This projection features a sequential branching structure known as the delay line, where collateral branches form terminal arbors and connect to the ventral dendritic layer of NL neurons. This structural organization plays a critical role in influencing the dynamic range of ITD detection by regulating conduction delays along the NM axon trunk.

The study by Seidl et al. (2010) is a pioneering work that measured diameter of NM axon using electron microscopy, providing highly reliable data. However, due to the technical  limitations of electron microscopy, which does not allow for the continuous tracing of individual axons, it is not entirely clear whether the axons measured in the ventral NL region correspond to terminal arbors of collateral branches or the main trunk of NM axons (see Figure 9E, F in their paper). Instead, they categorized axon diameters based on their distance from NL cell layer, showing that axon diameter increases distally (see Figure 9G in their paper). Notably, the diameters of ventral axons located more than 120 μm away from the NL cell layer is almost identical to those in the midline.

As illustrated in our Figure 4D and Supplementary Video 2, the main trunk of the contralateral NM projection is predominantly located in these distal regions. Therefore, our findings complement those of Seidl et al. (2010) rather than contradicting them. We made this point as clear as possible in text (page 7, line 3).

(3) The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?

In this study, we examined chick embryos from E9 to just before hatching (E21) and post-hatch chicks up to P9. Chickens begin to perceive sound around E12 and possess sound localization abilities at the time of hatching (Grier et al., 1967) (added to page 4, line 9). Therefore, by E21, the sound localization circuit is largely established.

On the other hand, additional refinement of the circuit with aging is certainly possible. A key cue for sound localization, interaural time difference (ITD), depends on the distance between the two ears, which increases as the animal grows. As shown in Figure 2G, internodal length increased by approximately 20% between E18 and P9 while maintaining regional differences. Given that NM axons are nearly fully myelinated by E21 (Figure 4D, 6C), this suggests that myelin extends in proportion to the overall growth of the head and brain volume. We described this possibility in text (page 5, line 21)

Thus, our study covers not only the early stages of myelination but also the post-functional maturation in the sound localization circuit.

(4) The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

In this study, we demonstrated that although vesicular release did not affect internodal length, it selectively promoted oligodendrogenesis, thereby supporting the full myelination and hence the pattern of nodal spacing along the NM axons. We believe that this finding falls within the broader scope of 'activity-dependent plasticity' involving oligodendrocytes and nodes.

As summarized in the excellent review by Bonetto et al. (2021), activity-dependent plasticity in oligodendrocytes encompasses a wide range of phenomena, not limited to changes in internodal length but also including oligodendrogenesis. Moreover, the effects of neuronal activity are not uniform but likely depend on the diversity of both neurons and oligodendrocytes. For example, in the mouse visual cortex, activity-dependent myelination occurs in interneurons but not in excitatory neurons (Yang et al., 2020). Additionally, expression of TeNT in axons affected myelination heterogeneously in zebrafish; some axons were impaired in myelination and the others were not affected at all (Koudelka et al., 2016). In the mouse corpus callosum, neuronal activity influences oligodendrogenesis, which in turn facilitates adaptive myelination (Gibson et al., 2014).

Thus, rather than refuting the role of activity-dependent plasticity in nodal spacing, our findings emphasize the diversity of underlying regulatory mechanisms. We described these explicitly in text (page 10, line 18).

Significance

This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

This paper does not argue against node plasticity, but rather demonstrates that oligodendrocytes in the NL region exhibit a form of plasticity; they proliferate in response to vesicular release from NM axons, yet do not undergo morphological changes, ensuring adequate oligodendrocyte density for the full myelination of the auditory circuit. Thus, activity-dependent plasticity involving oligodendrocytes would contributes in various ways to each neural circuit, which is presumably attributed to the fact that myelination is driven by complex multicellular interactions between diverse axons and oligodendrocytes. Oligodendrocytes are known to exhibit heterogeneity in morphology, function, responsiveness, and gene profiles (Foerster et al., 2019; Sherafat et al., 2021; Osanai et al., 2022; Valihrach et al., 2022), but functional significance of this heterogeneity remains largely unclear. This paper also provides insight into how oligodendrocyte heterogeneity may contribute to the fine-tuning of neural circuit function, adding further value to our findings. Importantly, our study covers the wide range of development in the sound localization circuit, from the pre-myelination (E9) to the postfunctional maturation (P9), revealing how the nodal spacing pattern along the axon in this circuit emerges and matures.

Reviewer #2:

Evidence, reproducibility and clarity

Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

Major points, detailed below, need to be addressed to overcome some limitations of the study.

Major comments:

(1) It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.

eTeNT is a widely used genetically encoded silencing tool and constructs similar to the one used in this study have been successfully applied in primates and rodents to suppress target behaviors via genetic dissection of specific pathways (Kinoshita et al., 2012; Sooksawate et al., 2013). However, precisely quantifying the extent of vesicular release inhibition from NM axons in the brainstem auditory circuit is technically problematic.

One major limitation is that while A3V efficiently infects NM neurons, its transduction efficiency does not reach 100%. In electrophysiological evaluations, NL neurons receive inputs from multiple NM axons, meaning that responses may still include input from uninfected axons. Additionally, failure to evoke synaptic responses could either indicate successful silencing or failure to stimulate NM axons, making a clear distinction difficult. Furthermore, unlike in motor circuits, we cannot assess the effect of silencing by observing behavioral outputs.

Thus, we instead opted to quantify the precise expression efficiency of GFP-tagged eTeNT in the cell bodies of NM neurons. The proportion of NM neurons expressing GFP-tagged eTeNT was 89.7 ± 1.6% (N = 6 chicks), which is consistent with previous reports evaluating A3V transduction efficiency in the brainstem auditory circuit (Matsui et al., 2012). These results strongly suggest that synaptic transmission from NM axons was globally silenced by eTeNT at the NL region. We described these explicitly in text (page 8, line 2).

(2) Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?

Figure 6D depicts a representative axon selected from a dense population of GFP-positive axons in a 200-μm-thick slice after A3V-eTeNT infection to bilateral NM. As shown in Supplementary Video 1 and 2, densely labeled GFP-positive axons can be traced along the main trunk. To prevent any misinterpretation, we have revised the description of Figure 6 in the main text and Figure legend (page 31, line 9), and stated the A3V-eTeNT infection efficiency was 89.7 ± 1.6% in NM neurons, as mentioned above. Based on this efficiency, we interpreted that the global occlusion of vesicular release from most of the NM axons altered the pericellular microenvironment of the NL region, which led to the regional effect on the oligodendrocyte density.

On the other hand, your question regarding whether sparse expression of eTeNT still has an effect is highly relevant. As we also discussed in our reply to comment 4 by Reviewer #1, the relationship between neuronal activity and oligodendrocytes is highly diverse. In some types of axons, vesicular release is essential for normal myelination, and this process was disrupted by TeNT (Koudelka et al., 2016), suggesting that direct interaction with oligodendrocytes via vesicle release may actively promote myelination in these types of axons.

To clarify whether the phenotype observed in Figure 6 arises from changes in the pericellular microenvironment at the NL region or from the direct suppression of axon-oligodendrocyte interactions, we included a new Supplementary Figure (Figure 6—figure supplement 1). In this figure, we evaluated the node formation on the axon sparsely expressing eTeNT by electroporation into the unilateral NM. The results showed that sparse eTeNT expression did not increase the percentages of heminodes or unmyelinated segments. This finding supports our conclusion that the increased unmyelinated segments by A3V-eTeNT resulted from impaired synaptic transmission at NM terminals and subsequent alterations of  pericellular microenvironment at the NL region.

(3) The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:

Thank you for your valuable suggestions to improve the rigor of our statistical analyses. We have reanalyzed all statistical tests using R software. In the revised Methods section and Figure Legends, we have clarified the rationale for selecting each statistical test, specified which test was used for each figure, and explicitly defined both n and N. After reevaluation with the Shapiro-Wilk test, we adjusted some analyses to non-parametric tests where appropriate. However, these adjustments did not alter the statistical significance of our results compared to the original analyses.

(3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or MannWhitney U test, which are much more common in the field. What is the rationale for the test choice?

We have revised the explanation of our statistical test choices to provide greater clarity and precision. For example, in Figure 2G, we first assessed the normality of the data in each of the four groups using the Shapiro-Wilk test, which revealed that some datasets did not follow a normal distribution. Given this, we selected the Kruskal-Wallis test, a commonly used non-parametric test for comparisons across three or more groups. Since the Kruskal-Wallis test indicated a significant difference, we conducted a post hoc Steel-Dwass test to determine which specific group comparisons were statistically significant.

(3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a ttest used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.

In the case of Figures 3H-K, we compared oligodendrocyte morphology between regions. However, since the number of sparsely labeled oligodendrocytes differs both between regions and across individual samples, there is no strict correspondence between paired measurements. On the other hand, in Figures 5B, C, and E, we compared the density of labeled cells between regions within the same slice, establishing a direct correspondence between paired data points. For these comparisons, we appropriately used a paired t-test.

(3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)

We have specified the statistical tests used for each figure in the Methods section and Figure Legends for better clarity. Additionally, we have revised the descriptions for Figure 4G, L, and M and their corresponding Figure Legends to explicitly indicate that Spearman’s rank correlation coefficient (rₛ) was used for evaluation.

(3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).

We have now clarified what “n” represents in each figure, as well as the number of animals (N) used in each experiment, in the Figure Legends.

In this study, developmental stages of chick embryos were defined by HH stage (Hamburger and Hamilton, 1951), minimizing individual variability. Additionally, since our study focuses on the distribution of morphological characteristics of individual cells, averaging measurements per animal would obscure important cellular-level variability and potentially mislead interpretation of data. Furthermore, we employed a strategy of sparse genetic labeling in many experiments, which naturally results in variability in the number of measurable cells per animal. Given the clear distinctions in our data distributions, we believe that averaging per biological replicate is not essential in this case.

To further ensure the robustness of our statistical analysis, data presented as boxplots were preliminarily assessed using PlotsOfDifferences, a web-based application that calculates and visualizes effect sizes and 95% confidence intervals based on bootstrapping (https://huygens.science.uva.nl/PlotsOfDifferences/; https://doi.org/10.1101/578575). Effect sizes can serve as a valuable alternative to p-values (Ho, 2018; https://www.nature.com/articles/s41592019-0470-3). The significant differences reported in our study are also supported by clear differences in effect sizes, ensuring that our conclusions remain robust regardless of the statistical approach used.

If requested, we would be happy to provide PlotsOfDifferences outputs as supplementary source data files, similar to those used in eLife publications, for each figure.

(3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6).

We have now incorporated individual data points into the bar graphs in Figures 5 and 6.

(4) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit?

The morphological differences of oligodendrocytes between white and gray matter are well established (i.e. shorter myelin at gray matter), but their correspondence with the nodal spacing pattern along the long axonal projections of cortical neurons is not well understood. Future research may find similarities with our findings. Additionally, as mentioned in the final section of the Discussion, the mammalian brainstem auditory circuit is functionally analogous to the avian ITD circuit. Regional differences in nodal spacing along axons have also been observed in the mammalian system, raising the important question of whether these differences are supported by regional heterogeneity in oligodendrocytes. Investigating this possibility will facilitate our understanding of the underlying logic and mechanisms for determining node spacing patterns along axons, as well as provide valuable insights into evolutionary convergence in auditory processing mechanisms. We described these explicitly in text (page 11, line 34).

(5) Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended.

As you summarized in your comment, our results demonstrated that A3V-eTeNT suppressed oligodendrogenesis in the NL region, leading to a reduction in oligodendrocyte density (Figures 6L, M), which caused the emergence of unmyelinated segments. While this is an indirect manipulation of oligodendrocyte density, it nonetheless provides evidence supporting a causal relationship between oligodendrocyte density and nodal spacing.

The emergence of unmyelinated segments at the NL region further suggests that the myelin extension capacity of oligodendrocytes differs between regions, highlighting regional differences in intrinsic properties of oligodendrocyte as the most prominent determinant of nodal spacing variation. However, as you correctly pointed out, our findings do not establish direct causation.

In the future, developing methods to artificially manipulate myelin length could provide a more definitive demonstration of causality. Given these considerations, we have modified the title to replace "determine" with "underlie", ensuring that our conclusions are presented with appropriate nuance.

(6) The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:

(6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)

(6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)

(6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812).

(6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

Thank you for your insightful suggestions. We have incorporated the relevant references you provided and revised the manuscript accordingly to contextualize our findings within the existing literature.

Minor comments:

(7) Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.

Response: 

Thank you for your suggestion. We have corrected the units in Figure 1G to µm

(8) The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?

Oligodendrocytes and OPCs have small cell bodies, often comparable in size to their nuclei. The central void in the ring-like Olig2 staining pattern appears too small to represent the nucleus. Additionally, a similar ring-like appearance is observed in BrdU labeling (Figure 5G), suggesting that this staining pattern may reflect nuclear morphology or other structural features.

Significance

In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

The main finding of our study is that the primary determinant of the biased nodal spacing pattern in the sound localization circuit is the regional heterogeneity in the morphology of oligodendrocytes due to their intrinsic properties (e.g., their ability to produce and extend myelin sheaths) rather than the density of the cells. This was based on our observations that a reduction of oligodendrocyte density by A3V-eTeNT expression caused unmyelinated segments but did not increase internodal length (Figure 6), further revealing the importance of oligodendrocyte density in ensuring full myelination for the axons with short internodes. Thus, we think that our study could propose the significance of oligodendrocyte heterogeneity in the circuit function as well as in the nodal spacing using experimental manipulation of oligodendrocyte density. 

Reviewer #3:

Evidence, reproducibility and clarity

The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing. I have some major concerns:

(1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.

Thank you for your insightful comment regarding the potential role of pre-nodal clusters in determining internodal length. Indeed, studies in zebrafish have suggested that pre-nodal clustering of node components prior to myelination may prefigure internodal length (Vagionitis et al., 2022). We have incorporated a discussion on whether such pre-nodal clusters could contribute to regional differences in nodal spacing in our manuscript (page 9, line 35).

Whether pre-nodal clusters are detectable before myelination appears to depend on neuronal subpopulation (Freeman et al., 2015). To investigate the presence of pre-nodal clusters along NM axons in the brainstem auditory circuit, we previously attempted to visualize AnkG signals at E13 and E14. However, we did not observe clear structures indicative of pre-nodal clusters; instead, we only detected sparse fibrous AnkG signals with weak Nav clustering at their ends, consistent with hemi-node features. This result does not exclude the possibility of pre-nodal clusters on NM axons, as the detection limit of immunostaining cannot be ruled out. In brainstem slices, where axons are densely packed, nodal molecules are expressed at low levels across a wide area, leading to a high background signal in immunostaining, which may mask weak pre-nodal cluster signals prior to myelination. Regarding the comment on Figure 1D, we assume you are referring to Figure 2D based on the context. The lack of clarity in the high-magnification images in Figure 2D results from both the high background signal and the limited penetration of the MAG antibody. Furthermore, we are unable to verify Neurofascin accumulation at pre-nodal clusters, as there is currently no commercially available antibody suitable for use in chickens, despite our over 20 years of efforts to identify one for AIS research. Therefore, current methodologies pose significant challenges in visualizing pre-nodal clusters in our model. Future advancements, such as exogenous expression of fluorescently tagged Neurofascin at appropriate densities or knock-in tagging of endogenous molecules, may help overcome these limitations.

However, a key issue to be discussed in this study is not merely the presence or absence of prenodal clusters, but rather whether pre-nodal clusters—if present—would determine regional differences in internodal length. To address this possibility, we have added new data in Figure 6I, measuring the length of unmyelinated segments that emerged following A3V-eTeNT expression.

If pre-nodal clusters were fixed before myelination and predetermined internodal length, then the length of unmyelinated segments should be equal to or a multiple of the typical internodal length. However, our data showed that unmyelinated segments in the NL region were less than half the length of the typical NL internodal length, contradicting the hypothesis that fixed pre-nodal clusters determine internodal length along NM axons in this region.

(2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.

As mentioned in our reply to comment 2 by Reviewer #1, the diameter of NM axons was already evaluated using electron microscopy (EM) in the pioneering study by Seidl et al., (2010). Additionally, EM-based analysis makes it difficult to clearly distinguish between the main trunk of NM axons and thin collateral branches at the NL region. Accordingly, we did not do the EM analysis in this revision. 

In Figure 4, we used palGFP, which is targeted to the cell membrane, allowing us to measure axon diameter by evaluating the distance between two membrane signal peaks. This approach minimizes the influence of the blurring of fluorescence signals on diameter measurements. Thus, we believe that our method is sufficient to evaluate the relative difference in axon diameters between regions and hence to show that axon diameter is not the primary determinant of the 3-fold difference in internodal length between regions. 

(3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

The heterogeneity in oligodendrocyte morphology would reflect differences in gene profiles, which, in turn, may arise from differences in their developmental origin and/or pericellular microenvironment of OPCs. We made this point as clear as possible in Discussion (page 9, line 21).

Significance

The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

I think this is a really great point to bring up and a great way to connect to the audience he's speaking to. In my experience at least, I know that I tend to overanalyze a lot of my work and knowing that someone else does that too makes me feel more at ease.

The blind man already has a image in his head of what the husband looks like, how he is as a person, what he does in life, etc

make sure head is turned to the opposite side to prevent them from rolling

!

Author response:

The following is the authors’ response to the previous reviews.

Reviewer #1 (Public Review):

The authors observed a decline in autophagy and proteasome activity in the context of Milton knockdown. Through proteomic analysis, they identified an increase in the protein levels of eIF2β, subsequently pinpointing a novel interaction within eIF subunits where eIF2β contributes to the reduction of eIF2α phosphorylation levels. Furthermore, they demonstrated that overexpression of eIF2β suppresses autophagy and leads to diminished motor function. It was also shown that in a heterozygous mutant background of eIF2β, Milton knockdown could be rescued. This work represents a novel and significant contribution to the field, revealing for the first time that the loss of mitochondria from axons can lead to impaired autophagy function via eIF2β, potentially influencing the acceleration of aging.

Thank you so much for your review and comments.

Reviewer #2 (Public Review):

In the manuscript, the authors aimed to elucidate the molecular mechanism that explains neurodegeneration caused by the depletion of axonal mitochondria. In Drosophila, starting with siRNA depletion of Milton and Miro, the authors attempted to demonstrate that the depletion of axonal mitochondria induces the defect in autophagy. From proteome analyses, the authors hypothesized that autophagy is impacted by the abundance of eIF2β and the phosphorylation of eIF2α. The authors followed up the proteome analyses by testing the effects of eIF2β overexpression and depletion on autophagy. With the results from those experiments, the authors proposed a novel role of eIF2β in proteostasis that underlies neurodegeneration derived from the depletion of axonal mitochondria.

The manuscript has several weaknesses. The reader should take extra care while reading this manuscript and when acknowledging the findings and the model in this manuscript.

The defect in autophagy by the depletion of axonal mitochondria is one of the main claims in the paper. The authors should work more on describing their results of LC3-II/LC3-I ratio, as there are multiple ways to interpret the LC3 blotting for the autophagy assessment. Lysosomal defects result in the accumulation of LC3-II thus the LC3-II/LC3-I ratio gets higher. On the other hand, the defect in the early steps of autophagosome formation could result in a lower LC3-II/LC3-I ratio. From the results of the actual blotting, the LC3-I abundance is the source of the major difference for all conditions (Milton RNAi and eIF2β overexpression and depletion).

Thank you so much for your review and comments. As the reviewer pointed out, LC3-II/LC3- I ratio changes do not necessarily indicate autophagy defects. However, since p62 accumulation (Figure 2B, 2E, 3E, Figure 8C, Figure 9C), these results collectively suggest that autophagy is lowered.

As the reviewer pointed out and we described in v2, milton knockdown, eIF2β overexpression and heterozygosity increase LC3-I abundance. We do not know how these conditions increase LC3-I at this moment. We will investigate the cause of the increase in LC3-I by milton knockdown and how it contribute to impaired autophagy. We added this discussion as:

Lines 388-393; ‘Our results also suggest that milton knockdown and overexpression of eIF2β affect autophagy via increased LC3-I abundance (Figures 2 and 7), suggesting an unconventional mechanism of autophagy suppression. To our knowledge, the roles of eIF2β in aging and autophagy independent of ISR have not been reported. Our results revealed a novel function of eIF2β to maintain proteostasis during aging, while further investigation is required to elucidate underlying mechanisms.’

Another main point of the paper is the up-regulation of eIF2β by depleting the axonal mitochondria leads to the proteostasis crisis. This claim is formed by the findings from the proteome analyses. The authors should have presented their proteomic data with much thorough presentation and explanation. As in the experiment scheme shown in Figure 4A, the author did two proteome analyses: one from the 7-day-old sample and the other from the 21-day-old sample. The manuscript only shows a plot of the result from the 7-day-old sample, but that of the result from the 21-day-old sample. For the 21-day-old sample, the authors only provided data in the supplemental table, in which the abundance ratio of eIF2β from the 21-day-old sample is 0.753, meaning eIF2β is depleted in the 21-day-old sample. The authors should have explained the impact of the eIF2β depletion in the 21-day-old sample, so the reader could fully understand the authors' interpretation of the role of eIF2β on proteostasis.

Thank you for pointing it out. Plots of the 21-day-old proteome results was included in the main figure (Figure 4C) in v2. In this revision, we further analyzed age-dependent changes of eIF2β levels by western blotting (Figure 4G). We found that eIF2β levels increased during aging until 49-day-old then reduced at 63-day-old (Figure 4G in the revised manuscript). At the young age, eIF2β levels were higher in milton knockdown brain compared to the control , and eIF2β levels were lower in milton knockdown brains than those in the control. These results suggest that milton knockdown accelerates age-dependent changes in eIF2β. We added these results and discussion in the revised manuscript.

Lines 240-243: ‘We also investigated age-dependent changes in eIF2β by western blotting of control flies at 7-, 21-, 35-, and 49-, and 63-day-old. eIF2β levels increased during aging until 49-day-old (Figure 4G). These results suggest that upregulation of eIF2β in milton knockdown fly brain reflects early an onset of age-dependent increase of eIF2β levels.’

Lines 363-368: ‘We also found that eIF2β protein levels increase in an age-dependent manner until 49-day-old and reduces after that (Figure 4G). In the brains with neuronal knockdown of milton, eIF2β levels were higher at 7-day-old than those in control and lower at the 21-day-old (Figure 4D and Supplementary table). These results suggest that milton knockdown is likely accelerating age-dependent changes rather than increasing their magnitude.’Our new data indicate that eIF2β levels increase during aging in control flies until 49-day-old, then reduce at 63-day-old (included as Figure 4G in the revised manuscript). These age- dependent changes might explain the reduction in eIF2β levels in Milton knockdown compared to the control in middle age: higher eIF2β levels in milton knockdown flies at a young age than control and lower eIF2β levels in the middle-aged flies may reflect premature aging.

We included these sentences in the discussion section:

Lines 240-243:‘We also investigated age-dependent changes in eIF2β by western blotting of control flies at 7-, 21-, 35-, and 49-, and 63-day-old. eIF2β levels increased during aging until 49-day-old (Figure 4G). These results suggest that upregulation of eIF2β in milton knockdown fly brain reflects early an onset of age-dependent increase of eIF2β levels.’

Lines 359-371: ‘Our results suggest that the loss of axonal mitochondria is an event upstream of proteostasis collapse during aging. The number of puncta of ubiquitinated proteins was higher in milton knockdown at 14-day-old, but there was no significant difference at 30-day-old (Figure 1). Proteome analyses also showed that age-related pathways, such as immune responses, are enhanced in young flies with milton knockdown (Table 2). We also found that eIF2β protein levels increase in an age-dependent manner until 49-day-old and reduces after that (Figure 4G). In the brains with neuronal knockdown of milton, eIF2β levels were higher at 7-day-old than those in control and lower at the 21-day-old (Figure 4D and Supplementary table). These results suggest that milton knockdown is likely accelerating age-dependent changes rather than increasing their magnitude. Disruption of proteostasis is expected to contribute neurodegeneration38 , and it would be interesting to analyze the sequence of protein accumulation and axonal degeneration in milton knockdown (24,29 and Figure 1) in detail with higher time resolution.’

With our new data, we revised some of our responses to the first round of reviewer’s comments.

Reviewer #1 (Public Review):

The authors observed a decline in autophagy and proteasome activity in the context of Milton knockdown. Through proteomic analysis, they identified an increase in the protein levels of eIF2β, subsequently pinpointing a novel interaction within eIF subunits where eIF2β contributes to the reduction of eIF2α phosphorylation levels. Furthermore, they demonstrated that overexpression of eIF2β suppresses autophagy and leads to diminished motor function. It was also shown that in a heterozygous mutant background of eIF2β, Milton knockdown could be rescued. This work represents a novel and significant contribution to the field, revealing for the first time that the loss of mitochondria from axons can lead to impaired autophagy function via eIF2β, potentially influencing the acceleration of aging. To further support the authors' claims, several improvements are necessary, particularly in the methods of quantification and the points that should be demonstrated quantitatively. It is crucial to investigate the correlation between aging and the proteins eIF2β and eIF2α.

Thank you so much for your review and comments. We included analyses of protein levels of eIF2α, eIF2β, and eIF2γ at 7 days and 21 days (Figure 4D). The manuscript was revised as below;

Lines 246-249 ‘As for the other subunits of eIF2 complex, proteome analysis did not detect a significant difference in the protein levels of eIF2α and eIF2γ between milton knockdown and control flies at 7 and 21 days (Figure 4D).’

NEW TEXT: We analyzed age-dependent changes of eIF2β levels in more detail by western blotting (Figure 4G). We found that eIF2β levels increased during aging until 49-day-old then reduced at 63-day-old (Figure 4G in the revised manuscript). At the young age, eIF2β levels were higher in milton knockdown brain compared to the control , and eIF2β levels were lower in milton knockdown brains than those in the control. These results suggest that Milton knockdown accelerates age-dependent changes in eIF2β.. We added these results and discussion in the revised manuscript.

NEW TEXT: Lines 240-243: ‘We also investigated age-dependent changes in eIF2β by western blotting of control flies at 7-, 21-, 35-, and 49-, and 63-day-old. eIF2β levels increased during aging until 49-day-old (Figure 4G). These results suggest that upregulation of eIF2β in milton knockdown fly brain reflects early an onset of age-dependent increase of eIF2β levels.’

NEW TEXT: Lines 363-368: ‘We also found that eIF2β protein levels increase in an age-dependent manner until 49-day-old and reduces after that (Figure 4G). In the brains with neuronal knockdown of milton, eIF2β levels were higher at 7-day-old than those in control and lower at the 21-day-old (Figure 4D and Supplementary table). These results suggest that milton knockdown is likely accelerating age-dependent changes rather than increasing their magnitude.’

Reviewer #2 (Public Review):

In the manuscript, the authors aimed to elucidate the molecular mechanism that explains neurodegeneration caused by the depletion of axonal mitochondria. In Drosophila, starting with siRNA depletion of Milton and Miro, the authors attempted to demonstrate that the depletion of axonal mitochondria induces the defect in autophagy. From proteome analyses, the authors hypothesized that autophagy is impacted by the abundance of eIF2β and the phosphorylation of eIF2α. The authors followed up the proteome analyses by testing the effects of eIF2β overexpression and depletion on autophagy. With the results from those experiments, the authors proposed a novel role of eIF2β in proteostasis that underlies neurodegeneration derived from the depletion of axonal mitochondria.

The manuscript has several weaknesses. The reader should take extra care while reading this manuscript and when acknowledging the findings and the model in this manuscript.

The defect in autophagy by the depletion of axonal mitochondria is one of the main claims in the paper. The authors should work more on describing their results of LC3-II/LC3-I ratio, as there are multiple ways to interpret the LC3 blotting for the autophagy assessment. Lysosomal defects result in the accumulation of LC3-II thus the LC3-II/LC3-I ratio gets higher. On the other hand, the defect in the early steps of autophagosome formation could result in a lower LC3-II/LC3-I ratio. From the results of the actual blotting, the LC3-I abundance is the source of the major difference for all conditions (Milton RNAi and eIF2β overexpression and depletion). In the text, the authors simply state the observation of their LC3 blotting. The manuscript lacks an explanation of how to evaluate the LC3-II/LC3-I ratio. Also, the manuscript lacks an elaboration on what the results of the LC3 blotting indicate about the state of autophagy by the depletion of axonal mitochondria.

Thank you for pointing it out, and we apologize for an insufficient description of the result. We included quantitation of the levels of LC3-I and LC3-II in Figures 2A, 2D, 3D, 7B (Figure 6B in the previous version), and 8B (Figure 7B in the previous version). As the reviewer pointed out, LC3-II/LC3-I ratio changes do not necessarily indicate autophagy defects. However, since p62 accumulation (Figure 2B, 2E, 3E, 7C (Figure 6C in the previous version), 8C (Figure 7C in the previous version)), these results collectively suggest that autophagy is lowered. We revised the manuscript to include this discussion as below:

Lines 174-186 ‘During autophagy progression, LC3 is conjugated with phosphatidylethanolamine to form LC3-II, which localizes to isolation membranes and autophagosomes. LC3-I accumulation occurs when autophagosome formation is impaired, and LC3-II accumulation is associated with lysosomal defects31,32. p62 is an autophagy substrate, and its accumulation suggests autophagic defects31,32. We found that milton knockdown increased LC3-I, and the LC3-II/LC3-I ratio was lower in milton knockdown flies than in control flies at 14-day-old (Figure 2A). We also analyzed p62 levels in head lysates sequentially extracted using detergents with different stringencies (1% Triton X-100 and 2% SDS). Western blotting revealed that p62 levels were increased in the brains of 14-day-old of milton knockdown flies (Figure 2B). The increase in the p62 level was significant in the Triton X-100- soluble fraction but not in the SDS-soluble fraction (Figure 2B), suggesting that depletion of axonal mitochondria impairs the degradation of less-aggregated proteins.’

Line 189-190: 'At 30 day-old, LC3-I was still higher, and the LC3-II/LC3-I ratio was lower, in milton knockdown compared to the control (Figure 2D).’

Line 202-203: ‘However, in contrast with milton knockdown, Pfk knockdown did not affect the levels of LC3-I, LC3-II or the LC3-II/LC3-I ratio (Figure 3D).’

Line 279-285: ‘Neuronal overexpression of eIF2β increased LC3-II, while the LC3-II/LC3-I ratio was not significantly different (Figure 7A and B). Overexpression of eIF2β significantly increased the p62 level in the Triton X-100-soluble fraction (Figure 7C, 4-fold vs. control, p <0.005 (1% Triton X-100)) but not in the SDS-soluble fraction (Figure 7C, 2-fold vs. control, p\= 0.062 (2% SDS)), as observed in brains of milton knockdown flies (Figure 2B). These data suggest that neuronal overexpression of eIF2β accumulates autophagic substrates.’

Line 311-319: ‘Neuronal knockdown of milton causes accumulation of autophagic substrate p62 in the Triton X-100-soluble fraction (Figure 2B), and we tested if lowering eIF2β ameliorates it. We found that eIF2β heterozygosity caused a mild increase in LC3-I levels and decreases in LC3-II levels, resulting in a significantly lower LC3-II/LC3-I ratio in milton knockdown flies (Figure 8B). eIF2β heterozygosity decreased the p62 level in the Triton X- 100-soluble fraction in the brains of milton knockdown flies (Figure 8C). The p62 level in the SDS-soluble fraction, which is not sensitive to milton knockdown (Figure 2B), was not affected (Figure 8C). These results suggest that suppression of eIF2β ameliorates the impairment of autophagy caused by milton knockdown.’

Another main point of the paper is the up-regulation of eIF2β by depleting the axonal mitochondria leads to the proteostasis crisis. This claim is formed by the findings from the proteome analyses. The authors should have presented their proteomic data with much thorough presentation and explanation. As in the experiment scheme shown in Figure 4A, the author did two proteome analyses: one from the 7-day-old sample and the other from the 21-day-old sample. The manuscript only shows a plot of the result from the 7-day-old sample, but that of the result from the 21-day-old sample. For the 21-day-old sample, the authors only provided data in the supplemental table, in which the abundance ratio of eIF2β from the 21-day-old sample is 0.753, meaning eIF2β is depleted in the 21-day-old sample. The authors should have explained the impact of the eIF2β depletion in the 21-day-old sample, so the reader could fully understand the authors' interpretation of the role of eIF2β on proteostasis.

NEW TEXT: Thank you for pointing it out. We included plots of the 21-day-old proteome results as a part of the main figure (Figure 4C). As the reviewer pointed out, eIF2β protein levels are lower in milton knockdown background at the 21-day-old compared to the control. Since a reduction in the eIF2_β_ ameliorated milton knockdown-induced locomotor defects in aged flies (Figure 7D), the reduction in eIF2β observed in the 21-day-old milton knockdown flies is not likely to negatively contribute to milton knockdown-induced defects. Our new data indicate that eIF2β levels increase during aging in control flies until 49-day-old, then reduce at 63-day-old (included as Figure 4G in the revised manuscript). These age-dependent changes might explain the reduction in eIF2β levels in Milton knockdown compared to the control in middle age: higher eIF2β levels in milton knockdown flies at a young age than control and lower eIF2β levels in the middle-aged flies may reflect premature aging.

NEW TEXT: We included these sentences in the discussion section:

NEW TEXT: Lines 240-243:‘We also investigated age-dependent changes in eIF2β by western blotting of control flies at 7-, 21-, 35-, and 49-, and 63-day-old. eIF2β levels increased during aging until 49-day-old (Figure 4G). These results suggest that upregulation of eIF2β in milton knockdown fly brain reflects early an onset of age-dependent increase of eIF2β levels.’

NEW TEXT: Lines 359-371: ‘Our results suggest that the loss of axonal mitochondria is an event upstream of proteostasis collapse during aging. The number of puncta of ubiquitinated proteins was higher in milton knockdown at 14-day-old, but there was no significant difference at 30-day-old (Figure 1). Proteome analyses also showed that age-related pathways, such as immune responses, are enhanced in young flies with milton knockdown (Table 2). We also found that eIF2β protein levels increase in an age-dependent manner until 49-day-old and reduces after that (Figure 4G). In the brains with neuronal knockdown of milton, eIF2β levels were higher at 7-day-old than those in control and lower at the 21-day-old (Figure 4D and Supplementary table). These results suggest that milton knockdown is likely accelerating age-dependent changes rather than increasing their magnitude. Disruption of proteostasis is expected to contribute neurodegeneration38 , and it would be interesting to analyze the sequence of protein accumulation and axonal degeneration in milton knockdown (24,29 and Figure 1) in detail with higher time resolution.’

The manuscript consists of several weaknesses in its data and explanation regarding translation.

(1) The authors are likely misunderstanding the effect of phosphorylation of eIF2α on translation. The P-eIF2α is inhibitory for translation initiation. However, the authors seem to be mistaken that the down-regulation of P-eIF2α inhibits translation.

We are sorry for our insufficient explanation in the previous version. As the reviewer pointed out, it is well known that the phosphorylated form of eIF2α inhibits translation initiation. Neuronal knockdown of milton caused a reduction in p-eIF2α (Figure 5D and E (Figure 4J and K in the previous version)), and it also lowered translation (Figure 6 (Figure 5 in the previous version)); the relationship between these two events is currently unclear. We do not think that a reduction in the p-eIF2α suppressed translation; rather, we propose that the unbalance of expression levels of the components of eIF2 complexes negatively affects translation. We revised discussion sections to describe our interpretation more in detail as below:

Line 374-384: ‘eIF2β is a component of eIF2, which meditates translational regulation and ISR initiation. When ISR is activated, phosphorylated eIF2α suppresses global translation and induces translation of ATF4, which mediates transcription of autophagy-related genes39,40. Since ISR can positively regulate autophagy, we suspected that suppression of ISR underlies a reduction in autophagic protein degradation. We found neuronal knockdown of milton reduced phosphorylated eIF2α, suggesting that ISR is reduced (Figure 5). However, we also found that global translation was reduced (Figure 6). Increased levels of eIF2β might disrupt the eIF2 complex or alter its functions. The stoichiometric mismatch caused by an imbalance of eIF2 components may inhibit ISR induction. Supporting this model, we found that eIF2β upregulation reduced the levels of p-eIF2α (Figure 7).’We have revised the graphical abstract and removed the eIF2 complex since its role in the loss of proteostasis caused by milton knockdown has not been elucidated yet.

(2) The result of polysome profiling in Figure 4H is implausible. By 10%-25% sucrose density gradient, polysomes are not expected to be observed. The authors should have used a gradient with much denser sucrose, such as 10-50%.

Thank you for pointing it out. It was a mistake of 10-50%, and we apologize for the oversight. It was corrected (Figure 6 (Figure 5 in the previous version)).

(3) Also on the polysome profiling, as in the method section, the authors seemed to fractionate ultra-centrifuged samples from top to bottom and then measured A260 by a plate reader. In that case, the authors should have provided a line plot with individual data points, not the smoothly connected ones in the manuscript.

Thank you for pointing it out. We revised the graph (Figure 6 (Figure 5 in the previous version)).

(4) For both the results from polysome profiling and puromycin incorporation (Figure 4H and I), the difference between control siRNA and Milton siRNA are subtle, if not nonexistent. This might arise from the lack of spatial resolution in their experiment as the authors used head lysate for these data but the ratio of Phospho-eIF2α/eIF2α only changes in the axons, based on their results in Figure 4E-G. The authors could have attempted to capture the spatial resolution for the axonal translation to see the difference between control siRNA and Milton siRNA.

Thank you for your comment. We agree that it would be an interesting experiment, but it will take a considerable amount of time to analyze axonal translation with spatial resolution. We will try to include such analyses in the future. For this manuscript, we revised the discussion section to include the reviewer's suggestion as below;

Lines 355-357: ‘Further analyses to dissect the effects of milton knockdown on proteostasis and translation in the cell body and axon by experiments with spatial resolution would be needed.’

Recommendations for the authors:

From the Reviewing Editor:

As the Reviewing Editor, I have read your manuscript and the associated peer reviews. I have concerns about publishing this work in its current form. I think that your manuscript cannot claim to have found a novel function of eIF2beta because of technical uncertainties and conceptual problems that should be addressed.

Thank you so much for your review and comments. We addressed all the concerns raised by the reviewers. Point-by-point responses are listed below.

First, your manuscript is based partly on what appears to be a mistaken understanding of the mechanistic basis of the ISR. Specifically, eIF2 is a heterotrimeric complex of alpha, beta, and gamma subunits. When eIF2a is phosphorylated, the heterotrimer adopts a new conformation. This conformation directly binds and inhibits eIF2B, the decameric GEF that exchanges the GDP bound to the gamma subunit of the eIF2 complex for GTP. Unless I misunderstood your paper, you seem to propose that decreasing levels of phospho-eIF2a will inhibit translation, but this is backward from what we know about the ISR.

Thank you for your insightful comment, and we are sorry for the confusion. We did not mean to propose that decreasing levels of phospho-eIF2_a_ inhibits translation. We apologize for our insufficient explanation, which might have caused a misunderstanding (Lines 312-318 in the original version). We agree with the reviewer that ‘mismatch due to elevated eIF2-beta could change the behavior of the ISR’. We revised the text in the result section as follows:

Lines 263-268 (in the Result section) ‘Phosphorylation of eIF2α induces conformational changes in the eIF2 complex and inhibits global translation36. To analyze the effects of milton knockdown on translation, we performed polysome gradient centrifugation to examine the level of ribosome binding to mRNA. Since p-eIF2α was downregulated, we hypothesized that milton knockdown would enhance translation. However, unexpectedly, we found that milton knockdown significantly reduced the level of mRNAs associated with polysomes (Figure 6A and B).’

Lines 374-384 (in the Discussion section): ‘eIF2β is a component of eIF2, which meditates translational regulation and ISR initiation. When ISR is activated, phosphorylated eIF2α suppresses global translation and induces translation of ATF4, which mediates transcription of autophagy-related genes39,40. Since ISR can positively regulate autophagy, we suspected that suppression of ISR underlies a reduction in autophagic protein degradation. We found neuronal knockdown of milton reduced phosphorylated eIF2α, suggesting that ISR is reduced (Figure 5). However, we also found that global translation was reduced (Figure 6). Increased levels of eIF2β might disrupt the eIF2 complex or alter its functions. The stoichiometric mismatch caused by an imbalance of eIF2 components may inhibit ISR induction. Supporting this model, we found that eIF2β upregulation reduced the levels of p-eIF2α (Figure 7).’

It may be possible that a stoichiometric mismatch due to elevated eIF2-beta could change the behavior of the ISR, but your paper doesn't adequately address the expression levels of all three eIF2 subunits: alpha, beta, and gamma. The proteomic data shown in Fig 4B is unconvincing on its own because the changes in the beta subunit are subtle. The Western blot in Figure 4C suggests that the KD changes the mass or mobility of the beta subunit, and most importantly, there are no Western blots measuring the levels of eIF2a, eIF2a-phospho, or eIF2-gamma.

We appreciate the reviewer’s comment and agree that the stoichiometric mismatch due to elevated eIF2β may interfere with ISR. We found overexpression of eIF2β lowered p-eIF2 alpha (Figure S2 in V1), which supports this model. We included this data in the main figure in the revised manuscript (Figure 7D) and revised the text as below:

Lines 286-289: ‘Since milton knockdown reduced the p-eIF2α level (Figure 5E), we asked whether an increase in eIF2β affects p-eIF2α. Neuronal overexpression of eIF2β did not affect the eIF2α level but significantly decreased the p-eIF2α level (Figure 7D and E).’

Expression data of eIF2α and eIF2γ from proteomic analyses has been extracted from proteome analyses and included as a table (Figure 4D). Western blots of phospho-eIF2a (Figure S1 in V1) in the main figure (Figure 5B). The result section was revised as below;

Lines 246-249: ‘As for the other subunits of eIF2 complex, proteome analysis did not detect a significant difference in the protein levels of eIF2α and eIF2γ between milton knockdown and control flies at 7 and 21 days (Figure 4D).’

NEW TEXT: We also analyzed age-dependent changes of eIF2β by western blotting and found that eIF2β increased during aging until 49-day-old. We included this result as Figure 4G and added these sentences in the result section:

NEW TEXT: Line 240-243: ‘We also investigated age-dependent changes in eIF2β by western blotting of control flies at 7-, 21-, 35-, and 49-, and 63-day-old. eIF2β levels increased during aging until 49-day-old (Figure 4G). These results suggest that upregulation of eIF2β in milton knockdown fly brain reflects early an onset of age-dependent increase of eIF2β levels.

Reviewer #1 (Recommendations For The Authors):

L125-128: In this section, while the efficiency of Milton knockdown is referenced from a previous publication, it is necessary to also mention that the Miro knockdown has been similarly reported in the literature. Additionally, the Methods section lacks details on the Miro RNAi line used, and Table 2 does not include the genotype for Miro RNAi. This information should be included for clarity and completeness.

Thank you for pointing it out. Knockdown efficiency with this strain has been reported (Iijima- Ando et al., PLoS Genet, 2012). We revised the text to include citation and knockdown efficiency as follows:

Lines 136-147: ‘There was no significant increase in ubiquitinated proteins in milton knockdown flies at 1-day old, suggesting that the accumulation of ubiquitinated proteins caused by milton knockdown is age-dependent (Figure S1). We also analyzed the effect of the neuronal knockdown of Miro, a partner of milton, on the accumulation of ubiquitin-positive proteins. Since severe knockdown of Miro in neurons causes lethality, we used UAS-Miro RNAi strain with low knockdown efficiency, whose expression driven by elav-GAL4 caused 30% reduction of Miro mRNA in head extract24. Although there was a tendency for increased ubiquitin- positive puncta in Miro knockdown brains, the difference was not significant (Figure 1B, p>0.05 between control RNAi and Miro RNAi). These data suggest that the depletion of axonal mitochondria induced by milton knockdown leads to the accumulation of ubiquitinated proteins before neurodegeneration occurs.’

L132-L136: The current phrasing in this section suggests an increase in ubiquitinated proteins for both Milton and Miro knockdowns. However, since there is no significant difference noted for Miro, it is incorrect to state an increase in ubiquitin-positive puncta. Furthermore, combining the results of Milton knockdown to claim an increase in ubiquitinated proteins prior to neurodegeneration is misleading. At the very least, the expression here needs to be moderated to accurately reflect the findings.

Thank you for pointing it out. We revised the text as above.

L137-L141: Results in Figure 1 indicate that Milton knockdown leads to an increase in ubiquitinated proteins at 14 days, while Miro knockdown shows no difference from the control at either 14 or 30 days. Conversely, both the control and Miro exhibit an increase in ubiquitinated proteins with aging, but this trend does not seem to apply to Milton knockdown. This observation suggests that Milton KD may not affect the changes in protein quality control associated with aging. It implies that Milton's function might be more related to protein homeostasis in younger cells, or that changes due to aging might overshadow the effects of Milton knockdown. These interpretations should be included in the Results or Discussion sections for a more comprehensive analysis.

NEW TEXT: Thank you for your insightful comment. As you mentioned, the accumulation of ubiquitinated proteins significantly increases only in young flies. Age-related pathways, such as immune responses, are highlighted in young milton knockdown flies but not in the aged flies. Our new result indicates that eIF2β increases during aging in control flies (included as Figure 4G in the revised manuscript), and upregulation of eIF2β in milton knockdown is only observed at a young age. These results suggest that milton knockdown does not increase the magnitude of age-dependent changes but accelerates their onset. We revised the text to include those points as follows:

NEW TEXT: Lines 152-153: ‘These results suggest that depletion of axonal mitochondria may have more impact on proteostasis in young neurons than in old neurons.’

NEW TEXT: Lines 359-371: ‘Our results suggest that the loss of axonal mitochondria is an event upstream of proteostasis collapse during aging. The number of puncta of ubiquitinated proteins was higher in milton knockdown at 14-day-old, but there was no significant difference at 30-day- old (Figure 1). Proteome analyses also showed that age-related pathways, such as immune responses, are enhanced in young flies with milton knockdown (Table 2). We also found that eIF2β protein levels increase in an age-dependent manner until 49-day-old and reduces after that (Figure 4G). In the brains with neuronal knockdown of milton, eIF2β levels were higher at 7-day-old than those in control and lower at the 21-day-old (Figure 4 and Supplementary table). These results suggest that milton knockdown is likely accelerating age-dependent changes rather than increasing their magnitude. Disruption of proteostasis is expected to contribute neurodegeneration38 , and it would be interesting to analyze the sequence of protein accumulation and axonal degeneration in milton knockdown (24,29 and Figure 1) in detail with higher time resolution.’

L143 : Please remove the erroneously included quotation mark.

Thank you for pointing it out. We corrected it.

L145-L147:

While it is understood that Milton knockdown results in a reduction of mitochondria in axons, as reported previously and seemingly indicated in Figure 1E, this paper repeatedly refers to axonal depletion of mitochondria. Therefore, it would be beneficial to quantitatively assess the number of mitochondria in the axonal terminals located in the lamina via electron microscopy. Such quantification would robustly reinforce the argument that mitochondrial absence in axons is a consequence of Milton knockdown.

Thank you for pointing it out. We included quantitation of the number of mitochondria in the synaptic terminals (Figure 1E).

The text and figure legend was revised accordingly:

Lines 156-157: ‘As previously reported24, the number of mitochondria in presynaptic terminals decreased in milton knockdown (Figure 1E).’

The knockdown of Milton is known to reduce mitochondrial transport from an early stage, but what about swelling? By observing swelling at 1 day and 14 days, it may be possible to confirm the onset of swelling and discuss its correlation with the accumulation of ubiquitinated proteins.

Quantitation of axonal swelling has also been included (Figure 1F).

We appreciate the reviewer's comments on the correlation between the accumulation of ubiquitinated proteins and axonal swelling. Axonal swelling was not observed at 3-days-old (Iijima-Ando et al., PLoS Genetics, 2012), indicating that axonal swelling is an age-dependent event. Dense materials are found in swollen axons more often than in normal axons, suggesting a positive correlation between disruption of proteostasis and axonal damage. It would be interesting to analyze the time course of events further; however, we feel it is beyond the scope of this manuscript. We revised the text to include this discussion as:

Lines 157-160: ‘The swelling of presynaptic terminals, characterized by the enlargement and roundness, was not reported at 3-day-old24 but observed at this age with about 4% of total presynaptic terminals (Figure 1F, asterisks).’

Lines 162-167: ‘Dense materials are rarely found in age-matched control neurons, indicating that milton knockdown induces abnormal protein accumulation in the presynaptic terminals (Figure 1G and H). In milton knockdown neurons, dense materials are found in swollen presynaptic terminals more often than in presynaptic terminals without swelling, suggesting a positive correlation between the disruption of proteostasis and axonal damage (Figure 1G).’

Lines 369-371: ‘Disruption of proteostasis is expected to contribute neurodegeneration38 , and it would be interesting to analyze the sequence of protein accumulation and axonal degeneration in milton knockdown (24,29 and Figure 1) in detail with higher time resolution.’

L147-L151: Though Figures 1F and 1G provide qualitative representations, it is advisable to quantitatively assess whether dense materials significantly accumulate. Such quantitative analysis would be required to verify the accumulation of dense materials in the context of the study.

Thank you for pointing it out. We included quantitation of the number of neurons with dense material (Figure 1G). We revised the manuscript as follows:

Line 162-164: ‘Dense materials are rarely found in age-matched control neurons, indicating that milton knockdown induces abnormal protein accumulation in the presynaptic terminals (Figure 1G and H).’

Regarding Figure 1B, C:

Even though the count of puncta in the whole brain appears to be fewer than 400, the magnification of the optic lobe suggests a substantial presence of puncta. Please clarify in the Methods section what constitutes a puncta and whether the quantification in the whole brain is based on a 2D or 3D analysis. Detail the methodology used for quantification.

Thank you for your comment. We revised the method section to include more details as below:

Lines 440-443: ‘Quantitative analysis was performed using ImageJ (National Institutes of Health) with maximum projection images derived from Z-stack images acquired with same settings. Puncta was identified with mean intensity and area using ImageJ.’

What about 1-day-old specimens? Does Milton knockdown already show an increase in ubiquitinated protein accumulation at this early stage? Investigating whether ubiquitin-protein accumulation is involved in aging promotion or is already prevalent during developmental stages is a necessary experiment.

Thank you for your comment. We carried out immunostaining with an anti-ubiquitin antibody in the brains at 1-day-old. No significant difference was detected between the control and milton knockdown. This result has been included as Figure S1 in the revised manuscript. The result section was revised as below:

Line 136-139 ‘There was no significant increase in ubiquitinated proteins in milton knockdown flies at 1-day old, suggesting that the accumulation of ubiquitinated proteins caused by milton knockdown is age-dependent (Figure S1).’

For Figure 1E: In the Electron Microscopy section of the Methods, define how swollen axons were identified and describe the quantification methodology used.

Thank you for your comment. Swollen axons are, unlike normal axons, round in shape and enlarged. We revised the text as below;

Lines 157-160: ‘The swelling of presynaptic terminals, characterized by the enlargement and roundness, was not reported at 3-day-old24 but observed at this age with about 4% of total presynaptic terminals (Figure 1F, asterisks).’

Lines 689-691, Figure 1 legend: ‘Swollen presynaptic terminals (asterisks in (F)), characterized by the enlargement and higher circularity, were found more frequently in milton knockdown neurons.’

L218-L219: Throughout the text, the expression 'eIF2β is "upregulated" in response to Milton knockdown' is frequently used. However, considering the presented results, it might be more accurate to interpret that under the condition of Milton knockdown, eIF2β is not undergoing degradation but rather remains stable.

Thank you for pointing it out. We replaced ‘upregulated’ with ‘increased’ throughout the text.

L234-L235: On what basis is the conclusion drawn that there is a reduction? Given that three experiments have been conducted, it would be possible and more convincing to quantify the results to determine if there is a significant decrease.

Thank you for pointing it out. We quantified the AUC of polysome fraction and carried out a statistical analysis. There is a significant decrease in polysome in milton knockdown, and this result has been included in Figure 5B. We revised the figure and the legend accordingly.

L236: 5H-> 4H

Thank you for pointing it out, and we are sorry for the confusion. We corrected it.

L238-L239: Since there is no significant difference observed, it may not be accurate to interpret a reduction in puromycin incorporation.

Thank you for pointing it out. As described above, quantification of polysome fractions showed that milton knockdown significantly reduced polysome (Figure 6B (Figure 5B in the previous version)). We revised the manuscript as below;

Lines 267-268: ‘However, unexpectedly, we found that milton knockdown significantly reduced the level of mRNAs associated with polysomes (Figure 6A and B).’

Figure 5D and Figure 6D: Climbing assays have been conducted, but I believe experiments should also be performed to examine whether overexpression or heterozygous mutants of eIF2β induce or suppress degeneration.

Thank you for pointing it out. We analyzed the eyes with eIF2β overexpression for neurodegeneration. Although there was a tendency of elevated neurodegeneration in the retina with eIF2β overexpression, the difference between control and eIF2β overexpression did not reach statistical significance (Figure S2). This result has been included as Figure S2 in the revised manuscript, and the following sentences have been included in the text:

Lines 292-297: ‘We asked if eIF2β overexpression causes neurodegeneration, as depletion of axonal mitochondria in the photoreceptor neurons causes axon degeneration in an age- dependent manner24. eIF2β overexpression in photoreceptor neurons tends to increase neurodegeneration in aged flies, while it was not statistically significant (p>0.05, Figure S2).’

L271-L272: The results in Figure 6B are surprising. I anticipated a greater increase compared to the Milton knockdown alone. While p62 appears to be reduced, it is not clear why these results lead to the conclusion that lowering eIF2β rescues autophagic impairment. Please add a discussion section to address this point.

Thank you for pointing it out. We apologize for the unclear description of the result. Milton knockdown flies show p62 accumulation (Figure 2), and deleting one copy of eIF2beta in milton knockdown background reduced p62 accumulation (Figure 8C (Figure 7C in the previous version)). We revised the text as below:

Lines 311-319: ‘Neuronal knockdown of milton causes accumulation of autophagic substrate p62 in the Triton X-100-soluble fraction (Figure 2B), and we tested if lowering eIF2β ameliorates it. We found that eIF2β heterozygosity caused a mild increase in LC3-I levels and decreases in LC3-II levels, resulting in a significantly lower LC3-II/LC3-I ratio in milton knockdown flies (Figure 8B). eIF2β heterozygosity decreased the p62 level in the Triton X-100-soluble fraction in the brains of milton knockdown flies (Figure 8C). The p62 level in the SDS-soluble fraction, which is not sensitive to milton knockdown (Figure 2B), was not affected (Figure 8C). These results suggest that suppression of eIF2β ameliorates the impairment of autophagy caused by milton knockdown.’

L369: Please specify the source of the anti-ubiquitin antibody used.

Thank you for pointing it out. We included the antibody information in the method section.

Figure 7: While the relationship between Milton knockdown and the eIF2β and eIF2α proteins has been elucidated through the authors' efforts, I would like to see an investigation into whether eIF2β is upregulated and eIF2α phosphorylation is reduced in simply aged Drosophila. This would help us understand the correlation between aging and eIF2 protein dynamics.

Thank you for your comment. We agree that it is an important question, and we are working on it. However, we feel that it is beyond the scope of the current manuscript.

L645-L646: If the mushroom body is identified using mito-GFP, then include mito-GFP in the genotype listed in Supplementary Table 2.

We are sorry for the oversight. We corrected it in Supplementary Table 2.

Additionally, while it is presumed that the mito-GFP signal decreases in axons with Milton RNAi, how was the lobe tips area accurately selected for analysis? Please include these details along with a comprehensive description of the quantification methodology in the Methods section.

Thank you for your comment. Although the mito-GFP signal in the axon is weak in the milton knockdown neurons, it is sufficient to distinguish the mushroom body structure from the background. We revised the method section to include this information in the method section:

Line 443-447: ‘For eIF2α and p-eIF2α immunostaining, the mushroom body was detected by mitoGFP expression.’

Before the court hearing, he was cheerful and optimistic, but after the hearing he sat slumped, with his shoulders dropping and his head rising barely above the back of his chair, indicating that the hearing did not go well.

Reviewer #1: Evidentiary Rating: Potentially Informative

Written Review: The authors claim that a spin-enhanced fluorescent nanodiamond (FND) lateral flow test for SARS-CoV-2 antigen detection achieves up to 1,100-fold greater sensitivity than conventional gold nanoparticle LFTs using identical antibodies. In a large, blinded clinical evaluation, the assay demonstrated 95.1% sensitivity (Ct ≤ 30) and 100% specificity, enabling SARS-CoV-2 detection on average two days earlier than conventional LFTs and within 0.6 days of RT-qPCR. They assert that this quantum-enhanced diagnostic platform could be adapted to other infectious and non-infectious diseases. 1. The study represents the first large-scale blinded clinical evaluation of spin-enhanced nanodiamond LFTs, moving beyond proof-of-concept to a performance assessment with real clinical samples. 2. The antibody screening process using biolayer interferometry is well executed, but screening on a single recombinant antigen source introduces potential epitope bias; inclusion of diverse antigen sources could further validate pair robustness. 3. The direct head-to-head comparison with in-house AuNP LFTs using identical reagents is a strong methodological choice, eliminating confounding variables common in cross-platform sensitivity claims. 4. While the assay achieves sub-pg/mL LoDs, residual non-specific binding limits ultimate sensitivity exploitation; more work on membrane chemistry or blocking strategies could push performance closer to the theoretical limit. 5. The sample size is adequate for preliminary evaluation, but a larger, more demographically and geographically diverse cohort is necessary to confirm real-world performance, especially in asymptomatic and early infection cases. 6. The assay’s 95.1% sensitivity at Ct ≤ 30 exceeds WHO “desirable” criteria, but the drop in sensitivity at lower viral loads (<10⁴ copies/mL) should be discussed in terms of balancing infectiousness detection with overdiagnosis risk. 7. The ROC, Bayesian regression, and infection dynamics modelling are sophisticated and well described, but providing raw Ct distribution histograms for positive samples would help readers assess viral load representativeness.

Reviewer #1 (Public review):

Summary:

The authors aimed to investigate the cellular mechanisms underlying place field formation (PFF) in hippocampal CA1 pyramidal cells by performing in vivo two-photon calcium imaging in head-restrained mice navigating a virtual environment. Specifically, they sought to determine whether BTSP-like (behavioral time scale synaptic plasticity) events, characterized by large calcium transients, are the primary mechanism driving PFFs or if other mechanisms also play a significant role. Through their extensive imaging dataset, the authors found that while BTSP-like events are prevalent, a substantial fraction of new place fields are formed via non-BTSP-like mechanisms. They further observed that large calcium transients, often associated with BTSP-like events, are not sufficient to induce new place fields, indicating the presence of additional regulatory factors (possibly local dendritic spikes).

Strengths

The study makes use of a robust and extensive dataset collected from 163 imaging sessions across 45 mice, providing a comprehensive examination of CA1 place cell activity during navigation in both familiar and novel virtual environments. The use of two-photon calcium imaging allows the authors to observe the detailed dynamics of neuronal activity and calcium transients, offering insights into the differences between BTSP-like and non-BTSP-like PFF events. The study's ability to distinguish between these two mechanisms and analyze their prevalence under different conditions is a key strength, as it provides a nuanced understanding of how place fields are formed and maintained. The paper supports the idea that BTSP is not the only driving fore behind PFF, and other mechanisms are likely sufficient to drive PFF, and BTSP events may also be insufficient to drive PFF in some cases. The longer-than-usual virtual track used in the experiment allowed place cells to express multiple place fields, adding a valuable dimension to the dataset that is typically lacking in similar studies. Additionally, the authors took a conservative approach in classifying PFF events, ensuring that their findings were not confounded by noise or ambiguous activity.

Weaknesses

The stand out weakness of the paper is the lack of direct measures of BTSP events. Without direct confirmation that large calcium transients correspond to actual BTSP events (including associated complex spikes and calcium plateau potentials), concluding that BTSP is not necessary or sufficient for PFF formation is speculative (although I do believe it).

Reviewer #2 (Public review):

Summary:

The authors of this manuscript aim to investigate the formation of place fields (PFs) in hippocampal CA1 pyramidal cells. They focus on the role of behavioral time scale synaptic plasticity (BTSP), a mechanism proposed to be crucial for the formation of new PFs. Using in vivo two-photon calcium imaging in head-restrained mice navigating virtual environments, employing a classification method based on calcium activity to categorize the formation of place cells' place fields into BTSP, non-BTSP-like, and investigated their properties.

Strengths:

This work shows that place fields formation could induced by both BSTP and non-BSTP events, and it also provided a new and solid method to classify BTSP and non-BTSP place field formation using calcium image to the field. This work offers novel knowledge and new methods and factual evidence for other researchers in the field.

The method enabled the authors to reveal that while many PFs are formed by BTSP-like events, a significant number of PFs emerge with calcium dynamics that do not match BTSP characteristics, suggesting a diversity of mechanisms underlying PF formation. The characteristics of place fields under the first two categories are comprehensively described, including aspects such as formation timing, quantity, and width.

Weaknesses:

The authors have addressed the weaknesses in the revised version.

Reviewer #3 (Public review):

Summary:

In this manuscript, Sumegi et al. use calcium imaging in head-fixed mice to test whether new place fields tend to emerge due to events that resemble behavioral time scale plasticity (BTSP) or other mechanisms. An impressive dataset was amassed (163 sessions from 45 mice with 500-1000 neurons per sample) to study spontaneous emergence of new place fields in area CA1 that had the signature of BTSP. The authors observed that place fields could emerge due to BTSP and non-BTSP-like mechanisms. Interestingly, when non-BTSP mechanisms seemed to generate a place field, this tended to occur on a trial with a spontaneous reset in neural coding (a remapping event). Novelty seemed to upregulate non-BTSP events relative to BTSP events. Finally, large calcium transients (presumed plateau potentials) were not sufficient to generate a place field.

Strengths:

I found this manuscript to be exceptionally well written, well powered, and timely given the outstanding debate and confusion surrounding whether all place fields must arise from BTSP event. Working at the same institute, Albert Lee (e.g. Epszstein et al., 2011 - which should be cited) and Jeff Magee (e.g. Bittner et al., 2017) showed contradictory results for how place fields arise. These accounts have not fully been put toe-to-toe and reconciled in the literature. This manuscript addresses this gap and shows that both accounts are correct - place fields can emerge due to a pre-existing map and due to BTSP.

Weaknesses:

I find only three significant areas for improvement in the present study:

First, can it be concluded that non-BTSP events occur exclusively due to a global remapping event, as stated in the manuscript "these PFF surges included a high fraction of both non-BTSP- and BTSP-like PFF events, and were associated with global remapping of the CA1 representation"? Global remapping has a precise definition that involves quantifying the stability of all place fields recorded. Without a color scale bar in Figure 3D (which should be added), we cannot know whether the overall representations were independent before and after the spontaneous reset. It would be good to know if some neurons are able to maintain place coding (more often than expected by chance), suggestive of a partial-remapping phenomenon.

Second, BTSP has a flip side that involves weakening of existing place fields when a novel field emerges. Was this observed in the present study? Presumably place fields can disappear due to this bidirectional-BTSP or due to global remapping. For a full comparison of the two phenomena, the disappearance of place fields must also be assessed.

Finally, it would be good to know if place fields differ according to how they are born. For example, are there differences in reliability, width, peak rate, out of field firing, etc for those that arise due BTSP vs non-BTSP.

Comments on revisions:

The authors have mostly addressed my feedback. Compelling evidence for a fundamental observation.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

Summary:

The authors aimed to investigate the cellular mechanisms underlying place field formation (PFF) in hippocampal CA1 pyramidal cells by performing in vivo two-photon calcium imaging in head-restrained mice navigating a virtual environment. Specifically, they sought to determine whether BTSP-like (behavioral time scale synaptic plasticity) events, characterized by large calcium transients, are the primary mechanism driving PFFs or if other mechanisms also play a significant role. Through their extensive imaging dataset, the authors found that while BTSP-like events are prevalent, a substantial fraction of new place fields are formed via non-BTSP-like mechanisms. They further observed that large calcium transients, often associated with BTSP-like events, are not sufficient to induce new place fields, indicating the presence of additional regulatory factors (possibly local dendritic spikes).

Strengths

The study makes use of a robust and extensive dataset collected from 163 imaging sessions across 45 mice, providing a comprehensive examination of CA1 place-cell activity during navigation in both familiar and novel virtual environments. The use of two-photon calcium imaging allows the authors to observe the detailed dynamics of neuronal activity and calcium transients, offering insights into the differences between BTSP-like and non-BTSP-like PFF events. The study's ability to distinguish between these two mechanisms and analyze their prevalence under different conditions is a key strength, as it provides a nuanced understanding of how place fields are formed and maintained. The paper supports the idea that BTSP is not the only driving force behind PFF, and other mechanisms are likely sufficient to drive PFF, and BTSP events may also be insufficient to drive PFF in some cases. The longer-than-usual virtual track used in the experiment allowed place cells to express multiple place fields, adding a valuable dimension to the dataset that is typically lacking in similar studies. Additionally, the authors took a conservative approach in classifying PFF events, ensuring that their findings were not confounded by noise or ambiguous activity.

Weaknesses

Despite the impressive dataset, there are several methodological and interpretational concerns that limit the impact of the findings. Firstly, the virtual environment appears to be poorly enriched, relying mainly on wall patterns for visual cues, which raises questions about the generalizability of the results to more enriched environments. Prior studies have shown that environmental enrichment can significantly influence spatial coding, and it would be important to determine how a more immersive VR environment might alter the observed PFF dynamics. Secondly, the study relies on deconvolution methods in some cases to infer spiking activity from calcium signals without in vivo ground truth validation. This introduces potential inaccuracies, as deconvolution is an estimate rather than a direct measure of spiking, and any conclusions drawn from these inferred signals should be interpreted with caution. Thirdly, the figures would benefit from clearer statistical annotations and visual enhancements. For example, several plots lack indicators of statistical significance, making it difficult for readers to assess the robustness of the findings. Furthermore, the use of bar plots without displaying underlying data distributions obscures variability, which could be better visualized with violin plots or individual data points. The manuscript would also benefit from a more explicit breakdown of the proportion of place fields categorized as BTSP-like versus non-BTSP-like, along with clearer references to figures throughout the results section. Lastly, the authors' interpretation of their data, particularly regarding the sufficiency of large calcium transients for PFF induction, needs to be more cautious. Without direct confirmation that these transients correspond to actual BTSP events (including associated complex spikes and calcium plateau potentials), concluding that BTSP is not necessary or sufficient for PFF formation is speculative.

Reviewer #2 (Public review):

Summary:

The authors of this manuscript aim to investigate the formation of place fields (PFs) in hippocampal CA1 pyramidal cells. They focus on the role of behavioral time scale synaptic plasticity (BTSP), a mechanism proposed to be crucial for the formation of new PFs. Using in vivo two-photon calcium imaging in head-restrained mice navigating virtual environments, employing a classification method based on calcium activity to categorize the formation of place cells' place fields into BTSP, non-BTSP-like, and investigated their properties.

Strengths:

A new method to use calcium imaging to separate BTSP and non-BTSP place field formation. This work offers new methods and factual evidence for other researchers in the field.

The method enabled the authors to reveal that while many PFs are formed by BTSP-like events, a significant number of PFs emerge with calcium dynamics that do not match BTSP characteristics, suggesting a diversity of mechanisms underlying PF formation. The characteristics of place fields under the first two categories are comprehensively described, including aspects such as formation timing, quantity, and width.

Weaknesses:

There are some issues about data and statistics that need to be addressed before these research findings can be considered as rigorous conclusions.

While the authors mentioned 3 features of PF generated by BTSP during calcium imaging in the Introduction, the classification method used features 1 and 2. The confirmation by feature 3 in its current form is important but not strong enough.

Some key data is missing such as the excluded PFs, the BTSP/non-BTSP of each animal, etc

Impact:

This work is likely to provide a new method to classify BTSP and non-BTSP place field formation using calsium image to the field.

Reviewer #3 (Public review):

Summary:

In this manuscript, Sumegi et al. use calcium imaging in head-fixed mice to test whether new place fields tend to emerge due to events that resemble behavioral time scale plasticity (BTSP) or other mechanisms. An impressive dataset was amassed (163 sessions from 45 mice with 500-1000 neurons per sample) to study the spontaneous emergence of new place fields in area CA1 that had the signature of BTSP. The authors observed that place fields could emerge due to BTSP and non-BTSP-like mechanisms. Interestingly, when non-BTSP mechanisms seemed to generate a place field, this tended to occur on a trial with a spontaneous reset in neural coding (a remapping event). Novelty seemed to upregulate non-BTSP events relative to BTSP events. Finally, large calcium transients (presumed plateau potentials) were not sufficient to generate a place field.

Strengths:

I found this manuscript to be exceptionally well-written, well-powered, and timely given the outstanding debate and confusion surrounding whether all place fields must arise from BTSP event. Working at the same institute, Albert Lee (e.g. Epszstein et al., 2011 - which should be cited) and Jeff Magee (e.g. Bittner et al., 2017) showed contradictory results for how place fields arise. These accounts have not fully been put toe-to-toe and reconciled in the literature. This manuscript addresses this gap and shows that both accounts are correct - place fields can emerge due to a pre-existing map and due to BTSP.

We thank the Reviewer for his/her appreciation of the importance of our study. We have included the additional reference.

Weaknesses:

I find only three significant areas for improvement in the present study:

First, can it be concluded that non-BTSP events occur exclusively due to a global remapping event, as stated in the manuscript "these PFF surges included a high fraction of both non-BTSP- and BTSP-like PFF events, and were associated with global remapping of the CA1 representation"? Global remapping has a precise definition that involves quantifying the stability of all place fields recorded. Without a color scale bar in Figure 3D (which should be added), we cannot know whether the overall representations were independent before and after the spontaneous reset. It would be good to know if some neurons are able to maintain place coding (more often than expected by chance), suggestive of a partial-remapping phenomenon.

We have performed the analysis suggested by the Reviewer and determined what fraction of CA1PCs retained its original tuning property after the representation switch. We found that the remapping was essentially global, as only a small fraction (5.4%) of CA1PCs retained their pre-switch tuning curve after the switch. This is now described in the Results.

We now state in the figure legend for the former Figure 3D (now Figure 3F) that the color scale applies to all subpanels.

We would like to note that we do not conclude that non-BTSP events occur exclusively during global remapping – we have found a sizable fraction of PFF by non-BTSP mechanism also in the familiar environment with no signs of change in the population representation. We agree nonetheless that PFF is dominated by BTSP under these conditions, whereas the contribution of non-BTSP is larger during global remapping events.

Second, BTSP has a flip side that involves the weakening of existing place fields when a novel field emerges. Was this observed in the present study? Presumably place fields can disappear due to this bidirectional BTSP or due to global remapping. For a full comparison of the two phenomena, the disappearance of place fields must also be assessed.

In this study we focused on the birth of new PFs – yet, PFs not only form but also disappear constantly. The factors driving PF weakening are even less explored and understood than those driving PF birth. In fact, we observed (as illustrated by several examples in our MS) that many PFs weaken, or disappear completely during the course of an imaging session. These effects are sometimes accompanied by a new PFF event elsewhere (e.g. Figure 2 – figure supplement 2E bottom), whereas in other cases they are not (e.g. Figure 5A, middle). Similarly, some BTSP events seem to coincide with disappearance of another PF, but others are not (e.g. Figure 2A bottom, first PF along the track; Figure 3 – figure supplement 1A left, first PF). The picture is further complicated in the case of global remapping events (i.e. representation switches, Figure 3 – figure supplement 2B) that, by definition, include both new PFF and PF disappearance. We feel that exploration of the complex mechanisms at play in PF disappearance is outside the scope of the current study, but could be the subject of an interesting future investigation.

Finally, it would be good to know if place fields differ according to how they are born. For example, are there differences in reliability, width, peak rate, out-of-field firing, etc for those that arise due to BTSP vs non-BTSP.

We have analyzed several properties of the PFs and found no significant difference in either their width (BTSP: 46.4 ± 24.4 cm; non-BTSP: 50.4 ± 32.5 cm, p = 0.28) or peak rates (BTSP: 19.0 ± 14.7 a.u./s; non-BTSP: 21.4 ± 16.8 a.u./s, p = 0.27) or the out-of-field firing rates (BTSP: 0.64 ± 0.68 a.u./s; non-BTSP: 0.83 ± 1.25 a.u./s, p = 0.09, all unpaired t-test). We have included these data into the Results section.

Reviewer #1 (Recommendations for the authors):

Consider adding additional visual cues or environmental elements to the virtual reality (VR) setup to create a more enriched and immersive environment. Collect data from a couple of mice in the enriched environment and compare the PFF dynamics to the original environment. This would help determine whether the findings on PFF dynamics hold in a setting where spatial coding may be more robust. Including floor cues, distal visual markers, or varying textures might provide a more comprehensive understanding of the factors influencing BTSP-like and non-BTSP-like events.

We thank the Reviewer for her/his suggestion of analyzing data obtained from a more enriched VR environment compared to the one we used in our study. We have now included data obtained in a profoundly different VR environment, which did not have sparse dominant visual landmarks, but the entire wall was covered with a rich pattern with different shapes of different colors. Our data from 11 imaging sessions from 4 mice revealed BTSP- and non-BTSP-like PFF events with approximately the same ratio to that found in our regular maze. These results are described in the Results section and are presented in a new supplementary figure (Figure 2 – figure supplement 2). 

Wherever deconvolved spikes were used for analysis, provide a comparison of results obtained directly from the GCaMP ΔF/F signals versus those derived from the deconvolved spiking data. This could illustrate any differences and help readers understand the limitations and reliability of the inference method.

We have adopted a currently widely accepted method in the field to infer spikes from fluorescent traces using the Suite2p software package. All of our analyses were then performed on the inferred spikes. To address the concerns of the Reviewer, we analyzed the relationship between the peak [Ca²⁺] transients and inferred spike activity (new Figure 3 – figure supplement 1C-E). Our results clearly demonstrate a robust, highly significant correlation between these measures at the level of individual cells (new Figure 3 – figure supplement 1D) and the Spearman correlation coefficients show a distribution that is very different from random distributions (new Figure 3 – figure supplement 1E). From these, we conclude that using directly the fluorescent data would have resulted in largely similar PF detection and identification.

Improve the visual clarity of figures by enlarging key elements such as arrows that indicate BTSP-like events. Consider using colors that stand out more clearly to guide readers' attention. Include annotations of statistical significance directly on the figures (e.g., adding NS or * indicators) to make it clear which comparisons are statistically significant. This will help readers quickly interpret the data without needing to refer back to the text.

Based on the suggestion of the Reviewer, we have enlarged the arrows. We have also indicated statistical results on the figures. Because some of the results of factorial ANOVA tests are difficult to be comprehensively indicated on our plots, we kept the description of the statistical results in the legends as well. We hope that these alterations will make data interpretation easier.

Replace or supplement bar plots with violin plots or scatter plots that show the distribution of individual data points. This change would offer a clearer picture of data variability and underlying trends, aiding readers in assessing the robustness of the results.

We have changed the plots and now present all data points.

Add more detailed quantification in the results section, specifying the total number of newly formed place fields, the proportion that are categorized as BTSP-like versus non-BTSP-like, and how many events did not fit these categories. Explicitly state what fraction of the total recorded place field formations are represented by the 59 non-BTSP-like events mentioned, as this is currently difficult to discern.

The number of BTSP- and non-BTSP-like PFF events are given in the MS. As described in the Methods, after identifying BTSP- and non-BTSP-like PFF events using the shift and gain criteria, we have manually checked each of these ROIs and the spatial footprint of every new PFF events for these cells and excluded ROIs with non-soma-like shapes and activities with spurious footprints suggesting contamination, creating a ‘cleaned’ dataset. We did not perform such visual inspection and manual curation of every ROI’s spatial footprints that belong to the two additional categories (no gain with shift, gain without shift, 872 events). Since these classes are also overestimated without curation, we cannot provide a precise fraction of the BTSP- and non-BTSP-like PFF events from the total recorded PFF population. However, - assuming that factors leading to exclusion affect all groups equally - we can provide their fractions by comparing the numbers of newly born PFs in all categories before the visual inspections. In the normal maze, we found 806 candidate BTSP-like (52%),164 non-BTSP-like (10%) PFFs and an additional 593 PFs (38%) could not be included in these two groups [40 PFs (3%) with formation lap gain and backward shift but significant backward drift; 238 PFs (15%) with formation lap gain but without backward shift; 315 PFs (20%) with no formation lap gain but with backward shift]. These data have been included in the Methods.

Ensure that all statements describing specific findings are consistently linked to the appropriate figures and panels. There are instances in the text where results are discussed without clear references, which can make it challenging for readers to verify the data. For example, the section on population remapping in a novel environment should point directly to the relevant figure panels to guide readers.

We regret that our text was not linked properly to the appropriate figures. We corrected this during the revision.

Given that BTSP-like events are inferred rather than directly confirmed, it would be prudent to frame conclusions about their sufficiency in more tentative terms, acknowledging the limitations of the current data. Consider adding a discussion of potential future experiments that could confirm whether these large transients truly represent BTSP events, including evidence for complex spikes or calcium plateau potentials.

The Reviewer is correct that we do not have direct evidence that all large somatic Ca²⁺ events represent dendritic plateau potentials. Now we discuss this and other limitations in the MS (Discussion section).

Reviewer #2 (Recommendations for the authors):

Although the author has outlined three characteristics of place fields (PFs) generated by behavioral time scale synaptic plasticity (BTSP) during calcium imaging in the Introduction section, as follows: ' First, the prolonged CSB results in large [Ca²⁺] transient during the initial PFF event, typically followed by weaker Ca2+ signals on consecutive traversals through the PF. Second, due to the long and asymmetric temporal kernel of the plasticity (favoring potentiation of inputs active 1-2 seconds before the CSB) a substantial backward shift in the spatial position of the PF center can be observed on linear tracks after the formation lap. Third, the width of the new PF is generally proportional to the running speed of the animal during the PFF event.' Figure 3B, which displays the third feature of classified BTSP and non-BTSP data, serves as an important confirmation of the classification results using the first two features. Even though the Spearman correlation indicated a significant difference, the raw data distributions of BTSP and non-BTSP appear similar, suggesting that a distribution of bootstrap and more stringent confirmation should be conducted to be convincing.

As described in the MS, because of the difference in the number of events in the two groups, we randomly subsampled the BTSP-like events to the sample size of the non-BTSP-like PFF events 10000 times and performed regression analysis. This bootstrapping revealed that both the r and p values of the fit to the non-BTSP data fell outside the 95% confidence interval of the bootstrapped BTSP values, indicating that the difference between the groups was robust.

In further analysis during the revision, we found that the PF width variance explained by distance from landmarks is substantially larger than the variance explained by the running speed during the formation lap. We performed a cross-validated analysis by these two factors (Figure 3D), which highlights that speed explains some of the PF width variance of BTSP-like PFFs, but none of the non-BTSP PFFs.

The proportions of the three types should be provided. page 6: ' Using a conservative approach, we categorized a new PF to be formed by a BTSP-like mechanism if it had both positive gain and negative shift values (Figure 2A; n = 310 new PFs), whereas new PFs exhibiting neither positive gain nor negative shift were considered as non-BTSP-like events (Figure 2B; n = 59). All other newly formed PFs (no-gain with backward shift and gain without backward shift) were excluded from further analysis.' The number of excluded newly formed PFs should be disclosed, as well as the distribution ratio of these three types in each animal.

The number of BTSP- and non-BTSP-like PFF events are given in the MS. As described in the Methods, after identifying BTSP- and non-BTSP-like PFF events using the shift and gain criteria, we have manually checked each of these ROIs and the spatial footprint of every new PFF events for these cells and excluded ROIs with non-soma-like shapes or spurious activities, creating a ‘cleaned’ dataset. We did not perform such visual inspection and manual curation of every ROI’s spatial footprints that belonged to the two additional categories (no gain with shift, gain without shift, 872 events). Since these classes are also overestimated without curation, we cannot provide a precise fraction of the BTSP- and non-BTSP-like PFF events from the total recorded PFF population. However, - assuming that factors leading to exclusion affect all groups equally - we can provide their fractions by comparing the numbers of newly born PFs in all categories before the visual inspections. In the normal maze, we found 806 candidate BTSP-like (52%),164 non-BTSP-like (10%) PFFs and an additional 593 PFs (38%) could not be included in these two groups [40 PFs (3%) with formation lap gain and backward shift but significant backward drift; 238 PFs (15%) with formation lap gain but without backward shift; 315 PFs (20%) with no formation lap gain but with backward shift]. These data have been included in the Methods.

Figure 2C, while showing an overall decrease in amplitude from the formation lap to the next lap, could benefit from a pairwise analysis of the corresponding formation lap and the following lap of each session to provide more convincing and detailed results.

We now present all data with connected lines across consecutive laps to illustrate the changes in each ROI. Our statistical analysis included the pairwise comparison of amplitudes.

The experiment's time range is broad (11-99 days); it is worth investigating whether different training intervals might influence the results.

Based on the suggestion of the Reviewer, we have analyzed the elapsed time and the number of sessions from the first training to the recording, and we demonstrate that there is no correlation of these parameters with the number of new PFFs. These data are now presented in Figure 2 – figure supplement 1C.

It is unclear whether the formation of place fields also generates characteristic features of dendritic properties.

It is not clear to us which ‘characteristic dendritic features of dendritic properties’ generated by PFF the Reviewer refers to. Since we did not image dendrites of individual CA1PCs, we have no information about dendritic properties of the neurons.

It may be necessary to add a clearer figure to illustrate the correlation between width and speed following the downsampling of non-BTSP-like events (refer to Figure 3B).

We have performed extensive additional analysis on the relationship of PF width with various behavioral factors, including the speed of the animal in the formation lap. Inspection of the PF width distributions along the track revealed a close association of PF width with the distance of the animal from the nearest visual landmark in the corridor, so that PFs close to landmarks were narrower than PFs between landmarks. We found that the PF width variance explained by distance from landmarks is substantially larger than the variance explained by the running speed during the formation lap. Nevertheless, there is a clear difference between BTSP-like and non-BTSP-like PFFs: running speed explains some variance in the case of BTSP-like PFFs, but none for non-BTSP-like PFFs.

We have included these findings into the Results section and created two new panels in Figure 3 (C, D) and Figure 3 – figure supplement 1 (A, B).

It is recommended that statistical results be labeled in the figures with n.s. or stars for better readability.

Based on the suggestion of the Reviewer, we have indicated statistical results on the figures. Because some of the results of factorial ANOVA tests are difficult to be comprehensively indicated on our plots, we kept the description of the statistical results in the legends as well. We hope that these alterations will make data interpretation easier. We hope that these alterations will make data interpretation easier.

This is a very crucial moment that highlights the human cost of blindly following tradition. Tessie's protest underscores the story's central theme: unquestioned social practices can lead to injustice and harm.

This section shows how being on the debate team helped a writer build new literacies. They learned not just to write and speak clearly, but also to use strategies, read signals, and control arguments, similar to Mirabelli's idea of power struggles in communication. Debate even shaped how the writer approaches writing today, always thinking about evidence and possible weaknesses in their arguments.

added ct https://discoversouthcarolina.com/trips/wild-wonders-the-raptor-migration

I like the idea of writing in a book you are reading verses on another piece of paper. Somehow it makes me feel more engaged with the text.

Something about this scene is almost terrifying. I like how he later describes his brother as a madman, and I feel like this works so well after the author really hammers home how essentially dead his brother was. Especially because he "comes back to life" at the mention of his brother's name.

This is by far the clearest way to illustrate the idea of the rhizome story. It is a rather complex idea to comprehend and this makes it much easier to wrap your head around.

Montezuma welcomed the Cortes.

I relate to X when he says how he feels limited when you don't have the words to express yourself because sometimes I feel like I know what I mean in my head, but I don't know how to put it into words.

You can believe with your heart a positive outlook can have results in its own right as it wills us and people forward but, you need to think with your head because believing fire won't burn you doesn't mean its not going to. Both sides work in tandem to create spectacular things using belief and logic to find and ask great things.

Families sacrifice a lot, especially foreign ones. They want the best for their children and for them to do more with their lives.

As educators, we need to come up with a way to help students see how smartphones in the classroom can have a negative effect on the outcome of their learning. I know a math teacher that has a calculator/ phone holder and when they walk in the room they put their phone where the calculator was and then head to their seat.

familiar

When we talked about the blue books in class, I definitely quietly wished in my head that school could go back to how I remember it ten years ago.

Tin Tin, Brains, Alan and I always like to play in the mud and get caked from head to toe in the stuff, always laughing like crazy and having mud fights - that is, if the buffalo don't mind!

O: Oates seems to be trying to enforce the idea that prose fiction is a space for readers to take in the 'art' of the novel/story/etc., and grow an interpretation. I think the earlier statement, "Where in prose fiction the writer is accustomed to shaping subtleties of meaning by way of carefully composed language," explains this best. While the play on the other hand is given directly to the viewer, with little space to fill in imaginative blanks (although the depth behind acting should not be disregarded.) I find this comparison impactful because it demonstrates the depth of prose fiction that may not be initially realized. A play becomes a predominately collective experience and interpretation, while prose fiction becomes a living document. I can think back to times when I was so immersed in a piece of prose fiction that I began to imagine it in my free time, the characters coming alive in my head, my imagination becoming incredibly involved in the story.

Montague family head is also on the scene now

I wonder if I can find examples of this online. I have an idea of what the author is discussing but I have a hard time visualizing it in my head.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

We thank the reviewer for his valuable input and careful assessment, which have significantly improved the clarity and rigor of our manuscript.

Summary:

Mazer & Yovel 2025 dissect the inverse problem of how echolocators in groups manage to navigate their surroundings despite intense jamming using computational simulations.

The authors show that despite the 'noisy' sensory environments that echolocating groups present, agents can still access some amount of echo-related information and use it to navigate their local environment. It is known that echolocating bats have strong small and large-scale spatial memory that plays an important role for individuals. The results from this paper also point to the potential importance of an even lower-level, short-term role of memory in the form of echo 'integration' across multiple calls, despite the unpredictability of echo detection in groups. The paper generates a useful basis to think about the mechanisms in echolocating groups for experimental investigations too.

Strengths:

(1) The paper builds on biologically well-motivated and parametrised 2D acoustics and sensory simulation setup to investigate the various key parameters of interest

(2) The 'null-model' of echolocators not being able to tell apart objects & conspecifics while echolocating still shows agents successfully emerge from groups - even though the probability of emergence drops severely in comparison to cognitively more 'capable' agents. This is nonetheless an important result showing the directionof-arrival of a sound itself is the 'minimum' set of ingredients needed for echolocators navigating their environment.

(3) The results generate an important basis in unraveling how agents may navigate in sensorially noisy environments with a lot of irrelevant and very few relevant cues.

(4) The 2D simulation framework is simple and computationally tractable enough to perform multiple runs to investigate many variables - while also remaining true to the aim of the investigation.

Weaknesses:

There are a few places in the paper that can be misunderstood or don't provide complete details. Here is a selection:

(1) Line 61: '... studies have focused on movement algorithms while overlooking the sensory challenges involved' : This statement does not match the recent state of the literature. While the previous models may have had the assumption that all neighbours can be detected, there are models that specifically study the role of limited interaction arising from a potential inability to track all neighbours due to occlusion, and the effect of responding to only one/few neighbours at a time e.g. Bode et al. 2011 R. Soc. Interface, Rosenthal et al. 2015 PNAS, Jhawar et al. 2020 Nature Physics.

We appreciate the reviewer's comment and the relevant references. We have revised the manuscript accordingly to clarify the distinction between studies that incorporate limited interactions and those that explicitly analyze sensory constraints and interference. We have refined our statement to acknowledge these contributions while maintaining our focus on sensory challenges beyond limited neighbor detection, such as signal degradation, occlusion effects, and multimodal sensory integration (see lines 58-64):

(2) The word 'interference' is used loosely places (Line 89: '...took all interference signals...', Line 319: 'spatial interference') - this is confusing as it is not clear whether the authors refer to interference in the physics/acoustics sense, or broadly speaking as a synonym for reflections and/or jamming.

To improve clarity, we have revised the manuscript to distinguish between different types of interference:

• Acoustic interference (jamming): Overlapping calls that completely obscure echo detection, preventing bats from perceiving necessary environmental cues.

• Acoustic interference (masking): Partial reduction in signal clarity due to competing calls.

• Spatial interference: Physical obstruction by conspecifics affecting movement and navigation.

We have updated the manuscript to use these terms consistently and explicitly define them in relevant sections (see lines 84-85, 119-120). This distinction ensures that the reader can differentiate between interference as an acoustic phenomenon and its broader implications in navigation.

(3) The paper discusses original results without reference to how they were obtained or what was done. The lack of detail here must be considered while interpreting the Discussion e.g. Line 302 ('our model suggests...increasing the call-rate..' - no clear mention of how/where call-rate was varied) & Line 323 '..no benefit beyond a certain level..' - also no clear mention of how/where call-level was manipulated in the simulations.

All tested parameters, including call rate dynamics and call intensity variations, are detailed in the Methods section and Tables 1 and 2. Specifically:

• Call Rate Variation: The Inter-Pulse Interval (IPI) was modeled based on documented echolocation behavior, decreasing from 100 msec during the search phase to 35 msec (~28 calls per second) at the end of the approach phase, and to 5 msec (200 calls per second) during the final buzz (see Table 2). This natural variation in call rate was not manually manipulated in the model but emerged from the simulated bat behavior.

• Call Intensity Variation: The tested call intensity levels (100, 110, 120, 130 dB SPL) are presented in Table 1 under the “Call Level” parameter. The effect of increasing call intensity was analyzed in relation to exit probability, jamming probability, and collision rate. This is now explicitly referenced in the Discussion. We have revised the manuscript to explicitly reference these aspects in the Results and Discussion sections – see lines 346-349, 372-375.

Reviewer #2 (Public review):

We are grateful for the reviewer’s insightful feedback, which has helped us clarify key aspects of our research and strengthen our conclusions.

This manuscript describes a detailed model of bats flying together through a fixed geometry. The model considers elements that are faithful to both bat biosonar production and reception and the acoustics governing how sound moves in the air and interacts with obstacles. The model also incorporates behavioral patterns observed in bats, like one-dimensional feature following and temporal integration of cognitive maps. From a simulation study of the model and comparison of the results with the literature, the authors gain insight into how often bats may experience destructive interference of their acoustic signals and those of their peers, and how much such interference may actually negatively affect the groups' ability to navigate effectively. The authors use generalized linear models to test the significance of the effects they observe.

In terms of its strengths, the work relies on a thoughtful and detailed model that faithfully incorporates salient features, such as acoustic elements like the filter for a biological receiver and temporal aggregation as a kind of memory in the system. At the same time, the authors' abstract features are complicating without being expected to give additional insights, as can be seen in the choice of a twodimensional rather than three-dimensional system. I thought that the level of abstraction in the model was perfect, enough to demonstrate their results without needless details. The results are compelling and interesting, and the authors do a great job discussing them in the context of the biological literature. 

The most notable weakness I found in this work was that some aspects of the model were not entirely clear to me. 

For example, the directionality of the bat's sonar call in relation to its velocity. Are these the same?

For simplicity, in our model, the head is aligned with the body, therefore the direction of the echolocation beam is the same as the direction of the flight. 

Moreover, call directionality (directivity) is not directly influenced by velocity. Instead, directionality is estimated using the piston model, as described in the Methods section. The directionality is based on the emission frequency and is thus primarily linked to the behavioral phases of the bat, with frequency shifts occurring as the bat transitions from search to approach to buzz phases. During the approach phase, the bat emits calls with higher frequencies, resulting in increased directionality. This is supported by the literature (Jakobsen and Surlykke, 2010; Jakobsen, Brinkløv and Surlykke, 2013). This phase is also associated with a natural reduction in flight speed, which is a well-documented behavioral adaptation in echolocating bats(Jakobsen et al., 2024).

To clarify this in the manuscript, we have updated the text to explicitly state that directionality follows phase-dependent frequency changes rather than being a direct function of velocity, see lines 543-545. 

If so, what is the difference between phi_target and phi_tx in the model equations? 

𝝓_{𝒕𝒂𝒓𝒈𝒆𝒕} represents the angle between the bat and the reflected object (target).

𝝓_𝑻𝒙 the angle [rad], between the masking bat and target (from the transmitter’s perspective)

𝝓_{𝑻𝒙𝑹𝒙} refers to the angle between the transmitting conspecific and the receiving focal bat, from the transmitter’s point of view.

𝝓_{𝑹𝒙𝑻𝒙} represents the angle between the receiving bat and the transmitting bat, from the receiver’s point of view.

These definitions have been explicitly stated in the revised manuscript to prevent any ambiguity (lines 525-530). Additionally, a Supplementary figure demonstrating the geometrical relations has been added to the manuscript.

What is a bat's response to colliding with a conspecific (rather than a wall)? 

In nature, minor collisions between bats are common and typically do not result in significant disruptions to flight (Boerma et al., 2019; Roy et al., 2019; Goldshtein et al., 2025). Given this, our model does not explicitly simulate the physical impact of a collision event. Instead, during the collision event the bat keeps decreasing its velocity and changing its flight direction until the distance between bats is above the threshold (0.4 m). We assume that the primary cost of such interactions arises from the effort required to avoid collisions, rather than from the collision itself. This assumption aligns with observations of bat behavior in dense flight environments, where individuals prioritize collision avoidance rather than modeling post-collision dynamics. See lines 479-484.

From the statistical side, it was not clear if replicate simulations were performed. If they were, which I believe is the right way due to stochasticity in the model, how many replicates were used, and are the standard errors referred to throughout the paper between individuals in the same simulation or between independent simulations, or both? 

The number of repetitions for each scenario is detailed in Table 1, but we included it in a more prominent location in the text for clarity. Specifically, we now state (Lines 110-111):

"The number of repetitions for each scenario was as follows: 1 bat: 240; 2 bats: 120; 5 bats: 48; 10 bats: 24; 20 bats: 12; 40 bats: 12; 100 bats: 6."

Regarding the reported standard errors, they are calculated across all individuals within each scenario, without distinguishing between different simulation trials. 

We clarified in the revised text (Lines 627-628 in Statistical Analysis) 

Overall, I found these weaknesses to be superficial and easily remedied by the authors. The authors presented well-reasoned arguments that were supported by their results, and which were used to demonstrate how call interference impacts the collective's roost exit as measured by several variables. As the authors highlight, I think this work is valuable to individuals interested in bat biology and behavior, as well as to applications in engineered multi-agent systems like robotic swarms.

Reviewer #3 (Public review):

We sincerely appreciate the reviewer’s thoughtful comments and the time invested in evaluating our work, which have greatly contributed to refining our study.

We would like to note that in general, our model often simplifies some of the bats’ abilities, under the assumption that if the simulated bats manage to perform this difficult task with simpler mechanisms, real better adapted bats will probably perform even better. This thought strategy will be repeated in several of the s below.

Summary:

The authors describe a model to mimic bat echolocation behavior and flight under high-density conditions and conclude that the problem of acoustic jamming is less severe than previously thought, conflating the success of their simulations (as described in the manuscript) with hard evidence for what real bats are actually doing. The authors base their model on two species of bats that fly at "high densities" (defined by the authors as colony sizes from tens to tens of thousands of individuals and densities of up to 33.3 bats/m2), Pipistrellus kuhli and Rhinopoma microphyllum. This work fits into the broader discussion of bat sensorimotor strategies during collective flight, and simulations are important to try to understand bat behavior, especially given a lack of empirical data. However, I have major concerns about the assumptions of the parameters used for the simulation, which significantly impact both the results of the simulation and the conclusions that can be made from the data. These details are elaborated upon below, along with key recommendations the authors should consider to guide the refinement of the model.

Strengths:

This paper carries out a simulation of bat behavior in dense swarms as a way to explain how jamming does not pose a problem in dense groups. Simulations are important when we lack empirical data. The simulation aims to model two different species with different echolocation signals, which is very important when trying to model echolocation behavior. The analyses are fairly systematic in testing all ranges of parameters used and discussing the differential results.

Weaknesses:

The justification for how the different foraging phase call types were chosen for different object detection distances in the simulation is unclear. Do these distances match those recorded from empirical studies, and if so, are they identical for both species used in the simulation? 

The distances at which bats transition between echolocation phases are identical for both species in our model (see Table 2). These distances are based on welldocumented empirical studies of bat hunting and obstacle avoidance behavior (Griffin, Webster and Michael, 1958; Simmons and Kick, 1983; Schnitzler et al., 1987; Kalko, 1995; Hiryu et al., 2008; Vanderelst and Peremans, 2018). These references provide extensive evidence that insectivorous bats systematically adjust their echolocation calls in response to object proximity, following the characteristic phases of search, approach, and buzz.

To improve clarity, we have updated the text to explicitly state that the phase transition distances are empirically grounded and apply equally to both modeled species (lines 499-508).

What reasoning do the authors have for a bat using the same call characteristics to detect a cave wall as they would for detecting a small insect? 

In echolocating bats, call parameters are primarily shaped by the target distance and echo strength. Accordingly, there is little difference in call structure between prey capture and obstacles-related maneuvers, aside from intensity adjustments based on target strength (Hagino et al., 2007; Hiryu et al., 2008; Surlykke, Ghose and Moss, 2009; Kothari et al., 2014). In our study, due to the dense cave environment, the bats are found to operate in the approach phase most of the time, which is consistent with natural cave emergence, where they are navigating through a cluttered environment rather than engaging in open-space search. For one of the species (Rhinopoma), we also have empirical recordings of individuals flying under similar conditions (Goldshtein et al., 2025). Our model was designed to remain as simple as possible while relying on conservative assumptions that may underestimate bat performance. If, in reality, bats fine-tune their echolocation calls even earlier or more precisely during navigation than assumed, our model would still conservatively reflect their actual capabilities. See lines 500-508.

The two species modeled have different calls. In particular, the bandwidth varies by a factor of 10, meaning the species' sonars will have different spatial resolutions. Range resolution is about 10x better for PK compared to RM, but the authors appear to use the same thresholds for "correct detection" for both, which doesn't seem appropriate.

The detection process in our model is based on Saillant’s method using a filterbank, as detailed in the paper (Saillant et al., 1993; Neretti et al., 2003; Sanderson et al., 2003). This approach inherently incorporates the advantages of a wider bandwidth, meaning that the differences in range resolution between the species are already accounted for within the signal-processing framework. Thus, there is no need to explicitly adjust the model parameters for bandwidth variations, as these effects emerge from the applied method.

Also, the authors did not mention incorporating/correcting for/exploiting Doppler, which leads me to assume they did not model it.

The reviewer is correct. To maintain model simplicity, we did not incorporate the Doppler effect or its impact on echolocation. The exclusion of Doppler effects was based on the assumption that while Doppler shifts can influence frequency perception, their impact on jamming and overall navigation performance is minor within the modelled context.

The maximal Doppler shifts expected for the bats in this scenario are of ~ 1kHz. These shifts would be applied variably across signals due to the semi-random relative velocities between bats, leading to a mixed effect on frequency changes. This variability would likely result in an overall reduction in jamming rather than exacerbating it, aligning with our previous statement that our model may overestimate the severity of acoustic interference. Such Doppler shifts would result in errors of 2-4 cm in localization (i.e., 200-400 micro-seconds) (Boonman, Parsons and Jones, 2003).

We have now explicitly highlighted this in the revised version (see 548-581).

The success of the simulation may very well be due to variation in the calls of the bats, which ironically enough demonstrates the importance of a jamming avoidance response in dense flight. This explains why the performance of the simulation falls when bats are not able to distinguish their own echoes from other signals. For example, in Figure C2, there are calls that are labeled as conspecific calls and have markedly shorter durations and wider bandwidths than others. These three phases for call types used by the authors may be responsible for some (or most) of the performance of the model since the correlation between different call types is unlikely to exceed the detection threshold. But it turns out this variation in and of itself is what a jamming avoidance response may consist of. So, in essence, the authors are incorporating a jamming avoidance response into their simulation. 

We fully agree that the natural variations in call design between the phases contribute significantly to interference reduction (see our discussion in a previous paper in Mazar & Yovel, 2020). However, we emphasize that this cannot be classified as a Jamming Avoidance Response (JAR). In our model, bats respond only to the physical presence of objects and not to the acoustic environment or interference itself. There is no active or adaptive adjustment of call design to minimize jamming beyond the natural phase-dependent variations in call structure. Therefore, while variation in call types does inherently reduce interference, this effect emerges passively from the modeled behavior rather than as an intentional strategy to avoid jamming. 

The authors claim that integration over multiple pings (though I was not able to determine the specifics of this integration algorithm) reduces the masking problem. Indeed, it should: if you have two chances at detection, you've effectively increased your SNR by 3dB.  

The reviewer is correct. Indeed, integration over multiple calls improves signal-tonoise ratio (SNR), effectively increasing it by approximately 3 dB per doubling of observations. The specifics of the integration algorithm are detailed in the Methods section, where we describe how sensory information is aggregated across multiple time steps to enhance detection reliability.

They also claim - although it is almost an afterthought - that integration dramatically reduces the degradation caused by false echoes. This also makes sense: from one ping to the next, the bat's own echo delays will correlate extremely well with the bat's flight path. Echo delays due to conspecifics will jump around kind of randomly. However, the main concern is regarding the time interval and number of pings of the integration, especially in the context of the bat's flight speed. The authors say that a 1s integration interval (5-10 pings) dramatically reduces jamming probability and echo confusion. This number of pings isn't very high, and it occurs over a time interval during which the bat has moved 5-10m. This distance is large compared to the 0.4m distance-to-obstacle that triggers an evasive maneuver from the bat, so integration should produce a latency in navigation that significantly hinders the ability to avoid obstacles. Can the authors provide statistics that describe this latency, and discussion about why it doesn't seem to be a problem? 

As described in the Methods section, the bat’s collision avoidance response does not solely rely on the integration process. Instead, the model incorporates real-time echoes from the last calls, which are used independently of the integration process for immediate obstacle avoidance maneuvers. This ensures that bats can react to nearby obstacles without being hindered by the integration latency. The slower integration on the other hand is used for clustering, outlier removal and estimation wall directions to support the pathfinding process, as illustrated in Supplementary Figure 1.

Additionally, our model assumes that bats store the physical positions of echoes in an allocentric coordinate system (x-y). The integration occurs after transforming these detections from a local relative reference frame to a global spatial representation. This allows for stable environmental mapping while maintaining responsiveness to immediate changes in the bat’s surroundings.

See lines 600-616 in the revised version.

The authors are using a 2D simulation, but this very much simplifies the challenge of a 3D navigation task, and there is an explanation as to why this is appropriate. Bat densities and bat behavior are discussed per unit area when realistically it should be per unit volume. In fact, the authors reference studies to justify the densities used in the simulation, but these studies were done in a 3D world. If the authors have justification for why it is realistic to model a 3D world in a 2D simulation, I encourage them to provide references justifying this approach. 

We acknowledge that this is a simplification; however, from an echolocation perspective, a 2D framework represents a worst-case scenario in terms of bat densities and maneuverability:

• Higher Effective Density: A 2D model forces all bats into a single plane rather than distributing them through a 3D volume, increasing the likelihood of overlap in calls and echoes and making jamming more severe. As described in the text: the average distance to the nearest bat in our simulation is 0.27m (with 100 bats), whereas reported distances in very dense colonies are 0.5m (Fujioka et al., 2021), as observed in Myotis grisescens (Sabol and Hudson, 1995) and Tadarida brasiliensis (Theriault et al., no date; Betke et al., 2008; Gillam et al., 2010)

• Reduced Maneuverability: In 3D space, bats can use vertical movement to avoid obstacles and conspecifics. A 2D constraint eliminates this degree of freedom, increasing collision risk and limiting escape options.

Thus, our 2D model provides a conservative difficult test case, ensuring that our findings are valid under conditions where jamming and collision risks are maximized. Additionally, the 2D framework is computationally efficient, allowing us to perform multiple simulation runs to explore a broad parameter space and systematically test the impact of different variables.

To address the reviewer’s concern, we have clarified this justification in the revised text and will provide supporting references where applicable (see Methods lines 450455).

The focus on "masking" (which appears to be just in-band noise), especially relative to the problem of misassigned echoes, is concerning. If the bat calls are all the same waveform (downsweep linear FM of some duration, I assume - it's not clear from the text), false echoes would be a major problem. Masking, as the authors define it, just reduces SNR. This reduction is something like sqrt(N), where N is the number of conspecifics whose echoes are audible to the bat, so this allows the detection threshold to be set lower, increasing the probability that a bat's echo will exceed a detection threshold. False echoes present a very different problem. They do not reduce SNR per se, but rather they cause spurious threshold excursions (N of them!) that the bat cannot help but interpret as obstacle detection. I would argue that in dense groups the mis-assignment problem is much more important than the SNR problem. 

There is substantial literature supporting the assumption that bats can recognize their own echoes and distinguish them from conspecific signals (Schnitzler, Bioscience and 2001, no date; Kazial, Burnett and Masters, 2001; Burnett and Masters, 2002; Kazial, Kenny and Burnett, 2008; Chili, Xian and Moss, 2009; Yovel et al., 2009; Beetz and Hechavarría, 2022)). However, we acknowledge that false echoes may present a major challenge in dense groups. To address this, we explicitly tested the impact of the self-echo identification assumption in our study see Results Figure 1: The impact of confusion on performance, and lines 399-404 in the Discussion.

Furthermore, we examined a full confusion scenario, where all reflected echoes from conspecifics were misinterpreted as obstacle reflections (i.e., 100% confusion). Our results show that this significantly degrades navigation performance, supporting the argument that echo misassignment is a critical issue. However, we also explored a simple mitigation strategy based on temporal integration with outlier rejection, which provided some improvement in performance. This suggests that real bats may possess additional mechanisms to enhance self-echo identification and reduce false detections. See lines 411-420 in the manuscript for further discussion. 

We actually used logarithmically frequency modulated (FM) chirps, generated using the MATLAB built-in function chirp(t, f0, t1, f1, 'logarithmic'). This method aligns with the nonlinear FM characteristics of Pipistrellus kuhlii (PK) and Rhinopoma microphyllum (RM) and provides a realistic approximation of their echolocation signals. We acknowledge that this was not sufficiently emphasized in the original text, and we have now explicitly highlighted this in the revised version to ensure clarity (see Lines 509-512 in Methods).

The criteria set for flight behavior (lines 393-406) are not justified with any empirical evidence of the flight behavior of wild bats in collective flight. How did the authors determine the avoidance distances? Also, what is the justification for the time limit of 15 seconds to emerge from the opening? Instead of an exit probability, why not instead use a time criterion, similar to "How long does it take X% of bats to exit?"  :

While we acknowledge that wild bats may employ more complex behaviors for collision avoidance, we chose to implement a simplified decision-making rule in our model to maintain computational tractability.

The avoidance distances (1.5 m from walls and 0.4 m from other bats) were selected as internal parameters to support stable and realistic flight trajectories while maintaining a reasonable collision rate. These values reflect a trade-off between maneuverability and behavioral coherence under crowding. To address this point, we added a sensitivity analysis to the revised manuscript. Specifically, we tested the effect of varying the conspecific avoidance distance from 0.2 to 1.6 meters at bat densities of 2 to 40 bats/3m². The only statistically significant impact was at the highest density (40 bats/3m²), where exit probability increased slightly from 82% to 88% (p = 0.024, t = 2.25, DF = 958). No significant changes were observed in exit time, collision rate, or jamming probability across other densities or conditions (GLM, see revised Methods). These results suggest that the selected avoidance distances are robust and not a major driver of model performance, see lines 469-47.

The 15-second exit limit was determined as described in the text (Lines 489-491): “A 15-second window was chosen because it is approximately twice the average exit time for 40 bats and allows for a second corrective maneuver if needed.” In other words, it allowed each bat to circle the ‘cave’ twice to exit even in the most crowded environment. This threshold was set to keep simulation time reasonable while allowing sufficient time for most bats to exit successfully.

We acknowledge that the alternative approach suggested by the reviewer— measuring the time taken for a certain percentage of bats to exit—is also valid. However, in our model, some outlier bats fail to exit and continue flying for many minutes, such simulations would lead to excessive simulation times making it difficult to generate repetitions and not teaching us much – they usually resulted from the bat slightly missing the opening (see video S1. Our chosen approach ensures practical runtime constraints while still capturing relevant performance metrics.

What is the empirical justification for the 1-10 calls used for integration?  

The "average exit time for 40 bats" is also confusing and not well explained. Was this determined empirically? From the simulation? If the latter, what are the conditions?

Does it include masking, no masking, or which species? 

Previous studies have demonstrated that bats integrate acoustic information received sequentially over several echolocation calls (2-15), effectively constructing an auditory scene in complex environments (Ulanovsky and Moss, 2008; Chili, Xian and Moss, 2009; Moss and Surlykke, 2010; Yovel and Ulanovsky, 2017; Salles, Diebold and Moss, 2020). Additionally, bats are known to produce echolocation sound groups when spatiotemporal localization demands are high (Kothari et al., 2014). Studies have documented call sequences ranging from 2 to 15 grouped calls (Moss and Surlykke, 2010), and it has been hypothesized that grouping facilitates echo segregation.

We did not use a single integration window - we tested integration sizes between 1 and 10 calls and presented the results in Figure 3A. This range was chosen based on prior empirical findings and to explore how different levels of temporal aggregation impact navigation performance. Indeed, the results showed that the performance levels between 5-10 calls integration window (Figure 3A)

Regarding the average exit time for 40 bats, this value was determined from our simulations, where it represents the mean time for successful exits under standard conditions with masking. We have revised the text to clarify these details see, lines 489-491.

Reviewer #1 (Recommendations for the authors):

(1) Data Availability:

As it stands now, this reviewer cannot vouch for the uploaded code as it wasn't accessible according to F.A.I.R principles. The link to the code/data points to a private company's file-hosting account that requires logging in or account creation to see its contents, and thus cannot be accessed.

This reviewer urges the authors to consider uploading the code onto an academic data repository from the many on offer (e.g. Dryad, Zenodo, OSF). Some repositories offer an option to share a private link (e.g. Zenodo) to the folder that can then be shared only with reviewers so it is not completely public.

This is a computational paper, and the credibility of the results is based on the code used to generate them.

The code is available at GitHub as required:

https://github.com/omermazar/Colony-Exit-Bat-Simulation

(2) Abstract:

Line 22: 'To explore whether..' - replace 'whether' with 'how'?

The sentence was rephrased as suggested by the reviewer.

(2) Main text:

Line 43: '...which may share...' - correct to '...which share...', as elegantly framed in the authors' previous work - jamming avoidance is unavoidable because all FM bats of a species still share >90% of spectral bandwidth despite a few kHz shift here and there.

The sentence was rephrased as suggested by the reviewer.

Line 49: The authors may wish to additionally cite the work of Fawcett et al. 2015 (J. Comp. Phys A & Biology Open)

Thank you for the suggestion. We have included a citation to the work of Fawcett et al. (2015) in the revised manuscript.

Line 61: This statement does not match the recent state of the literature. While the previous models may have assumed that all neighbours can be detected, there are models that specifically study the role of limited interaction arising from the potential inability to track all neighbours, and the effect of responding to only one/few neighbours at a time e.g. Bode et al. 2011 R. Soc. Interface, Jhawar et al. 2020 Nature Physics.

We have added citations to the important studies suggested by the reviewer, as detailed in the Public Review above.

Line 89: '..took all interference signals into account...' - what is meant by 'interference signals' - are the authors referring to reflections, unclear.

We have revised the sentence and detailed the acoustic signals involved in the process: self-generated echoes, calls from conspecifics, and echoes from cave walls and other bats evoked by those calls, see lines 99-106.

Figure 1A: The colour scheme with overlapping points makes the figure very hard to understand what is happening. The legend has colours from subfigures B-D, adding to the confusion.

What does the yellow colour represent? This is not clear. Also, in general, the color schemes in the simulation trajectories and the legend are not the same, creating some amount of confusion for the reader. It would be good to make the colour schemes consistent and visually separable (e.g. consp. call direct is very similar to consp. echo from consp. call), and perhaps also if possible add a higher resolution simulation visualisation. Maybe it is best to separate out the colour legends for each sub-figure.

The updated figure now includes clearer, more visually separable colors, and consistent color coding across all sub-panels. The yellow trajectory representing the focal bat’s flight path is now explicitly labeled, and we adjusted the color mapping of acoustic signals (e.g., conspecific calls vs. echoes) to improve distinction. We also revised the figure caption accordingly and ensured that the legend is aligned with the updated visuals. These modifications aim to enhance interpretability and reduce ambiguity for the reader.

Figure C3: What is 'FB Channel', this is not explained in the legend.

FB Channel’ stands for ‘Filter Bank Channel’. This clarification has been added to the caption of Figure 1. 

Figure 3: Visually noticing that the colour legend is placed only on sub-figure A is tricky and readers may be left searching for the colour legend. Maybe lay out the legend horizontally on top of the entire figure, so it stands out?

We have adjusted the placement of the color legend in Figure 3 to improve visibility and consistency.

Line 141: '..the probability of exiting..' - how is this probability calculated - not clear.

We have clarified in the revised text that the probability of exiting the cave within 15 seconds is defined as the number of bats that exited the cave within that time divided by the total number of bats in each scenario, see lines 159160.

Line 142: What are the sample sizes here - i.e. how many simulation replicates were performed?

We have clarified the number of repetitions in each scenario the revised text, as detailed in the Public Review above.

Line 151: 'The jamming probability,...number of jammed echoes divided by the total number of reflected echoes' - it seems like these are referring to 'own' echoes or first-order reflections, it is important to clarify this.

The reviewer is right. We have clarified it in the revised text, see lines 173175.

Line 153: '..with a maximum difference of ...' - how is this difference calculated? What two quantities are being compared - not clear.

We have revised the text to clarify that the 14.3% value reflects the maximum difference in jamming probability between the RM and PK models, which occurred at a density of 10 bats. The values at each density are shown in Figure 2D, see lines 175-177.

Line 221: '..temporal aggregation helps..' - I'm assuming the authors meant temporal integration? However, I would caution against using the exact term 'temporal integration' as it is used in the field of audition to mean something different. Perhaps something like 'sensory integration' , or 'multi-call integration'

To avoid ambiguity and better reflect the process modeled in our work, we have replaced the term "temporal aggregation" with "multi-call integration" throughout the revised manuscript. This term more accurately conveys the idea of combining information from multiple echolocation calls without conflicting with existing terminology.

(4) Discussion

Lines 302: 'Our model suggests...increasing the call-rate..' - not clear where this is explicitly tested or referred to in this manuscript. Can't see what was done to measure/quantify the effect of this variable in the Methods or anywhere else.

We have rephrased this paragraph as detailed in the Public Review above, see lines 346-349.

Line 319: 'spatial interference' - unclear what this means. This reviewer would strongly caution against creating new terms unless there is an absolute need for it. What is meant by 'interference' in this paper is hard to assess given that the word seems to be used as a synonym for jamming and also for actual physical wave-based interference.

We have rephrased this paragraph as detailed in the Public Review above, see line 119-120, 366-367.

Line 323: '..no benefit beyond a certain level...' - also not clear where this is explicitly tested. It seems like there was a set of simulations run for a variety of parameters but this is not written anywhere explicitly. What type of parameter search was done, was it all possible parameter combinations - or only a subset? This is not clear.

We have rephrased this paragraph as detailed in the Public Review above, see lines 372-375.

Line 324: '..ca. 110 dB-SPL.' - what reference distance?

All call levels were simulated and reported in dB-SPL, referenced at 0.1 meters from the emitting bat. We have clarified it in the revised text in the relevant contexts and specifically in line 529.

(5) Methods

Line 389 : '...over a 2 x 1.5 m2 area..' It took a while to understand this statement and put it in context. Since there is no previous description of the entire L-arena, the reviewer took it to mean the simulations happened over the space of a 2 x 1.5 m2 area. Include a top-down description of the simulation's spatial setup and rephrase this sentence.

To address the confusion, we revised the text to clarify that the full simulation environment represents a corridor-shaped cave measuring 14.5 × 2.5 meters, with a right-angle turn located 5.5 meters before the exit, as shown in Figure 1A. The 2 × 1.5 m area refers specifically to the small zone at the far end of the cave where bats begin their flight. The revised description now includes a clearer spatial overview to prevent ambiguity, see lines 456-460.

Line 398: Replace 'High proximity' with 'Close proximity'

Replaced.

Line 427: 'uniform target strength of -23 dB' - at what distance is this target strength defined? Given the reference distance can vary by echolocation convention (0.1 or 1 m), one can't assess if this is a reasonable value or not.

The reference distance for the reported target strength is 1 meter, in line with standard acoustic conventions. We have revised the text to clarify this explicitly (line 531).

Also, independent of the reference distance, particularly with reference to bats, the target strength is geometry-dependent, based on whether the wings are open or not. Using the entire wingspan of a bat to parametrise the target strength is an overestimate of the available reflective area. The effective reflective area is likely to be somewhere closer to the surface area of the body and a fraction of the wingspan together. This is important to note and/or mention explicitly since the value is not experimentally parametrised.

For comparison, experimentally based measurements used in Goetze et al. 2016 are -40 dB (presumably at 1 m since the source level is also defined at 1 m?), and Beleyur & Goerlitz 2019 show a range between -43 to -34 dB at 1 m.

We agree with the reviewer that target strength in bats is strongly influenced by their geometry, particularly wing posture during flight. In our model, we simplified this aspect by using a constant target strength, as the detailed temporal variation in body and wing geometry is pseudo-random and not explicitly modeled. We acknowledge that this is a simplification, and have now stated this limitation clearly in the revised manuscript. We chose a fixed value of –23 dB at 1 meter to reflect a plausible mid-range estimate, informed by anatomical data and consistent with values reported for similarly sized species (Beleyur and Goerlitz, 2019). To support this, we directly measured the target strength of a 3D-printed RM bat model, obtaining –32dB. 

Moreover, a sensitivity analysis across a wide range (–49 to –23 dB) confirmed that performance metrics remain largely stable, indicating that our conclusions are not sensitive to this parameter, and suggesting that our results hold for different-sized bats. See lines 384-390, 533-538, and Supplementary Figures 3 and 4 in the revised article. 

Line 434: 'To model the bat's cochlea...'. Bats have two cochleas. This model only describes one, while the agents are also endowed with the ability to detect sound direction - which requires two ears/cochleas.... There is missing information about the steps in between that needs to be provided.

We appreciate the reviewer’s observation. Indeed, our model is monaural, and simulates detection using a single cochlear-like filter bank receiver. We have clarified this in the revised text to avoid confusion. This paragraph specifically describes the detection stage of the auditory processing pipeline. The localization process, which builds on detection and includes directional estimation, is described in the following paragraph (see line 583 onward), as discussed in the next comment and response.

Line 457: 'After detection, the bat estimates the range and Direction of Arrival...' This paragraph describes the overall idea, but not the implementation. What were the inputs and outputs for the range and DOA calculation performed by the agent? Or was this information 'fed' in by the simulation framework? If there was no explicit DOA step that the agent performed, but it was assumed that agents can detect DOA, then this needs to be stated.

In the current simulation, the Direction of Arrival (DOA) was not modeled via an explicit binaural processing mechanism. Instead, based on experimental studies (Simmons et al., 1983; Popper and Fay, 1995).  we assumed that bats can estimate the direction of an echo with an angular error that depends on the signal-to-noise ratio (SNR). Accordingly, the inputs to the DOA estimation were the peak level of the desired echo, noise level, and the level of acoustic interference. The output was an estimated direction of arrival that included a random angular error, drawn from a normal distribution whose standard deviation varied with the SNR. We have revised the relevant paragraph (Lines 583-592) to clarify this implementation.

Line 464: 'To evaluate the impact of the assumption...' - the 'self' and 'non-self' echoes can be distinguished perhaps using pragmatic time-delay cues, but also using spectro-temporal differences in individual calls/echoes. Do the agents have individual call structures, or do all the agents have the same call 'shape'? The echolocation parameters for the two modelled species are given, but whether there is call parameter variation implemented in the agents is not mentioned.

In our relatively simple model, all individuals emit the same type of chirp call, with parameters adapted only based on the distance to the nearest detected object. However, individual variation is introduced by assigning each bat a terminal frequency drawn from a normal distribution with a standard deviation of 1 kHz, as described in the revised version -lines 519-520. This small variation is not used explicitly as a spectro-temporal cue for echo discrimination.

In our model, all spectro-temporal variations—whether due to call structure or variations resulting from overlapping echoes from nearby reflectors—are processed through the filter bank, which compares the received echoes to the transmitted call during the detection stage. As such, the detection process itself can act as a discriminative filter, to some extent, based on similarity to the emitted call.

We acknowledge that real bats likely rely on a variety of spectro-temporal features for distinguishing self from non-self-echoes—such as call duration, received level, multi-harmonic structure, or amplitude modulation. In our simulation, we focus on comparing two limiting conditions: full recognition of self-generated echoes versus full confusion. Implementing a more nuanced self-recognition mechanism based on temporal or spectral cues would be a valuable extension for future work.

(6) References

Reference 22: Formatting error - and extra '4' in the reference.

The error has been fixed.

(7) Thoughts/comments

Even without 'recogntion' of walls & conspecifics, bats may be able to avoid obstacles - this is a neat result. Also, using their framework the authors show that successful 'blind' object-agnostic obstacle avoidance can occur only when supported by some sort of memory. In some sense, this is a nice intermediate step showing the role of memory in bat navigation. We know that bats have good long-term and long-spatial scale memory, and here the authors show that short-term spatial memory is important in situations where immediate sensory information is unreliable or unavailable.

We appreciate the reviewer’s thoughtful summary. Indeed, one of the main takeaways of our study is that successful obstacle avoidance can occur even without explicit recognition of walls or conspecifics—provided that a clustered multi-call integration is in place. Our model shows that when immediate sensory information is unreliable, integrating detections over time becomes essential for effective navigation. This supports the broader view that memory, even on short timescales, plays an important role in bat behavior.

(8) Reporting GLM results

The p-value, t-statistic, and degrees of freedom are reported consistently across multiple GLM results. However, the most important part which is the effect size is not consistently reported - and this needs to be included in all results, and even in the table. The effect size provides an indicator of the parameter's magnitude, and thus scientific context.

We agree that the effect size provides essential scientific context. In fact, we already include the effect size explicitly in Table 1, as shown in the “Effect Size” column for each tested parameter. These values describe the magnitude of each parameter’s effect on exit probability, jamming probability, and collision rate. In the main text, effect sizes are presented as concrete changes in performance metrics (e.g., “exit probability increased from 20% to 87%,” or “with a decrease of 3.5%±8% to 5.5%±5% (mean ± s.e.)”), which we believe improves interpretability and scientific relevance.  

To further clarify this in the main text, we have reviewed the reported results and ensured that effect sizes are mentioned more consistently wherever GLM outcomes are discussed. Additionally, we have added a brief note in the table caption to emphasize that effect sizes are provided for all tested parameters.

The 'tStat' appears multiple times and seems to be the output of the MATLAB GLM function. This acronym is specific to the MATLAB implementation and needs to be replaced with a conventionally used acronym such as 't', or the full form 't-statistic' too. This step is to keep the results independent of the programming language used.

We have replaced all instances of tStat with the more conventional term ‘t’ throughout the manuscript to maintain consistency with standard reporting practices.

Reviewer #2 (Recommendations for the authors):

In addition to my public review, I had a few minor points that the authors may want to consider when revising their paper.

(1) Figures 2, 3, and 4 may benefit from using different marker styles, in addition to different colors, to show the different cases.

Thank you for the suggestion. In Figures 2–4, the markers represent means with standard error bars. To maintain clarity and consistency across all conditions, we have chosen to keep a standardized marker style – and we clarify this in the legend. We found that varying only the colors is sufficient for distinguishing between conditions without introducing visual clutter.

(2) The text "PK" in the inset for Figure 2A is very difficult to read. I would suggest using grey as with "RM" in the other inset.

We have updated the insert in Figure 2A to improve legibility.

(3) Are the error bars in Figure 3 very small? I wasn't able to see them. If that is the case, the authors may want to mention this in the caption.

You are correct—the error bars are present in all plots but appear very small due to the large number of simulation repetitions and low variability. We have revised the caption to explicitly mention this.

(4) The species name of PK is spelled inconsistently (kuhli, khulli, and kuhlii).

We have corrected the species name throughout the manuscript.

(5) Table 1 is a great condensation of all the results, but the time to exit is missing. It may be helpful if summary statistics on that were here as well.

We have added time-to-exit to the effect size column in Table 1, alongside the other performance metrics, to provide a more complete summary of the simulation results.

(6) I may have missed it, but why are there two values for the exit probability when nominal flight speed is varied?

The exit probability was not monotonic with flight speed, but rather showed a parabolic trend with a clear optimum. Therefore, we reported two values representing the effect before and after the peak. We have clarified this in the revised table and updated the caption accordingly.

(7) Table 2 has an extra header after the page break on page 18.

The extra header in Table 2 after the page break has been removed in the revised manuscript.

(8) The G functions have 2 arguments in their definitions and Equation 1, but only one argument in Equations 2 and 3. I wasn't able to see why.

Thank you for pointing this out. You are correct—this was a typographical error. We have corrected the argument notation in Equations 2 and 3 and explicitly included the frequency dependence of the gain (G) functions in both equations.

(9) D_txrx was not defined but it was used in Equation 2.

The variable D_txrx is defined in the equation notation section as: D_₍ₜₓr_ₓ – the distance [m] between the transmitting conspecific and the receiving focal bat, from the transmitter’s perspective. We have now ensured that this definition is clearly linked to Equation 2 in the revised text. Moreover, we have added a supplementary figure that illustrates the geometric configuration defined by the equations to further support clarity, as described in the Public Review above.

(10) It was hard for me to understand what was meant by phi_rx and phi_tx. These were described as angles between the rx or tx bats and the target, but I couldn't tell what the point defining the angle was. Perhaps a diagram would help, or more precise definitions.

We have revised the caption to provide clearer and more precise definitions Additionally, we have included a geometric diagram as a supplementary figure, as noted in the Public Review above, to visually clarify the spatial relationships and angle definitions used in the equations, see lines 498-499.

(11) Was the hearing threshold the same for both species?

Yes. We have clarified it in the revised version.

(12) Collision avoidance is described as turning to the "opposite direction" in the supplemental figure explaining the model. Is this 90 degrees or 180 degrees? If 90 degrees, how do these turns decide between right and left?

In our model, the bat does not perform a fixed 90° or 180° turn. Instead, the avoidance behavior is implemented by setting the maximum angular velocity in the direction opposite to the detected echo. For example, if the obstacle or conspecific is detected on the bat’s right side, the bat begins turning left, and vice versa.

This turning direction is re-evaluated at each decision step, which occurs after every echolocation pulse. The bat continues turning in the same direction if the obstacle remains in front, otherwise it resumes regular pathfinding. We have clarified this behavior in the updated figure caption and model description, see lines 478-493.

Reviewer #3 (Recommendations for the authors):

(1) Lines 27-31: These sentences mischaracterize the results. This claim appears to equate "the model works" with "this is what bats actually do." Also, the model does not indicate that bats' echolocation strategies are robust enough to mitigate the effects of jamming - this is self-evident from the fact that bats navigate successfully via echolocation in dense groups.

Thank you for the comment. Our aim was not to claim that the model confirms actual bat behavior, but rather to demonstrate that simple and biologically plausible strategies—such as signal redundancy and basic pathfinding—are sufficient to explain how bats might cope with acoustic interference in dense settings. We have revised the wording to better reflect this goal and to avoid overinterpreting the model's implications.

See abstract in the revised version.  

(2) Line 37: This number underestimates the number of bats that form some of the largest aggregations of individuals worldwide - the free-tailed bats can form aggregations exceeding several million bats.

We have revised the text to reflect that some bat species, such as free-tailed bats, are known to form colonies of several million individuals, which exceed the typical range. The updated sentence accounts for these extreme cases, see lines 36-37.

(3) The flight densities explained in the introduction and chosen references are not representative of the literature - without providing additional justification for the chosen species, it can be interpreted that the selection of the species for the simulation is somewhat arbitrary. If the goal is to model dense emergence flight, why not use a species that has been studied in terms of acoustic and flight behavior during dense emergence flights---such as Tadarida brasiliensis?

Our goal was to develop a general model applicable to a broad class of FMecholocating bat species. The two species we selected—Pipistrellus kuhlii (PK) and Rhinopoma microphyllum (RM)—span a wide range of signal characteristics: from wideband (PK) to narrowband (RM), providing a representative contrast in call structure. 

Although we did not include Tadarida brasiliensis (TB) specifically, its echolocation calls are acoustically similar to RM in terminal frequency and fall between PK and RM in bandwidth. Therefore, we believe our findings are likely to generalize to TB and other FM-bats.

Moreover, as noted in a previous response, the average inter-bat distance in our highest-density simulations (0.27 m) is still smaller than those reported for Tadarida brasiliensis during dense emergences—further supporting the relevance of our model to such scenarios.

To support broader applicability, we also provide a supplementary graphical user interface (GUI) that allows users to modify key echolocation parameters and explore their impact on behavior—making the framework adaptable to additional species, including TB.

(4) Line 78: It is not clear how (or even if) the simulated bats estimate the direction of obstacles. The explanation given in lines 457-463 is quite confusing. What is the acoustic/neurological mechanism that enables this direction estimation? If there is some mechanism (such as binaural processing), how does this extrapolate to 3D?

This comment echoes a similar concern raised by a previous reviewer. As explained earlier, in the current simulation, the Direction of Arrival (DOA) was not modeled via an explicit binaural processing mechanism. The complete  is detailed in  to Reviewer #1, Line 457. This implementation is now clarified in the revised text, and a detailed description of the localization process is also provided in the Methods section (lines 583-592).

(5) The authors propose they are modeling the dynamic echolocation of bats in the simulation (line 79), but it appears (whether this is due to a lack of information in the manuscript or true lack in the simulation) that the authors only modeled a flight response. How did the authors account for bats dynamically changing their echolocation? This is unclear and from what I can tell may just mean that the bats can switch between foraging phase call types depending on the distance to a detected obstacle. Can the authors elaborate more on this?

The echolocation behavior of the bats—including dynamic call adjustments— was implemented in the simulation and is described in detail in the Methods section (lines 498-520 and Table 2). To avoid redundancy, the Results chapter originally referred to this section, but we have now added a brief explanation in the Results to clarify that the bats’ call parameters (IPI, duration, and frequency range) adapt based on the distance to detected objects, following empirically documented echolocation phases ("search," "approach," "buzz"). These dynamics are consistent with established bat behavior during navigation in cluttered environments such as caves.

(6) Figure 1 C3: "Detection threshold": what is this and how was it derived?

The caption also mentions yellow arrows, but they are absent from the figure. C4: Each threshold excursion is marked with an asterisk, but there are many more excursions than asterisks. Why are only some marked? Unclear.

C3: The detection threshold is determined dynamically. It is set to the greater of either 7 dB above the noise level (0 dB-SPL)(Kick, 1982; Saillant et al., 1993; Sanderson et al., 2003; Boonman et al., 2013) or the maximal received level minus 70 dB, effectively applying a dynamic range of 70 dB. This clarification has been added to the Methods section. The yellow arrow has been added.

C4: Thank you for this important observation. Only peaks marked with asterisks represent successful detections—those that were identified in both the interference-free and full detection conditions, as explained in the Methods. Other visible peaks result from masking signals or overlapping echoes from nearby reflectors, but they do not meet the detection criteria. To keep the figure caption concise, we have elaborated on this process more clearly in the revised Methods section. We added this information to the legend

(7) Figure 2: A line indicating RM, No Masking is absent

Thank you for pointing this out. The missing line for RM, No Masking has now been added in the revised version of Figure 2.

(8) Line 121: "reflected off conspecifics". Does this mean echoes due to conspecifics?

The phrase "reflected off conspecifics" refers to echoes originating from the bat’s own call and reflected off the bodies of nearby conspecifics. We have clarified the wording in the revised text to avoid confusion

(9) Line 125: Why are low-frequency channels stimulated by higher frequencies? This needs further clarification.

The cochlear filter bank in our model is implemented using gammatone filters, each modeled as an 8th-order Butterworth filter. Due to the non-ideal filter response and relatively broad bandwidths—especially in the lower-frequency channels—strong energy from the beginning of the downward FM chirp (at higher frequencies) can still produce residual activation in lower-frequency channels. While these stimulations are usually below the detection threshold, they may still be visible as early sub-threshold responses. Given the technical nature of this explanation (a property of the filter implementation) and it does not influence the detection outcomes, we have chosen not to elaborate on it in the figure caption or Methods.

(10) Lines 146-150: This is an interesting finding. Is there a theoretical justification for it?

This outcome arises directly from the simulation results. As noted in the Discussion (lines 359-365), although Pipistrellus kuhlii (PK) shows a modest advantage in jamming resistance due to its broader bandwidth, the redundancy in sensory information across calls—enabled by frequent echolocation—appears to compensate for these signal differences. As a result, the small variations in echo quality between species do not translate into significant differences in performance. We speculate that if the difference in jamming probability had been larger, performance disparities would likely have emerged.

(11) Line 151: The authors define a jammed echo as an echo entirely missed due to masking. Is this appropriate? Doesn't echo mis-assignment also constitute jamming?

We agree that echo mis-assignment can also degrade performance; however, in our model, we distinguish between two outcomes: (1) complete masking (echo not detected), and (2) detection with a localization error. As explained in the Methods (lines 500–507), we run the detection analysis twice—once with only desired echoes (“interference-free detection”) and once including masking signals (“full detection”). If a previously detected echo is no longer detected, it is classified as a jammed echo. If the echo is still detected but the delay shifts by more than 100 µs compared to the interference-free condition, it is also considered jammed. If the delay shift is smaller, it is treated as a detection with localization error rather than full jamming. We have clarified this distinction in the revised Methods section.

(12) Figure 2-E: Detection probability statistics are of limited usefulness without accompanying false alarm rate (FAR) statistics. Do the authors have FAR numbers?

We understand FAR to refer to instances where masking signals or other acoustic phenomena are mistakenly interpreted as real echoes from physical objects. As explained in the manuscript, we implemented two model versions: one without confusion, and one with full confusion.

Figure 2E reports detection performance under the non-confusion model, in which only echoes from actual physical reflectors are used, and no false detections occur—hence, the false alarm rate is effectively zero in this condition. In the full-confusion model, all detected echoes—including those originating from masking signals or conspecific calls—are treated as valid detections, which may include false alarms. However, we did not explicitly quantify the false alarm rate as a separate metric in this simulation.

We agree that tracking FAR could be informative and will consider incorporating it into future versions of the model.

(13) Line 161: RM bats suffered from a significantly higher probability of the "desired conspecific's echoes" being jammed. What does "desired conspecific's echoes" mean? This is unclear.

The term “desired conspecific's echoes” refers to echoes originating from the bat’s own call, reflected off nearby conspecifics, which are treated as relevant reflectors for collision avoidance. We have revised the wording in the text for clarity.

(14) Line 188: Why didn't the size of the integration window affect jamming probability? I couldn't find this explained in the discussion.

The jamming probability in our analysis is computed at the individual-echo level, prior to any temporal integration. Since the integration window is applied after the detection step, it does not influence whether a specific echo is masked (i.e., jammed) or not. Therefore, as expected, we did not observe a significant effect of integration window size on jamming probability.

(15) Line 217-218: Why do the authors think this would be?

Thank you for the thoughtful question. We agree that, in theory, increasing call intensity should raise the levels of both desired echoes and masking signals proportionally. However, in our model, the environmental noise floor and detection threshold remain constant, meaning that higher call intensities increase the signal-to-noise ratio (SNR) more effectively for weaker echoes, especially those at longer distances or with low reflectivity. This could lead to a higher likelihood of those echoes crossing the detection threshold, resulting in a small but measurable reduction in jamming probability.

Additionally, the non-linear behavior of the filter-bank receiver—including such as thresholding at multiple stages—can introduce asymmetries in how increased signal levels affect the detection of target versus masking signals.

That said, the effect size was small, and the improvement in jamming probability did not translate into any significant gain in behavioral performance (e.g., exit probability or collision rate), as shown in Figure 3C.

(16) Line 233: I'm not sure I understand how a slightly improved aggregation model that clustered detected reflectors over one-second periods is different. Doesn't this just lead to on average more calls integrated into memory?

While increasing the memory duration does lead to more detections being available, the enhanced aggregation model (we now refer to as multi-call clustering) differs fundamentally from the simpler one. As detailed in the Methods, it includes additional processing steps: clustering spatially close detections, removing outliers, and estimating wall directions based on the spatial structure of clustered echoes. In contrast, the simpler model treats each detection as an isolated point without estimating obstacle orientation. These additional steps allow for more robust environmental interpretation and significantly improve performance under high-confusion conditions. We have clarified it in revised text (lines 606-616) and added a Supplementary Figure 2B.

(17) Table 1: What about conspecific target strength?

We have now added the conspecific target strength as a tested parameter in Table 1, along with its tested range, default value, and measured effect sizes. A detailed sensitivity analysis is also presented in Supplementary Figure 4, demonstrating that variations in conspecific target strength had relatively minor effects on performance metrics.  

(18) Figure 3-A: The x-axis is the number of calls in the integration window. But the leftmost sample on each curve is at 0 calls. Shouldn't this be 1?

“0 calls” refers to the case where only the most recent call is used for pathfinding—without integrating any information from prior calls. The x-axis reflects the number of previous calls stored in memory, so a value of 0 still includes the current call. We’ve clarified this terminology in the figure caption.

(19) Lines 282-283: This statement needs to be clarified that it is with the constraints of using a 2D simulation with at most 33 bats/m^2. It also should be clarified that it is assumed the bat can reliably distinguish between its own echoes and conspecific echoes, which is a very important caveat.

We have revised the text to clarify that the results are based on a 2D simulation with a maximum tested density of 33 bats/m². We also now explicitly state that the model assumes bats can distinguish between their own echoes and those generated by conspecifics—an assumption we recognize as a simplification. These clarifications help place the results within the scope and constraints of the simulation. Moreover, as described in the text (and noted in previous response): the average distance to the nearest bat in our simulation is 0.27m (with 100 bats), whereas reported distances in very dense colonies are 0.5m

(20) Line 294: What is this sentence referring to?

The sentence refers to the finding that, even under high bat densities, a substantial portion of the echoes—particularly those reflected from nearby obstacles (e.g., 1 m away)—were jammed due to masking. Nevertheless, the bats in the simulation were still able to navigate successfully using partial sensory input. We have clarified the sentence in the revised text to make this point more explicit, see line 333-336.

(21) Line 302: Was jamming less likely when IPI was higher or lower? I could not find this demonstrated anywhere in the manuscript.

We agree that the original text was not sufficiently clear on this point. While we did not explicitly test fixed IPI values as a parameter, the model does simulate the natural behavior of decreasing IPI as bats approach obstacles. This behavior is supported by empirical observations and is incorporated into the echolocation dynamics of the simulation. We have clarified this point in the revised text (see Lines 346-351) and explained that while lower IPI introduces more acoustic overlap, it also increases redundancy and improves detection through temporal integration.

(22) Lines 313-314: This is an interesting assumption, but it is not evident that is substantiated by the references.

The claim is based on well-established principles in signal processing and bioacoustics. Wideband signals—such as those emitted by PK bats— distribute their energy over a broader frequency range, which makes them inherently more resistant to narrowband interference and masking. This concept is commonly applied in both biological and artificial sonar systems and is supported by empirical studies in bats and theory in acoustic sensing.

For example, Beleyur & Goerlitz (2019) demonstrate that broader bandwidth calls improve detection in cluttered and jamming-prone environments. Similarly, Ulanovsky et al. (2004) and Schnitzler & Kalko (200) discuss how FM bats' wideband calls enhance temporal and spatial resolution, helping to reduce the impact of overlapping signals from conspecifics. These findings align with communication theory where spread-spectrum techniques improve robustness in noisy environments.

We agree with the reviewer that this is an important point and we have updated the manuscript to clarify this rationale and cite the relevant literature accordingly – lines 631-363,

(23) Lines 318-319: What is the justification for "probably"? Isn't this just a supposition?

We agree with the reviewer’s point and have rephrased the sentence

(24) Line 320: How does this 63% performance match the sentence in line 295?

The sentence in Line 295 refers to the overall ability of the bats to navigate successfully despite high jamming levels, highlighting the robustness of the strategy under challenging conditions. The figure in Line 320 (63%) quantifies this performance under the most extreme simulated scenario (100 bats / 3 m²), where both spatial and acoustic interferences are maximal. We have rephrased the text in the revised version (lines 324-327).

(25) Lines 341-345: It seems like this is more likely to be the main takeaway of the paper.

As noted in the Public Review above, there is substantial literature supporting the assumption that bats can recognize their own echoes and distinguish them from those of conspecifics (e.g., Schnitzler, Bioscience, 2001; Kazial et al., 2001, 2008; Burnett & Masters, 2002; Chiu et al., 2009; Yovel et al., 2009; Beetz & Hechavarría, 2022). Therefore, we consider our assumption of selfrecognition to be well-supported, at least under typical conditions. That said, we agree that the impact of echo confusion on performance is significant and highlights a critical challenge in dense environments.

To our knowledge, this is the first computational model to explicitly simulate both self-recognition and full echo confusion under high-density conditions. We believe that the combination of modeled constraints and the demonstrated robustness of simple sensorimotor strategies, even under worst-case assumptions, is what makes this contribution both novel and meaningful.

(26) Lines 349-350: What is the aggregation model? What is meant by "integration"?

We have revised the text to clarify that the “aggregation model” refers to a multi-call clustering process that includes clustering of detections, removal of outliers, and estimation of wall orientation, as described in detail in the revised Methods and Results sections.

(27) Line 354: Again, why isn't this the assumption we're working under?

As addressed in our response to Comment 25, our primary model assumes that bats can recognize their own echoes—an assumption supported by substantial empirical evidence. The alternative "full confusion" model was included to explore a worst-case scenario and highlight the behavioral consequences of failing to distinguish self from conspecific echoes. We assume that real bats may experience some degree of echo misidentification; however, our assumption of full confusion represents a worst-case scenario.

(28) Line 382: "Under the assumption that..." I agree that bats probably can, but if we assume they can differentiate them all, where's the jamming problem?

The assumption that bats can theoretically distinguish between different signal sources applies after successful detection. However, the jamming problem arises during the detection and localization stages, where acoustic interference can prevent echoes from crossing the detection threshold or distort their timing.

(29) Lines 386-387: The paper referenced focused on JAR in the context of foraging. What changes were made to the simulation to switch to obstacle avoidance?

While the simulation framework in Mazar & Yovel (2020) was developed to study jamming avoidance during foraging, the core components—such as the acoustic calculations, receiver model, and echolocation behavior—remain applicable. For the current study, we adapted the simulation extensively to address colony-exit behavior. These modifications include modeling cave walls as acoustic reflectors, implementing a pathfinding algorithm, integrating obstacle-avoidance maneuvers, and adapting the integration window and integration processes. These updates are detailed throughout the Methods section.

(30) Line 400-402: Something doesn't add up with the statement: each decision relies on an integration window that records estimated locations of detected reflectors from the last five echolocation calls, with the parameter being tested between 1 and 10 calls. Can the authors reword this to make it less confusing?

We have reworded the sentence to clarify that the default integration window includes five calls, while we systematically tested the effect of using 1 to 10 calls, see lines 486-487.

(31) Line 393: "30 deg/sec" why was this value chosen?

The turning rate of 30 deg/sec was manually selected to approximate the curvature of natural foraging flight paths observed in Rhinopoma microphyllum using on-board tags. Moreover, in Mazar & Yovel (2020), we showed that the flight dynamics of simulated bats in a closed room closely matched those of Pipistrellus kuhlii flying in a room of similar dimensions. However, in the current simulation, bats rarely follow a random-walk trajectory due to the structured environment and frequent obstacle detection. As a result, this parameter has no meaningful impact on the simulation outcomes.

(32) Line 412: "Harmony" --- do you mean harmonic? And what is the empirical evidence that RM bats use the 2nd harmonic compared to the 1st?

Perhaps showing a spectrogram of a real RM signal would be helpful.

The typo-error was corrected. For reference See (Goldshtein et al., 2025)

(33) Table 2: Something is incorrect with the table. The first row on the next page is the wrong species name. Also, where are the citations for these parameter values?

The table header has been corrected in the revised version. The parameter values for flight and echolocation behavior were derived from existing literature and empirical data: Pipistrellus kuhlii parameters were based on Kalko (1995), and Rhinopoma microphyllum parameters were extracted from our own recordings using on-board tags, as described in Goldstein et al. (2025). We have added the appropriate citations to Table 2.

(34) Line 442: How was the threshold level chosen?

The detection threshold in each level is set to the greater of either 7 dB above the noise level (0 dB-SPL) or the maximal received level minus 70 dB, effectively applying a dynamic range of 70 dB.

(35) Line 445: 100 micros: This is about 3cm. The resolution of PK is about 1cm. For RM it's about 10cm. So, this window is generous for PK, but too strict for RM.

To keep the model simple and avoid introducing species-specific detection thresholds, we selected a biologically plausible compromise that could reasonably apply to both species. This simplification ensures consistency across simulations while remaining within the known behavioral range.

(36) Line 448: What is the spectrum of the Gaussian noise, and did it change between PK and RM?

We used the same white Gaussian noise with a flat spectrum across the relevant frequency range (10–80 kHz) for both species. We have clarified this in the revised text in lines 570-572.

(37) Line 451: 4 milliseconds is 1.3m. Is this appropriate?

The 4 milliseconds window was selected based on established auditory masking thresholds described in Mazar & Yovel (2020), and supported by (Popper and Fay, 1995) ch. 2.4.5, ((Blauert, 1997),  ch. 3.1 and (Mohl and Surlykke, 1989). These values provide conservative lower bounds on bats’ ability to cope with masking (Beleyur and Goerlitz, 2019). For simplicity, we used constant thresholds within each window, see lines 574-576.  

(38) Line 452: Citation for the forward and backward masking durations?

See the  to the previous comment.

(39) Lines 460-461: This is unclear. How does the bat get directional information? The authors claim to be able to measure direction-of-arrival for each detection, but it is not clear how this is done

As noted in our response to Reviewer 1 (Comment on Line 457), directional information is not computed via an explicit binaural model. Instead, we assume the bat estimates the direction of arrival with an angular error that depends on the SNR, based on established studies (e.g., Simmons et al., 1983; Popper & Fay, 1995). We have clarified this in the revised text in lines 583-592.

(40) Line 467: It seems like the authors are modeling pulse-echo ambiguity, at least in this one alternative model, which is good! However the alternative model doesn't get much attention in the paper. Is there a reason for this?

We would like to clarify that we did not model pulse-echo. In our confusion model, all echoes received within the IPI are attributed to the bat’s most recent call. This includes echoes that may in fact originate from conspecific calls, but the model does not assign self-echoes to earlier pulses or span multiple IPIs. Therefore, while the model captures echo confusion, it does not include true pulse-echo ambiguity. We have clarified this point in the revised text in lines 551-553.

(41) Line 41: "continuous" is more appropriate than "constant".

Thank you, we have rephrased the text accordingly.

(42) Line 69: "band width" should be one word.

Thank you, we have corrected it to “bandwidth”.

(43) Line 79: "bats" should be in the possessive.

Thank you, the text has been rephrased.

(44) Line 128: "convoluted" don't you mean "convolved"?

We have replaced “convoluted” with the correct term “convolved” in the revised text.

(45) Please check your references, as there are some incomplete citations and typos.

Thank you, we have reviewed and corrected all references for completeness and consistency.

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good way to understand what the author is getting at

...and still is. One of the biggest learning curves I had during my internships was how hard it was to actually effectively implement movies and videos into the class. As a student, I just thought my teacher was showing us a video so they didn't have to teach a full lesson, or so that they could grade papers. I thought the questions they asked were logical and just thought of on the spot.

I was at a different district working under a Social Studies teacher that always showed movies in their class. It was to the points that the kids didn't want to do it (even though the did not have to fill anything out). The other teachers were saying negative things about the head teacher and I was confused. Once I saw there was not much constructive activity to go along with the movie, I was worried about its effectiveness.

I taught an Immigration unit and showed a video of Ellis Island. I attempted to make Guided Notes for them to fill out. I thought listening to it in pieces would be best. Turns out I needed to have them watch it through once, and then replay the specific clips multiple times. Creating thought provoking questions students could answer after the video was also more difficult than I thought. All of this to say that it is actually not much easier to teach using videos because you need to make sure you are using them in moderation, with the appropriate activities, and in a way that engages students. There are so many factors that videos may not work in a classroom.

I wonder if one of the reasons is student attention. I know when I am sitting watching a video, it doesn't hold my attention as closely as a live person talking to me. Is there also an interaction piece to this? Sitting and watching a talking head on TV and not being able to ask that person to re-phrase their comment can make learning more difficult for some.

I usually tend skip the About Us part and head straight to the required skills section to see how I can align my skills with those the job role demands. I think moving forward I am certainly going t dedicate some time to look into this and make sure the organization's values align with mine, which would maybe also prove to be useful during behavioral interview rounds.

Reading like a writer is like not just getting a story in my head but thinking about the authors point of you and getting into the author's head.

Usually when I read, it was just a story in my head.

" Yes, some nights she does, in snatches. bows her poor head against the wall and snatches a little sleep. [Pause.] Still speak? Yes, some nights she does, when she fancies none can hear. " This specific section right here can be broke down to presence vs absence, strange of voice, and strand of time. All three work perfectly. When they say she sleeps in "snatches" it means her naps are not restful its like a continuous pattern of awake and then sleep which creates presence vs absence. Then when it says "bows her poor head against the wall" makes her seem tired and restless like she is consistently clinging on to life in a sense? or even a sense of being trapped? which this is a strand of time because this event feels repeated and it feels continuous, and it makes me feel like Shes hardly even there and it makes me think that she is suffering. Then when it says "Still speak? Yes, some nights she does, when she fancies none can hear." It shows she speaks to herself when nobody else is listening It makes her voice seem eerie or ghostly and it really makes me wonder if she is fading away, however still trying to cling to life. Her voice is like half present, shes halfway here, clinging for life.

I find that this is common amongst a lot of people. I can even say so for myself, when I wouldn’t do as well as I thought I did on a test or in a class I would get discouraged. It made me grow resentment towards a class I once enjoyed. That led me to procrastinate even more and made me dread doing the work for that class. Although failing can really mess with your head, I think this story points out the importance of determination despite all the discouragement

Then Hrothgar left with his friend, the lord of Scyldings, from the building. With the War-Chieftan wishing them good fortune in their journey. The Kings had created a watcher, as the people had recalled, the king of the Danemen did a special service and gave the watcher his trust because of his warlike strength. The Watchers armor was then taken off, he gave his helmet to his henchman and his chain sword and thanked him for holding his items, then someone said words of Defiance Beowulf Geatman stood from his bed and said”I am no meaner in prowess, or in war time achievements than Grendel. So I seek to not defeat Grendel with a sword even though I could.

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Reply to the reviewers

We thank the reviewers for their careful assessment and enthusiastic appreciation of our work.

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __In this article, Thomas et al. use a super-resolution approach in living cells to track proteins involved in the fusion event of sexual reproduction. They study the spatial organization and dynamics of the actin fusion focus, a key structure in cell-cell fusion in Schizosaccharomyces pombe. The researchers have adapted a high-precision centroid mapping method using three-color live-cell epifluorescence imaging to map the dynamic architecture of the fusion focus during yeast mating. The approach relies on tracking the centroid of fluorescence signals for proteins of interest, spatially referenced to Myo52-mScarlet-I (as a robust marker) and temporally referenced using a weakly fluorescent cytosolic protein (mRaspberry), which redistributes strongly upon fusion. The trajectories of five key proteins, including markers of polarity, cytoskeleton, exocytosis and membrane fusion, were compared to Myo52 over a 75-minute window spanning fusion. Their observations indicate that secretory vesicles maintain a constant distance from the plasma membrane whereas the actin network compacts. Most importantly, they discovered a positive feedback mechanism in which myosin V (Myo52) transports Fus1 formin along pre-existing actin filaments, thereby enhancing aster compaction.

This article is well written, the arguments are convincing and the assertions are balanced. The centroid tracking method has been clearly and solidly controlled. Overall, this is a solid addition to our understanding of cytoskeletal organization in cell fusion.

Major comments: No major comment.

Minor comments: _ Page 8 authors wrote "Upon depletion of Myo52, Ypt3 did not accumulate at the fusion focus (Figure 3C). A thin, wide localization at the fusion site was occasionally observed (Figure 3C, Movies S3)" : Is there a quantification of this accumulation in the mutant?

We will provide the requested quantification. The localization is very faint, so we are not sure that quantification will capture this faithfully, but we will try.

_ The framerate of movies could be improved for reader comfort: For example, movie S6 lasts 0.5 sec.

We agree that movies S3 and S6 frame rates could be improved. We will provide them with slower frame rate.

Reviewer #1 (Significance (Required)):

This study represents a conceptual and technical breakthrough in our understanding of cytoskeletal organization during cell-cell fusion. The authors introduce a high-precision, three-color live-cell centroid mapping method capable of resolving the spatio-temporal dynamics of protein complexes at the nanometer scale in living yeast cells. This methodological innovation enables systematic and quantitative mapping of the dynamic architecture of proteins at the cell fusion site, making it a powerful live-cell imaging approach. However, it is important to keep in mind that the increased precision achieved through averaging comes at the expense of overlooking atypical or outlier behaviors. The authors discovered a myosin V-dependent mechanism for the recruitment of formin that leads to actin aster compaction. The identification of Myo52 (myosin V) as a transporter of Fus1 (formin) to the fusion focus adds a new layer to our understanding of how polarized actin structures are generated and maintained during developmentally regulated processes such as mating.

Previous studies have shown the importance of formins and myosins during fusion, but this paper provides a quantitative and dynamic mapping that demonstrates how Myo52 modulates Fus1 positioning in living cells. This provides a better understanding of actin organization, beyond what has been demonstrated by fixed-cell imaging or genetic perturbation.

Audience: Cell biologists working on actin dynamics, cell-cell fusion and intracellular transport. Scientists involved in live-cell imaging, single particle tracking and cytoskeleton modeling.

I have expertise in live-cell microscopy, image analysis, fungal growth machinery and actin organization.

We thank the reviewer for their appreciation of our work.

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __ A three-color imaging approach to use centroid tracking is employed to determine the high resolution position over time of tagged actin fusion focus proteins during mating in fission yeast. In particular, the position of different protein components (tagged in a 3rd color) were determined in relation to the position (and axis) of the molecular motor Myo52, which is tagged with two different colors in the mating cells. Furthermore, time is normalized by the rapid diffusion of a weak fluorescent protein probe (mRaspberry) from one cell to the other upon fusion pore opening. From this approach multiple important mechanistic insights were determined for the compaction of fusion focus proteins during mating, including the general compaction of different components as fusion proceeds with different proteins having specific stereotypical behaviors that indicate underlying molecular insights. For example, secretory vesicles remain a constant distance from the plasma membrane, whereas the formin Fus1 rapidly accumulates at the fusion focus in a Myo52-dependent manner.

I have minor suggestions/points: (1) Figure 1, for clarity it would be helpful if the cells shown in B were in the same orientation as the cartoon cells shown in A. Similarly, it would be helpful to have the orientation shown in D the same as the data that is subsequently presented in the rest of the manuscript (such as Figure 2) where time is on the X axis and distance (position) is on the Y axis.

We have turned each image in panel B by 180° to match the cartoon in A. For panel D, we are not sure what the reviewer would like. This panel shows the coordinates of each Myo52 position, whereas Figure 2 shows oriented distance (on the Y axis) over time (on the X axis). Perhaps the reviewer suggests that we should display panel D with a rotation onto the Y axis rather than the X axis. We feel that this would not bring more clarity and prefer to keep it as is.

(2) Figure 2, for clarity useful to introduce how the position of Myo52 changes over time with respect to the fusion site (plasma membrane) earlier, and then come back to the positions of different proteins with respect to Myo52 shown in 2E. Currently the authors discuss this point after introducing Figure 2E, but better for the reader to have this in mind beforehand.

We have added a sentence at the start of the section describing Figure 2, pointing out that the static appearance of Myo52 is due to it being used as reference, but that in reality, it moves relative to the plasma membrane: “Because Myo52 is the reference, its trace is flat, even though in reality Myo52 also moves relative to other proteins and the plasma membrane (see Figure 2E)”. This change is already in the text.

(3) First sentence of page 8 "..., peaked at fusion time and sharply dropped post-fusion (Figure S3)." Figure S3 should be cited so that the reader knows where this data is presented.

Thanks, we have added the missing figure reference to the text.

(4) Figure 3D-H, why is Exo70 used as a marker for vesicles instead of Ypt3 for these experiments? Exo70 seems to have a more confusing localization than Ypt3 (3C vs 3D), which seems to complicate interpretations.

There are two main reasons for this choice. First, the GFP-Ypt3 fluorescence intensity is lower than that of Exo70-GFP, which makes analysis more difficult and less reliable. Second, in contrast to Exo70-GFP where the endogenous gene is tagged at the native genomic locus, GFP-Ypt3 is expressed as additional copy in addition to endogenous untagged Ypt3. Although GFP-Ypt3 was reported to be fully functional as it can complement the lethality of a ypt3 temperature sensitive mutant (Cheng et al, MBoC 2002), its expression levels are non-native and we do not have a strain in which ypt3 is tagged at the 5’ end at the native genomic locus. For these reasons, we preferred to examine in detail the localization of Exo70. We do not think it complicates interpretations. Exo70 faithfully decorates vesicles and exhibits the same localization as Ypt3 in WT cells (see Figure 2D) and in myo52-AID (see Figure 3C-D). We realize that our text was a bit confusing as we opposed the localization of Exo70 and Ypt3, when all we wanted to state was that the Exo70-GFP signal is stronger. We have corrected this in the text.

(5) Page 10, end of first paragraph, "We conclude...and promotes separation of Myo52 from the vesicles." This is an interesting hypothesis/interpretation that is consistent with the spatial-temporal organization of vesicles and the compacting fusion focus, but the underlying molecular mechanism has not be concluded.

This is an interpretation that is in line with our data. Firm conclusion that the organization of the actin fusion focus imposes a steric barrier to bulk vesicle entry will require in vitro reconstitution of an actin aster driven by formin-myosin V feedback and addition of myosin V vesicle-like cargo, which can be a target for future studies. To make clear that it is an interpretation and not a definitive statement, we have added “likely” to the sentence, as in: “We conclude that the distal position of vesicles in WT cells is a likely steric consequence of the architecture of the fusion focus, which restricts space at the center of the actin aster and promotes separation of Myo52 from the vesicles”.

(6) Figure 5F and 5G, the results are confusing and should be discussed further. Depletion of Myo52 decreases Fus1 long-range movements, indicating that Fus1 is being transported by Myo52 (5F). Similarly, the Fus1 actin assembly mutant greatly decreases Fus1 long-range movements and prevents Myo52 binding (5G), perhaps indicating that Fus1-mediated actin assembly is important. It seems the author's interpretations are oversimplified.

We show that Myo52 is critical for Fus1 long-range movements, as stated by the reviewer. We also show that Fus1-mediated actin assembly is important. The question is in what way.

One possibility is that FH2-mediated actin assembly powers the movement, which in this case represents the displacement of the formin due to actin monomer addition on the polymerizing filament. A second possibility is that actin filaments assembled by Fus1 somehow help Myo52 move Fus1. This could be for instance because Fus1-assembled actin filaments are preferred tracks for Myo52-mediated movements, or because they allow Myo52 to accumulate in the vicinity of Fus1, enhancing their chance encounter and thus the number of long-range movements (on any actin track). Based on the analysis of the K1112A point mutant in Fus1 FH2 domain, our data cannot discriminate between these three different options, which is why we concluded that the mutant allele does not allow us to make a firm conclusion. However, the Myo52-dependence clearly shows that a large fraction of the movements requires the myosin V. We have clarified the end of the paragraph in the following way: “Therefore, analysis of the K1112A mutant phenotype does not allow us to clearly distinguish between Fus1-powered from Myo52-powered movements. Future work will be required to test whether, in addition to myosin V-dependent transport, Fus1-mediated actin polymerization also directly contributes to Fus1 long-range movements.”

(7) Figure 6, why not measure the fluorescence intensity of Fus1 as a proxy for the number of Fus1 molecules (rather than the width of the Fus1 signal), which seems to be the more straight-forward analysis?

The aim of the measurement was to test whether Myo52 and Fus1 activity help focalize the formin at the fusion site, not whether these are required for localization in this region. This is why we are measuring the lateral spread of the signal (its width) rather than the fluorescence intensity of the signal. We know from previous work that Fus1 localizes to the shmoo tip independently of myosin V (Dudin et al, JCB 2015), and we also show this in Figure 6. However, the precise distribution of Fus1 is wider in absence of the myosins.

We can and will measure intensities to test whether there is also a quantitative difference in the number of molecules at the shmoo tip.

(8) Figure 7, the authors should note (and perhaps discuss) any evidence as to whether activation of Fus1 to facilitate actin assembly depends upon Fus1 dissociating from Myo52 or whether Fus1 can be activated while still associated with Myo52, as both circumstances are included in the figure.

This is an interesting point. We have no experimental evidence for or against Fus1 dissociating from Myo52 to assemble actin. However, it is known that formins rotate along the actin filament double helix as they assemble it, a movement that seems poorly compatible with processive transport by myosin V. In Figure 7, we do not particularly want to imply that Myo52 associates with Fus1 linked or not with an actin filament. The figure serves to illustrate the focusing mechanism of myosin V transporting a formin, which is more evident when we draw the formin attached to a filament end. We have now added a sentence in the figure legend to clarify this point: “Note that it is unknown whether Myo52 transports Fus1 associated or not with an actin filament.”

(9) Figure 7, the color of secretory vesicles should be the same in A and B.

This is now corrected.

Reviewer #2 (Significance (Required)):

This is an impactful and high quality manuscript that describes an elegant experimental strategy with important insights determined. The experimental imaging strategy (and analysis), as well as the insight into the pombe mating fusion focus and its comparison to other cytoskeletal compaction events will be of broad scientific interest.

We thank the reviewer for their appreciation of our work.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary:

Fission yeast cell-cell fusion during mating is mediated by an actin-based structure called the 'fusion focus', which orchestrates actin polymerization by the mating-specific formin, Fus1, to direct polarized secretion towards the mating site. In the current study, Thomas and colleagues quantitatively map the spatial distribution of proteins mediating cell-cell fusion using a three-color fluorescence imaging methodology in the fission yeast Schizosaccharomyces pombe. Using Myo52 (Type V myosin) as a fluorescence reference point, the authors discover that proteins known to localize to the fusion focus have distinct spatial distributions and accumulation profiles at the mating site. Myo52 and Fus1 form a complex in vivo detected by co-immunoprecipitation and each contribute to directing secretory vesicles to the fusion focus. Previous work from this group has shown that the intrinsically disordered region (IDR) of Fus1 plays a critical role in forming the fusion focus. Here, the authors swap out the IDR of fission yeast Fus1 for the IDR of an unrelated mammalian protein, coincidentally called 'fused in sarcoma' (FUS). They express the Fus1∆IDR-FUSLC-27R chimera in mitotically dividing fission yeast cells, where Fus1 is not normally expressed, and discover that the Fus1∆IDR-FUSLC-27R chimera can travel with Myo52 on actively polymerizing actin cables. Additionally, they show that acute loss of Myo52 or Fus1 function, using Auxin-Inducible Degradation (AID) tags and point mutations, impair the normal compaction of the fusion focus, suggesting that direct interaction and coordination of Fus1 and Myo52 helps shape this structure.

Major Comments:

(1) In the Results section for Figure 2, the authors claim that actin filaments become shorter and more cross-linked they move away from the fusion site during mating, and suggest that this may be due to the presence of Myo51. However, the evidence to support this claim is not made clear. Is it supported by high-resolution electron microscopy of the actin filaments, or some other results? This needs to be clarified.

Sorry if our text was unclear. The basis for the claim that actin filaments become shorter comes from our observation that the average position of tropomyosin and Myo51, both of which decorate actin filaments, is progressively closer to both Fus1 and the plasma membrane. Thus, the actin structure protrudes less into the cytosol as fusion progresses. The basis for claiming that Myo51 promotes actin filament crosslinking comes mainly from previously published papers, which had shown that 1) Myo51 forms complexes with the Rng8 and Rng9 proteins (Wang et al, JCB 2014), and 2) the Myo51-Rng8/9 not only binds actin through Myo51 head domain but also binds tropomyosin-decorated actin through the Rng8/9 moiety (Tang et al, JCB 2016; reference 27 in our manuscript). We had also previously shown that these proteins are necessary for compaction of the fusion focus (Dudin et al, PLoS Genetics 2017; reference 28 in our manuscript). Except for measuring the width of Fus1 distribution in myo51∆ mutants, which confirms previous findings, we did not re-investigate here the function of Myo51.

We have now re-written this paragraph to present the previous data more clearly: “The distal localization of Myo51 was mirrored by that of tropomyosin Cdc8, which decorates linear actin filaments (Figure 2B) (Hatano et al, 2022). The distal position of the bulk of Myo51-decorated actin filaments was confirmed using Airyscan super-resolution microscopy (Figure 2B, right). Thus, the average position of actin filaments and decreasing distance to Myo52 indicates they initially extend a few hundred nanometers into the cytosol and become progressively shorter as fusion proceeds. Previous work had shown that Myo51 cross-links and slides Cdc8-decorated actin filaments relative to each other (Tang et al, 2016) and that both proteins contribute to compaction of the fusion focus in the lateral dimension along the cell-cell contact area (perpendicular to the fusion axis) (Dudin et al, 2017). We confirmed this function by measuring the lateral distribution of Fus1 along the cell-cell contact area (perpendicular to the fusion axis), which was indeed wider in myo51∆ than WT cells (see below Figure 6A-B).”

(2) In Figure 4, the authors comment that disrupting Fus1 results in more disperse Myo52 spatial distribution at the fusion focus, raising the possibility that Myo52 normally becomes focused by moving on the actin filaments assembled by Fus1. This can be tested by asking whether latrunculin treatment phenocopies the 'more dispersed' Myo52 localization seen in fus1∆ cells? If Myo52 is focused instead by its direct interaction with Fus1, the latrunculin treatment should not cause the same phenotype.

This is in principle a good idea, though it is technically challenging because pharmacological treatment of cell pairs in fusion is difficult to do without disturbing pheromone gradients which are critical throughout the fusion process (see Dudin et al, Genes and Dev 2016). We will try the experiment but are unsure about the likelihood of technical success.

We note however that a similar experiment was done previously on Fus1 overexpressed in mitotic cells (Billault-Chaumartin et al, Curr Biol 2022; Fig 1D). Here, Fus1 also forms a focus and latrunculin A treatment leads to Myo52 dispersion while keeping the Fus1 focus, which is in line with our proposal that Myo52 becomes focused by moving on Fus1-assembled actin filaments. Similarly, we showed in Figure 5B that Latrunculin A treatment of mitotic cells expressing Fus1∆IDR-FUSLC-27R also results in Myo52, but not Fus1 dispersion.

(3) The Fus1∆IDR-FUSLC-27R chimera used in Figure 5 is an interesting construct to examine actin-based transport of formins in cells. I was curious if the authors could provide the rates of movement for Myo52 and for Fus1∆IDR-FUSLC-27R, both before and after acute depletion of Myo52. It would be interesting to see if loss of Myo52 alters the rate of movement, or instead the movement stems from formin-mediated actin polymerization.

We will measure these rates.

(4) Also, Myo52 is known to interact with the mitotic formin For3. Does For3 colocalize with Myo52 and Fus1∆IDR-FUSLC-27R along actin cables?

This is an interesting question for which we do not have an answer. For technical reasons, we do not have the tools to co-image For3 with Fus1∆IDR-FUSLC-27R because both are tagged with GFP. We feel that this question goes beyond the scope of this paper.

(5) If Fus1∆IDR-FUSLC-27R is active, does having ectopic formin activity in mitotic cells affect actin cable architecture? This could be assessed by comparing phalloidin staining for wildtype and Fus1∆IDR-FUSLC-27R cells.

We are not sure what the purpose of this experiment is, or how informative it would be. If it is to evaluate whether Fus1∆IDR-FUSLC-27R is active, our current data already demonstrates this. Indeed, Fus1∆IDR-FUSLC-27R recruits Myo52 in a F-actin and FH2 domain-dependent manner (shown in Figure 5B and 5G), which demonstrates that Fus1∆IDR-FUSLC-27R FH2 domain is active. Even though Fus1∆IDR-FUSLC-27R assembles actin, we predict that its effect on general actin organization will be weak. Indeed, it is expressed under endogenous fus1 promoter, leading to very low expression levels during mitotic growth, such that only a subset of cells exhibit a Fus1 focus. Furthermore, most of these Fus1 foci are at or close to cell poles, where linear actin cables are assembled by For3, such that they may not have a strong disturbing effect. Because analysis of actin cable organization by phalloidin staining is difficult (due to the more strongly staining actin patches), cells with clear change in organization predicted to be rare in the population, and the gain in knowledge not transformative, we are not keen to do this experiment.

Minor Comments:

Prior studies are referenced appropriately. Text and figures are clear and accurate. My only suggestion would be Figure 1E-H could be moved to the supplemental material, due to their extremely technical nature. I believe this would help the broad audience focus on the experimental design mapped out in Figure 1A-D.

We are relatively neutral about this. If this suggestion is supported by the Editor, we can move these panels to supplement.

Reviewer #3 (Significance (Required)):

Significance: This study provides an improved imaging method for detecting the spatial distributions of proteins below 100 nm, providing new insights about how a relatively small cellular structure is organized. The use of three-color cell imaging to accurately measure accumulation rates of molecular components of the fusion focus provides new insight into the development of this structure and its roles in mating. This method could be applied to other multi-protein structures found in different cell types. This work uses rigorously genetic tools such as knockout, knockdown and point mutants to dissect the roles of the formin Fus1 and Type V myosin Myo52 in creating a proper fusion focus. The study could be improved by biochemical assays to test whether Myo52 and Fus1 directly interact, since the interaction is only shown by co-immunoprecipitation from extracts, which may reflect an indirect interaction.

Indeed, future studies should dissect the Fus1-Myo52 interaction, to determine whether it is direct and identify mutants that impair it.

I believe this work advances the cell-mating field by providing others with a spatial and temporal map of conserved factors arriving to the mating site. Additionally, they identified a way to study a mating specific protein in mitotically dividing cells, offering future questions to address.

This study should appeal to a range of basic scientists interested in cell biology, the cytoskeleton, and model organisms. The three-colored quantitative imaging could be applied to defining the architecture of many other cellular structures in different systems. Myosin and actin scientists will be interested in how this work expands the interplay of these two fields.

I am a cell biologist with expertise in live cell imaging, genetics and biochemistry.

We thank the reviewer for their appreciation of our work.

Slope is going to be heading upward meaning it head postive. Heading in a constasnt way which is the same way and let the VI in constasnt way.

Reviewer #3 (Public review):

Summary:

The authors use calcium recordings from STN to measure STN activity during spontaneous movement and in a multi-stage avoidance paradigm. They also use optogenetic excitation, optogenetic inhibition, and lesion approaches to increase or decrease the activity of STN during the avoidance paradigm. The paper reports a large amount of data and makes many claims, some seem well supported to this Reviewer, others not so much.

Strengths:

Well-supported claims include data showing that during spontaneous movements, especially contraversive ones, STN calcium activity is increased using bulk photometry measurements. Single-cell measures back this claim but also show that it is only a modest minority of STN cells that respond strongly, with most showing no response during movement, and a similar number showing smaller inhibitions during movement.

Similar data during cued active avoidance procedures show that STN calcium activity sharply increases in response to auditory cues, and during cued movements to avoid a footshock. Optogenetic and lesion experiments are consistent with an important role for STN in generating cue-evoked avoidance. And a strength of these results is that multiple bi-directional approaches were used.

Weaknesses:

I found the experimental design and presentation convoluted and the results over-interpreted.

(1) I really don't understand or accept this idea that delayed movement is necessarily indicative of cautious movements. Is the distribution of responses multi-modal in a way that might support this idea, or do the authors simply take a normal distribution and assert that the slower responses represent 'caution'? Even if responses are multi-modal and clearly distinguished by 'type', why should readers think this that delayed responses imply cautious responding instead of say: habituation or sensitization to cue/shock, variability in attention, motivation, or stress; or merely uncertainty which seems plausible given what I understand of the task design where the same mice are repeatedly tested in changing conditions. This relates to a major claim (i.e., in the work's title).

(2) Related to the last, I'm struggling to understand the rationale for dividing cells into 'types' based the their physiological responses in some experiments (e.g., Figure 7).

(3) The description and discussion of orienting head movements were not well supported, but were much discussed in the avoidance datasets. The initial speed peaks to cue seem to be the supporting data upon which these claims rest, but nothing here suggests head movement or orientation responses.

(4) Similar to the last, the authors note in several places, including abstract, the importance of STN in response timing, i.e., particularly when there must be careful or precise timing, but I don't think their data or task design provides a strong basis for this claim.

(5) I think that other reports show that STN calcium activity is recruited by inescapable foot shock as well. What do these authors see? Is shock, independent of movement, contributing to sharp signals during escapes?

(6) In particular, and related to the last point, the following work is very relevant and should be cited: https://elifesciences.org/reviewed-preprints/104643#tab-content. Note that the focus of this other paper is on a subset of VGLUT2+ Tac1 neurons in paraSTN, but using VGLUT2-Cre to target STN will target both STN and paraSTN.

(7) In multiple other instances, claims that were more tangential to the main claims were made without clearly supporting data or statistics. E.g., claim that STN activation is related to translational more than rotational movement; claim that GCaMP and movement responses to auditory cues were small; claims that 'some animals' responded differently without showing individual data.

(8) In several figures, the number of subjects used was not described. This is necessary. Also necessary is some assessment of the variability across subjects. The only measure of error shown in many figures relates to trial-to-trial or event variability, which is minimal because, in many cases, it appears that hundreds of trials may have been averaged per animal, but this doesn't provide a strong view of biological variability. When bar/line plots are used to display data, I recommend showing individual animals where feasible.

(9) Can the authors consider the extent to which calcium imaging may be better suited to identify increases compared to decreases and how this may affect the results, particularly related to the GRIN data when similar numbers of cells show responses in both directions (e.g., Figure 3)?

(10) Raw example traces are not provided.

(11) The timeline of the spontaneous movement and avoidance sessions was not clear, nor was the number of events or sessions per animal nor how this was set. It is not clear if there was pre-training or habituation, if many or variable sessions were combined per animal, or what the time gaps between sessions were, or if or how any of these parameters might influence interpretation of the results.

(12) It is not clear if or how the spread of expression outside of the target STN was evaluated, and if or how many mice were excluded due to spread or fiber placements.

Alan taming the 'itchy monster' on my toddler self's head

Reviewer #1 (Public review):

Summary:

The authors introduce a densely-sampled dataset where 6 participants viewed images and sentence descriptions derived from the MS Coco database over the course of 10 scanning sessions. The authors further showcase how image and sentence decoders can be used to predict which images or descriptions were seen, using pairwise decoding across a set of 120 test images. The authors find decodable information widely distributed across the brain, with a left-lateralized focus. The results further showed that modality-agnostic models generally outperformed modality-specific models, and that data based on captions was not explained better by caption-based models but by modality-agnostic models. Finally, the authors decoded imagined scenes.

Strengths:

(1) The dataset presents a potentially very valuable resource for investigating visual and semantic representations and their interplay.

(2) The introduction and discussion are very well written in the context of trying to understand the nature of multimodal representations and present a comprehensive and very useful review of the current literature on the topic.

Weaknesses:

(1) The paper is framed as presenting a dataset, yet most of it revolves around the presentation of findings in relation to what the authors call modality-agnostic representations, and in part around mental imagery. This makes it very difficult to assess the manuscript, whether the authors have achieved their aims, and whether the results support the conclusions.

(2) While the authors have presented a potential use case for such a dataset, there is currently far too little detail regarding data quality metrics expected from the introduction of similar datasets, including the absence of head-motion estimates, quality of intersession alignment, or noise ceilings of all individuals.

(3) The exact methods and statistical analyses used are still opaque, making it hard for a reader to understand how the authors achieved their results. More detail in the manuscript would be helpful, specifically regarding the exact statistical procedures, what tests were performed across, or how data were pooled across participants.

(4) Many findings (e.g., Figure 6) are still qualitative but could be supported by quantitative measures.

(5) Results are significant in regions that typically lack responses to visual stimuli, indicating potential bias in the classifier. This is relevant for the interpretation of the findings. A classification approach less sensitive to outliers (e.g., 70-way classification) could avoid this issue. Given the extreme collinearity of the experimental design, regressors in close temporal proximity will be highly similar, which could lead to leakage effects.

(6) The manuscript currently lacks a limitations section, specifically regarding the design of the experiment. This involves the use of the overly homogenous dataset Coco, which invites overfitting, the mixing of sentence descriptions and visual images, which invites imagery of previously seen content, and the use of a 1-back task, which can lead to carry-over effects to the subsequent trial.

(7) I would urge the authors to clarify whether the primary aim is the introduction of a dataset and showing the use of it, or whether it is the set of results presented. This includes the title of this manuscript. While the decoding approach is very interesting and potentially very valuable, I believe that the results in the current form are rather descriptive, and I'm wondering what specifically they add beyond what is known from other related work. This includes imagery-related results. This is completely fine! It just highlights that a stronger framing as a dataset is probably advantageous for improving the significance of this work.

For Jourdain, it allowed her to 1) pursue a career in higher education; 2) enrol for a doctorate in Paris 3) write a dissertation 4) be hired as Vice-head at St. Hughe

This passage explains vectored interrupts and how they handle multiple devices efficiently. Since there are often more devices than vector entries, interrupt chaining links multiple handlers to a single vector entry. When an interrupt occurs, handlers are checked in order until the correct one services the request, balancing speed and memory usage.

The humor was superb and I could really imagine the fish's eyes popping out of his head.

add a comma before and after this sentence

He thinks of her as his good luck charm.

the lighting department can be very dangerous someone could get electrocuted. i never knew that a skilled electrician is called a gaffer. Although this is why you need skilled electricians in the first place for safety.

ENIGORIO then created his way of light into the darkness of the world by using his deceased mother. He used the head to create an orb. It created the sun.

Personally, I don't necessarily agree with this take--I think your first read-through of any article or essay should be a full analysis and your second read-through should focus on the nuances you missed... the reason for this is because a 'lazy read-through' is not as efficient.

Reviewer #1 (Public review):

Summary:

In this work, Wang et al. use a combination of genetic tools, novel experimental approaches and biomechanical models to quantify the contribution of passive leg forces in Drosophila. They also deduce that passive forces are not sufficient to support the body weight of the animal. Overall, the contribution of passive forces reported in this work is much less than what one would expect based on the size of the organism and previous literature from larger insects and mammals. This is an interesting finding, but some major caveats in their approach remain unanswered.

Strengths:

(1) The authors combine experimental measurements and modeling to quantify the contributions of passive forces at limb joints in Drosophila.

(2) The authors replicate a previous experimental strategy (Hooper et al 2009, J. Neuro) to suspend animals in air for measuring passive forces and, as in previous studies, find that passive forces are much stronger than gravitational forces acting on the limbs. While in these previous studies using large insects, a lot of invasive approaches for accurately quantifying passive forces are possible (e.g., physically cutting of nerves, directly measuring muscle forces in isolated preparations, etc), the small size of Drosophila makes this difficult. The authors overcome this using a novel approach where they attach additional weight to the leg (changes gravitational force) and inactivate motor neurons (remove active forces). With a few approximations and assumptions, the authors then deduce the contribution of passive forces at each joint for each leg.

(3) The authors find interesting differences in passive forces across different legs. This could have behavioral implications.

(4) Finally, the authors compare experimental results of how a free-standing Drosophila is lowered ("falls down") on silencing motor neurons, to a biomechanical "OpenSim" model for deducing the role of passive forces in supporting the body weight of the fly. Using this approach, they conclude that passive forces are not sufficient to support the body weight of the fly.

Weaknesses:

(1) Line 65 "(Figure 1A). Inactivation causes a change in the leg's rest position; however, in preliminary experiments, the body rotation did not have a large effect on the rest positions of the leg following inactivation. This result is consistent with the one already reported for stick insects and shows that passive forces within the leg are much larger than the gravitational force on a leg and dominate limb position [1]." This is the direct replication of the previous work by Hooper et al 2009 and therefore authors should ideally show the data for this condition (no weight attached).

(2) The authors use vglut-gal4, a very broad driver for inactivating motor neurons. The driver labels all glutamatergic neurons, including brain descending neurons and nerve cord interneurons, in addition to motor neurons. Additionally, the strength of inactivation might differ in different neurons (including motor neurons) depending on the expression levels of the opsins. As a result, in this condition, the authors might not be removing all active forces. This is a major caveat that authors do not address. They explore that they are not potentially silencing all inputs to muscles by using an additional octopaminergic driver, but this doesn't address the points mentioned above. At the very least, the authors should try using other motor neuron drivers, as well as other neuronal silencers. This driver is so broad that authors couldn't even use it for physiology experiments. Additionally, the authors could silence VGlut-labeled motor neurons and record muscle activity (potentially using GCaMP as has been done in several recent papers cited by the authors, Azevedo et al, 2020) as a much more direct readout.

(3) Figure 4 uses an extremely simplified OpenSim model that makes several assumptions that are known to be false. For example, the Thorax-Coxa joint is assumed to be a ball and socket joint, which it is not. Tibia-tarsus joint is completely ignored and likely makes a major contribution in supporting overall posture, given the importance of the leg "claw" for adhering to substrates. Moreover, there are a couple of recent open-source neuromechanical models that include all these details (NeuromechFly by Lobato-Rios et al, 2022, Nat. Methods, and the fly body model by Vaxenburg et al, 2025, Nature). Leveraging these models to rule in or rule out contributions at other joints that are ignored in the authors' OpenSim model would be very helpful to make their case.

(4) Figure 5 shows the experimental validation of Figure 4 simulations; however, it suffers from several caveats.

a) The authors track a single point on the head of the fly to estimate the height of the fly. This has several issues. Firstly, it is not clear how accurate the tracking would be. Secondly, it is not clear how the fly actually "falls" on VGlut silencing; do all flies fall in a similar manner in every trial? Almost certainly, there will be some "pitch" and "role" in the way the fly falls. These will affect the location of this single-tracked point that doesn't reflect the authors' expectations. Unless the authors track multiple points on the fly and show examples of tracked videos, it is hard to believe this dataset and, hence, any of the resulting interpretations.

b) As described in the previous point, the "reason" the fly falls on silencing all glutamatergic neurons could be due to silencing all sorts of premotor/interneurons in addition to the silencing of motor neurons.

c) (line 175) "The first finding is that there was a large variation in the initial height of the fly (Figure 5C), consistent with a recent study of flies walking on a treadmill[20]." The cited paper refers to how height varies during "walking". However, in the current study, the authors are only looking at "standing" (i.e. non-walking) flies. So it is not the correct reference. In my opinion, this could simply reflect poor estimation of the fly's height based on poor tracking or other factors like pitch and role.

d) "The rate at which the fly fell to the ground was much smaller in the experimental flies than it was in the simulated flies (Figure 5E). The median rate of falling was 1.3 mm/s compared to 37 mm/s for the simulated flies (Figure 5F). (Line 190) The most likely reason for the longer than expected time for the fly to fall is delays associated with motor neuron inactivation and muscle inactivation." I don't believe this reasoning. There are so many caveats (which I described in the above points) in the model and the experiment, that any of those could be responsible for this massive difference between experiment and modeling. Simply not getting rid of all active forces (inadequate silencing) could be one obvious reason. Other reasons could be that the model is using underestimates of passive forces, as alluded to in point 3.

(5) Final figure (Figure 6) focuses on understanding the time course of neuronal silencing. First of all, I'm not entirely sure how relevant this is for the story. It could be an interesting supplemental data. But it seems a bit tangential. Additionally, it also suffers from major caveats.

a) The authors now use a new genetic driver for which they don't have any behavioral data in any previous figures. So we do not know if any of this data holds true for the previous experiments. The authors perform whole-cell recordings from random unidentified motor neurons labeled by E49-Gal4>GtACR1 to deduce a time constant for behavioral results obtained in the VGlut-Gal4>GtACR1 experiments.

b) The DMD setup is useful for focal inactivation, however, the appropriate controls and data are not presented. Line 200 "A spot of light on the cell body produces as much of the hyperpolarization as stimulating the entire fly (mean of 11.3 mV vs 13.1 mV across 9 neurons). Conversely, excluding the cell body produces only a small effect on the MN (mean of 2.6 mV)." First of all, the control experiment for showing that DMD is indeed causing focal inactivation would be to gradually move the spot of light away from the labeled soma, i.e. to the neighboring "labelled" soma and show that there is indeed focal inactivation. Instead authors move it quite a long distance into unlabeled neuropil. Secondly, I still don't get why the authors are doing this experiment. Even if we believe the DMD is functioning perfectly, all this really tells us is that a random subset motor neurons (maybe 5 or 6 cells, legend is missing this info) labeled by E49-Gal4 is strongly hyperpolarized by its own GtACR1 channel opening, rather than being impacted because of hyperpolarizations in other E49-Gal4 labeled neurons. This has no relevance to the interpretation of any of the VGlut-Gal4 behavioral data. VGLut-Gal4 is much broader and also labels all glutamatergic neurons, most of which are inhibitory interneurons whose silencing could lead to disinhibition of downstream networks.

Author response:

Reviewer 1:

(1) Line 65 "(Figure 1A). Inactivation causes a change in the leg's rest position; however, in preliminary experiments, the body rotation did not have a large effect on the rest positions of the leg following inactivation. This result is consistent with the one already reported for stick insects and shows that passive forces within the leg are much larger than the gravitational force on a leg and dominate limb position [1]." This is the direct replication of the previous work by Hooper et al 2009 and therefore authors should ideally show the data for this condition (no weight attached).

We did not present this data – the effect of inactivation on the leg’s rest position in unweighted leg - because it was already reported in the case of stick insects. However, we understand the reviewer’s point that it is important to present the data showing this replication. We will do the same in the revised version.

(2) The authors use vglut-gal4, a very broad driver for inactivating motor neurons. The driver labels all glutamatergic neurons, including brain descending neurons and nerve cord interneurons, in addition to motor neurons. Additionally, the strength of inactivation might differ in different neurons (including motor neurons) depending on the expression levels of the opsins. As a result, in this condition, the authors might not be removing all active forces. This is a major caveat that authors do not address. They explore that they are not potentially silencing all inputs to muscles by using an additional octopaminergic driver, but this doesn't address the points mentioned above. At the very least, the authors should try using other motor neuron drivers, as well as other neuronal silencers. This driver is so broad that authors couldn't even use it for physiology experiments. Additionally, the authors could silence VGlut-labeled motor neurons and record muscle activity (potentially using GCaMP as has been done in several recent papers cited by the authors, Azevedo et al, 2020) as a much more direct readout.

This reviewer critique is related to the use of vglut-gal4 –a broad driver– to inactivate motor neurons (MNs). The reviewer argues that the use of a broad driver might result in some effects that are not due to MN inactivation. Conversely, it is possible that not all MNs are inactivated. These critiques raise important points that we will address in the revision by 1) performing experiments with other MN drivers as suggested by the reviewer, 2) performing experiments in flies that are inactivated by freezing. These measurements will provide other estimates of passive forces allowing us to better triangulate the range of values for the passive forces. Moreover, it appears that one of the reviewer’s main concern is that the passive forces are overestimated because of the residual active forces. We will discuss this possibility in detail. It is important to note that in the end what we hope to accomplish is to provide a useful estimate of the passive forces. It is unlikely that the passive force will be a precise number like a physical constant as the passive forces likely depend on recent history.

(3) Figure 4 uses an extremely simplified OpenSim model that makes several assumptions that are known to be false. For example, the Thorax-Coxa joint is assumed to be a ball and socket joint, which it is not. Tibia-tarsus joint is completely ignored and likely makes a major contribution in supporting overall posture, given the importance of the leg "claw" for adhering to substrates. Moreover, there are a couple of recent open-source neuromechanical models that include all these details (NeuromechFly by Lobato-Rios et al, 2022, Nat. Methods, and the fly body model by Vaxenburg et al, 2025, Nature). Leveraging these models to rule in or rule out contributions at other joints that are ignored in the authors' OpenSim model would be very helpful to make their case.

Our OpenSim model predates the newer mechanical model. In the revised manuscript, we will revisit the model in light of recent developments.

(4) Figure 5 shows the experimental validation of Figure 4 simulations; however, it suffers from several caveats.

a) The authors track a single point on the head of the fly to estimate the height of the fly. This has several issues. Firstly, it is not clear how accurate the tracking would be. Secondly, it is not clear how the fly actually "falls" on VGlut silencing; do all flies fall in a similar manner in every trial? Almost certainly, there will be some "pitch" and "role" in the way the fly falls. These will affect the location of this single-tracked point that doesn't reflect the authors' expectations. Unless the authors track multiple points on the fly and show examples of tracked videos, it is hard to believe this dataset and, hence, any of the resulting interpretations.

b) As described in the previous point, the "reason" the fly falls on silencing all glutamatergic neurons could be due to silencing all sorts of premotor/interneurons in addition to the silencing of motor neurons.

c) (line 175) "The first finding is that there was a large variation in the initial height of the fly (Figure 5C), consistent with a recent study of flies walking on a treadmill[20]." The cited paper refers to how height varies during "walking". However, in the current study, the authors are only looking at "standing" (i.e. non-walking) flies. So it is not the correct reference. In my opinion, this could simply reflect poor estimation of the fly's height based on poor tracking or other factors like pitch and role.

d) "The rate at which the fly fell to the ground was much smaller in the experimental flies than it was in the simulated flies (Figure 5E). The median rate of falling was 1.3 mm/s compared to 37 mm/s for the simulated flies (Figure 5F). (Line 190) The most likely reason for the longer than expected time for the fly to fall is delays associated with motor neuron inactivation and muscle inactivation." I don't believe this reasoning. There are so many caveats (which I described in the above points) in the model and the experiment, that any of those could be responsible for this massive difference between experiment and modeling. Simply not getting rid of all active forces (inadequate silencing) could be one obvious reason. Other reasons could be that the model is using underestimates of passive forces, as alluded to in point 3.

(4a) Although we agree that measuring different points on the body would allow us to estimate the moments, we disagree that the height of the fly cannot be evaluated from the measurement of a single point. The measurements have been performed using the same techniques that we used to assess the fly’s height in a different study where we estimated the resolution of our imaging system to be ~20 mm(Chun et. al. 2021). We will include these details in the revised manuscript. The video showing the falling experiments are not available or referenced in the manuscript. These will be made available.

b) We will repeat the “falling” experiment with a more restrictive driver.

c) We disagree with the reviewer on this point. The system has a resolution of ~20 mm and is sufficient to make conclusion about the difference in the height of the fly. We will clarify this point in the revised manuscript.

d) We do not follow the reviewer’s rationale here. The passive forces in the model (along with any residual forces) are the same in the model as well as in the experiment. Moreover, there will be a delay between light onset, neuronal inactivation and muscle inactivation. These processes are not instantaneous. In Figure 6, we estimate these delays and have concluded that they will cause substantial delay. In the revised manuscript, we will discuss other reasons for the delay suggested by the reviewer.

(5) Final figure (Figure 6) focuses on understanding the time course of neuronal silencing. First of all, I'm not entirely sure how relevant this is for the story. It could be an interesting supplemental data. But it seems a bit tangential. Additionally, it also suffers from major caveats.

a) The authors now use a new genetic driver for which they don't have any behavioral data in any previous figures. So we do not know if any of this data holds true for the previous experiments. The authors perform whole-cell recordings from random unidentified motor neurons labeled by E49-Gal4>GtACR1 to deduce a time constant for behavioral results obtained in the VGlut-Gal4>GtACR1 experiments.

b) The DMD setup is useful for focal inactivation, however, the appropriate controls and data are not presented. Line 200 "A spot of light on the cell body produces as much of the hyperpolarization as stimulating the entire fly (mean of 11.3 mV vs 13.1 mV across 9 neurons). Conversely, excluding the cell body produces only a small effect on the MN (mean of 2.6 mV)." First of all, the control experiment for showing that DMD is indeed causing focal inactivation would be to gradually move the spot of light away from the labeled soma, i.e. to the neighboring "labelled" soma and show that there is indeed focal inactivation. Instead authors move it quite a long distance into unlabeled neuropil. Secondly, I still don't get why the authors are doing this experiment. Even if we believe the DMD is functioning perfectly, all this really tells us is that a random subset motor neurons (maybe 5 or 6 cells, legend is missing this info) labeled by E49-Gal4 is strongly hyperpolarized by its own GtACR1 channel opening, rather than being impacted because of hyperpolarizations in other E49-Gal4 labeled neurons. This has no relevance to the interpretation of any of the VGlut-Gal4 behavioral data. VGLut-Gal4 is much broader and also labels all glutamatergic neurons, most of which are inhibitory interneurons whose silencing could lead to disinhibition of downstream networks.

(5 a) However, we can address the reviewer critique by recording from the Vglut line while using a MN line to target the recordings to MNs.

b) Once we use the Vglut driver to perform these recordings, it will help assess how much of the MN inactivation is due to the GtACR expressed in the MN versus other neurons.

Reviewer 2:

While (as mentioned above) the study's conclusions are well-supported by the results and modeling, limitations arise because of the assumptions made. For instance, using a linear approximation may not hold at larger joint angles, and future studies would benefit from accounting for nonlinearities. Future studies could also delve into the source of passive forces, which is important for more deeply understanding the anatomical and physical basis of the results in this study. For instance, assessments of muscle or joint properties to correlate stiffness values with physical structure might be an area of future consideration.

We agree with these comments but believe that these studies represent avenues for future work.

Reviewer 3:

(1) Passive torques are measured, but only some short speculative statements, largely based on previous work, are offered on their functional significance; some of these claims are not well supported by experimental evidence or theoretical arguments. Passive forces are judged as "large" compared to the weight force of the limb, but the arguably more relevant force is the force limb muscles can generate, which, even in equilibrium conditions, is already about two orders of magnitude larger. The conclusion that passive forces are dynamically irrelevant seems natural, but contrasts with the assertion that "passive forces [...] will have a strong influence on limb kinematics". As a result, the functional significance of passive joint torques in the fruit fly, if any, remains unclear, and this ambiguity represents a missed opportunity. We now know the magnitude of passive joint torques - do they matter and for what? Are they helpful, for example, to maintain robust neuronal control, or a mechanical constraint that negatively impacts performance, e.g., because they present a sink for muscle work?

To us, measuring passive forces was the first step to understanding neural/biomechanical control of limb. In general, we agree with these comments and would like to understand the role of passive forces in overall control of limb. A complete discussion of the role of the significance of passive forces in the control of limb is beyond the scope of this study. We would like to note that it is unlikely that the active forces are two orders of magnitude larger during unloaded movement of the limb. However, these issues will have to be settled in future work.

(2) The work is framed with a scaling argument, but the assumptions that underpin the associated claims are not explicit and can thus not be evaluated. This is problematic because at least some arguments appear to contradict textbook scaling theory or everyday experience. For example, active forces are assumed to scale with limb volume, when every textbook would have them scale with area instead; and the asserted scaling of passive forces involves some hidden assumptions that demand more explicit discussion to alert the reader to associated limitations. Passive forces are said to be important only in small animals, but a quick self-experiment confirms that they are sufficient to stabilize human fingers or ankles against gravity, systems orders of magnitude larger than an insect limb, in seeming contradiction with the alleged dominance of scale. Throughout the manuscript, there are such and similar inaccuracies or ambiguities in the mechanical framing and interpretation, making it hard to fairly evaluate some claims, and rendering others likely incorrect.

We interpret this comment as making two separate points. The first one is that the reviewer says that our statement that active forces depend on the third power of the limb or L³ is incorrect. We agree and apologize for this oversight. Specifically, on L6-7 we say, “both inertial forces and active forces scale with the mass if the limb which in turn scales with the volume of the limb and therefore depends on the third power of limb length (L³)”. Instead, this statement should read “inertial forces scale with the mass if the limb which in turn scales with the volume of the limb and therefore depends on the third power of limb length (L³)”. However, this oversight does not affect the scaling argument as the scaling arguments in the rest of the manuscript only involves inertial forces and not active forces.

The second point is about the scaling law that governs passive forces. In the current manuscript, we have assumed that the passive forces scale as L² based on previous work. The reviewer has pointed out that this assumption might be incorrect or at the very least needs a rationale. We agree with this assessment: passive forces that arise in the muscle are likely to scale as L² but passive forces that arise in the joint might not. In the revised manuscript, we will discuss this concern.

Response to the public comment:

There was a comment from a reader: “None of our work cited in various places in this preprint (i.e., Zakotnik et al. 2006, Guschlbauer et al. 2007, Page et al. 2008, Hooper et al. 2009, Hooper 2012, Ache and Matheson 2012, Blümel et al. 2012, Ache and Matheson 2013, von Twickel et al. 2019, and Guschlbauer et al. 2022) claims or implies that passive forces could be sufficient to support the weight of an insect or any animal. To claim or suggest otherwise (as done in lines 33-35) is incorrect and sets up a misleading straw man that misrepresents our work. All statements in the preprint regarding our work related to this specific matter need to be removed or edited accordingly. For instance, the investigations, calculations, and interpretations in Hooper et al. 2009 are solely about limbs that are not being used in stance or other loaded tasks (indeed, the article's title specifically refers to "unloaded" leg posture and movements). Trying to use this work to predict whether passive muscle forces alone can support a stick insect against gravity requires considering much more than the oversimplified calculation given in lines 290-292. Other “back of the envelope calculations” (lines 299-300) are likely also insufficient and erroneous. The discussion in lines 289-304 needs to be edited accordingly”

We thank the reader for their comment. However, we interpret these studies differently. The studies above rightly focused on unloaded legs because it would be difficult to study passive forces in an intact insect without genetic tools. The commenter correctly points out that these studies do not comment on whether passive forces are strong enough to support the weight of the fly. However, we disagree that our arguments based on their results are unreasonable or strawman. We think that our interpretation of their measurements is correct. Moreover, we were motivated by Yox et. el. 1982 who states in so many words: “Stiffness of the muscles in the joints of all the legs might be sufficient to support a resting arthropod. A more rigorous analysis of all supporting limbs and joint angles would be required to prove this hypothesis”. We were inspired by this comment. In the revised manuscript, we will make it clear that the statement made in Line 33 is based on Yox. et. al. and our interpretation of measurements made by others.

In my country, there are many monopolies in different industries. For example, Samsung dominates semiconductors and electronics, while KakaoTalk leads in messaging, taxi services, and even its own payment system. Due to the preemption of few head coorporations, they are in charge of control of the market prices, which makes me question about how small businesses compete and survive.

This is like a claim of assault, and it's been blamed on Rebekah, it would seem. The note explains that there is a requirement of absolutely no real evidence, other than the girl simply saying that it happened, which is important. A historical note shows that no evidence was needed back then. If someone felt they were assaulted in their sleep, or saw it in a vision it was generally accepted, and it could put someone in serious danger. The seemingly obvious logic of the era still has me scratching my head to this very moment.

i never knew back then the media was controlled by 50 different companies that shortened it dramatically down to just 4.

The answer to "Why you should subscribe" is to hand over your money to a person hiding behind an avatar that itself has a bag over its head. Want to know more? Cut and paste the "About" page into ChatGPT and ask (1) if the biography credible, and (2) can all the assertions of activity in the public sector be verified by official or press records.

A fool and their money are soon separated.

Ukraine comes up in my head - Trump is suddenly Putin's stooge for meeting with him in Alaska but is nothing when he meets with Zelenskyy and EU leaders the next week.

Ultimately a result of partisanship and immense privilege of watching all of these conflicts on the sidelines for centuries

https://www.youtube.com/watch?v=9bR-bsvi0Nk

Annotating on hypothesis as a resource for others in the Fellowship of the Sacred Commons

Four Sessoins in September

Gifting was way of life in Brehon Life Cumann was the original Commons Even in feudal times, the people had a Cumann Capitalism destroyed the Cumann - it is anti-commons The state regulates the market Commons as a social welfare system 1980's globalisation - nations can no longer regulate Restore the Cumann and use it to regulate the present into the future No place for Religion No place for Ideaology Local - Translocal - Cosmo-Local Web3 - Crypto as models

SESSION 1 Macro History Review Patterns Cyclical Humanity COmmons arrises when Market and State become hegemonic Commons can also become hegenomic Transition now What kind of commons? Physical Commons Social Commons Digital Commons Phy-gital Commons An interesting time in humanity Who are the commoners? Thesis and Empirical Evidence of 20 years observation of Seed Forms Verifiable Data that something is happening Coherent Speculation

Commons in Feudalism Commons in Capitalism

Alan Paige Fisk - The structure of social life Communal Shareholding - Family Equality Matching - Gifting Authority Ranking - Protect & Reward The Market

Humanity wants to create Mode A at a higher level of com-lexity - people, family, but we want it at civilisational scale

Religion was the first attempot to civilise civilisation - to civilise warriors - Caim Aideain -

Capitalism is mass ideaology

Immination of the Eschaton Life is a vale of tears but we can go to heaven The age of the Father - The Age of the Son - The Age of the Spirit - live like monks, we do not need church

Paradise is in the future Reformation Mass Utopia - past present future

BUT THIS IS OVER!!! 1789 to 1989

The Age of Cosmo-Local Networks

Software Currencies Our Hopes are invested in Blockchain which is a form of Jesus

Civilisations Spiral - there is always somethign left behind It bifurcates - a reaction against the failure of the previous cycle

In Rome, work was slavery In Christianity - work is sacred to make heaven on earth

Commonsres think translocally - cosmically We do nto need the nation state

Fair BnB can replace Air BnB - it does not need the nation state

The Spirit as modes of exchange

Matter and Spirit are at the same level - the physical is sacred

Ideaology and Materialism can be integrated

Michel's very traumatic childhood

Marxism explained his suffering HIs abandonment of Trotsky-ism Shifted to self change Looking at the world world his marxist glasses Joined a Reich commune Women compete for the Alpa Males - and Michel was not an alpa male

People are confused when they have no methodology at all In order to be free, one has to know how one is determined

A tourist sees a building, but a local sees the people who inhabited the building as a culture over time

We learn from people that we disagree with

Listenign to right wing people - I do nto agree with you, AND I get you

3D is light and shadow - it is perspecival which also then creates a shadow

Christian Atheism Conservative Communism

Michel was cancelled in 2018 - it was his shock Universities used to be mostly men, now there are two women for every man in univesities

Something has gone wrong with the Left and Masculinity

The network state world is antithetical to family

In a society that has to struggle, the male is dominant When that society becomes richer, the female becomes dominant (Dominus = Lord)

Rich women have an advantage over poor women becase their kids will survive In an egqlitarian sopciety, all kids survive, so the rich women discourage other women to have children

Being a mother, growing life has to have prestige

when reproduction rates are low, society suicides itself

Commons is a practice and an institution

Network State Movement = collect money with crypto and then buy land - create a new corporate state - be a shareholder not a citizen

Commons as cosmo local domain relevancies

Crypto has the idea of contribution to the common good

Michel started to read the Dark Masters when he was canceleld in 2018

We can only want what oher peopel want - so we compete Then we need a scapegoat - we find a common enemy Religion is a way to sacreficie people Sacrifice is a potion regulated through religion - until Christ appears to end the sacrifice

In feudal times, a merchant could not wear fur, it was only for royalty

In egalitarisn societies, everyone can have everything, but the only way to solve the demand is mass production with mass consumption

A rich woman can own an expensive bag yet in five years, everyoine can have it

Capitalism regulates this through over-consumpotion

Capitalism is now violent - billions of little cuts for thousands of years

LEFT : Man is good instituions are bad RIGHT: Man is bad institutions are good

We can be egalitarian by confronting our dark nature - we are envious - we are animalistic - we needed the ten commandments - but we were given 30 of them

We began to go into thertapy in the 1970's to confront ourselves

IN the 1980's we invited medication to suppporess the symptoms

Generations have nto done their own inner work

The Woke accuse old white men in the way they used to blame the Jews - scapegoating machineries

Neo-Liberalals now medicate the psyche - medicalisation of symptoms

We need to become somatic - we have to work it out in the body, thropugh the body - spirtituality arises in somatic experiences

Michel then looked to the East - a supermarket of therapy

Rashneesh Commune - preparign the world for neo-liberalism

Forget guilt and shame - forget the others - follwo your own path - follow your own bliss - transcend yourself - become selfish - go fot it - have the life youw ant - INDIVIDUALLISTIC

But if you did not have money - you cooudl not joiun that club

People prostituted themselves to go to the Ashram for €75 a day - it was for upper middle class people

How it becase a mastery of seduction - you want this

Watch Wild Wild Country - OSHO

Michel then looked at the traditions of the West - Templar - Rosicrucian -

He had to reconcile hismelf with Western Civilisation

Catholics were hostile to slavery - the church bought slaves from the ottomans - a low number of salves in Europe (it aboloshed slavery)

Buddhists had slaves since the 1930's

A Christian has to love their neighbour

Keith Chandler - beyond civilisation - relationship between order and chaos

Tribal people are not alienated, family is known as are enemies Civilisations are alienated - solider, slave, serf, religion

Commons is not utopian New relationship with nature Social contracts Not paradise Not classless The transition is messy

We can do better than destroying ourselves

We are in a new phase of history

Commons is a realistic hypothesis of cnage It's real already

In the 14th century: we had purgatory so then we could lend money, we had the printing press to spead ideas and we had double entry book-keeping to create capitalism : THEY WERE SEED FORMS

Social Change needs SEED FORMS - e,g, Commons

P2P WIKI - 40,000 entries

Ken Wilbur - Integral Studies

Levels Schema Quadrants

Golden Rosicrucians in Holland

Gnostic Dialoistic New Christian - Saklas - Jehovah - Yeldabaoth - Pleroma - Monad - Barbelo - Aeon - Archon

With Gnostics, MIchel could not accept a hateful world and decided to be creative - he was tired of introspection

Transhumanism Documentary

Head of Digital Strategy for a Telco 90's + Burnout

William James - once born people - twice born people - we have to do a lot of work - crisis, we com eout of it - so many things - a richer integratveo capacity -dark night of the soul to be integrated 1998 (Age of 42)

The world is tragic and I can do something without teh rage of being 18

P2P Foundation - sabbatical

Came back to his engagement - tremendous energy until 2018 - massive productivity

10,000 academic references 4,000 google scholar references

2018 - shared critical video of jordan petersen - fascist - disagreed with him, but Michelw as cancelled

Michel is a librarian

People reacted - where am I?? Its like Salma Rushdie beign attacked without reading him

Nobody defended him from the extremists

"You cannat talk becase youa re white" Deminised - defunded - debanked depaypalled - blacklisted - height of popularity to zero - unjust - questioning is thsi the left - am I still on the left?

A good thing - he did not know anythign about the right

From Trotsky to New Right and all in between - no labs

He is a conservative communist - we need to improve and preserve - there are no contradictions

In 2016 - counter culture switches to the right

We lost the leftist opinion - it was a mourning

Leftism used to be a brotherhood - now to go being a white person, we are sub-human

Genital mutilation is not normal

The woke right commits genocide and the jews cannot be criticised

Identity is layered - over many years in a real life

Life experiecne made him a compelx layered being

In the USA, the Reds fear china, the blues fear the internet They are tribes - only 4% of blues marry reds In the USA, they are physically separating by relocaing to theri own states

Ruling classs 1% Managers class with many factions

After world war 2, one had to be Kenseyian - this was the faction supported

In 1973, Oil Crisis - maybe Friedman??? the new neo-liberal elites that becase hegemonic

Occupy 2014 - against the 1%

6,000% increase in woke vocabularoty in press

Elites decide to shift to anotehr faction - e.g Holland hegemonic abolished guilds - but then the peopel stopped fightign fot the elite

We are in an accelerated disintegration of the western model

Importance of Web3 and P2P

Course on The Commons

Michel's Higher Standpoint now

Integrative Capacity Motivation (not rage and resentment) Activism is high on the dark triad Capacity to see relatvie truths of different human groups A new breed of people who are politically homeless Pragmatic - making sense

MIchel's dream (web 3) social change if we have willing alliacnes from the top, the middle and the top

Monasteries - Roman Elite - People

Attract capital from sections of the elite who have the money

There is huge capital flow

if you are a parasite, they will come after you If you share, you will be welcome everywhere

A SEED FORM dependent on our participation

Event in Brussels - Commons Hob Brussels

706 - cultural events in people's own houses

Michel has something to say and wants to share it

In timews like this, going deep is very important Too much action Deep Exchange Make time to step back NOT ACT ACTUALLY

THINK DONT ACT!

us as people never appreciate what we have we want what others have and the world can be a bad place because we are never content with what we have

the word choice in this sentence sets a scenery, makes me feel the emotion and I was able to see an image in my head while I was reading it

eLife Assessment

In this valuable study, the authors use a cutting-edge method to perform voltage imaging of CA1 pyramidal cells in head-fixed mice running on a track while local field potentials (LFPs) were recorded in the contralateral hemisphere. The authors provide solid evidence of synchronous ensembles of CA1 pyramidal neurons that are associated with contralaterally recorded theta rhythms but not with contralaterally recorded sharp wave-ripples during exploration of a novel environment. The paper will be of interest to scientists who are interested in hippocampal neuronal coding of novel environments, particularly those with experimental questions that can benefit from this cutting-edge imaging technique.

We may not use RStudio. Instead, try head(flights).

His downfall was caused by his own hands and will. His emotions got the better of him.

Bad luck around every corner! Okonkwo couldn't catch a break. Will his high motivation lead to his downfall?

Supernatural forces also operate in this story

Struggled with this in high school. The teacher asks, "Do you understand?" and no one truly does, but we shake our head anyways.

Reviewer #1 (Public review):

In their manuscript, Michelson et al use a combination of mesoscopic 1p and single-cell resolution 2p imaging to characterise cortical encoding of grooming behaviour. Despite their subcortical locus of control (and non-reliance on cortex), the authors report that grooming movements are accompanied by widespread activation of dorsal cortex. Different grooming movements elicit distinct spatiotemporal cortical activity patterns. They find that cortical engagement is greater at the beginning of grooming episodes than at their end. They also report greater cortical activation for atypical unilateral grooming movements seen under head-restraint in comparison to cortical activity during bilateral movements typical of unrestrained or spontaneous grooming.

While this is not the first study to report cortical representations of subcortically controlled behaviours, and the authors themselves cite many previous reports of cortical activation during locomotion and even grooming (Sjöbom et al 2020), the value of the present study lies in their use of imaging to reveal the widespread nature of cortical activation during execution of a complex, innate behaviour. I also appreciate the systematic approach used by the authors to break down grooming episodes into their constituent movements and reveal their transition structure.

I do have concerns, however, that some of the authors' claims are insufficiently supported by their results, and more analysis is required to convincingly rule out alternative interpretations.

(1) One possible explanation for the gradual decline in cortical activity is that unilateral movements associated with greater cortical activation dominate early in grooming episodes, whereas bilateral movements that elicit weaker cortical activity dominate later (Figure 3G and 2C). The authors could check whether cortical activity associated with the *same* grooming movement is constant or declines during such episodes. A related point: doesn't the regression analysis shown in Figure 3, Supplement 2, assume that a stationary relationship between movement and spatiotemporal patterns of cortical activity?

(2) From the decline in cortical responses during long grooming episodes, the authors suggest that "mesoscale cortical activity mostly reflects the initiation of subcortically-mediated behaviors, rather than the behavior itself". The authors have taken a lot of trouble to come up with a rich, detailed segmentation and clustering of the grooming behaviour into its constituent movements (Figure 1). Therefore, I am somewhat surprised that they make this claim solely from analysis of averaged cortical activity during nearly minute-long grooming episodes rather than a higher time resolution analysis of transitions between distinct grooming movements (like the prior study by Sjöbom et al and related work in striatal encoding of innate movement sequences by Markowitz et al).

(3) The authors find that unilateral, atypical grooming movements elicit cortical activity that is distinct from the more naturalistic bilateral movements. They interpret this as reflecting the temporal transition structure of the behaviour. However, an alternative explanation is that the differences (or similarities) in evoked activity simply reflect differences (or similarities) in the kinematics of these movements, with bilateral movements appearing more similar to each other than to unilateral movements. A related point: there is little description of the "non-grooming forelimb movements". Were these kinematically similar to the unilateral forelimb movements, which may explain why they cluster together in Figure 4H?

(4) Page 13, last paragraph: the authors suggest that similar encoding of non-grooming forelimb movements and unilateral grooming movements may reflect a shared reliance on the cortex. This is rather speculative. Several studies have demonstrated that voluntary unilateral movements employed for reaching or lever pressing are not generally reliant on the cortex (Whishaw et al, Beh Brain Res, 1991; Kawai et al, 2015). There isn't, in my opinion, a broad consensus for the authors' statement that "reaching for food is a cortex-dependent action". Rather than extrapolating from past studies, could the authors not experimentally assess whether unilateral grooming movements are more sensitive to cortical silencing than bilateral ones, possibly revealing a cortical locus of control?

Reviewer #2 (Public review):

Summary:

In this manuscript, Michelson, Gupta, and Murphy use calcium imaging to map the distribution of neural activity across the cerebral cortex of grooming, head-restrained mice. Animals groomed spontaneously and in response to wetting of the face. Individual movement elements, such as bilateral strokes across the face, resembled those observed in freely-moving animals. Sequencing of movement elements was structured, but did not consist of full "syntactic grooming chains." Widefield imaging across the cortex revealed distinct patterns of activity for distinct movement elements. Individual neurons responded strongly during movement and had largely similar properties across cortical areas.

Strengths:

In my opinion, this is a solid paper that will be of interest to the mouse sensorimotor neuroscience community. The experiments are technically sound, the text is well-written, and the figures are clear. The activity maps are presented in standardized Allen Atlas coordinates, and I expect they will be very useful for future studies of orofacial and limb movement.

Weaknesses:

While the manuscript provides a valuable description of cortical activity during head-restrained grooming, I think it could engage a bit more with contemporary theories and debates in cortical physiology and motor control. The Abstract nicely highlights an apparent paradox: the motor cortex sends strong projections to the spinal cord, and is strongly modulated during behaviors like grooming. Nevertheless, blocking corticospinal traffic by inactivating or lesioning the motor cortex leaves such behaviors intact. There are several potential resolutions to this paradox. First, cortical activity during grooming could be confined to an "output-null" subspace that is responsible for monitoring sensorimotor events and preparing voluntary movements, but does not drive muscle activity (c.f. work in the macaque: Kaufman et al., Nature Neuroscience 2014; Churchland & Shenoy, Nature Reviews Neuroscience 2024). Second, cortical activity during grooming could be transmitted to lower centers, but gated out through inhibition. Third, it is possible that cortical activity in intact animals does contribute to muscle activation during grooming, but following a lesion or inactivation, other descending pathways compensate for the cortical deficit. The authors might wish to discuss their findings in light of these considerations.

In the first paragraph of the Introduction, it could be made clearer which results are specific to mice. The Niell & Stryker finding, for example, holds in mice, but not marmosets (Liska et al., eLife 2024).

The "hotspots" in Figure 3G appear to be more anterior during bilateral elliptical than unilateral elliptical movements. How do the authors interpret this finding?

The distribution of single-neuron responses looks relatively similar across cortical areas, including forelimb, hindlimb, and trunk somatosensory cortex, and primary and secondary forelimb motor cortex. What do the authors make of this?

Reviewer #3 (Public review):

Summary:

The authors use a combination of a head-fixed grooming paradigm, single-photon mesoscale, and wide-field-of-view two-photon calcium imaging to characterize cortical activity patterns during evoked grooming. Previous work has shown that grooming behavior does not require cortex, but that there are neuronal representations of grooming in motor cortex. The authors extend these findings by showing cortex-wide activation patterns at the meso-scale that relate to distinct grooming elements. This activation is strongest at grooming onset, but declines over the course of extended grooming periods. They also find similar activity patterns during licking/drinking behavior. Two-photon imaging further revealed that individual neurons across the cortex are preferentially activated by grooming. While their activity also declines after grooming onset, they remain active throughout grooming periods. This work extends previous findings by revealing that grooming and other subcortically-generated behaviors may be represented not only in motor cortex, but across dorsal cortex, both on the mesoscale and single neuron levels. These findings may lead to further investigation into the role of cortical activity during subcortically generated behaviors.

Strengths:

(1) Detailed characterization of grooming behavior in a head-fixed paradigm.

(2) Combination of single photon mesoscale and two-photon wide field-of-view imaging to characterize grooming (and licking)-related activity across dorsal cortex on multiple levels

Weaknesses:

(1) The behavior observed in the head-fixed grooming paradigm only partially resembles spontaneous grooming, lacking typical elements of the syntactic chain, while additionally evoking non-typical behaviors, resembling unilateral reaches, making the interpretation of the observations and their relevance to natural behaviors difficult. Furthermore, the nature of the non-typical movements (which may be cortex-dependent while typical grooming is not) is not explored.

(2) Two important findings in relation to the neural representations of individual grooming behaviors remain unclear:

a) The authors state that individual grooming behaviors did not have distinct neuronal representations (except unilateral grooming; Figure 4G) - it remains unclear how this fits with the observation of distinct activation maps during the different grooming behaviors. Should this differential activation not also correspond to distinct activation patterns of 'grooming' neurons across the cortex? Or do they mean that the activity in the 'grooming' neurons is not consistent across grooming instances and therefore no distinct representation can be detected?

b) The authors state that the 'typical' grooming behaviors do not have consistent activation patterns across animals (Figure 3 and supplements). It remains, therefore, unclear what the averaged activation maps really represent. Furthermore, this observation leaves several open questions: Are the activation patterns consistent in individual animals? Do differences across animals emerge due to differences in their behavior? And most importantly, can the actual behavior be decoded from the activation patterns?

(3) Multiple statements/conclusions are not supported by quantification of the data, but only by qualitative assessments, e.g.: lines 433-435: "In general, the maximally activated networks involved in licking and unilateral grooming behaviors 'appeared' to be the most consistent across animals compared to the bilateral grooming movements (Figure 3G)."; 436-437: "Averaged cortical activation maps associated with licking and elliptical behaviors were 'qualitatively similar' between evoked and spontaneous sessions, where the water drop was not applied".; 480-482: "The unique explained variance maps for the licking behavior 'differed' in the drinking context compared to the grooming context (Figure 3-figure supplement 3F)." The lack of quantification leaves the significance of these observations unclear.

(4) It remains unclear what the ongoing activity in 'grooming' neurons represents, since there is no detailed analysis of the relationship between activity and the detailed kinematics of the grooming movements.

The authors show that neuronal representations of grooming and other subcortical behaviors can be found across dorsal cortex and that these representations are at least to some degree specific to distinct behavioral elements. While this study does not reveal functional insights into the role of cortical representations of subcortically-generated behaviors, it is a step towards more in-depth studies. In the future, it will be important to determine whether these representations are efference copies or sensory-driven, or whether they affect the behavior, and if so, under which circumstances.

Reviewer #2 (Public review):

General comments

We thank the reviewers and editor for their thoughtful feedback. We are glad that the minor comments appear resolved. In this revision, we added subject-specific analyses, further FC comparisons, and clarified our rationale for stimulation parameters. We acknowledge that two concerns remain: (1) the 1 mA-2 mA sequence may introduce confounds, and (2) electric field modeling was not included due to technical limitations. We now explicitly note these as limitations in the manuscript and provide justification and discussion accordingly.

Major comments

R.2.1. For the anesthetized monkeys, the anode location differs between subjects, with the electrode positioned to stimulate the left DLFPC in monkey R and the right DLPFC in monkey N. The authors mention that this discrepancy does not result in significant differences in the electric field due to the monkeys' small head size. However, this is incorrect, as placing the anode on the left hemisphere would result in a much lower EF in the right DLPFC than placing the anode on the right side. Running an electric field simulation would confirm this. Additionally, the small electrode size suggested by the Easy cap configuration for NHP appears sufficient to stimulate the targeted regions focally. If this interpretation is correct, the authors should provide additional evidence to support their claim, such as a computational simulation of the EF distribution.

R.2.1 Authors' answer: We thank the Reviewer for the comments. First, regarding the reviewer's statement that placing the anode on the left hemisphere would result in a much lower EF in the right DLPFC than placing the anode on the right side, we would like to clarify that we did not use a typical 4 x 1 concentric ring high-definition setup (which consists of a small centre electrode surrounded by four return electrodes), but a two-electrode montage, with one electrode over the left or right PFC and the other one over the contralateral occipital cortex. According to EF modelling papers, a 4 x 1 high-definition setup would produce an EF that is focused and limited to the cortical area circumscribed by the ring of the return electrodes (Datta et al. 2009; Alam et al. 2016). Therefore, targeting the left or right DLPFC with a 4 x 1 setup would produce an EF confined to the targeted hemisphere of the PFC. In contrast, we expect the brain current flow generated with our 2-electrode setup to be broader, despite the small size of the electrodes, because there is no constraint from return electrodes. Thus, with our setup, the current is expected to flow between the PFC and the occipital cortex (see also our responses to comments R3.3., R.E.C.#2.1. and R.E.C.#2.2.).

Second, we would like to point out that in awake experiments, in which we stimulated the right PFC of both monkeys, there was no gross evidence of left or right asymmetry in the computed functional connectivity patterns (Figure 3A, Figure 3 - figure supplement 2A; Figure 5A). These results, showing that our stimulation montages did not induce asymmetric dynamic FC changes in NHPs, support the idea that our setups did not generate EFs that were spatially focused enough to alter brain activity in one hemisphere substantially more than the other.

Third, it is also worth noting that current evidence suggests that human brains are significantly more lateralized than those of macaques. Macaque monkeys have been found to have some degree of lateralized networks, but these are of lower complexity, and the lateralization is less pronounced and functionally organized than in humans. (Whey et al., 2014; Mantini et al., 2013). This suggests that, even if the stimulation were focal enough to stimulate the left or the right part of the PFC only, the behavioural effects would likely be similar.

Follow-up comment: Thank you for the detailed response and for referencing both experimental data and prior literature. While I appreciate the discussion on the lack of functional asymmetry and reduced lateralization in macaques, my original concern was about the physical distribution of the electric field (EF) due to different anode placements. Functional connectivity outcomes do not necessarily reflect EF symmetry, and without EF modeling, it's difficult to determine whether the stimulation affected both hemispheres equally. I understand the challenges of NHP-specific modeling, but even a simplified simulation or acknowledgment of this limitation in the manuscript would help clarify the interpretability of your results.

R.2.2. For the anesthetized monkeys, the authors applied 1 mA tDCS first, followed by 2 mA tDCS. A 20-minute stimulation duration of 1 mA tDCS is strong enough to produce after-effects that could influence the brain state during the 2 mA tDCS. This raises some concerns. Previous studies have shown that 1 mA tDCS can generate EF of over 1 V/m in the brain, and the effects of stimulation are sensitive to brain state (e.g., eye closed vs. eye open). How do the authors ensure that there are no after-effects from the 1 mA tDCS? This issue makes it challenging to directly compare the effects of 1 mA and 2 mA stimulation.<br /> R.2.2 Authors' answer: We agree with the reviewer's comment that 1 mA tDCS may induce aftereffects, as has been observed in several human studies (e.g., (Jamil et al. 2017, 2020). Although the differences between the 1 mA post-stimulation and baseline conditions were not significant in our analyses, it's still possible that the stimulation produced some effects below the threshold of significance that may contribute, albeit weakly, to the changes observed during

Follow-up comment: Thank you for the clarification and for acknowledging the potential for 1 mA after-effects. While I appreciate the authors' transparency and the amendment to the manuscript, I still find it important that the limitation be clearly stated in the Discussion section. The fact that 2 mA stimulation always followed 1 mA introduces a potential confound, making it difficult to attribute observed changes uniquely to 2 mA. If a counterbalanced design was not feasible, I would recommend explicitly noting this as a limitation in the interpretation of dose-dependent effects.

R.2.3. The occurrence rate of a specific structural-functional coupling pattern among random brain regions shows significant effects of tDCS. However, these results seem counterintuitive. It is generally understood that non-invasive brain stimulation tends to modulate functional connectivity rather than structural or structural-functional connectivity. How does the occurrence rate of structural-functional coupling patterns provide a more suitable measure of the effectiveness of tDCS than functional connectivity alone? I would recommend that the authors present the results based on functional connectivity itself. If there is no change in functional connectivity, the relevance of changes in structural-functional coupling might not translate into a meaningful alteration in brain function, making it unclear how significant this finding is without corresponding functional evidence.

R.2.3. Authors' answer: First of all, we would like to make it clear that the occurrence rate of patterns as a function of their SFC is not intended to be used or seen as a 'better' measure of the efficacy of tDCS. Instead, it is one aspect of the effects of tDCS on whole-brain functional cortical dynamics, obtained from refined measures (phase-coherences), that specifically addresses the coupling between structure and function. This type of analysis is further motivated by its increasing use in the literature due to its suspected relationship to wakefulness (e.g., (Barttfeld et al. 2015, Demertzi et al. 2019; Castro et al. 2023)). Also, in our analysis, the structure is kept constant: the connectivity matrix used to correlate the functional brain states is always the same (CoCoMac82). Thus, the influence of tDCS on the structure-function side can only be explained by modulating the functional aspects, as suggested by intuition and previous results.

Then, we agree with the reviewer that studying the functional changes induced by tDCS alone could be valuable. However, usual metrics used in FC analysis are usually done statistically: FC-states are either computed through averaging spatial correlations over time, then analyzed through graph-theoretical properties for instance (or by just directly computing the element-wise differences), or either by considering the properties of the different visited FC-states by computing spatial correlations over a sliding time-window, and then similar analysis can be done as previously explained. But these are static metrics, if the states visited are essentially the same (which is expected from non-invasive neuromodulations that haven't already demonstrated strong and/or characteristic impact), but the dynamical process of visiting said states changes, one would see no difference in that regard. As such, in the case of resting-state fMRI, differences in FCs are hard to interpret given that between-sessions within-condition differences are usually found with some degree of variance for the respective conditions. Trying then to interpret between-condition differences is quite tricky in the case of subtle modulations of the system's activity. On the other hand, more subtle differences can be captured by considering more detailed analysis, such as using phase-based methods like we did, by incorporating some statistical learning component with regard to the dynamicity of the system (supervised learning for instance like we did followed by temporal & transition-based methodology), and by adding some dimensions along which one will be able to give some interpretation to the analysis. In our case we were interested in characterizing resting-state differences between stimulation conditions, which have nuanced and subtle interactions with the biological system. As such, classical measures of differences between FC states are likely to not be refined and precise enough. In fact, we propose additional files investigating those classically used measures such as differences in average FC matrices, or changes in functional graph properties (like modularity, efficiency and density) of the visited FC states. These figures show that, for the first case, comparing region-to-region specific FCs provides very few statistically significant results. With respect to the second part, we show that virtually no differences are observed in the properties of the functional states visited. These results suggest, as expected, that the actual brain states visited across the different stimulation conditions are topologically quite similar, and that only very few region-specific pairwise functional connectivities are particularly modulated by specific tDCS montages while, on the other hand, the actual dynamical process dictating how the brain activity passes from one state to another is in fact being influenced as shown by the dynamical analysis presented in the main figures in a more apparent and meaningful way (in that it is dependent on the montage, somewhat consistent with regard to the post-stimulations conditions, and can be made sense of by considering the theoretical effect of near-anodal versus near-cathodal neuromodulatory effects).

Actions in the text: We have added new supplementary files showing the effects of the stimulations on FC matrices and on classical functional graph properties in awake and anesthesia datasets (Supplementary Files 3 & 4). We have added new sentences about these new analyses on the effects of the stimulations on FC matrices and on classical functional graph properties in the Results section:<br /> Follow-up comment: Thank you for the detailed and comprehensive response. The clarification regarding the use of SFC dynamics and the additional analyses provided are convincing.

R2.4. The authors recorded data from only two monkeys, which may limit the investigation of the group effects of tDCS. As the number of scans for the second monkey in each consciousness condition is lower than that in the first monkey, there is a concern that the main effects might primarily reflect the data from a single monkey. I suggest that the authors should analyze the data for each monkey individually to determine if similar trends are observed in both subjects.

R.2.4. Authors' answer: We agree that the small number of subjects is a limitation of our study. However, we have already addressed these aspects by reporting statistical analyses that consider them, using linear models of such variables, and running them through ANOVA tests. In addition, we experimentally ensured that we recorded a relatively high number of sessions over a period of several years. Regardless, we agree that our study would benefit from further investigation into this matter. We have therefore prepared complementary figures showing the main analysis performed separately for the two monkeys as proposed, as well as further investigations into the inter-condition variability outmatching the inter-individual variability, itself being also outmatched by intra-individual changes.

Actions in the text: We have added a supplementary file showing the main analyses performed separately for the two monkeys (Supplementary File 2) and further investigations into the inter-condition variability (Supplementary Files 3 & 4). We have added new sentences about these analyses performed separately for the two monkeys in the Results section:

Follow-up comment: Thank you for addressing this concern and for providing the individual monkey analysis. The additional figures and statistical explanations are helpful and appreciated.

R2.5. Anodal tDCS was only applied to anesthetized monkeys, which limits the conclusion that the authors are aiming for. It raises questions about the conclusion regarding brain state dependency. To address this, it would be better to include the cathodal tDCS session for anesthetized monkeys. If cathodal tDCS changes the connectivity during anesthesia, it becomes difficult to argue that the effects of cathodal tDCS vary depending on the state of consciousness as discussed in this paper. On the other hand, if cathodal tDCS would not produce any changes, the conclusion would then focus on the relationship between the polarity of tDCS and consciousness. In that case, the authors could maintain their conclusion but might need to refine it to reflect this specific relationship more accurately.

R.2.5. Authors' answer: We agree with the reviewer that it would have been interesting to investigate the effects of cathodal tDCS in anesthetized monkeys. However, due to the challenging nature of the experimental procedures under anesthesia, we had to limit the investigations to only one stimulation modality. We chose to deliver anodal stimulation because, from a translational point of view, we aimed to provide new information on the effects of tDCS under anesthesia as a model for disorders of consciousness. It also made much more sense to increase the cortical excitability of the prefrontal cortex in an attempt to wake up the sedated monkeys rather than doing the opposite.

Actions in the text: We have added a new sentence in the Results section:

"Due to the challenging nature of the experimental procedures under anesthesia, we limited the investigations to only one stimulation modality. We chose to deliver anodal stimulation to provide new information on the effects of tDCS under anesthesia as a model for disorders of consciousness and to increase the cortical excitability of the PFC in an attempt to wake up the sedated monkeys."

Follow-up comment: Thank you for clarifying the rationale behind applying only anodal stimulation under anesthesia. While I appreciate the experimental constraints and the translational motivation, I would still encourage the authors to explicitly acknowledge in the Discussion that the absence of a cathodal condition under anesthesia limits the ability to dissociate polarity-specific effects from state-dependent effects. This clarification would help temper the conclusions and better reflect the scope of the current dataset.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review): 

Summary: 

In this work, the authors apply TDCS to awake and anesthetized macaques to determine the effect of this modality on dynamic connectivity measured by fMRI. The question is to understand the extent to which TDCS can influence conscious or unconscious states. Their target was the PFC. During the conscious states, the animals were executing a fixation task. Unconsciousness was achieved by administering a constant infusion of propofol and a continuous infusion of the muscle relaxant cisatracurium. They observed the animals while awake receiving anodal or cathodal hd-TDCS applied to the PFC. During the cathodal stimulation, they found disruption of functional connectivity patterns, enhanced structure-function correlations, a decrease in Shannon entropy, and a transition towards patterns that were more commonly anatomically based. In contrast under propofol anesthesia anodal hd-TDCS stimulation appreciably altered the brain connectivity patterns and decreased the correlation between structure and function. The PFC stimulations altered patterns associated with consciousness as well as those associated with unconsciousness.

Strengths: 

The authors carefully executed a set of very challenging experiments that involved applying tDCS in awake and anesthetized non-human primates while conducting functional imaging.

We thank the Reviewer for summarising our study and for his appreciation of the highly challenging experiments we performed.

Weaknesses:

The authors show that tDCS can alter functional connectivity measured by fMRI but they do not make clear what their studies teach the reader about the effects of tDCS on the brain during different states of consciousness. No important finding is stated contrary to what is stated in the abstract. It is also not clear what the work teaches us about how tDCS works nor is it clear what are the "clinical implications for disorders of consciousness." The deep anesthesia is akin to being in a state of coma. This was not discussed.  

While the authors have executed a set of technically challenging experiments, it is not clear what they teach us about how tDCS works, normal brain neurophysiology, or brain pathological states such as disorders of consciousness.

We thank the reviewer for his comments. We agree that we could better highlight the value and implications of our work, and we take this opportunity to improve our manuscript according to the suggestions.

Actions in the text: We have added several new paragraphs in the Discussion section, considering these comments and other related remarks from the Reviewing Editor (see below our answer to the first comment of the Reviewing Editor: REC#1).

Reviewer #2 (Public review): 

General comments: 

The authors investigated the effects of tDCS on brain dynamics in awake and anesthetized monkeys using functional MRI. They claim that cathodal tDCS disrupts the functional connectivity pattern in awake monkeys while anodal tDCS alters brain patterns in anesthetized monkeys. This study offers valuable insight into how brain states can influence the outcomes of noninvasive brain stimulation. However, there are several aspects of the methods and results sections that should be improved to clarify the findings.

We thank the Reviewer for the summary and appreciation of our study.  

Major comments 

For the anesthetized monkeys, the anode location differs between subjects, with the electrode positioned to stimulate the left DLFPC in monkey R and the right DLPFC in monkey N. The authors mention that this discrepancy does not result in significant differences in the electric field due to the monkeys' small head size. However, this is incorrect, as placing the anode on the left hemisphere would result in a much lower EF in the right DLPFC than placing the anode on the right side. Running an electric field simulation would confirm this. Additionally, the small electrode size suggested by the Easy cap configuration for NHP appears sufficient to stimulate the targeted regions focally. If this interpretation is correct, the authors should provide additional evidence to support their claim, such as a computational simulation of the EF distribution.

We thank the Reviewer for the comments. First, regarding the reviewer’s statement that placing the anode on the left hemisphere would result in a much lower EF in the right DLPFC than placing the anode on the right side, we would like to clarify that we did not use a typical 4 x 1 concentric ring high-definition setup (which consists of a small centre electrode surrounded by four return electrodes), but a two-electrode montage, with one electrode over the left or right PFC and the other one over the contralateral occipital cortex. According to EF modelling papers, a 4 x 1 high-definition setup would produce an EF that is focused and limited to the cortical area circumscribed by the ring of the return electrodes (Datta et al. 2009; Alam et al. 2016). Therefore, targeting the left or right DLPFC with a 4 x 1 setup would produce an EF confined to the targeted hemisphere of the PFC. In contrast, we expect the brain current flow generated with our 2-electrode setup to be broader, despite the small size of the electrodes,  because there is no constraint from return electrodes. Thus, with our setup, the current is expected to flow between the PFC and the occipital cortex (see also our responses to comments R3.3., R.E.C.#2.1. and R.E.C.#2.2.). 

Second, we would like to point out that in awake experiments, in which we stimulated the right PFC of both monkeys, there was no gross evidence of left or right asymmetry in the computed functional connectivity patterns (Figure 3A, Figure 3 - figure supplement 2A; Figure 5A). These results, showing that our stimulation montages did not induce asymmetric dynamic FC changes in NHPs, support the idea that our setups did not generate EFs that were spatially focused enough to alter brain activity in one hemisphere substantially more than the other.

Third, it is also worth noting that current evidence suggests that human brains are significantly more lateralized than those of macaques. Macaque monkeys have been found to have some degree of lateralized networks, but these are of lower complexity, and the lateralization is less pronounced and functionally organized than in humans. (Whey et al., 2014; Mantini et al., 2013). This suggests that, even if the stimulation were focal enough to stimulate the left or the right part of the PFC only, the behavioural effects would likely be similar.

We strongly agree with the reviewer that conducting an EF simulation would be valuable to confirm our expectations and to gain a comprehensive view of the characteristics of the EFs generated with our different setups in NHPs. However, the challenge is in the fact that EF computational models have been developed for humans, and their use in NHPs is not straightforward due to significant anatomical differences. For example, macaque monkeys are distinct from humans in terms of brain size, shape and cortical organisation, skull thickness, and the presence of muscles, as well as different tissue conductivities (Lee et al. 2015; Datta et al.2016; Mantell et al. 2023). We plan to address this in future work.

Actions in the text: In the Materials and Methods section, we have modified the sentence: “Because of the small size of the monkey's head and because we did not use return electrodes to restrict the current flow (as is achieved with typical high-definition montages (Datta et al. 2009; Alam et al. 2016)), we expected that tDCS stimulation with the two symmetrical montages would result in nearly equivalent electric fields across the monkey’s head and produce roughly similar effects on brain activity.” 

We also added a new sentence about EF simulation: 

“This would need to be confirmed by running an electric field simulation. However, computational electric field models have been developed for humans, and their use in NHPs is not straightforward due to anatomical specificities. Indeed, monkeys differ from humans in terms of brain size, shape and cortical organization, skull thickness, tissue conductivities and the presence of muscles (Lee et al. 2015; Datta et al. 2016; Mantell et al. 2023). Modelling of EFs generated with the specific tDCS montages employed in this study will be performed in future work.”

For the anesthetized monkeys, the authors applied 1 mA tDCS first, followed by 2 mA tDCS. A 20-minute stimulation duration of 1 mA tDCS is strong enough to produce after-effects that could influence the brain state during the 2 mA tDCS. This raises some concerns. Previous studies have shown that 1 mA tDCS can generate EF of over 1 V/m in the brain, and the effects of stimulation are sensitive to brain state (e.g., eye closed vs. eye open). How do the authors ensure that there are no after-effects from the 1 mA tDCS? This issue makes it challenging to directly compare the effects of 1 mA and 2 mA stimulation.

We agree with the reviewer's comment that 1 mA tDCS may induce aftereffects, as has been observed in several human studies (e.g., (Jamil et al. 2017, 2020). Although the differences between the 1 mA post-stimulation and baseline conditions were not significant in our analyses, it's still possible that the stimulation produced some effects below the threshold of significance that may contribute, albeit weakly, to the changes observed during and after 2 mA stimulation. We have, therefore, amended the paper in line with the reviewer's comments.

Actions in the text: We have added the following text in the Result section: 

“While several human studies have reported that 1 mA transcranial stimulation induces aftereffects (e.g., (Jamil et al. 2017, 2020; Monte-Silva et al. 2010), the differences between the 1 mA post-stimulation and baseline conditions were not significant in our analyses. However, it is still possible that the 1 mA stimulation produced some effects below the threshold of significance that may contribute to the changes observed during and after the 2 mA stimulation.”

The occurrence rate of a specific structural-functional coupling pattern among random brain regions shows significant effects of tDCS. However, these results seem counterintuitive. It is generally understood that noninvasive brain stimulation tends to modulate functional connectivity rather than structural or structural-functional connectivity. How does the occurrence rate of structural-functional coupling patterns provide a more suitable measure of the effectiveness of tDCS than functional connectivity alone? I would recommend that the authors present the results based on functional connectivity itself. If there is no change in functional connectivity, the relevance of changes in structural-functional coupling might not translate into a meaningful alteration in brain function, making it unclear how significant this finding is without corresponding functional evidence.

First, of all, we would like to make it clear that the occurrence rate of patterns as a function of their SFC is not intended to be used or seen as a ‘better’ measure of the efficacy of tDCS. Instead, it is one aspect of the effects of tDCS on whole-brain functional cortical dynamics, obtained from refined measures (phase-coherences), that specifically addresses the coupling between structure and function. This type of analysis is further motivated by its increasing use in the literature due to its suspected relationship to wakefulness (e.g., (Barttfeld et al. 2015, Demertzi et al. 2019; Castro et al. 2023)). Also, in our analysis, the structure is kept constant: the connectivity matrix used to correlate the functional brain states is always the same (CoCoMac82). Thus, the influence of tDCS on the structure-function side can only be explained by modulating the functional aspects, as suggested by intuition and previous results.

Then, we agree with the reviewer that studying the functional changes induced by tDCS alone could be valuable. However, usual metrics used in FC analysis are usually done statistically: FC-states are either computed through averaging spatial correlations over time, then analyzed through graph-theoretical properties for instance (or by just directly computing the element-wise differences), or either by considering the properties of the different visited FC-states by computing spatial correlations over a sliding time-window, and then similar analysis can be done as previously explained. But these are static metrics, if the states visited are essentially the same (which is expected from non-invasive neuromodulations that haven’t already demonstrated strong and/or characteristic impact), but the dynamical process of visiting said states changes, one would see no difference in that regard. As such, in the case of resting-state fMRI, differences in FCs are hard to interpret given that between-sessions within-condition differences are usually found with some degree of variance for the respective conditions. Trying then to interpret between-condition differences is quite tricky in the case of subtle modulations of the system’s activity. On the other hand, more subtle differences can be captured by considering more detailed analysis, such as using phase-based methods like we did,  by incorporating some statistical learning component with regard to the dynamicity of the system (supervised learning for instance like we did followed by temporal & transition-based methodology), and by adding some dimensions along which one will be able to give some interpretation to the analysis.  In our case we were interested in characterizing resting-state differences between stimulation conditions, which have nuanced and subtle interactions with the biological system. 

As such, classical measures of differences between FC states are likely to not be refined and precise enough. In fact, we propose additional files investigating those classically used measures such as differences in average FC matrices, or changes in functional graph properties (like modularity, efficiency and density) of the visited FC states. These figures show that, for the first case, comparing region-to-region specific FCs provides very few statistically significant results. With respect to the second part, we show that virtually no differences are observed in the properties of the functional states visited. 

These results suggest, as expected, that the actual brain states visited across the different stimulation conditions are topologically quite similar, and that only very few region-specific pairwise functional connectivities are particularly modulated by specific tDCS montages while, on the other hand, the actual dynamical process dictating how the brain activity passes from one state to another is in fact being influenced as shown by the dynamical analysis presented in the main figures in a more apparent and meaningful way (in that it is dependent on the montage, somewhat consistent with regard to the post-stimulations conditions, and can be made sense of by considering the theoretical effect of near-anodal versus near-cathodal neuromodulatory effects).

Actions in the text: We have added new supplementary files showing the effects of the stimulations on FC matrices and on classical functional graph properties in awake and anesthesia datasets (Supplementary Files 3 & 4).

We have added new sentences about these new analyses on the effects of the stimulations on FC matrices and on classical functional graph properties in the Results section:

“In addition, we performed the main analyses separately for the two monkeys, explored the inter-condition variability (Supplementary File 2), and computed classical measures of functional connectivity such as average FC matrices and functional graph properties (modularity, efficiency and density) of the visited FC states (Supplementary File 3).... In contrast, classical FC metrics did not show significant differences across stimulation conditions, highlighting the value of dynamic FC metrics to capture the neuromodulatory effects of tDCS.”

“Analyses of the two monkeys separately showed that the changes in slope and Shannon entropy were bigger in one of the two monkeys but went in the same direction (Supplementary File 2), while classical FC metrics did not capture any statistical differences between the different stimulation conditions (Supplementary File 3).”

The authors recorded data from only two monkeys, which may limit the investigation of the group effects of tDCS. As the number of scans for the second monkey in each consciousness condition is lower than that in the first monkey, there is a concern that the main effects might primarily reflect the data from a single monkey. I suggest that the authors should analyze the data for each monkey individually to determine if similar trends are observed in both subjects.

We agree that the small number of subjects is a limitation of our study. However, we have already addressed these aspects by reporting statistical analyses that consider them, using linear models of such variables, and running them through ANOVA tests. In addition, we experimentally ensured that we recorded a relatively high number of sessions over a period of several years. Regardless, we agree that our study would benefit from further investigation into this matter. We have therefore prepared complementary figures showing the main analysis performed separately for the two monkeys as proposed, as well as further investigations into the inter-condition variability outmatching the inter-individual variability, itself being also outmatched by intra-individual changes. 

Actions in the text: We have added a supplementary file showing the main analyses performed separately for the two monkeys (Supplementary File 2) and further investigations into the inter-condition variability (Supplementary Files 3 & 4).

We have added new sentences about these analyses performed separately for the two monkeys in the Results section:

“In addition, we performed the main analyses separately for the two monkeys, explored the inter-condition variability (Supplementary File 2), and computed classical measures of functional connectivity such as average FC matrices and functional graph properties (modularity, efficiency and density) of the visited FC states (Supplementary File 3). The separate analyses showed that the changes in slope and Shannon entropy were substantially more pronounced in one of the two monkeys, corroborating some of the effects captured in the ANOVA tests.”

“Analyses of the two monkeys separately showed that the changes in slope and Shannon entropy were bigger in one of the two monkeys but went in the same direction (Supplementary

File 2)”.

Anodal tDCS was only applied to anesthetized monkeys, which limits the conclusion that the authors are aiming for. It raises questions about the conclusion regarding brain state dependency. To address this, it would be better to include the cathodal tDCS session for anesthetized monkeys. If cathodal tDCS changes the connectivity during anesthesia, it becomes difficult to argue that the effects of cathodal tDCS vary depending on the state of consciousness as discussed in this paper. On the other hand, if cathodal tDCS would not produce any changes, the conclusion would then focus on the relationship between the polarity of tDCS and consciousness. In that case, the authors could maintain their conclusion but might need to refine it to reflect this specific relationship more accurately. 

We agree with the reviewer that it would have been interesting to investigate the effects of cathodal tDCS in anesthetized monkeys. However, due to the challenging nature of the experimental procedures under anesthesia, we had to limit the investigations to only one stimulation modality. We chose to deliver anodal stimulation because, from a translational point of view, we aimed to provide new information on the effects of tDCS under anesthesia as a model for disorders of consciousness. It also made much more sense to increase the cortical excitability of the prefrontal cortex in an attempt to wake up the sedated monkeys rather than doing the opposite.

Actions in the text: We have added a new sentence in the Results section:

“Due to the challenging nature of the experimental procedures under anesthesia, we limited the investigations to only one stimulation modality. We chose to deliver anodal stimulation to provide new information on the effects of tDCS under anesthesia as a model for disorders of consciousness and to increase the cortical excitability of the PFC in an attempt to wake up the sedated monkeys.”

Reviewer #3 (Public review): 

Summary: 

This study used transcranial direct current stimulation administered using small 'high-definition' electrodes to modulate neural activity within the non-human primate prefrontal cortex during both wakefulness and anaesthesia. Functional magnetic resonance imaging (fMRI) was used to assess the neuromodulatory effects of stimulation. The authors report on the modification of brain dynamics during and following anodal and cathodal stimulation during wakefulness and following anodal stimulation at two intensities (1 mA, 2 mA) during anaesthesia. This study provides some possible support that prefrontal direct current stimulation can alter neural activity patterns across wakefulness and sedation in monkeys. However, the reported findings need to be considered carefully against several important methodological limitations. 

Strengths: 

A key strength of this work is the use of fMRI-based methods to track changes in brain activity with good spatial precision. Another strength is the exploration of stimulation effects across wakefulness and sedation, which has the potential to provide novel information on the impact of electrical stimulation across states of consciousness.

We thank the Reviewer for the summary and for highlighting the strengths of our study. 

Weaknesses: 

The lack of a sham stimulation condition is a significant limitation, for instance, how can the authors be sure that results were not affected by drowsiness or fatigue as a result of the experimental procedure?

We agree with the reviewer that adding control conditions could have strengthened our study. Control conditions usually consist of a sham condition or active control conditions. However, as mentioned in response to one of Reviewer 2 comments (R.2.5), we had to make choices as we could not perform as many experiments due to their demanding nature, especially under anesthesia. 

In the awake state, we acquired data with two experimental conditions; the monkeys were exposed to either anodal (F4/O1) or cathodal (O1/F4) PFC tDCS. As anodal tDCS of the PFC induced only minor changes in brain dynamics, it could be considered as an active control condition for the cathodal condition, which had striking effects on the cortical dynamics. It is also worth noting that doubts have been raised about the neurobiological inertia of certain sham protocols. Indeed, different sham protocols have been employed in the literature, some of which may produce unintended effects (Fonteneau et al. 2019). Therefore, active control conditions, such as reversing the polarity of the stimulation or targeting a different brain region, have been proposed to provide better control (Fonteneau et al. 2019). Furthermore, in the context of experiments performed under anesthesia, the relevance of a sham control condition typically used to achieve adequate blinding is questionable. 

With regard to drowsiness and fatigue as a result of the experimental procedure, we agree with the reviewer that this is a common problem in functional imaging due to the length of the recording sessions. We assumed, as was done in previous work (Uhrig, Dehaene, and Jarraya 2014; Wang et al. 2015), that the monkeys' performance on the fixation task during acquisition would capture these periods of fatigue. Therefore, only sessions with fixation rates above 85% were included in our analysis. 

Actions in the text: We have now specified, in the Materials and Methods section, the fact that only runs with a high fixation rate (> 85%) were included in the study: 

“To ensure that the results were not biased by fatigue or drowsiness due to the lengthy

In the anaesthesia condition, the authors investigated the effects of two intensities of stimulation (1 mA and 2 mA). However, a potential confound here relates to the possibility that the initial 1 mA stimulation block might have caused plasticity-related changes in neural activity that could have interfered with the following 2 mA block due to the lack of a sufficient wash-out period. Hence, I am not sure any findings from the 2 mA block can really be interpreted as completely separate from the initial 1 mA stimulation period, given that they were administered consecutively. Several previous studies have shown that same-day repeated tDCS stimulation blocks can influence the effects of neuromodulation (e.g., Bastani and Jaberzadeh, 2014, Clin Neurophysiol; Monte-Silva et al., J. Neurophysiology). 

We agree with the reviewer’s comment that the initial 1 mA stimulation block might have induced changes in neural activity and that the 20-minute post 1 mA block would not be long enough to wash out these changes. This comment is very similar to the second comment made by Reviewer 2 (R.2.2). Although our experimental data do not support this possibility (as the differences between the 1 mA post-stimulation and baseline conditions were not significant), it is still conceivable that the stimulation produced some effects below the threshold of significance and that these might weakly contribute to the changes observed during and after the 2 mA stimulation. 

Actions in the text: We have modified the paper according to the reviewers' comments (please see our answer and actions in the text to R.2.2.).

The different electrode placement for the two anaesthetised monkeys (i.e., Monkey R: F3/O2 montage, Monkey N: F4/O1 montage) is problematic, as it is likely to have resulted in stimulation over different brain regions. The authors state that "Because of the small size of the monkey's head, we expected that tDCS stimulation with these two symmetrical montages would result in nearly equivalent electric fields across the monkey's head and produce roughly similar effects on brain activity"; however, I am not totally convinced of this, and it really would need E-field models to confirm. It is also more likely that there would in fact be notable differences in the brain regions stimulated as the authors used HD-tDCS electrodes, which are generally more focal.

We thank the Reviewer for the remark, which is very similar to the second comment from Reviewer 2. Please see our answer to the first comment of Reviewer 2 

Actions in the text: We have modified the paper according to the reviewers' comments (please see the actions taken in response to R.2.1.).

Given the very small sample size, I think it is also important to consider the possibility that some results might also be impacted by individual differences in response to stimulation. For instance, in the discussion (page 9, paragraph 2) the authors contrast findings observed in awake animals versus anaesthetised animals. However, different monkeys were examined for these two conditions, and there were only two monkeys in each group (monkeys J and Y for awake experiments [both male], and monkeys R and N [male and female] for the anaesthesia condition). From the human literature, it is well known that there is a considerable amount of inter-individual variability in response to stimulation (e.g., Lopez-Alonso et al., 2014, Brain Stimulation; Chew et al., 2015, Brain Stimulation), therefore I wonder if some of these differences could also possibly result from differences in responsiveness to stimulation between the different monkeys? At the end of the paragraph, the authors also state "Our findings also support the use of tDCS to promote rapid recovery from general anesthesia in humans...and suggest that a single anodal prefrontal stimulation at the end of the anesthesia protocol may be effective." However, I'm not sure if this statement is really backed-up by the results, which failed to report "any behavioural signs of awakening in the animals" (page 7)?

We thank the Reviewer for this comment. Because working with non-human primates is expensive and labor intensive, the sample sizes in classical macaque experiments are generally small (typically 2-4 subjects per experiment). Our sample size (i.e. 2 rhesus macaques in awake experiments and 2 macaques under sedation, 11 +/- 9 scan sessions per animal, 288 and 136 runs in the awake and anesthesia state, respectively) is comparable to other previous work in non-human primates using fMRI (Milham et al. 2018; Yacoub et al. 2020; Uchimura, Kumano, and Kitazawa 2024). In addition, we would like to point out that the baseline cortical dynamics we found before stimulation, whether in the awake or sedated state, are comparable to previous studies (Barttfeld et al. 2015; Uhrig et al. 2018; Tasserie et al. 2022). This suggests our results are reproducible across datasets, despite the small sample size.

That being said, we agree with the reviewer that inter-individual variability in response to stimulation can be considerable, as shown by a large body of literature in the field. It seems possible that the two monkeys studied in each condition responded differently to the stimulation. But even if that’s the case, our results suggest that at least in one of the two monkeys, cathodal PFC stimulation in the awake state and anodal PFC stimulation under propofol anesthesia induced striking changes in brain dynamics, which we believe is a significant contribution to the field. 

In fact, supplementary analysis, as proposed by Reviewer 2 (cf R2.4), investigating how the different measurables we’ve used were differently affected by tDCS show that indeed monkey Y’s case is more apparent and significant than monkey J’s. Still, the effects observed in monkey J’s case are still congruent with what is observed in monkey Y’s and at the population level (though less flagrant). We also show that these inter-individual variabilities are outmatched by the inter-condition variability, (as indicated by our initially strong statistical results at the population levels), thus showing that, even though we have different responses depending on the subject, the effects observed at the population level cannot be only accounted for by the differences in subjects’ specificities.

Lastly, the Reviewer questioned whether our results support that a single anodal prefrontal stimulation at the end of the anesthesia protocol could effectively promote rapid recovery from general anesthesia, because the stimulation did not wake the animals in our experiments. It should be emphasized that in our case, the monkeys were stimulated while they were still receiving continuous propofol perfusion. In contrast, during the recovery process from anesthesia, the delivery of the anesthetic drug is stopped. It is therefore conceivable that anodal PFC tDCS, which successfully enriched brain dynamics in sedated monkeys in our experiments, may accelerate the recovery from anesthesia when the drug is no longer administered. 

Actions in the text: We have added a line in the Materials and Methods to compare to other studies:

“Our sample size is comparable to previous work in NHP using fMRI (Milham et al. 2018; Yacoub et al. 2020; Uchimura, Kumano, and Kitazawa 2024).”

Reviewing Editor Comments: 

In some cases, authors opt to submit a revised manuscript. Should you choose to do so, please be aware that the reviewers have indicated that their appraisal is unlikely to change unless some of the suggested field modelling is incorporated into the work. This may change the evaluation of the strength of evidence, but the final wording will be subject to reviewer discretion. Details for responding to the reviews are provided at the bottom of this email.

Reviewer #1 (Recommendations for the authors): 

The work should discuss the implications of their experiments for using tDCS to arouse a patient from a coma. The anesthetized animal is effectively in a drug-induced coma. While they observed connectivity changes, these changes did not map nicely onto behavioral changes. 

I would suggest that the authors spell out more clearly what they view as the clinical implications of their work in terms of new insights into how tDCS may be used to either understand and or treat disorders of consciousness.

We thank the Reviewer for his thoughtful comments. We appreciate the opportunity to clarify and expand on the key findings and implications of our work, particularly regarding the new insights into how tDCS can be used to understand and treat disorders of consciousness. We therefore provide a broader perspective on the clinical implications of our experiments regarding coma and disorders of consciousness. We also agree with the Reviewer that the absence of behavioral changes but the presence of functional differences should be more clearly addressed. 

Actions in the text: We have added a few lines about the relevance of anesthesia as a model for disorders of consciousness in the Introduction part:

“Anesthesia provides a unique model for studying consciousness, which, similarly to DOC, is characterized by the disruption or even  the loss of consciousness (Luppi 2024). Additionally, anesthesia mechanisms involve several subcortical nuclei that are key components of the brain's sleep and arousal circuits (Kelz and Mashour 2019).”

In the Discussion section, we have modified and expanded a paragraph about the effects of tDCS in DOC patients and how this technique could be further used to study consciousness: From another clinical perspective, our results demonstrating that 2 mA anodal PFC tDCS decreased the structure-function correlation and modified the dynamic repertoire of brain patterns during anesthesia (Figures 6 and 7) are consistent with the beneficial effects of such stimulation in DOC patients (Thibaut et al., 2014; Angelakis et al., 2014; Thibaut et al., 2017; Zhang et al., 2017; Martens et al., 2018; Cavinato et al., 2019; Wu et al., 2019; Hermann et al., 2020; Peng et al., 2022; Thibaut et al., 2023). Although some clinical trials investigated the effects of stimulating other brain regions, such as the motor cortex (Martens et al., 2019; Straudi et al., 2019) or the parietal cortex (Huang et al., 2017; Guo et al., 2019; Zhang et al., 2022; Wan et al., 2023; Wang et al., 2020), the DLPFC appears to be the most effective target for patients with a minimally conscious state (Liu et al., 2023). In terms of neuromodulatory effects in DOC patients, DLPFC tDCS has been reported to increase global excitability (Bai et al., 2017), increase the P300 amplitude (Zhang et al., 2017; Hermann et al., 2020), improve the fronto-parietal coherence in the theta band (Bai et al., 2018), enhance the putative EEG markers of consciousness (Bai et al., 2018; Hermann et al., 2020) and reduce the incidence of slow-waves in the resting state (Mensen et al., 2020). Our findings further support the PFC as a relevant target for modulating consciousness level and align with growing evidence showing that the PFC plays a key role in conscious access networks (Mashour, Pal, and Brown 2022; Panagiotaropoulos 2024). Nevertheless, we hypothesize that other brain targets for tDCS may be of interest for consciousness restoration, potentially using multi-channel tDCS (Havlík et al., 2023). Among transcranial electrical stimulation techniques, tDCS has the great advantage of facilitating either excitation or inhibition of brain regions, depending on the polarity of the stimulation (Sdoia et al., 2019) exploited this advantage to investigate the causal involvement of the DLPFC in conscious access to a visual stimulus during an attentional blink paradigm. While conscious access was enhanced by anodal stimulation of the left DLPFC compared to sham stimulation, opposite effects were found with cathodal stimulation compared to sham over the same locus. Finally, this literature and our findings suggest that tDCS constitutes a non-invasive, reversible, and powerful tool for studying consciousness.”

We have added a new paragraph about patients with cognitive-motor dissociation and dissociation between consciousness and behavioral responsiveness:

“Changes in the state of consciousness are generally closely associated with changes in behavioural responsiveness, although some rare cases of dissociation have been described. Cognitive-motor dissociation (CMD) is a condition observed in patients with severe brain injury, characterized by behavior consistent with unresponsive wakefulness syndrome or a minimally conscious state minus (Thibaut et al., 2019). However, in these patients, specific cortical brain areas activate in response to mental imagery tasks (e.g., imagining playing tennis or returning home) in a manner indistinguishable from that of healthy controls, as shown through fMRI or EEG (Thibaut et al., 2019; Owen et al., 2006; Monti et al., 2010; Bodien et al., 2024). Thus, although CMD patients are behaviorally unresponsive, they demonstrate cognitive awareness that is not outwardly apparent. It is worth noting that both the structure-function correlation and the rate of the pattern closest to the anatomy were shown to be significantly reduced in unresponsive patients showing command following during mental imagery tasks compared to those who do not show command following (Demertzi et al., 2019). These observations would be compatible with our findings in anesthetized macaques exposed to 2 mA anodal PFC tDCS. The richness of the brain dynamics would be recovered (at least partially, in our experiments), but not the behaviour. This hypothesis also fits with a recent longitudinal fMRI study on patients recovering from coma (Crone et al., 2020). The researchers examined two groups of patients: one group consisted of individuals who were unconscious at the acute scanning session but regained consciousness and improved behavioral responsiveness a few months later, and the second group consisted of patients who were already conscious from the start and only improved behavioral responsiveness at follow-up. By comparing these two groups, the authors could distinguish between the recovery of consciousness and the recovery of behavioral responsiveness. They demonstrated that only initially conscious patients exhibited rich brain dynamics at baseline. In contrast, patients who were unconscious in the acute phase and later regained consciousness had poor baseline dynamics, which became more complex at follow-up. Complete recovery of both consciousness and responsiveness under general anesthesia is possible through electrical stimulation of the central thalamus (Redinbaugh et al., 2020; Tasserie et al., 2022).”

Reviewer #2 (Recommendations for the authors): 

Method 

(1) The authors mentioned that they used HD-tDCS in their experiments; however, they used 1 x 1 tDCS, which is not HD-tDCS but rather single-channel tDCS.

We thank the Reviewing Editor for pointing out this ambiguous wording. We understand that "HD-tDCS", which we used in our paper to refer to high-density 1x1 tDCS (because we used small carbon electrodes instead of the large sponge electrodes employed in conventional tDCS), may cause some confusion with high-definition tDCS, which uses compact ring electrodes and most commonly refers to a 4x1 montage (1 active central electrode over the target area and 4 return electrodes placed around the central electrode).

Therefore, to avoid any confusion, we will use the term "tDCS" rather than “HD-tDCS” to qualify the technique used in this paper and suppress mentions of high-density or high-definition tDCS.

Actions in the text: We have replaced the abbreviation “HD-tDCS” with “tDCS” throughout the paper. We have also suppressed the sentence about high-definition tDCS in the Introduction (“While conventional tDCS relies on the use of relatively large rectangular pad electrodes, high-density tDCS (HD-tDCS) utilizes more compact ring electrodes, allowing for increased focality, stronger electric fields, and presumably, greater neurophysiological changes (Datta et al. 2009; Dmochowski et al. 2011)”) and the two related citations in the References section.

(2) Please provide the characteristics of electrodes, including their size, shape, and thickness.

We thank the Reviewing Editor for this recommendation. We now provide the complete characteristics of the tDCS electrodes used in the paper.

Actions in the text: We have added a sentence describing the characteristics of the tDCS electrodes in the Materials and Methods section:

“We used a 1x1 electrode montage with two carbon rubber electrodes (dimensions: 1.4 cm x 1.85 cm, 0.93  cm thick) inserted into Soterix HD-tES MRI electrode holders (base diameter: 25 mm; height: 10.5 mm), which are in contact with the scalp. These electrodes (2.59 cm2) are smaller than conventional tDCS sponge electrodes (typically 25 to 35 cm²).”

(3) Could the authors clarify why they chose to stimulate the right DLPFC? Is there a specific rationale for this choice? Additionally, could the authors explain how they ensured that the stimulation targeted the DLPFC, given that the monkey cap might differ from human configurations? In many NHP studies, structural MRI is used to accurately determine electrode placement. Considering that a single channel F4 - O2 montage was used, even a small displacement of the frontal electrode laterally could result in the electric field not adequately covering the DLPFC. Could the authors provide structural MRI images and details of electrode positioning to help readers better understand targeting accuracy?

We thank the Reviewing Editor for the thoughtful comments and recommendations. We appreciate the opportunity to further clarify our rationale for stimulating the right DLPFC and also the suggestion to provide structural MRI images and details of electrode positioning, which we think will improve the quality of the paper by showing targeting accuracy.

First, we would like to clarify that our initial decision to stimulate the right PFC in most animals was driven by experimental constraints. Indeed, we had limited access to the left PFC in three of the four macaques, either due to the presence of cement (spreading asymmetrically from the centre of the head) used to fix the head post in awake animals or due to a scar in one of the two animals studied under anesthesia. 

Second, we agree with the Reviewing Editor on the importance of showing details of electrode positioning and evidence of targeting accuracy across MRI sessions. Therefore, we now provide structural images showing the positions of anodal and cathodal electrodes in almost all acquired sessions: 10 sessions (out of 10) under anesthesia and 30 sessions in the awake state (out of 34 sessions, because we could not acquire structural images in four sessions). These images show that, in anesthesia experiments, the anodal electrode was positioned over the dorsal prefrontal cortex and the cathodal electrode was placed over the contralateral occipital cortex (at the level of the parieto–occipital junction) in both monkeys. In the awake state, the montage still targeted the prefrontal cortex and the occipital cortex, but with a slightly different placement. One of the electrodes was placed over the prefrontal cortex, closer to the premotor cortex than in anesthesia experiments, while the other one was placed over the occipital cortex (V1), slightly more posterior than in anesthesia experiments. These images therefore show that the placement was relatively accurate across sessions and reproducible between monkeys in each of the two arousal conditions.

Actions in the text: We have added a supplementary file showing electrode positioning in 40 of the 44 acquired MRI sessions (Supplementary File 1). We have also added a new supplement figure (Figure 1 - figure supplement 1) showing electrode positioning in representative MRI sessions of the awake and anesthetized experiments in the main manuscript. 

We added a few sentences referring to these figures in the Result section: 

“Representative structural images showing electrode placements on the head of the two awake monkeys are shown in Figure 1 - figure supplement 1A). Supplementary File 1 displays the complete set of structural images, showing that the two electrodes were accurately placed over the prefrontal cortex and the occipital cortex in a reproducible manner across awake sessions.”

Figure 1 - figure supplement 1. Structural images displaying electrode placements on the head of monkeys. A) Awake experiments. Representative sagittal, coronal and transverse MRI sections, and the corresponding skin reconstruction images showing the position of the prefrontal and the occipital electrodes on the head of monkeys J. and Y. B) Anesthesia experiments. Representative sagittal, coronal and transverse MRI sections, and the corresponding skin reconstruction images showing the position of the prefrontal and occipital electrodes over the occipital cortex on the head of monkeys R. and N.

Supplementary File 1 (see attached file). Structural images showing the position of the tDCS electrodes on the monkey's head across sessions. Sagittal, coronal and transverse MRI sections, and corresponding skin reconstruction images showing the position of the prefrontal and occipital electrodes on the monkey's head for each MRI session (except for 4 sessions in which no anatomical scan was acquired). The two electrodes were accurately placed over the prefrontal cortex and the occipital cortex in a reproducible manner across sessions and between the two monkeys studied in each arousal state. In anesthesia experiments, the anodal electrode was placed over the dorsal prefrontal cortex, while the cathodal electrode was positioned over the parieto-occipital junction. In awake experiments, the prefrontal electrode was positioned over the dorsal prefrontal cortex/pre-motor cortex, while the occipital electrode was placed over the visual area 1. The position of the two electrodes differed slightly between the anesthetized and awake experiments due to different body positions (the prone position of the sedated monkeys prevented a more posterior position of the occipital electrode) and also due to the presence of a headpost on the head of the two monkeys in awake experiments (the monkeys we worked with in anesthesia experiments did not have an headpost).

(4) If the authors did not analyze the data for the passive event-related auditory response, it may be helpful to remove the related sentence to avoid potential confusion for readers.

We thank the Reviewing Editor for the comment. Although we understand the reviewer’s point of view, we decide to keep this information in the paper to inform the reader that the macaques were passively engaged in an auditory task, as this could have some influence on the brain state. In the Materials and Methods section, we already mentioned that the analysis of the cerebral responses to the auditory paradigm is not part of the paper. We have modified the sentence to make it clearer and to avoid potential confusion for readers.

Actions in the text: We have modified the sentence referring to the passive event-related auditory response in the Materials and Methods section:

“All fMRI data were acquired while the monkeys were engaged in a passive event-related auditory task, the local-global paradigm, which is based on local and global deviations from temporal regularities (Bekinschtein et al. 2009; Uhrig, Dehaene, and Jarraya 2014). The present paper does not address how tDCS perturbs cerebral responses to local and global deviants, which will be the subject of future work.”

(5) Could the authors clarify what x(t) represents in the equation? Additionally, it would be better to number the equations.

We apologize for the confusion,  x(t) represents the evolution of the BOLD signals over time. We have numbered the equations as suggested. 

Actions in the text: We have added explanations about the notation and numerotation of equations.

(6) It would be much better to provide schematic illustrations to explain what the authors did for analyzing fMRI data.

We thank the Reviewing Editor for the suggestion and now provide a new figure as suggested.  

Actions in the text: We have added a new figure (Figure 2) graphically showing the overall analysis performed. We have added a sentence about the new Figure 2 in the Results section:  “A graphical overview of the overall analysis is shown in Figure 2.” We have renumbered Figure 2 - supplement figures accordingly.

Figure 2. fMRI Phase Coherence analysis. A) Left) Animals were scanned before, during and after PFC tDCS stimulation in the awake state (two macaques) or under deep propofol anesthesia (two macaques). Right) Example of Z-scored filtered BOLD time series for one macaque, 111 time points with a TR of 2.4 s. B) Hilbert transform of the z-scored BOLD signal of one ROI into its time-varying amplitude A(t) (red) and the real part of the phase φ (green). In blue, we recover the original z-scored BOLD signal as A(t)cos(φ). C) Example of the phase of the Hilbert transform for each brain region at one TR. D) Symmetric matrix of cosines of the phase differences between all pairs of brain regions. E) We concatenated the vectorized form of the triangular superior of the phase difference matrices for all TRs for all participants, in all the conditions for both datasets separately obtaining using the K-means algorithm, the brain patterns whose statistics are then analyzed in the different conditions.

Results 

(1) In Figures 3A, 5A, and 6A showing brain connectivity, it is difficult to relate the connectivity variability among the brain regions. Instead of displaying connection lines for nodes, it would be more effective if the authors highlighted significant, strong connectivity within specific brain regions using additional methods, such as bootstrapping.

We thank the Reviewing Editor for the comment and suggestion. The connection lines indeed represent all the synchronizations above 0.5 and all the anti-synchronization below -0.5 between all pairs of brain regions. As suggested, another element we haven’t addressed is the heterogeneity in coherences between individual brain regions. We hence propose additional supplementary figures showing, for all centroids mentioned in main figures, the variance in phase-based connectivity of the distributions of coherence of all brain regions to the rest of the brain. High value would then indicate a wide range of values of coherence, while low would indicate the different coherence a region has with the rest of the brain have similar values. Thus, a brain with uniform color would indicate high homogeneity in coherence among brain regions, while sharp changes in colors would reveal that certain regions are more subject to high variance in their coherence distributions. We expect this new figure to more clearly expose the connectivity variability among the brain regions.

Actions in the text: We have added new figures showing, for all centroids mentioned in the main figures, the variances in phase-based connectivity of the distributions of coherence  (Figure 3 - figure supplement 3;  Figure 5 - figure supplement 2; Figure 6 - figure supplement 3; Figure 7 - figure supplement 2). One of them is shown below for the only awake analysis (Figure 3 - figure supplement 3).

Figure 3 - figure supplement 3. Variance in inter-region phase coherences of brain patterns. Low values (red and light red) indicate that the distribution of synchronizations between a brain region and the rest of the brain has relatively low variance, while high values (blue and light blue) indicate relatively high variance. Are displayed both supra (top) and subdorsal (bottom) views for each brain pattern from the main figure, ordered similarly as previously: from left (1) to right (6) as their respective SFC increases. 

We added a few sentences about variances in phase-based connectivity of the distributions of coherence in the Result section: 

“Further investigation of the variances in inter-region phase coherences of brain patterns, presented in Figure 3 - figure supplement 3, revealed two main findings. First, all the patterns exhibited some degree of lateral symmetry. Second, except for the pattern with the highest SFC, most patterns displayed high heterogeneity in their coherence variances and striking inter-pattern differences. These observations reflect both the segmentation of distinct functional networks across patterns and a topological organization within the patterns themselves: some regions showed a broader spectrum of synchrony with the rest of the brain, while others exhibited narrower distributions of coherence variances. For instance, unlike other brain patterns, pattern 5 was characterized by a high coherence variance in the frontal premotor areas and low variance in the occipital cortex, whereas pattern 3 had a high variance in the frontal and orbitofrontal regions. In addition, we performed the main analyses separately for the two monkeys, explored the inter-condition variability (Supplementary File 2), and computed classical measures of functional connectivity such as average FC matrices and functional graph properties (modularity, efficiency and density) of the visited FC states (Supplementary File 3).”

“The variance in inter-regional phase coherence across brain patterns showed notably that pattern 4, in contrast to most other patterns, was characterized by a high variance in frontal premotor areas and a low variance in the occipital cortex (Figure 5 - figure supplement 2)." 

“The variance in inter-region phase coherences of the brain patterns is displayed in Figure 6 - figure supplement 3 and showed a striking heterogeneity between the patterns. For example, pattern 5 had a low overall variance (except in the frontal cortex), while pattern 1 was the only pattern with a high variance in the occipital cortex.”

“The variance in inter-region phase coherences of brain patterns is displayed in Figure 6 - figure supplement 2.”

(2) For both conditions, only 2 to 3 out of 6 patterns showed significant effects of tDCS on the occurrence rate. Is it sufficient to claim the authors' conclusion?

We thank the Reviewer Editor for the comment. We would like to point out that similar kinds of differences in the occurrence rates of specific brain patterns (particularly in patterns at the extremities of the SFC scale) have already been reported previously. Prior works in patients suffering from disorders of consciousness, in healthy humans or in non-human primates,  have shown, by using a similar method of analysis, that not all brain states are equally disturbed by loss of consciousness, even in different modalities of unconscious transitioning (Luppi et al. 2021; Z. Huang et al. 2020; Demertzi et al. 2019; Castro et al. 2023; Golkowski et al. 2019; Barttfeld et al. 2015). Therefore, yes we believe that our conclusions are still supported by the results.

(3) If the authors want to assert that the brain state significantly influences the effects of tDCS as discussed in the manuscript, further analysis is necessary. First, it would be great to show the difference in connectivity between two consciousness conditions during the baseline (resting state) to see how resting state connectivity or structural connectivity varies. Second, demonstrating the difference in connectivity between the awake and anesthetized conditions (e.g., awake during cathodal vs. anesthetized cathodal) to show how the connectivity among the brain regions was changed by the brain state during tDCS. This would strengthen the authors' conclusion.

We thank the reviewer for this comment. Firstly, we’d like to clarify that the structural connectivity doesn’t change from one session to another in the same animal and minimally between subjects. Secondly, we agree with the Reviewing Editor that it is informative to show the differences between the baselines and this is what we have done. The results are shown in Figures 5 and 7. Regarding the comparison of the stimulating conditions across arousal levels, the only contrast that we could make is to compare 2 mA anodal awake with 2 mA anodal anesthetized (during and post-stimulation). However, as 2 mA anodal stimulation in the awake state did not affect the connectivity much (compared to the awake baseline), the results would be almost similar to the comparison of the awake baseline with 2 mA anodal anesthetized, which is shown in Figure 7. Therefore, we believe that this would result in minimal informative gains and even more redundancy. 

Reviewer #3 (Recommendations for the authors): 

Introduction, par 2: HD-tDCS does not necessarily produce stronger electric fields (E-fields) in the brain. The E-field is largely montage-dependent, and some configurations such as the 4x1 configuration can actually have weaker E-fields compared to conventional tDCS designs (i.e., with two sponge electrodes) as electrodes are often closer together resulting in more current being shunted by skull, scalp, and CSF. I would consider re-phrasing this section.

We agree with the Reviewer Editor that high-definition tDCS does not necessarily produce stronger electric fields in the brain and apologize for the confusion caused by our use of HD-tDCS to refer to high-density tDCS. To avoid any confusion, we have removed the sentence mentioning that HD-tDCS produces stronger electric fields. 

Actions in the text: We have removed the sentence about high-definition tDCS in the Introduction (“While conventional tDCS relies on the use of relatively large rectangular pad electrodes, high-density tDCS (HD-tDCS) utilizes more compact ring electrodes, allowing for increased focality, stronger electric fields, and presumably, greater neurophysiological changes (Datta et al. 2009; Dmochowski et al. 2011)”) and the two related citations in the References section.

eLife Assessment

This valuable study combines anatomical tracing and slice physiology to examine how anterior thalamic and retrosplenial inputs converge in the presubiculum, a key region in the navigation circuit. The authors show that near-simultaneous co-activation of retrosplenial and thalamic inputs drives supra-linear presubiculum responses, revealing a potential cellular mechanism for anchoring the brain's head direction system to external visual landmarks. Their thorough experimental approach and analyses provide convincing evidence for the cellular basis of how the brain's internal compass may be anchored to the external world, laying the groundwork for future experimental testing in vivo.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors use anatomical tracing and slice physiology to investigate the integration of thalamic (ATN) and retrosplenial cortical (RSC) signals in the dorsal presubiculum (PrS). This work will be of interest to the field, as postsubiculum is thought to be a key region for integrating internal head direction representations with external landmarks. The main result is that ATN and RSC inputs drive the same L3 PrS neurons, which exhibit superlinear summation to near-coincident inputs. Moreover, this activity can induce bursting in L4 PrS neurons, which can pass the signals LMN (perhaps gated by cholinergic input).

Strengths:

The slice physiology experiments are carefully done. The analyses are clear and convincing, and the figures and results are well composed. Overall, these results will be a welcome addition to the field.

Weaknesses:

The conclusions about the circuit-level function of L3 PrS neurons sometimes outstrip the data, and their model of the integration of these inputs is unclear. I would recommend some revision of the introduction and discussion. I also had some minor comments about the experimental details and analysis.

Specific major comments:

(1) I found that the authors' claims sometimes outstrip their data, given that there were no in vivo recordings during behavior. For example, in the abstract that their results indicate "that layer 3 neurons can transmit a visually matched HD signal to medial entorhinal cortex", and in the conclusion they state "[...] cortical RSC projections that carry visual landmark information converge on layer 3 pyramidal cells of the dorsal presubiculum". However, they never measured the nature of the signals coming from ATN and RSC to L3 PrS (or signals sent to downstream regions). Their claim is somewhat reasonable with respect to ATN, where the majority of neurons encode HD, but neurons in RSC encode a vast array of spatial and non-spatial variables other than landmark information (e.g., head direction, egocentric boundaries, allocentric position, spatial context, task history to name a few), so making strong claims about the nature of the incoming signals is unwarranted.

(2) Related to the first point, the authors hint at, but never explain, how coincident firing of ATN and RSC inputs would help anchor HD signals to visual landmarks. Although the lesion data (Yoder et al. 2011 and 2015) support their claims, it would be helpful if the proposed circuit mechanism was stated explicitly (a schematic of their model would be helpful in understanding the logic). For example, how do neurons integrate the "right" sets of landmarks and HD signals to ensure a stable anchoring? Moreover, it would be helpful to discuss alternative models of HD-to-landmark anchoring, including several studies that have proposed that the integration may (also?) occur in RSC (Page & Jeffrey, 2018; Yan, Burgess, Bicanski, 2021; Sit & Goard, 2023). Currently, much of the Discussion simply summarizes the results of the study, this space could be better used in mapping the findings to the existing literature on the overarching question of how HD signals are anchored to landmarks.

Comments on revised version:

The authors addressed all of my major points and most of my minor points in the revised submission.

In the text it states "They are willing to risk being wrong every time they collect data". Upon reading about the theory data cycle It immediately clicks in my head that I use this cycle probably every day whether it was thinking of a plan to get my mom to say yes to something or if it was being used in an actual science class. I got the urge to look up on google and see if "All" scientist use this cycle when trying to be a producer of researcher, to my surprise it says No..What other ways (and how) are they doing things with out going through numerous trials and errors?

In the text it states "They are willing to risk being wrong every time they collect data". Upon reading about the theory data cycle It immediately clicks in my head that I use this cycle probably every day whether it was thinking of a plan to get my mom to say yes to something or if it was being used in an actual science class. I got the urge to look up on google and see if "All" scientist use this cycle when trying to be a producer of researcher, to my surprise it says No..What other ways (and how) are they doing things with out going through numerous trials and errors?

Calc II is not entry-level, many people fail it and still go into engineering, this is head-in-assed

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Manuscript number: RC- 2025-03073

Corresponding author(s): Shaul Yogev

1. General Statements [optional]

We kindly thank our reviewers for their enthusiasm, thoughtful feedback, and constructive suggestions on how to strengthen our manuscript. Below, we provide a point-by-point response to reviewer comments and outline the experiments we will do to address every concern that has been raised.

2. Description of the planned revisions

• *

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This interesting study uses an unbiased genetic screen in C. elegans to identify SAX-1/NDR kinase as a regulator of dendritic branch elimination. Loss of SAX-1 results in an excess branching phenotype that is striking and highly penetrant. The authors identify several additional regulators of branch elimination (SAX-2, MOB-1, RABI-1, RAB-11.2) by using a candidate genetic screen aimed at factors that interact physically or genetically with SAX-1. They propose that SAX-1 acts by promoting membrane retrieval based on the nature of these interactors and the results of an imaging-based in vivo assay for endocytic puncta.

Major comments.

1. My biggest concern is that the phenotypes are only observed in temperature-sensitive dauer-constitutive mutant backgrounds, and not in wild-type dauers. That is, wild-type animals exiting dauer do not require SAX-1 for dendrite elimination. While this does not undermine the importance of the results, it does require more explanation. The authors write that "the requirement for sax-1... relies on specific physiological states of the dauer stage," but I do not understand what this means. Are they saying that daf-7 and daf-2 dauers are in a different "physiological state" than wild-type dauers? In what way? What is the evidence for this? A more rigorous explanation is needed. We agree that this is puzzling, and we thank the reviewer for recognizing that this does not undermine the importance of the results. There is ample evidence that daf-2 and daf-7 differ from starvation-induced dauers. For example, a recent preprint finds that the transcriptomes of these two mutants at dauer cluster much closer to each other than to starvation-induced dauers (Corchado et al. 2024). Older work has noted other differences, such as the time the dauer entry decision is made (Swanson and Riddle 1981), the synchronicity of dauer exit, the ability to force dauer entry in daf-d mutants, as well as additional dauer-unrelated phenotypes (reviewed in Karp 2018). We agree with the reviewer that this merits further clarifications and will perform the experiments suggested by the reviewer below:

To me, the simplest genetic explanation is that daf-7 and daf-2 are partially required for branch retraction in a manner redundant with sax-1, and the ts mutants are not fully wild-type at 15C. Thus, the sax-1 requirement is revealed only in these mutant backgrounds. Can the authors examine starvation-induced dauers of daf-7 or daf-2 raised continuously at 15C?

We will do this experiment.

daf-7 and daf-2 ts strains can form "partial dauers" that have a dauer-like appearance but are not SDS resistant. Could the difference between partial dauers and full dauers account for the difference in sax-1-dependence? The authors could use SDS selection of the daf-7 strain at 25C to ensure they are examining full dauers.

We tested daf-7 mutants with 1% SDS when we set up the system – they are fully dauer at 25°C and are SDS sensitive after exit. We will repeat this important control with daf-7; sax-1 double mutants.

The Bargmann lab has created a daf-2 FLP-OUT strain (ky1095ky1087) that allows cell-type-specific removal of daf-2. Could this be used to test for a cell-autonomous role of daf-2 in IL2Q related to branch elimination?

We can attempt this experiment. However, since IL2 promoters turn on prior to dauer, the interpretation would not be straightforward – it would be hard to exclude that a cell autonomous defect in dauer entry does not account for the IL2 dauer exit phenotype, even if branching appears normal.

These ideas are not a list of specific experiments the authors need to complete, rather they are meant to illustrate some possible approaches to the question. Whatever approach they use, it is important for them to more rigorously explain why SAX-1 is not required for branch removal in wild-type animals.

We completely agree. We will carry out the 15°C experiment, examine morphological characteristics and test SDS resistance. In addition, we will test neuronal markers that differ between dauers and non-dauers to determine whether the mutants are full or partial dauers at the relevant timepoints.

The SAX-2 localization (Fig. 4) and endocytosis assay (Fig. 6) results were not clear to me from the data shown. Overall a more rigorous analysis and presentation of the data would be important to make these conclusions convincing. This may involve refining the data presentation in the figures, modifying the claims (e.g., "we propose" vs "we find"), or saving some of the data to be more fully explored in a future paper. In my view, these figures are the biggest weak point of the manuscript and also are not important for the central conclusions (which are well supported and convincing), indeed these results are barely mentioned in the Abstract or last paragraph of Introduction.

We agree that the analysis and presentation of Figures 4 and 6 need to be improved. The presentation has already been updated, and the figures are clearer now. In the revision, we will increase sample size to provide stronger conclusions, consolidate some of the analysis and further improve presentation. While we agree with the reviewer that conclusions from these figures are not as strong as those drawn from genetic experiments, they do complement and support the conclusions of those other figures.

• In Fig. 4D, why is SAX-2 visible throughout the entire neuron and why is the "punctum" marked with an arrow also seen in the tagRFP channel? One gets the impression that some of the puncta may be background, bleed-through, or artifacts due to cell varicosities.

There is no bleed-through: this is most evident by looking at the brightest signals in the cell body (now labelled with an asterisk in a zoomed-out image) and noting that they do not bleed between channels. In sax-1 mutants, the SAX-2::GFP puncta are very obvious and distinguishable from the tagRFP channel. In control, SAX-2::GFP is very faint in the dendrite, so we increased the contrast to allow visualization. The reviewer is correct that under these conditions, some puncta look like the cytosolic fill. In the revision, we will re-analyze the data and will not consider these as bona-fide SAX-2 puncta, but rather cytosolic SAX-2 that accumulates due to constrictions and varicosities in the dendrite.

• Related to both Fig. 4 and Fig. 6, where does SAX-1 localize in IL2Q in dauer and post-dauer? Does its expression or localization change during branch retraction? Does it co-localize with SAX-2 or endocytic puncta?

We generated an endogenously tagged sax-1 with a 7xspGFP11 tag; however, this was below detection in the IL2s. For the revisions, we can test an overexpressed cDNA construct.

**Referee cross-commenting**

I think we all touched on similar points. I wanted to follow up on Reviewer 3's comment, "Is the failure to eliminate branches an indication of incomplete dauer recovery? Do sax-1 mutants retain additional characteristics of dauer morphology in post dauer adults." I thought this was an excellent point. It made me wonder if that might explain why the defect is only seen in daf-7 and daf-2 mutant backgrounds - maybe these strains retain partial dauer traits even after exit. Is there a specific experiment that they could do? Did you have specific characteristics of dauer morphology in mind for them to check? (Ideally something in the nervous system that can be scored quantitatively.)

Please see response to point #1 regarding experiments we will do to confirm the “dauer state” of daf-7 and daf-7; sax-1 double mutants.

Reviewer #1 (Significance (Required)):

A major strength of this work is the pioneering use of a novel system to study neuronal branch retraction. C. elegans has provided a powerful model for studying how dendrite branches form, but much less attention has been paid to how excess neuronal branches are removed. The post-dauer remodeling of IL2Q neurons provides an exciting and dramatic physiological example to explore this question.

This paper is notable for taking the first steps towards developing this innovative model. It does exactly what is needed at the outset of a new exploration - a forward genetic screen to discover the main regulators of the process. Using a combination of classical and modern genetic approaches, the authors bootstrap their way to a sizeable list of factors and a solid understanding of the properties of this system, for example that retraction of higher vs lower order dendrites show different genetic requirements.

We thank the reviewer for recognizing the novelty and significance of our work.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript, the authors establish C. elegans IL2 neurons as a system in which to study dendrite pruning. They use the system to perform a genetic screen for pruning regulators and find an allele of sax-1. Unexpectedly sax-1 is only required for post-dauer pruning in two different genetic backgrounds that induce dauer formation, but not starvation-induced dauer formation. Sax-1/NDR kinase reduction has previously been associated with increased outgrowth and branching in other systems, so this is a new role for this protein. However, the authors show that proteins that work with Sax-1 in other systems, like sax-2/fry, also play a role in this pathway. The genetic experiments are beautiful and the findings are all clearly explained and strongly supported. The authors also examine sax-2 localization, which localizes sax-1 in other systems, and show it in puncta in dendrites that increase with dauer exit, consistent with function at the time of pruning. They also show that membrane trafficking regulators associated with NDR kinases function in the same pathway here, hinting that endocytosis may play a role during pruning as in Drosophila. The link to endocytosis was a little weak (see Major point below). Overall, this study describes a new system to study pruning and identifies NDR/fry/Rabs as regulators of pruning during dauer exit. The work is very high quality and both the imaging and genetics are extremely well done.

We thank the reviewer for their positive assessment of the manuscript.

Major points

1. The only place where there were any questions about the data was the last figure (6G and I). Here they use uptake of GFP secreted from muscle as a readout of endocytosis in IL2 neurons. They nicely show that more internalized puncta accumulate as animals exit dauer. The claim that this is reduced in sax-1 mutants doesn't seem to match the images shown well. In the image there are many more puncta in the GFP channel and much more accumulation of the RFP-tagged receptor everywhere. It seems like some additional analysis of this data is important to fully capture what is going on and whether this really represents an endocytic defect. We agree and will provide additional data in Figure 6. The specific discrepancy between the image and the quantification is because we showed a single focal plane rather than a projection. This does not capture all the puncta in a neurite. The current version shows a projection, making it evident that the mutants has fewer puncta compared to the control.

Reviewer #2 (Significance (Required)):

Neurite pruning is important in all animals with neurons. Genetic approaches have primarily been applied to the problem using Drosophila, so identifying a new model system in which to study it is an important step. Using this system, a pathway known to function in a different context is linked to pruning. Thus the study provides new insights into both pruning and this pathway.

We thank the reviewer for the positive assessment of our study’s significance.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

Summary: Figueroa-Delgado et al. use a C. elegans neuro plasticity model to examine how dendrites are eliminated upon recovery from the stress induced larval stage, dauer. The authors performed a mutagenesis screen to identify novel regulators of dendrite elimination and revealed some surprising results. Branch elimination mechanism varies between 2{degree sign}, 3{degree sign}, and 4{degree sign} branches. The NDR kinase, SAX-1 and it's interactors (SAX-2 and MOB-2) are required for elimination of second and third order branches but not fourth order branches. Interestingly they showed that branch elimination varies depending on the stimulus of dendrite outgrowth such that the NDR kinase is required for branch elimination after genetically inducing the dauer stage but is not required if dauers are produced through food deprivation. The authors go a step further to include a small candidate screen looking at various pathways of membrane remodeling and identify additional regulators of dendrite elimination related to membrane trafficking including RABI-1, RAB-8, RAB-10, and RAB-11.2.

We thank the reviewer for their time and suggestions below

Major comments:

• While I find the data promising and exciting, several of the experiments have concerningly low sample sizes. Fig 3G, Fig 4G, Fig 5J and L, and Fig 6I all contain data sets that are fewer than 10 animals. Sample sizes should be stated specifically in the figure legends for all data represented in the graphs. We thank the reviewer for finding the data exciting. We agree that the sample sizes in some panels is low and will increase it in the revised version. Sample sizes are now specifically listed in the figure legends.

• All statements based on data not shown should be amended to include the data as a supplemental figure or edited to omit the statement based on withheld data. We agree. Some “not shown” data are already added to the current version of the manuscript and the rest will be added to the fully revised version, or the statements will be omitted.

• Rescue experiments (Fig 2J) should demonstrate failure to rescue from neighboring tissue types (hypodermis and muscle) to conclude cell autonomous rescue rather than a broadly acting factor. Thank you for the suggestion. We will use a hypodermal promoter and a muscle promoter driving SAX-1 cDNA expression to strengthen the claim of cell autonomy.

• Fig 4 needs quantification of higher order branches and SAX-2 proximity to branch nodes as these are discussed in the text. We will add this quantification.

Minor comments:

• Fig 1C-F, It appears like the shy87 allele produces animals of significantly different body sizes. It would improve rigor to normalize the dendrite coverage to body size in the quantification. We do not see a biologically meaningful size difference between shy87 and control, it may be the specific image shown. We will confirm this by measuring animal size for the final revision.

• Is the failure to eliminate branches an indication of incomplete dauer recovery? Do sax-1 mutants retain additional characteristics of dauer morphology in post dauer adults. This important point was also raised by Reviewer 1. We will test SDS sensitivity, morphological markers, and molecular markers to determine the dauer “state” of the mutants used in this study. The results will be included in the final revision.

• The text references multiple transgenic lines tested in Fig 2I-J but only one line is shown. Additional lines were visually examined under a fluorescent compound microscope but not imaged or quantified. We will add this quantification to the final revision.

• Fig 4F, Additional timepoints would enhance the sax-1 localization result and might provide insight into mechanism of action for sax-1. We will add the localization in post-dauer adults.

• Fig 6I Control and sax-1(ky491) example images should be provided in the supplement. We will add these images to the final revision.

**Referee cross-commenting**

I agree that we shared many of the same concerns.

There are several general assays for dauer characteristics that could be used here to determine if the post-dauer animals retain other characteristics of the dauer stage in addition to IL2 branches (SDS resistance, alae remodeling, pharyngeal bulb morphology, nictation behavior). The nictation behavior has been connected very nicely with IL2 neurons (Junho Lee's group). Additionally, FLP dendrites occupy the same space as the IL2 branches and outgrowth in post-dauers occurs in coordination with IL2 branch elimination - this might be another optional experiment, to check if FLP growth is impeded by persistent IL2 branches. All of these could be quantified similar to how the authors have already established with their IL2 model (FLP dendrite branches) or with a binary statistic.

Please see responses to Reviewer 1 and 3 above for the list of experiments to determine whether the animals fail to completely enter or exit dauer.

Reviewer #3 (Significance (Required)):

SIGNIFICANCE ============ These results describe a new role for the NDR kinase complex in dendrite pruning that has clinical significance to our understanding of human brain development and human health concerns in which pruning is dysregulated, such as observed in the case of autism. The authors use an established neuro-plasticity, C. elegans model (Schroeder et al. 2013) which provides a tractable and reproduceable platform for discovering the mechanism of dendrite pruning. These results would influence future work in the fields of cell biology of the neuron and disease models of brain development.

My expertise is in the field of C. elegans neuroscience and stress biology and have sufficient expertise to evaluate all aspects of this work.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer #1

• In Fig. 4C, the distinction between puncta in the primary or higher-order dendrites is not clear to me, and several puncta that I would have scored as primary are marked as higher-order.

We apologize for a mistake in the arrowhead color and overall presentation of this figure. It has been fixed in the current version.

• Related to this, in Fig. 4B are the two arrows meant to be white as in the top panel, or yellow as in the bottom panel?

We thank Reviewer #1 for their observation, and we apologize for our oversight. We fixed this in the current version.

• In Fig. 4, where in the head are we looking? It would help to show a more low-magnification view of the entire cell.

We added zoomed-out images and indicated where the zoomed in insets are taken from. We thank the reviewer for helping us improve the clarity of the data.

• The main sax-1 phenotype is increased SAX-2 puncta in dauer, but the branch retraction defect is in post-dauers. How is this relevant to the phenotype?

This is a very good point. The increase in SAX-2 puncta in sax-1 mutants is stronger during dauer-exit than in dauer, consistent with this being the time when SAX-1 functions. We agree that some earlier activity of SAX-1 cannot be excluded, and we do not assume that the effect on SAX-2 completely accounts for the pruning defects. This is now acknowledged in the text. However, given that both proteins function together in pruning, and given that the effect is strongest during dauer exit, we do believe that this data is informative and worth showing.

• The number of SAX-2 puncta in sax-1 mutants decreases almost to normal in post dauers. Is there a correlation between the number of remaining branches and the number of SAX-2 puncta? That is, do the many wild-type animals with "excess" SAX-2 puncta also fail to retract branches?

There is no correlation. In other words, the number of SAX-2 puncta does not instruct the extent of pruning. Please note the quantifications underestimate the number of SAX-2 puncta in the mutants, since they were only done on the primary dendrite. This is necessary because the mutant and control have different arbor size, so only branch order that can be appropriately compared are primary dendrites.

• The control post-dauer data in Fig. 4F and 4H are identical (re-used data) but the corresponding control dauer data in Fig. 4F and 4G are different. What is going on here?

We thank the reviewer for raising this point and apologize for the oversight in data presentation. In the revised manuscript, we now show all control and experimental data integrated into a single graph, ensuring that each dataset is represented accurately to provide a comparison between dauer and post dauer recovery conditions.

• Why are sample sizes so small for both strains in Fig. 4G compared to Fig. 4F and 4H?

We sincerely apologize for this mistake, some of the data was erroneously grouped in the original submission. The revised version contains an updated number of neurons, presented on the same graph, and in the final revision we will further increase sample size. We apologize again for this error.

• In Fig. 6C, why are the tagRFP (blue) puncta larger than the neurite? Aren't these meant to represent vesicles inside the surrounding neurite? One gets the impression that this is bleed-through from the GFP channel.

Based on EM, both an endocytic punctum and the diameter of the neuron are smaller than a single pixel. The apparent difference in size in fluorescence microscopy is because the puncta are brighter (they contain more membrane) and thus appear larger. In the current version, the improved presentation of the figure contains zoomed out images that clearly show that there is no bleed-through.

• In Fig. 6E and 6F, why are there no tagRFP (blue) puncta? Is CD8 not endocytosed at all if it lacks the nanobody sequence? One would expect the tagRFP (blue) signal to be the same in both strains and simply to lack yellow if the nanobody is not present.

CD8 lacks clear endocytosis motifs, which is why it is advantageous for labelling neurites and testing endocytosis when paired with an endocytic signal (Lee and Luo 1999; Kozik et al. 2010). Conversely, extracellular GFP binding to a membrane GFP antibody can induce endocytosis (for example, see (Tang et al., 2020)), likely by inducing clustering, although we are not familiar with work that explored the mechanism. In the updated version we included a rare example of an mCD8 punctum.

• The authors report a decrease in endocytic events in sax-1, but qualitatively it looks like there are vastly more puncta inside the neuron in Fig. 6H than in 6G.

We apologize for the presentation in the original version of Figure 6. This impression was because we showed single focal planes that only captured some of the signal. In the revised version we show projections, which makes it evident that there are fewer endocytic events in the mutant.

• In Fig. 6E and 6H, why are there so many GFP (yellow) puncta outside the neuron? What are these structures and why are they absent in the strain with the nanobody?

These puncta are secreted or muscle-associated GFP that has not been internalized by IL2Q neurons. They are present in all strains in this figure, this can be clearly seen in the zoomed-out images that have been added to the updated figure.

• What is the large central blue structure in Fig. 6H - is this the soma? - and why are puncta in this region not counted?

This is indeed the soma. In the updated version this can be clearly seen in the zoom-out. The large puncta in the soma were not counted because they may arise from the fusion of an unknown number of smaller puncta, and their precise number cannot be determined at the resolution of fluorescence microscopy.

• minor: there is text reading "40-" in the bottom panel of Fig. 6H. It is visible when printed but not on screen - adjust levels in Photoshop to reveal it.

We thank the reviewer for catching this oversight, it is now fixed.

Minor points:

1. At several points the authors emphasize the relationship of neurite remodeling to stress, e.g. Abstract and Discussion: "we adapted C. elegans IL2 sensory dendrites as a model [of...] stress-mediated dendrite pruning". It seems unnecessary and potentially misleading to treat this as a neuronal stress response. First, it conflates organismal and cellular stress - there is no reason to think that IL2 neurons are under cellular stress in dauer. In fact parasitic nematodes go through dauer-like stages as part of healthy development and probably have similar remodeling of IL2. Second, dendrite pruning occurs during dauer exit, which is the opposite of a stress response - it reflects a return to favorable conditions. We agree. We modified the abstract and discussion to avoid conflating organismal stress (the alleviation of which is relevant for triggering pruning) and cellular stress. Thank you for pointing this out.

In Fig. 1A, C. elegans is shown going directly from L1 to dauer in response to unfavorable conditions, which is incorrect. Animals proceed through L2 (in many cases actually an alternative L2d pre-dauer) and then molt into dauer (an alternative L3 stage) after completing L2.

We updated the schematic to include the L2d stage where commitment to dauer entry or resumption to reproductive development is made.

In Fig. 1B, please check if it is correct that hypodermis contacts the pharynx basement membrane as drawn. The schematic in the top panel makes it look like there is a single secondary branch and the quaternary branches are similar in length to the primary dendrite. The schematic in the bottom panel makes it look like the entire neuron is a small fraction of the length of the pharynx. Could these be drawn closer to scale?

The hypodermis does contact the pharynx basement membrane. We redrew the schematic for clarity.

Reviewer #2

For context, it might be helpful to know whether branching of other dendrites is increased in sax-1 mutants (as expected based on phenotypes in other animals) or decreased like IL2 neurons.

We examined the branching pattern of PVD, a polymodal nociceptive neuron (new Supplemental Figure 3). We find no significant difference between control and sax-1 or sax-2 mutants, suggesting that these genes function in the context of pruning. Recent work (Zhao et al. 2022) confirms that sax-1 is not required for PVD branching.

Minor:

"shy87 mutant dauers showed a minor reduction in secondary and tertiary branches compared to control (Figure 1G). These results indicate that shy87 is specifically required for the elimination of dauer-generated dendrite branches." Maybe temper the specificity claim some as the reduction in branches is definitely there.

We agree, the claim was tempered.

"three complimentary approaches" should be complementary

Thank you for noticing. We fixed this.

"In control animals, SAX-2 was mostly concentrated in the cell body (data not shown)" It might be nice to include some overview images that show the cell body for completeness.

We added zoomed-out images to the revised figure, thank you for the suggestion.

Reviewer #3

Minor comments:

• Fig 1G-H, are shy87 second and third order branch counts statistically different between dauer and post dauer adults? This comparison would strengthen the claim that these order branches fail to eliminate all together rather than undergo a partial elimination. We added this to Figure S2. The shy87 mutants show a complete failure in eliminating secondary branches (i.e. no difference between dauer and post-dauer) and a strong but incomplete defect in eliminating tertiary branches.

• Fig 4B-E Indicate branch order in the images, this is unclear and a point that is focused on in the text. Done.

• Discussion of Fig 1G from the text claims that shy87 is specifically required for branch elimination yet the data shows significant defects in branch outgrowth as well. This raises the question, are the branches abnormally stabilized that results in early underdevelopment and late atrophy? Authors should acknowledge alternative hypotheses. We agree and will revise the text accordingly. The difference between shy87 and control dauers, while statistically significant, is relatively minor and can only be detected by careful quantification, it is not apparent from looking at the images (in contrast for example to rab-8 and rab-10 mutants, where we acknowledge in the text that their branching defects might affect subsequent pruning.

• Authors reference a branch elimination process but don't outline what this would entail and where their results fit in. We apologize for being unclear. Given that sax-1 and sax-2 function together, one would intuitively expect to see SAX-2 being reduced in sax-1 mutants, yet the opposite is observed. On potential explanation is that SAX-1 does not directly control SAX-2 abundance, but that clearance of SAX-2 is part of the pruning process that both proteins regulate. This would explain the enrichment of SAX-2 in sax-1 mutants. However, additional models cannot be excluded, and we acknowledge this in the revised text.

References:

Corchado, Johnny Cruz, Abhishiktha Godthi, Kavinila Selvarasu, and Veena Prahlad. 2024. “Robustness and Variability in Caenorhabditis Elegans Dauer Gene Expression.” Preprint, bioRxiv, August 26. https://doi.org/10.1101/2024.08.15.608164.

Karp, Xantha. 2018. “Working with Dauer Larvae.” WormBook, August 9, 1–19. https://doi.org/10.1895/wormbook.1.180.1.

Kozik, Patrycja, Richard W Francis, Matthew N J Seaman, and Margaret S Robinson. 2010. “A Screen for Endocytic Motifs.” Traffic (Copenhagen, Denmark) 11 (6): 843–55. https://doi.org/10.1111/j.1600-0854.2010.01056.x.

Lee, T., and L. Luo. 1999. “Mosaic Analysis with a Repressible Cell Marker for Studies of Gene Function in Neuronal Morphogenesis.” Neuron 22 (3): 451–61.

Swanson, M. M., and D. L. Riddle. 1981. “Critical Periods in the Development of the Caenorhabditis Elegans Dauer Larva.” Developmental Biology 84 (1): 27–40. https://doi.org/10.1016/0012-1606(81)90367-5.

Tang, Rui, Christopher W Murray, Ian L Linde, et al. n.d. “A Versatile System to Record Cell-Cell Interactions.” eLife 9: e61080. https://doi.org/10.7554/eLife.61080.

Zhao, Ting, Liying Guan, Xuehua Ma, Baohui Chen, Mei Ding, and Wei Zou. 2022. “The Cell Cortex-Localized Protein CHDP-1 Is Required for Dendritic Development and Transport in C. Elegans Neurons.” PLOS Genetics 18 (9): e1010381. https://doi.org/10.1371/journal.pgen.1010381.

4. Description of analyses that authors prefer not to carry out

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Referee #1

Evidence, reproducibility and clarity

This interesting study uses an unbiased genetic screen in C. elegans to identify SAX-1/NDR kinase as a regulator of dendritic branch elimination. Loss of SAX-1 results in an excess branching phenotype that is striking and highly penetrant. The authors identify several additional regulators of branch elimination (SAX-2, MOB-1, RABI-1, RAB-11.2) by using a candidate genetic screen aimed at factors that interact physically or genetically with SAX-1. They propose that SAX-1 acts by promoting membrane retrieval based on the nature of these interactors and the results of an imaging-based in vivo assay for endocytic puncta.

Major comments.

1. My biggest concern is that the phenotypes are only observed in temperature-sensitive dauer-constitutive mutant backgrounds, and not in wild-type dauers. That is, wild-type animals exiting dauer do not require SAX-1 for dendrite elimination.

While this does not undermine the importance of the results, it does require more explanation. The authors write that "the requirement for sax-1... relies on specific physiological states of the dauer stage," but I do not understand what this means. Are they saying that daf-7 and daf-2 dauers are in a different "physiological state" than wild-type dauers? In what way? What is the evidence for this? A more rigorous explanation is needed.

To me, the simplest genetic explanation is that daf-7 and daf-2 are partially required for branch retraction in a manner redundant with sax-1, and the ts mutants are not fully wild-type at 15C. Thus, the sax-1 requirement is revealed only in these mutant backgrounds. Can the authors examine starvation-induced dauers of daf-7 or daf-2 raised continuously at 15C?

daf-7 and daf-2 ts strains can form "partial dauers" that have a dauer-like appearance but are not SDS resistant. Could the difference between partial dauers and full dauers account for the difference in sax-1-dependence? The authors could use SDS selection of the daf-7 strain at 25C to ensure they are examining full dauers.

The Bargmann lab has created a daf-2 FLP-OUT strain (ky1095ky1087) that allows cell-type-specific removal of daf-2. Could this be used to test for a cell-autonomous role of daf-2 in IL2Q related to branch elimination?

These ideas are not a list of specific experiments the authors need to complete, rather they are meant to illustrate some possible approaches to the question. Whatever approach they use, it is important for them to more rigorously explain why SAX-1 is not required for branch removal in wild-type animals. 2. The SAX-2 localization (Fig. 4) and endocytosis assay (Fig. 6) results were not clear to me from the data shown. Overall a more rigorous analysis and presentation of the data would be important to make these conclusions convincing. This may involve refining the data presentation in the figures, modifying the claims (e.g., "we propose" vs "we find"), or saving some of the data to be more fully explored in a future paper. In my view, these figures are the biggest weak point of the manuscript and also are not important for the central conclusions (which are well supported and convincing), indeed these results are barely mentioned in the Abstract or last paragraph of Introduction.

• In Fig. 4, where in the head are we looking? It would help to show a more low-magnification view of the entire cell.
• In Fig. 4D, why is SAX-2 visible throughout the entire neuron and why is the "punctum" marked with an arrow also seen in the tagRFP channel? One gets the impression that some of the puncta may be background, bleed-through, or artifacts due to cell varicosities.
• In Fig. 4C, the distinction between puncta in the primary or higher-order dendrites is not clear to me, and several puncta that I would have scored as primary are marked as higher-order.
• Related to this, in Fig. 4B are the two arrows meant to be white as in the top panel, or yellow as in the bottom panel?
• The main sax-1 phenotype is increased SAX-2 puncta in dauer, but the branch retraction defect is in post-dauers. How is this relevant to the phenotype?
• The number of SAX-2 puncta in sax-1 mutants decreases almost to normal in post dauers. Is there a correlation between the number of remaining branches and the number of SAX-2 puncta? That is, do the many wild-type animals with "excess" SAX-2 puncta also fail to retract branches?
• The control post-dauer data in Fig. 4F and 4H are identical (re-used data) but the corresponding control dauer data in Fig. 4F and 4G are different. What is going on here?
• Why are sample sizes so small for both strains in Fig. 4G compared to Fig. 4F and 4H?
• In Fig. 6C, why are the tagRFP (blue) puncta larger than the neurite? Aren't these meant to represent vesicles inside the surrounding neurite? One gets the impression that this is bleed-through from the GFP channel.
• In Fig. 6E and 6F, why are there no tagRFP (blue) puncta? Is CD8 not endocytosed at all if it lacks the nanobody sequence? One would expect the tagRFP (blue) signal to be the same in both strains and simply to lack yellow if the nanobody is not present.
• In Fig. 6E and 6H, why are there so many GFP (yellow) puncta outside the neuron? What are these structures and why are they absent in the strain with the nanobody?
• What is the large central blue structure in Fig. 6H - is this the soma? - and why are puncta in this region not counted?
• The authors report a decrease in endocytic events in sax-1, but qualitatively it looks like there are vastly more puncta inside the neuron in Fig. 6H than in 6G.
• minor: there is text reading "40-" in the bottom panel of Fig. 6H. It is visible when printed but not on screen - adjust levels in Photoshop to reveal it.
• Related to both Fig. 4 and Fig. 6, where does SAX-1 localize in IL2Q in dauer and post-dauer? Does its expression or localization change during branch retraction? Does it co-localize with SAX-2 or endocytic puncta?

Minor points:

1. At several points the authors emphasize the relationship of neurite remodeling to stress, e.g. Abstract and Discussion: "we adapted C. elegans IL2 sensory dendrites as a model [of...] stress-mediated dendrite pruning". It seems unnecessary and potentially misleading to treat this as a neuronal stress response. First, it conflates organismal and cellular stress - there is no reason to think that IL2 neurons are under cellular stress in dauer. In fact parasitic nematodes go through dauer-like stages as part of healthy development and probably have similar remodeling of IL2. Second, dendrite pruning occurs during dauer exit, which is the opposite of a stress response - it reflects a return to favorable conditions.
2. In Fig. 1A, C. elegans is shown going directly from L1 to dauer in response to unfavorable conditions, which is incorrect. Animals proceed through L2 (in many cases actually an alternative L2d pre-dauer) and then molt into dauer (an alternative L3 stage) after completing L2.
3. In Fig. 1B, please check if it is correct that hypodermis contacts the pharynx basement membrane as drawn. The schematic in the top panel makes it look like there is a single secondary branch and the quartenary branches are similar in length to the primary dendrite. The schematic in the bottom panel makes it look like the entire neuron is a small fraction of the length of the pharynx. Could these be drawn closer to scale?

Referee cross-commenting

I think we all touched on similar points. I wanted to follow up on Reviewer 3's comment, "Is the failure to eliminate branches an indication of incomplete dauer recovery? Do sax-1 mutants retain additional characteristics of dauer morphology in post dauer adults." I thought this was an excellent point. It made me wonder if that might explain why the defect is only seen in daf-7 and daf-2 mutant backgrounds - maybe these strains retain partial dauer traits even after exit. Is there a specific experiment that they could do? Did you have specific characteristics of dauer morphology in mind for them to check? (Ideally something in the nervous system that can be scored quantitatively.)

Significance

A major strength of this work is the pioneering use of a novel system to study neuronal branch retraction. C. elegans has provided a powerful model for studying how dendrite branches form, but much less attention has been paid to how excess neuronal branches are removed. The post-dauer remodeling of IL2Q neurons provides an exciting and dramatic physiological example to explore this question.

This paper is notable for taking the first steps towards developing this innovative model. It does exactly what is needed at the outset of a new exploration - a forward genetic screen to discover the main regulators of the process. Using a combination of classical and modern genetic approaches, the authors bootstrap their way to a sizeable list of factors and a solid understanding of the properties of this system, for example that retraction of higher vs lower order dendrites show different genetic requirements.

This text is a personal narrative from a writer who describes their experience and censorship and book banning. This all serves as a direct ,clear statement of the authors position on the issue.

Hopkins being uninvited from the school event because a student's parent pointed out that her books may be inappropriate, For middle school students. Having her books removed from all middle schools in the district and her being prohibited from speaking at one is unfair.

Reviewer #1 (Public review):

Summary:

The authors have studied how a virus (EMCV) uses its RNA (Type 2 IRES) to hijack the host's protein-making machinery. They use cryo-EM to extract structural information about the recruitment of viral Type 2 IRES to ribosomal pre-IC. The authors propose a novel interaction mechanism in which the EMCV Type 2 IRES mimics 28S rRNA and interacts with ribosomal proteins and initiator tRNA (tRNAi).

Strengths:

(1) Getting structural insights about the Type 2 IRES-based initiation is novel.

(2) The study allows a good comparison of other IRES-based initiation systems.

(3) The manuscript is well-written and clearly explains the background, methods, and results.

Weaknesses:

(1) The main weakness of the work is the low resolution of the structure. This limits the possibility of data interpretation at the molecular level.

However, despite the moderate resolution of the cryo-EM reconstructions, the model fits well into the density. The analysis of the EMCV IRES-48S PIC structure is thorough and includes meaningful comparisons to previously published structures (e.g., PDB IDs - 7QP6 and 7QP7). These comparisons showed that Map B1 represents a closed conformation, in contrast to Map A in the open state (Figure 2). Additionally, the proposed 28S rRNA mimicry strategy supported by structural superposition with the 80S ribosome and sequence similarity between the I domain of the IRES and the h38 region of 28S rRNA (Fig. 4) is well-justified.

(2) The lack of experimental validation of the functional importance of regions like the GNRA and RAAA loops is another limitation of this study.

(3) Minor modifications related to data processing and biochemical studies will further validate and strengthen the findings.

a) In the cryo-EM data section, the authors should include an image showing rejected particles during 2D classification. This would help readers understand why, despite having over 22k micrographs with sufficient particle distribution and good contrast, only a smaller number of particles were used in the final reconstruction. Additionally, employing map-sharpening tools such as Ewald sphere correction, Bayesian polishing, or reference-based motion correction might further improve the quality of the maps. Targeting high-resolution structures would be particularly informative.

b) The strategic modelling of different IRES domains into the density, particularly the domain into the region above the 40S head, is appreciable. However, providing the full RNA tertiary structure (RNAfold) of the EMCV IRES (nucleotides 280-905) would better explain the logic behind the model building and its molecular interpretation.

c) Although the authors compare their findings with other types of IRESs (Types 1, 3, and 4), there is no experimental validation of the functional importance of regions like the GNRA and RAAA loops. Including luciferase-based assays or mutational studies of these regions for validation of structural interpretations is strongly recommended.

Author response:

Reviewer #1 (Public review):

Summary:

The authors have studied how a virus (EMCV) uses its RNA (Type 2 IRES) to hijack the host's protein-making machinery. They use cryo-EM to extract structural information about the recruitment of viral Type 2 IRES to ribosomal pre-IC. The authors propose a novel interaction mechanism in which the EMCV Type 2 IRES mimics 28S rRNA and interacts with ribosomal proteins and initiator tRNA (tRNAi).

Strengths:

(1) Getting structural insights about the Type 2 IRES-based initiation is novel.

(2) The study allows a good comparison of other IRES-based initiation systems.

(3) The manuscript is well-written and clearly explains the background, methods, and results.

We thank Reviewer 1 for appreciating our efforts and finding structural insights about the type 2 IRES-based initiation presented in this study as novel.

Weaknesses:

(1) The main weakness of the work is the low resolution of the structure. This limits the possibility of data interpretation at the molecular level.

However, despite the moderate resolution of the cryo-EM reconstructions, the model fits well into the density. The analysis of the EMCV IRES-48S PIC structure is thorough and includes meaningful comparisons to previously published structures (e.g., PDB IDs - 7QP6 and 7QP7). These comparisons showed that Map B1 represents a closed conformation, in contrast to Map A in the open state (Figure 2). Additionally, the proposed 28S rRNA mimicry strategy supported by structural superposition with the 80S ribosome and sequence similarity between the I domain of the IRES and the h38 region of 28S rRNA (Fig. 4) is welljustified.

We agree that the low resolution of the map has compromised the data interpretation at the molecular level, and we thank the reviewer for appreciating our findings at this resolution. Due to the compromise in resolution, we have reported findings related to stretches or regions such as loops and stems, rather than individual nucleotides and interactions.  

(2) The lack of experimental validation of the functional importance of regions like the GNRA and RAAA loops is another limitation of this study.

We agree with the lack of any additional experiments other than Cryo-EM for probing the importance of regions such as GNRA and RAAA loops in this study. However, we have cited earlier reports that demonstrate the importance of these regions for overall IRES activity. The essentiality of RAAA loop for type 2 IRES was demonstrated in earlier report López de Quinto and Martínez-Salas, 1997 (Cited in manuscript). Further, the conservation of this loop across the type 2 IRES family adds to the importance of this loop (Manuscript Figure 6B). This loop and its flanking G-C stem are similar to h38 of 28S rRNA, and it appears that RAAA loop adopts a mimicry mechanism to interact with the 40S ribosomal protein- uS19, thus highlighting its importance for interaction with 40S. Experiments destabilising the G-C stem also compromise IRES activity, as shown in the case of FMDV IRES (Fernández et al 2011). Previous studies related to the mutation of the GNRA or GCGA loop in EMCV IRES have shown a deficiency in IRES activity (Roberts and Belsham, 1997; Robertson et al 1999), suggesting the importance of these regions in the viral IRES biology, and these reports are cited in the manuscript. Not only EMCV IRES, but mutation in the GUAA (representative of GNRA) loop of FMDV IRES also showed significant reduction in IRES activity (López de Quinto and Martínez-Salas, 1997). In our study, we observe that GCGA loop interacts with tRNA_i in EMCV IRES-48S PIC, thus implicating the importance of this loop. Moreover, incubation of FMDV IRES with 40S ribosomes has shown a decrease in SHAPE reactivity in domain 3 apex (position 170- 200 nucleotides) (Lozano et al 2018), which corresponds to EMCV IRES domain I apex. Further, we will attempt to address the concern of lack of experimental validation of GNRA and RAAA loops by performing biochemical assays.

(3) Minor modifications related to data processing and biochemical studies will further validate and strengthen the findings.

a) In the cryo-EM data section, the authors should include an image showing rejected particles during 2D classification. This would help readers understand why, despite having over 22k micrographs with sufficient particle distribution and good contrast, only a smaller number of particles were used in the final reconstruction. Additionally, employing mapsharpening tools such as Ewald sphere correction, Bayesian polishing, or reference-based motion correction might further improve the quality of the maps. Targeting high-resolution structures would be particularly informative.

We thank the reviewer for the suggestions, and we would employ suggested processes that may help improve the quality of the maps further. We will include image for rejected 2D classes in the revised manuscript. We agree with the Reviewer’s query related to the substantial number of micrographs and smaller number of particles for the final reconstruction. The total number of micrographs is the summation of multiple datasets, prepared and collected at various times. Among these, around 8000 micrographs have extremely poor particle number and distribution. As a result, the number of particles per micrograph is heterogeneous in the compiled dataset. We obtained only 237054 ‘good particles’ after multiple rounds of 2D & 3D classifications, and the final reconstruction has 28439 particles (~12%). This class was obtained after masked classification for IRES and ternary complex density. Hence, only the particles that show the best density for both IRES and ternary complex are used for reconstructing this map. Another set of particles that have only a portion of IRES and tRNA but NO density for eIF2 forms another map (26792 particles, 11.3%). Thus, we obtained a total of 55231 particles (23.3%) with IRES density.  

b) The strategic modelling of different IRES domains into the density, particularly the domain into the region above the 40S head, is appreciable. However, providing the full RNA tertiary structure (RNAfold) of the EMCV IRES (nucleotides 280-905) would better explain the logic behind the model building and its molecular interpretation.

We thank the reviewer for appreciating the modelling of the domain I apex in the cryo-EM density. We tried to predict the full tertiary structure of the IRES, however, inclusion of the full-length sequence from 280-905 gave models of extremely low confidence, and few domains do not abide by the secondary structure of EMCV IRES as reported in Duke et al 1992. Hence, we used individual domains of EMCV IRES and predicted the tertiary structure independent of other IRES domains. Furthermore, 3D models of FMDV IRES domains 2, 3, and 4 (corresponding to EMCV IRES domains- H, I, and J-K) were predicted from SHAPE reactivity values and RNAComposer server (Figure 3 in Lozano et al 2018). The predicted architecture of domain 3 apex (FMDV IRES) coincides with our I domain apex model (EMCV IRES).

c)  Although the authors compare their findings with other types of IRESs (Types 1, 3, and 4), there is no experimental validation of the functional importance of regions like the GNRA and RAAA loops. Including luciferase-based assays or mutational studies of these regions for validation of structural interpretations is strongly recommended.

We have discussed the possibility of how the other IRESs, such as type 1 and type 5 (Aichi virus), might use similar strategies as EMCV IRES to assemble the 48S PIC, given the similarity in the motif sequence and position across the viral IRESs. Like EMCV IRES, the type 1 IRES (e.g. Poliovirus, Coxsackie virus) also harbours the GNRA loop, preceded by a C-rich loop at its longest domain, known for long-range RNA-RNA interactions. The segment harbouring GNRA loop is highly conserved across the type 1 family of IRESs (Kim et al 2015).The Aichi viral IRES (type 5) harbours a GNRA loop in its longest domain, which is domain J. Deletion of the GNRA loop has compromised the IRES activity; however, substitution mutations in this region either elevated the IRES activity or it remained unaltered (Yu et al 2011). We have hypothesized that these IRESs (type 1 and type 5) might use the GNRA motifs in their longest domain (domain IV in type 1, and domain J in type 5) similar to that of EMCV IRES, where GNRA is present in the longest domain (I) and preceded by a C-rich loop. Thus, GNRA can potentially mediate long-range interactions with tRNA_i as all these IRESs require eIF2-ternary complex for the formation of 48S PIC. Parallelly, like EMCV IRES, type 1 and type 5 IRESs also have similar placement of GNRA motif-containing domain before the eIF4G-binding domain (domain J-K in EMCV IRES, domain V in poliovirus, domain K in Aichi virus). Hence, we suggest the possibility of a similar strategy by these IRESs to interact with tRNA_i during the formation of 48S PIC.  

Reviewer #2 (Public review):

Summary:

The field of protein translation has long sought the structure of a Type 2 Internal Ribosome Entry Site (IRES). In this work, Das and Hussain pair cryo-EM with algorithmic RNA structure prediction to present a structure of the Type 2 IRES found in Encephalomyocarditis virus (EMCV). Using medium to low resolution cryo-EM maps, they resolve the overall shape of a critical domain of this Type 2 IRES. They use algorithmic RNA prediction to model this domain onto their maps and attempt to explain previous results using this model.

Strengths:

(1) This study reveals a previously unknown/unseen binding modality used by IRESes: a direct interaction of the IRES with the initiator tRNA.

(2) Use of an IRES-associated factor to assemble and pull down an IRES bound to the small subunit of the ribosome from cellular extracts is innovative.

(3) Algorithmic modeling of RNA structure to complement medium to low resolution cryoEM maps, as employed here, can be implemented for other RNA structures.

We thank Reviewer 2 for positive and encouraging comments on our work, appreciating our ‘innovative’ approach of using IRES-associated factor to assemble and pull down IRES-bound ribosomal complex.  

Weaknesses:

(1) Maps at the resolution presented prevent unambiguous modelling of the EMCV-IRES. This, combined with the lack of any biochemical data, calls into question any inferences made at the level of individual nucleotides, such as the GNRA loop and CAAA loop (Figure 4).

We understand the concerns raised by the reviewer related to the resolution of the EMCV IRES-48S PIC map. However, we would like to mention that we refrained from commenting on individual nucleotides or molecular interactions in the manuscript. Instead, we discuss about loops, RNA stretches or motifs that could be inferred with more confidence as shown in Manuscript Figure 4. The EMCV IRES can directly interact with the 40S ribosome using its domain H and I (Chamond et al 2014), however, the details this interaction was unknown. We observe that the CAAA loop of domain I apex interacts with 40S ribosome based on the placement of portion of domain I in the cryo-EM map. This is also reflected in the earlier reported SHAPE data (Supplementary figures 2, and 8 in Chamond et al 2014), where a decrease in reactivity is evident in the presence of 40S ribosome. In addition, incubation of EMCV IRES with rabbit reticulocyte lysate (RRL) offered protection to domain I apex regions, which included the CAAA loop (Figure 4b in Maloney and Joseph, 2024).

Furthermore, this decrease in SHAPE reactivity pattern is also evident for FMDV IRES domain 3 apex (like domain I in EMCV IRES) in the presence of 40S ribosome (Lozano et al 2018).

Thus, these studies are consistent with the placement of IRES model in the cryo-EM map.

We aim to improve the resolution of the maps for better clarity and add biochemical experiments to justify the possible interactions.

(2) The EMCV IRES contains an upstream AUG at position 826, where the PIC can assemble (Pestova et al 1996; PMID 8943341). It is unclear if this start codon was mutated in this study. If it were not mutated, placement of AUG-834 over AUG-826 in the P-site is unexplained.

We thank the reviewer for bringing up this point, as we missed mentioning this in the manuscript. The EMCV IRES does not require scanning and directly positions the AUG-834 at the P site (Pestova et al 1996). In Pestova et al 1996, the intensity of the toeprint at AUG-834 is much more intense than that of AUG-826. Further, AUG-834 lies in the Kozak context, whereas AUG-826 has a poor Kozak context. Furthermore, the synthesis of the polypeptide requires placement of AUG-834 at the P site. In our cryo-EM map, we observed that the tRNA_i is in a P_IN state, which indicates the recognition of the start codon, and we reasoned that it is more likely that AUG-834 is placed at the P site than AUG-826. We will mention this in the revised manuscript, as we had NOT mutated AUG-826.

(3) The claims the authors make about (i) the general overall shape and binding site of the IRES, (ii) its gross interaction with the two ribosomal proteins, (iii) the P-in state of the 48S, (iv) the rearrangement of the ternary complex are all warranted. Their claims about individual nucleotides or smaller stretches of the IRES-without any supporting biochemical data-is not warranted by the data.

We thank the reviewer for warranting major claims, and we wish to make further improvements to support our assessment of small stretches and individual nucleotides.

Reviewer #3 (Public review):

Summary:

Type II IRES, such as those from encephalomyocarditis virus (EMCV) and foot-and-mouth disease virus (FMDV), mediate cap-independent translation initiation by using the full complement of eukaryotic initiation factors (eIFs), except the cap-binding protein eIF4E. The molecular details of how IRES type II interacts with the ribosome and initiation factors to promote recruitment have remained unclear. Das and Hussain used cryo-electron microscopy to determine the structure of a translation initiation complex assembled on the EMCV IRES. The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Strengths:

The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Weaknesses:

While this reviewer acknowledges the technical challenges inherent in determining the structure of such a highly flexible complex, the overall resolution remains insufficient to fully support the authors' conclusions, particularly given that cryo-EM is the sole experimental approach presented in the manuscript.

The study is biologically significant; however, the authors should improve the resolution or include complementary biochemical validation.

We thank Reviewer 3 for acknowledging the technical challenges in this study and finding our study biologically significant. We understand the concerns related to low resolution and the requirement of complementary biochemical validation for our reported observations and interpretations in the manuscript. We are attempting to improve the resolution and complement the interpretations with biochemical experiments.

Reviewer #1 (Public review):

Summary:

Praegel et al. explore the differences in learning an auditory discrimination task between adolescent and adult mice. Using freely-moving (Educage) and head-fixed paradigms, they compare behavioral performance and neuronal responses over the course of learning. The mice were initially trained for seven days on an easy pure frequency tone Go/No-go task (frequency difference of one octave), followed by seven days of a harder version (frequency difference of 0.25 octave). While adolescents and adults showed similar performance on the easy task, adults performed significantly better on the harder task. Quantifying the lick bias of both groups, the authors then argue that the difference in performance is not due to a difference in perception, but rather to a difference in cognitive control. The authors then used neuropixel recordings across 4 auditory cortical regions to quantify the neuronal activity related to the behavior. At the single cell level, the data shows earlier stimulus-related discrimination for adults compared to adolescents in both the easy and hard tasks. At the neuronal population level, adults displayed a higher decoding accuracy and lower onset latency in the hard task as compared to adolescents. Such differences were not only due to learning, but also to age as concluded from recordings in novice mice. After learning, neuronal tuning properties had changed in adults but not in adolescent. Overall, the differences between adolescent and adult neuronal data correlates with the behavior results in showing that learning a difficult task is more challenging for younger mice.

Strengths:

The behavioral task is well designed, with the comparison of easy and difficult tasks allowing for a refined conclusion regarding learning across age. The experiments with optogenetics and novice mice are completing the research question in a convincing way.

The analysis, including the systematic comparison of task performance across the two age groups, is most interesting and reveals differences in learning (or learning strategies?) that are compelling.

Neuronal recording during both behavioral training and passive sound exposure is particularly powerful, and allows interesting conclusions.

Weaknesses:

The weaknesses listed by this reviewer were addressed by adequate revisions.

Reviewer #2 (Public review):

Summary:

The authors aimed to find out how and how well adult and adolescent mice discriminate tones of different frequencies and whether there are differences in processing at the level of the auditory cortex that might explain differences in behavior between the two groups. Adolescent mice were found to be worse at sound frequency discrimination than adult mice. The performance difference between the groups was most pronounced when the sounds are close in frequency and thus difficult to distinguish and could, at least in part, be attributed to the younger mice' inability to withhold licking in no-go trials. By recording the activity of individual neurons in the auditory cortex when mice performed the task or were passively listening as well as in untrained mice the authors identified differences in the way that the adult and adolescent brains encode sounds and the animals' choice that could potentially contribute to the differences in behavior.

Strengths:

The study combines behavioural testing in freely-moving and head-fixed mice, optogenetic manipulation and high density electrophysiological recordings in behaving mice to address important open questions about age differences in sound-guided behavior and sound representation in the auditory cortex.

Weaknesses:

The weaknesses listed by this reviewer were addressed by adequate revisions.

Author response:

The following is the authors’ response to the previous reviews.

Reviewer #1 (Public review):

Summary:

Praegel et al. explore the differences in learning an auditory discrimination task between adolescent and adult mice. Using freely-moving (Educage) and head-fixed paradigms, they compare behavioral performance and neuronal responses over the course of learning. The mice were initially trained for seven days on an easy pure frequency tone Go/No-go task (frequency difference of one octave), followed by seven days of a harder version (frequency difference of 0.25 octave). While adolescents and adults showed similar performance on the easy task, adults performed significantly better on the harder task. Quantifying the lick bias of both groups, the authors then argue that the difference in performance is not due to a difference in perception, but rather to a difference in cognitive control. The authors then used neuropixel recordings across 4 auditory cortical regions to quantify the neuronal activity related to the behavior. At the single cell level, the data shows earlier stimulus-related discrimination for adults compared to adolescents in both the easy and hard tasks. At the neuronal population level, adults displayed a higher decoding accuracy and lower onset latency in the hard task as compared to adolescents. Such differences were not only due to learning, but also to age as concluded from recordings in novice mice. After learning, neuronal tuning properties had changed in adults but not in adolescent. Overall, the differences between adolescent and adult neuronal data correlates with the behavior results in showing that learning a difficult task is more challenging for younger mice.

Strengths:

The behavioral task is well designed, with the comparison of easy and difficult tasks allowing for a refined conclusion regarding learning across age. The experiments with optogenetics and novice mice are completing the research question in a convincing way.

The analysis, including the systematic comparison of task performance across the two age groups, is most interesting, and reveals differences in learning (or learning strategies?) that are compelling.

Neuronal recording during both behavioral training and passive sound exposure is particularly powerful, and allows interesting conclusions.

Weaknesses:

The presentation of the paper must be strengthened. Inconsistencies, missing information or confusing descriptions should be fixed.

We have carefully re-read the manuscript and reviewed it for inconsistencies. We made several corrections in the figures. For example, we removed redundant lines from violin plots and statistics, applied consistent labels, matched y- and x-limits of graphics, and adjusted labels. We also clarified descriptions of some experiment by adding explanations to the text.

The recording electrodes cover regions in the primary and secondary cortices. It is well known that these two regions process sounds quite differently (for example, one has tonotopy, the other not), and separating recordings from both regions is important to conclude anything about sound representations. The authors show that the conclusions are the same across regions for Figure 4, but is it also the case for the subsequent analysis? Comparing to the original manuscript, the authors have now done the analysis for AuDp and AUDv separately, and say that the differences are similar in both regions. The data however shows that this is not the case (Fig S7). And even if it were the case, how would it compatible with the published literature?

To address this and previous concerns about regional differences, the manuscript now includes 4 figures (4-1, 4-3, 6-2, 7-1) and 5 supplemental tables (3,4, 5, 6, 8) that explicitly compare results across brain regions.

Following the reviewer’s request for subsequent analysis, we now added a new supplemental figure (Fig. S6-2) and two new supplementary tables (Tables S5, S6). We show that similar to expert mice (supplementary Table 3, and supplementary Table 4), the firing properties of adolescent and adult novice mice differ across auditory subregions (supplementary Table 5). We also show that the different auditory subregions have different firing properties (supplementary Table 6). With respect to task engagement, we show that (similar to Fig. S4-2) the neuronal discriminability in different auditory subregions is similar in both novice and expert mice (Fig. S6-2).

Following the comment on Fig. S7-1, we made three changes to the revised manuscript. First, we now highlight that the differences firing properties between adolescent and adult neurons in AUDp and AUDv were distinct, but not significantly different within age-group comparisons. Second, we clearly state that the learning related changes in the measured parameters are different between AUDp and AUDv. Note, however, the greater changes in adult neurons after learning remains consistent between AUDp and AUDv. Third, we softened our original claim but still highlighted the stronger learning-induced plasticity in adults.

Regarding the concern that different regions should show different patterns due to their known differences (e.g. tonotopy). Of course we agree that different areas differ functionally (as shown in our own previous work and here as well). However, it is still plausible, and biologically reasonable, that developmental changes may proceed in a similar direction across different areas, even if their baseline coding properties differ.

Reviewer #2 (Public review):

Summary:

The authors aimed to find out how and how well adult and adolescent mice discriminate tones of different frequencies and whether there are differences in processing at the level of the auditory cortex that might explain differences in behavior between the two groups. Adolescent mice were found to be worse at sound frequency discrimination than adult mice. The performance difference between the groups was most pronounced when the sounds are close in frequency and thus difficult to distinguish and could, at least in part, be attributed to the younger mice' inability to withhold licking in no-go trials. By recording the activity of individual neurons in the auditory cortex when mice performed the task or were passively listening as well as in untrained mice the authors identified differences in the way that the adult and adolescent brains encode sounds and the animals' choice that could potentially contribute to the differences in behavior.

Strengths:

The study combines behavioural testing in freely-moving and head-fixed mice, optogenetic manipulation and high density electrophysiological recordings in behaving mice to address important open questions about age differences in sound-guided behavior and sound representation in the auditory cortex.

Weaknesses:

For some of the analyses that the authors conducted it is unclear what the rationale behind them is and, consequently, what conclusion we can draw from them.

We have carefully re-read the manuscript and reviewed it for analyses that lacked a clear rationale or conclusion. To address this, we have made several changes to clarify the reasoning and strengthen the interpretation of the results.

Reviewer #1 (Recommendations for the authors):

It would have helped if the authors had highlighted the changes they made to the manuscript compared to the original version - especially since many replies to the reviewers' comments were as vague as "...we fixed some of the wording so it adheres to the data shown", or "we refined our interpretation", without further details.

The revised version has improved substantially, and the main claims have been discussed in a more objective way. Important new analyses have been added to allow for a refined interpretation of the results. However, the presentation of the data could still be strengthened significantly (in response to comment A from last review).

We apologize for the lack of detail in some of our previous responses. Our intention was to keep the replies concise, assuming that the side-by-side version with tracked changes would make the edits sufficiently clear. However, we understand the need for greater transparency. Thus, below we provide the following five lists describing the major changes: (1) List of specific reviewer recommendations, (2) list of corrections in figures, (3) list of clarity issues, (4) list of fixed mistakes, (5) list of new figures. We hope this breakdown makes the revisions clearer and more accessible.

List of specific reviewer recommendations:

l.108 mentions a significant change in the vertical line of Fig 1F - Could this significance be indicated and quantified in the figure?

We quantified and indicated the significance of the vertical line in Fig. 1f and Fig. 1i.

Fig.1G - the thick and thin lines should be defined, as well as the grey and white dots (same values for adolescents, not for adults).

(a) We removed the thin inner lines from the violin plot. We define the bar (thick line) of the violin plot in an additional sentence in the methods section under data analysis (LL820-823). b) We adjusted the marker outlines in the adult data (Fig. 1G).

the figure axis legends should be consistent (trails in Fig D vs # trails in Fig 1F)

We adjusted the axis legend to # trials in Fig. 1D.

l.110: is d' always calculated based on the 100 last trials of a session, or is it just for Figure 1F? -etc...

d’ is always calculated based on the last 100 trials. To clarify this, we added a description in the methods section (L830).

List of corrections in the figures:

(1) We removed the internal lines from violin plots in throughout Fig. 1-7.

(2) We removed the underline of the statistics throughout Fig. 1-7.

(3) We consistently applied ‘adolescent’ and ‘adult’ figure labels and titles with lowercase letters throughout Fig. 1-7.

(4) We applied consistent labelling of ‘time (ms)’ throughout Fig. 1-7.

(5) We matched the size of dashed lines throughout Fig. 1-6.

(6) We adjusted the x-label of Fig. 1d, Fig. S-1-1 a, Fig. 3c, Fig. 3h-i, Fig, 4d to ‘# trials’.

(7) We removed the x-label of ‘Experimental Group’ from Fig. 1 to enhance consistency with other figures.

(8) We removed misaligned dots from the violin plots in Fig. 1g, Fig. 2f, Fig. 3f,g.

(9) We corrected the plot in Fig. S1-1b.

(10) We adjusted the y-limits of Fig. S1-1c to be consistent with Fig. S1-1d,e.

(11) We adjusted the x-labels and y-labels of Fig, 2, Fig. S3-1, Fig, S3-2 and Fig. 3b to ‘freq. (kHz)’.

(12) We added the age of adolescent and adult mice to the schematic timeline in Fig. 2a.

(13) We added a label of the reinforcement delay to the schematic trial structure in Fig. 3b.

(14) We added within-group statistics to Fig. 3e and the figure legend.

(15) We adjusted the x-label of Fig. 3d to ‘# sessions’.

(16) We adjusted the x-label of Fig. 3d and Fig. S3-1b to ‘# licks’.

(17) We changed the y-label in Fig. S3-1a, and Fig. S3-2d, e to ‘lick ratio’ to avoid confusion with the lick rate (Hz) that was calculated in Fig. 4 and Fig. 6.

(18) We replaced the titles ‘CAMKII’ with ‘dTomato’ in Fig. S3-2 to correctly highlight that both the experimental and control injection were CAMKII injections.

(19) We adjusted the x-labels and y-labels of Fig, 2, Fig. S3-1, Fig, S3-2 and Fig. 3b to ‘freq. (kHz)’.

(20) We adjusted the y-label of Fig. S4-1c to ‘# neurons’.

(21) We matched the x-ticks in Fig. 4e,f.

(22) We matched the x-ticks in Fig. 6d-g.

(23) We changed the x-label in Fig. 4g, S4-2 and S6-2 to ‘duration (ms)’ to match the figure label with the manuscript.

(24) We consistently label ‘Hit’, ‘Miss’, ‘FA’ and ‘CR’ with capital letters in Fig. 4d-e.

(25) We replaced the double figure label ‘C.’ in Fig. S4-2 with ‘D.’.

(26) We adjusted the dot-size in Fig. 5 to be equal for all graphs.

(27) We added ticks to the experimental timeline in Fig. 6a.

(28) We corrected the y-label in Fig.7c. Now it correctly reflects 5 attenuations from 72-32 dB SPL.

(29) We matched the y-label of Fig. 7e-h and Fig. S7-1.

List of clarity issues:

(1) We replaced the term ‘lower response bias’ with ‘higher lick bias’ (L24) to accurately describe the more negative (lower) criterion-bias, which highlights a higher tendency to lick.

(2) We replaced the term ‘response bias’ with ‘lick bias’ to consistently describe the calculated criterion-bias (L24, L149, L164, L455, L456, L468).

(3) We clarify that the age-related differences were ‘more pronounced’ instead of simply ‘higher’ to accurately reflect not simply the increase in adolescent lick-bias, but also the decrease in adult lick-bias (L31).

(4) We clarified that adolescent sound representations are not merely ’distinct’, but ‘not fully mature’ in L83.

(5) We clarified in L180 that the impulsive responses we observed in adolescent mice could be related to being ‘less impacted by punishments’.

(6) We clarified the differences in firing properties of auditory sub-regions analyzed in Supplementary Table 3 (L287-295).

(7) We explained and clarified the reference to Fig. 3j (LL252-253).

(8) We added statistics to Fig.S4-2 to support our claim that there are no differences in the onset-latency, duration of discriminability and maximal discriminability between different sub-regions within age-groups (LL 314-315).

(9) We expanded our explanation of the results in Table 3 (LL370-379).

(10) We separated the reference to Fig. 6b and Fig. 6c to clarify their meaning (LL358-361).

(11) We clarified the differences in basic firing properties during the FRA protocol in Fig. 7 (LL409-418).

(12) We expanded our explanation of the differences of the learning related firing properties in AUDp and AUDv of Fig. S7-1 (LL426-433).

(13) We changed the term ‘plasticity profiles’ to ‘learning related plasticity’ to further clarify our limitation that L5/6 and L2/3 may exhibit distinct learning related changes (L496).

(14) We changed the term ‘sluggish’ (L481) to ‘delayed’ to more precisely explain differences between adolescent and adult tuning properties.

(15) We clarified that the running d’ was calculated in bins of 25 trials, instead of ‘the last 25 trials’ (LL845-846).

List of fixed mistakes:

(1) We corrected and matched the age to more accurately reflect the age mice were recorded (P37-42 and P77-82).

(2) We corrected the attenuation range from 72-42 to 72-32 dB SPL to correctly reflect the 5 attenuations used in the protocol.

(3) We corrected the number of channels shown in the voltage trace from 10 to 11 (Fig. S4-1a)

(4) We corrected the number of neurons recorded in novice adolescent mice in the legend of Fig. 6 from 140 to 130 (Fig. 6b).

(5) We removed redundant, or double brackets, commas, dots, and semi-colons in the figure legends.

(6) We corrected the LME statistics Table 2.

List of new figures and tables:

(1) We added a new supplementary figure to accompany Figure 6. Specifically, Fig. S6-2, shows the interaction of the three measured discriminability properties (onset delay, duration of discriminability, and maximal discriminability) in novice compared to expert mice in the easy and hard task (Go compared to No Go). The figure compares the different auditory sub-regions (similar to Fig. S4-2). We show that the discriminability properties within different groups is not significantly different among the four different sub-regions.

(2) Supplementary Table 5: We compared the firing properties in different auditory subregions in novice mice, and found (similar to expert mice) that the firing properties differ between adult and adolescent mice across the four different sub-regions.

(3) Supplementary Table 6: We compared the firing properties between different subregions, separately for adolescent and adult novice mice. Similar to expert mice, we found that different auditory subregions differ in their auditory firing properties.

Reviewer #2 (Recommendations for the authors):

The authors largely addressed my suggestions.

Comparing hit vs correct rejection trials in the population decoding analysis (L313-314): The authors acknowledge that comparing these two trial types conflates choice and stimulus decoding but I am not convinced that the changes to the manuscript text make this clear enough to the reader.

Thank you for pointing this out. We have made additional revisions to clarify this, and other issues more explicitly, as follows:

(1) We have expanded the explanation of how our population decoding analysis conflates stimulus and choice, and we acknowledge the limitations of this approach in the Abstract (L28), the Results section (L324-326, LL367-370) and the Discussion (LL516-519).

(2) We replaced the analysis of impulsivity on the head-fixed task. Instead of analyzing all it is, we focus only on ITIs following FA trials (Fig. S3-1c,d). This is more consistent with the analysis in the Educage (Fig. S2-1), where we show that adolescents exhibit increased impulsivity after FA trials. We found a similar result for ITIs following FA trials in the head-fixed task.

(3) To provide complementary insight, we now further justify our use of the Fisher separation metric alongside decoding accuracy in Figure 5, with a clearer rationale provided in LL343-345

(4) We also clarified our reasoning for focusing on 62 dB SPL in the FRA-based analysis in LL400-403.

Author response:

We thank all three anonymous reviewers for their thoughtful evaluations of our manuscript and for recognizing the conceptual advance in combining agent-based behavioral simulations with systems neuroscience models. We are especially encouraged by the acknowledgement of the framework’s potential to support simulation of neural control of individual animal behavior in realistic sensory environments.

Below, we respond to each reviewer’s public comments in turn. Throughout, we have aimed to clarify our rationale for modeling choices, acknowledge limitations, and outline concrete steps for improvement in the revised manuscript.

Furthermore, the call for a better description of the model implementation as voiced by all three reviewers and additional requests from community members has prompted us to formulate a separate technically detailed description of the publicly available larvaworld software package as well as of the readily implemented models in form of a preprint paper (Sakagiannis et al., 2025, bioRxiv, DOI: https://doi.org/10.1101/2025.06.15.659765).

Reviewer #1:

We are happy to read that this reviewer considers the proposed behavioral architecture ‘a significant step forward in the field’, and that she/he recognizes the strengths of our work in the modular and hierarchical approach that provides connections to influential theories of motor control in the brain, in the experimental evidence it is based on, and in the valuable abstractions that we have chosen for the larval behavioral modeling.

The reviewer raises important points about the simplifications we have made, both conceptually and in the specific implementation of larval behaviors. Our main goal in this study is to introduce a conceptual framework that integrates agent-based modeling with systems neuroscience models in a modular fashion. To serve this purpose, we aimed for a minimal yet representative implementation at the motor layer of the architecture, calibrated to larval locomotion kinematics. This choice enables efficient simulation while allowing us to test top-down modulation and adaptive mechanisms in higher layers without the computational overhead of a full neuromechanical model. In addition to chemotaxis, we have recently used this simplified approach to model thermotaxis in larvae (Kafle et al., 2025, iScience, DOI: https://doi.org/10.1016/j.isci.2025.112809).

The reviewer notes the absence of explicit segmental neuromuscular control or central pattern generators (CPGs). We deliberately abstracted from these mechanisms, representing the larval body as two segments with basic kinematic control, to focus on reproducing overall locomotor patterns. This bisegmental simplification, which we illustrate in Supplemental Video “Bisegmental larva-body simplification”, retains the behavioral features relevant to our current aims. However, the modular structure of the framework means that more detailed neuromechanical models—incorporating CPG dynamics or connectome-derived circuit models—can be integrated in future work without altering the architecture as a whole.

We fully agree that real neural circuits are more complex than a strict subsumption architecture implies. In the Drosophila larva, there is clear evidence for ascending sensory feedback from the motor periphery to premotor and higher brain circuits, as well as neuromodulatory influences. These add layers of complexity beyond the predominantly descending control in our present model. At the same time, both larval and adult connectome data show that across-level descending and ascending connections are sparse compared to the dense within-layer connectivity. We see value in casting our model as a hierarchical control system precisely to make the strengths and limitations of such an abstraction explicit. The revised manuscript will include further discussion of these points.

In summary, our design choices reflect a trade-off: by limiting the biological detail in the lower layers, we gain computational efficiency and maintain a clear modular structure that can host models at different levels of abstraction. This ensures that the architecture remains both a tool for immediate behavioral simulation and a scaffold for integrating richer neural and biomechanical models as they become available.

Reviewer #2:

We thank the reviewer for recognizing the novelty of our locomotory model, particularly the implementation of peristaltic strides based on our new analyses of empirical larval tracks, and for providing constructive feedback that will help us improve the manuscript.

The reviewer highlights the need for clearer explanations of the chemotaxis and odor preference modules. We expand these sections in the revised manuscript with more explicit descriptions of model structure, parameterization, and calibration. As mentioned above, we have also prepared a separate preprint dedicated to the larvaworld Python package, which contains detailed implementation notes and hands-on tutorials that allow users to adapt or extend individual modules.

Regarding the comparison to empirical behavior in chemotaxis, our present analysis is indeed primarily qualitative. However, we would like to emphasize that the temporal profile of odor concentration at the larval head in our simulations matches that measured in Gomez-Marin et al. (Nature Comm., 2011, DOI: https://doi.org/10.1038/ncomms1455) using only one additional free parameter, while all parameters of the basic locomotory model had been fitted to a separate exploration dataset before and were kept fixed in the chemotaxis experiments. In addition to the simulation of chemotaxis in the present paper, we recently used larvaworld in a practical model application to estimate a species-specific parameter of thermotaxis from experiments across different drosophilids (Kafle et al., 2025, iScience, DOI: https://doi.org/10.1016/j.isci.2025.112809).

The preference index in our simulations was computed using the same definition as in the established experimental group assay for larval memory retention, enabling a direct quantitative comparison between simulated and empirical results. Variability in the simulated outcomes arose naturally from inter-individual differences in body length and locomotory parameters, derived from real larval measurements, as well as from the random initial orientation of each individual in the arena. These factors contributed to variation in individual tracks and ultimately produced preference index values that closely matched those observed experimentally. In the revised manuscript, we also discuss handedness, as highlighted by the reviewer, as another meaningful expression of inter-individual variability in Drosophila larvae and insects more generally.

Finally, we acknowledge the reviewer’s concern about the scalability and broader applicability of the model. While the present paper focuses on three specific behavioral paradigms (exploration, chemotaxis, odor preference), the modular structure of the architecture is designed for flexibility: modules at any layer can be exchanged for more detailed or alternative implementations, and new sensory modalities or behaviors can be integrated without redesigning the system. The larvaworld package, associated codebase, and documentation are openly available to encourage adoption and adaptation by the larval research community.

Reviewer #3:

This public review provides an excellent account of our central aim to build an easily configurable, well-documented platform for organism-scale behavioral simulation and we are happy to read that the reviewer considers this an excellent goal.

We thank the reviewer for her/his account of our well-organized code using contemporary Python tooling. We are currently further improving code readability and code documentation, and we will release a new version of the larvaworld Python package. We further agree with the reviewer’s assessment that understanding the model calibration currently requires reading of the appendix. For the revised manuscript we thus aim at improving our description of all calibration and modeling steps along the way. We will also make sure to improve the description of the experimental datasets used for calibration.

We recognize that our description of the paper’s scientific contribution could be clearer. In revision, we will sharpen the Introduction and Discussion to highlight our main contributions:

(1) Promoting a shift from isolated neural circuit modeling to integrated agent-based simulations in realistic environments.

(2) Proposing the layered behavioral architecture, adopting the subsumption paradigm for modular integration.

(3) Providing the larvaworld software as a ready-to-use, extensible modeling platform.

(4) Implementing an empirically calibrated locomotory model and demonstrating its integration with navigation and learning modules in replicated behavioral paradigms.

We agree with the reviewer that the next challenge is to integrate the empirically based behavioral simulations presented here with functional brain models capable of reproducing or predicting experimental findings at the level of cellular neurophysiology, including the effects of cell-type-specific manipulations such as gene knock-down or optogenetic activation/inhibition. However, based on our experience with systems-level modeling, we deliberately invested in behavioral simulation because functional models of the nervous system—including our own—often lack translation into simulated agent behavior. In many cases, model output is limited to one or more variables that can at best be interpreted as a behavioral bias, and most often represents an “average animal” that fails to capture inter-individual differences. By linking our spiking mushroom body model to behavioral simulations in a group of individual agents during memory retention tests (Figure 6C,D), we were able to achieve a first successful direct comparison between simulated and experimental behavior metrics—in this case, the behavioral preference index reported in Jürgensen et al. (iScience, 2024, DOI: https://doi.org/10.1016/j.isci.2023.108640).

Finally, we reiterate that the layered behavioral architecture is designed to promote a modular modeling paradigm. Our adoption of a subsumption architecture does not conflict with the concept of behavioral primitives; on the contrary, the notion that such primitives follow (semi-)autonomous motor programs and can be combined into more complex behaviors was the starting point for our implementation of the architecture in the fly larva. In our view, a genuinely contradictory paradigm for neural control of behavior would require a non-modular, strictly non-hierarchical organization of the nervous system and, by extension, of behavioral control.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review)

Comment 

Koonce et al. have generated a web-based visualization tool for exploring C. elegans neuronal morphology, contact area between neurons, and synaptic connectivity data. Here, the authors integrate volumetric segmentation of neurons and visualization of contact area patterns of individual neurons generated from Diffusion Condensation and C-PHATE embedding based on previous work from adult volumetric electron microscopy (vEM) data, extended to available vEM data for earlier developmental stages, which effectively summarizes modularity within the collated C. elegans contactomes to date. Overall, NeuroSC's relative ease of use for generating visualizations, its ability to quickly toggle between developmental stages, and its integration of a concise visualization of individual neurons' contact patterns strengthen its utility.

We thank that reviewer for this positive assessment of our work.

Comment

NeuroSC provides an accessible and convenient platform. However, many of the characteristics of NeuroSC overlap with that of an existing tool for visualizing connectomics data, Neuroglancer, which is a widely-used and shared platform with data from other organisms. The authors do not make clear their motivation for generating this new tool rather than building on a system that has already collated previous connectomics data. Although the field will benefit from any tool that collates connectomics data and makes it more accessible and user-friendly, such a tool is only useful if it is kept up-to-date, and if data formatting for submitting electron microscopy data to be added to the tool is made clear. It is unclear from this manuscript whether NeuroSC will be updated with recently published and future C. elegans connectomes, or how additional datasets can be submitted to be added in the future.

We have added new language to more explicitly state the motivations for developing NeuroSC (Introduction, lines 98-111, and discussion lines 375-384). In a new discussion section, we also include comparisons of the features of NeuroSC with other existing tools, like Neuroglancer and Webknossos, (lines 393-417).

Briefly, the functional features of NeuroSC are substantially different (and do not exist) in other web-based tools for navigating EM datasets, including NeuroGlancer. This is because the intended use of NeuroSC is substantially different (and purposefully synergistic) to the intended use, and tools available, in NeuroGlancer. 

NeuroGlancer is a versatile tool designed primarily for web-based visualizations and sharing of large EM datasets. NeuroSC was not designed to enable this type of access to the primary EM data (purposefully done because these features were already available through tools like NeuroGlancer). 

Instead, the explicit goal of NeuroSC is to provide a platform specifically optimized for examining neuronal relationships across connectomic datasets. NeuroSC builds on the segmentations emerging from programs like NeuroGlancer, but the tools are tailored to explore relationships such as contact profiles in the context of neuronal morphologies and synaptic positions, and across datasets that represent different animals or different developmental stages. 

To achieve this, all datasets in NeuroSC were optimized to facilitate comparisons across different connectomes of segmented neuronal features, including: 1) alignment of the neurons that are compared upon the display of the segmentations; 2) synchronization of the 3D windows; 3) implementation of a ‘universal color code’ across datasets for each neuron and relationship for easy visual comparisons; 4) use of the specific neuronal names to label instances of the same cells across all available datasets. The use of precise neuronal names among separate data sets allows integration of these objects with other catalogued datasets, including genomic and neuronal activity profiles.

The formatting and display of the datasets used in NeuroSC was accompanied by the development of new tools including: 1) Rendering of the contact profiles of all neurons in the context of the morphology of the cell and the synapses and 2) C-PHATE diagrams to inspect multidimensional relationship hierarchies based on these contact profiles. In NeuroSC, C-PHATEs can be navigated and compared across multiple stages of development while visualizing neuronal reconstructions, allowing users to compare neuronal relationships across individual datasets.

We agree with the reviewer that these tools are most useful when integrated. With that intention in mind, we designed NeuroSC as a series of modular, open-source tools that could be integrated into other programs, including Neuroglancer. In that sense our intent was not to produce another free-standing tool, but a set of tools that, if useful, could be integrated to other existing web-based connectomic resources to enhance the user experience of navigating complex EM datasets and draw biological meaning from the relationships between the neurons. Additionally, we intentionally designed NeuroSC to enable the ability to integrate new methods of understanding neuron relationships as they arise. We have dedicated a more detailed section to the discussion (lines 369- 417) to better convey this intention and directly address the unique abilities of NeuroSC as a complementary tool to the powerful existing tools, including Neuroglancer.

Comment

The interface for visualizing contacts and synapses would be improved with better user access to the quantitative underlying data. When contact areas or synapses are added to the viewer, adding statistics on the magnitude of the contact area, the number of synapses, and the rank of these values among the neuron's top connections, would make the viewer more useful for hypothesis generation. Furthermore, synapses are currently listed individually, with names that are not very legible to the web user. Grouping them by pre- and postsynaptic neurons and linking these groups across developmental stages would also be an improvement.

[what do they even mean by linking?]

We thank the reviewer for this insightful comment and have implemented several improvements to address these suggestions. Specifically, we have added new features to enhance user access to quantitative data within the NeuroDevSCAN viewer:

Cell, Patch, and Synapse Statistics: Users can now see a statistics panel when clicking on a rendered neuron, contact patch, or a synapse. These panels provide the following information, respectively, and are highlighted in lines 303-315):

Cell Stats: Click on a cell rendering to show cell stats which displays the total volume and surface area of the selected neuron within the defined neuropil area of our datasets (see Methods). 

Contact Stats: Click on a patch rendering to show ‘contact stats’. This pop up displays quantifications of the selected contact relationship. Rank compares the summed surface area of contacts ("patches") between these two neurons relative to all other contact relationships for the primary neuron for the cell and the whole nerve ring. A rank of 1, for example, means this neuron pair shares the largest contact surface area of the examined relationship. “Total surface area” is displayed in nanometers, and is the summed surface area of all patches of this identity. Contact percentages are presented in two ways: (1) as the proportion of the primary cell's total surface area occupied by the contact in question, and (2) as the proportion of the total surface area of the nerve ring occupied by that same contact. (Showcased in figure S5). 

Synapse Stats: A click on a synapse rendering now shows ‘synapse stats’, which displays the number of synapses of the selected identity within the primary neuron, including any polyadic synapse combinations involving the primary neurons. (Showcased in figure S7).

(1) Grouping and Readability Improvements: While individual synapses are still visualized, their display has been improved for legibility. We have condensed the lengthy naming scheme to improve clarity and codified the synapse type by using superscript letters C, E, U to represent chemical, electrical and undefined synapses, respectively. This is explained and shown in figure S7, we added arrows to indicate the directionality of presumed information flow at each synapse. 

(2) Developmental Linkage: We can link objects across datasets via cellular identity, but each synapse in the dataset does not yet have an identity attributed to its spatial coordinates, preventing us from linking specific synapses across development beyond their connectivity (ie, that a given synapses connects cell X to cell Y, for instance), also addressed in R1.11.  

Together, these improvements substantially enhance the utility of the viewer for hypothesis generation by making key quantitative data readily accessible.

Comment

While the DC/C-PHATE visualizations are a useful tool for the user, it is difficult to understand when grouping or splitting of cell contact patterns is biologically significant. DC is a deterministic algorithm applied to a contactome from a single organism, and the authors do not provide quantitative metrics of distances between individual neurons or a number of DC iterations on the C-PHATE plot, nor is the selection process for the threshold for DC described in this manuscript. In the application of DC/C-PHATE to larval stage nerve ring strata organization shown by the authors, qualitative observations of C-PHATE plots colored based on adult data seem to be the only evidence shown for persistent strata during development (Figure 3) or changing architectural motifs across stages (Figure 4). Quantitation of differences in neuron position within the DC hierarchy, or differences in modularity across stages, is needed to support these conclusions. Furthermore, illustrating the quantitative differences in C-PHATE plots used to make these conclusions will provide a more instructive guide for users of NeuroSC in generating future hypotheses.

There are several ways to visualize DC outputs, and one way to quantitatively compare DC clustering events of neurons is via Sankey diagrams. To make the inclusion of these resources more clear, we have highlighted them in lines 175-178 (Supplemental Tables 3-6). ‘DC outputs for each strata across animals can also be inspected using Sankey diagrams (Supplemental Tables 3-6). These spreadsheets detail the neuron members at each iteration of DC, allowing the user to derive quantitative comparisons of clustering events.’

As the reviewer points out, DC is a deterministic algorithm that will iteratively cluster neurons based on the similarity of their contact profiles. To better explain the selection process for the threshold, the number of DC iterations and the quantitative metrics between the neurons, we have added new text in the Diffusion Condensation methods section.  Briefly:

Number of DC iterations: During diffusion Condensation (DC) we track the modularity of the resulting clusters at each iteration and select the iteration with the highest modularity to define the clusters that represent the strata  (Moyle et al., 2021), (Brugnone et al., 2019). Mathematically, modularity is calculated by comparing the actual number of edges within clusters to the expected number of such edges in a randomized network with the same degree distribution (Newman et al., 2006). A higher modularity value implies that nodes within the same cluster are more densely connected to each other than to nodes in other clusters. We now better explain this in lines 562-567.

Threshold for merging points: The threshold (epsilon) used to merge data points in each iteration is set as a small fraction of the spatial extent of the data: for each coordinate dimension (x, y, z), we compute the range (maximum minus minimum), take the maximum of these three values, and divide it by 10,000. This process is performed iteratively for each round of clustering until all data points cluster into a single point. We have updated the manuscript to clarify this threshold selection and included this information in the revised algorithm description and pseudocode. We now better explain this in lines 556-559.

Distances between neurons in DC C-PHATE: In our previous description in Box 1 algorithm 1, we had provided a general algorithm for DC for any high dimensional dataset. We have now revised the algorithm to indicate how we used DC for these EM datasets. 

Distances between neurons are determined by the pixel overlap between their segmented shapes in the EM dataset. We use these distances to build a graph with weighted edges, in which the weight of the edge represents the pixel overlap (the adjacency in the actual EM segmentation). Affinities between neurons, which are a proxy for their distance in the graph, are then computed as now revised in Box 1, Algorithm 1. This process is done iteratively as neurons cluster. To better communicate this, we have changed the text in lines 533-538.  

Comment

R1.5. While the case studies presented by the authors help to highlight the utility of the different visualizations offered by the NeuroSC platform, the authors need to be more careful with the claims they make from these correlative observations. For example, in Figure 4, the authors use C-PHATE clustering patterns to make conclusions about changes in clustering patterns of individual neurons across development based on single animal datasets. In this and many other cases presented in this study with the limited existing datasets, it is difficult to differentiate between developmental changes and individual variability between the neurite positions, contacts, and synapse differences within these data. This caveat needs to be clearly addressed.

We now better explain in the manuscript that the selected case study, of the AVF neuron outgrowth, is not one of just correlation based solely on an EM dataset. Instead, the case study represents the NeuroSC-driven exploration of a biologically significant event supported by several independent datasets, as now explained in lines 257-276.

Briefly, we agree with the reviewer that examining differences across individual EM datasets is insufficient evidence to make conclusions about developmental changes. But the strength of NeuroSC is in its ability to combine and compare multiple datasets, bolstering observations that are not possible by looking at just one dataset, and providing new insights on the way to new hypotheses. We now better explain that we are not looking at single connectomes in isolation and then deriving conclusions, but instead using NeuroSC to compare across 9 EM datasets. We better explain how the tools in NeuroSC, including C-PHATE, enabled comparisons across these multiple connectomes to identify apparent differences in neuronal relationships. We then explain that by using NeuroSC, we could examine these variations in neuronal relationships at the level of individual, cell biological differences of neuronal morphologies between the developmental datasets. This could be due, as pointed by the reviewer, to differences due to development, or just differences between individual animals. In the case of AVF, that features are absent in all early specimens, then arise and persist in all specimens after a certain time point, which lead us to hypothesize they result from a developmental event. Because the segmented objects in NeuroSC are linked to neuronal identities, we are also able to cross reference our observations from the EM datasets with information in other datasets and the literature. In the specific case of postembryonic development of AVF outgrowth, we can now tie the knowledge, from developmental lineage information and molecular profiles, that AVF is a postembryonically born neuron (Sulston et al. 1977, Sun et al 2022, Poole et al 2024, wormatlas.org) to the outgrowth dynamics of its neurites using the postembryonic EM datasets. Our findings using  NeuroSC provide a proof of concept of the utility of the resource and extended our understanding of how the outgrowth of this neuron affects the relationships between the neural circuits in the nerve ring.

Comment

R1.6. Given that recent studies have also quantified contact area between neurons across multiple connectomes (Cook et al., Current Biology, 2023; Yim et al., Nature Communications, 2024), and that the authors use a slightly different approach to quantify contact area, a direct comparison between contact area values obtained in this study with prior studies seems appropriate.

We acknowledge that there are multiple different approaches to calculate adjacencies. In the papers cited above, there are 3 different algorithms used:

(1) Brittin 2019 (python parse Track EM, boundary thresholds), used in Cook et al 2023, Moyle 2021, and this study).

(2) Witvliet 2021 (Matlab 2D masks), used in Cook et al 2023.

(3) Yim 2024 (3D masks), used in Yim et al 2024.

To briefly describe the different approaches, and the methods we chose for this paper:

Algorithm 1 (used in this study) defines adjacency based on distances between boundary points in TrakEM2 segmentations, allowing threshold tuning to accommodate differences in resolution and image quality across datasets—an important feature for consistent cross-dataset comparisons.

Algorithm 2 infers contact via morphological dilation of VAST segmentations, identifying adjacency through overlapping expanded boundaries. 

Algorithm 3 uses voxelwise contact detection with directional surface area measurements and normalization to account for dataset size differences. 

In NeuroSC, we use algorithm 1, mostly because we had tested the rigor of this method in (Moyle et al. 2021), where we have shown that results were robust across a range of thresholds. This flexibility enables tailored application across datasets of varying quality and scale, critical for NeuroSC’s mission of curating data sets across differing methodologies to allow for direct relationship comparisons. We detail the methodology for defining thresholds for each dataset in methods section lines 492-521, defined in Supplementary table 1. Another difference between our analysis and the previously cited work is that for our analysis we also chose to include all individually resolved neurons, including post-embryonic cells, without collapsing them into left/right or dorsal/ventral symmetry classes. In this way our approach retains the full cellular resolution of the nervous system. 

Comment

Neuroglancer is not mentioned at all in the manuscript, despite it being a very similar and widely accepted platform for vEM data visualization across model organisms. An explicit comparison of NeuroSC and Neuroglancer would be appropriate, given the similarity of the tools. Currently, published C. elegans data (Witvliet et al., 2021; Yim et al., 2024) use Neuroglancer-based viewers, and directly comparing NeuroSC and highlighting its strengths relative to Neuroglancer would strengthen the paper.

In the original manuscript we had not mentioned tools like Neuroglancer because we envisioned them as distinct, in intended use and output, from NeuroSC. But, as explained in R1.2 comment, in the revised version we have included a section in the Introduction lines 98-108 and in the Discussion (lines 369- 417) that compares these types of web-based tools and highlights synergies. 

Comment

Assigning shorthand names to strata, such as "shallow reflex circuit" (page 4, line 172), may oversimplify this group of neurons. Either more detailed support for shorthand names of C-PHATE modules should be included, or less speculative names for strata should be used.

We appreciate this comment and understand that the original language used in the manuscript to describe strata categorizations may run the risk of oversimplification. We have now clarified the text to communicate that: 1) Strata are labeled by numbers (Strata 1, Strata 2, Strata 3 and Strata 4), rather than functional features of the neurons forming part of the strata, and that 2) the assignment of ‘strata’ is just one level of classification available via DC/CPHATE (as explained below). 

To be sure, we have observed and published (Moyle et. al. Nature 2021) that within a given stratum, many neurons share the functional identities that we have used as summary descriptors for the strata (eg, shallow reflex circuits for Stratum 1; sensory and integrative circuits in Strata 3 and Strata 4; command interneurons in Strata 2, etc). However, those cell types are not the only members of the strata. We have adjusted the language in lines 197-204 to reflect this more clearly. “Stratum 1, which contains most neurons contributing to shallow reflex circuits that control aversive head movements in response to noxious stimuli, displayed the fewest changes among the developmental connectomes (Figure 3B–F; Supplementary Table 3). In contrast, C. elegans exhibit tractable behaviors that adapt to changing environmental conditions (Flavell et al., 2020). Strata 3 and 4 contain most neurons involved in circuits associated with such learned behaviors, including mechano- and thermo-sensation. This is reflected in Strata 3 and 4 showing the most change in neuronal relationships across postembryonic development.“

Comment

The authors state that NeuroSC can be applied to other model organisms. Since model organisms with greater neuron numbers include more individual neurons per cell class, the authors should support this by quantitatively demonstrating how DC/C-PHATE relationships correlate with shared functional roles among C. elegans neurons.

We now clarify in the manuscript that, like in other organisms, C. elegans neurons are also grouped into functional classes with shared characteristics. In the context of the cylindrical nerve ring of the animal, these neuronal classes are sometimes bilaterally symmetric (forming left-right pairs), four-fold symmetric and six-fold symmetric. We now explain in the discussion that the DC/CPHATE analyses group these neuron classes and their relationships (lines 442-451). In the specific section mentioned by the reviewer, we now also add new text to contextualize this concept and how it might relate to the possible use of these tools in organisms with larger nervous systems: ‘However, our previous work has demonstrated that DC/CPHATE clustering of C. elegans neurons consistently pulls out clusters of shared neuron classes and shared functional roles Moyle et al. (2021). Building on this foundation, we envision applying similar clustering approaches to larger connectomes, aiming to identify classes and functionally related neuronal groups in more complex nervous systems. We suggest that contact profiles, along with neuron morphologies and synaptic partners, can act as ‘fingerprints’ for individual neurons and neuron classes. These ‘fingerprints’ can be aligned across animals of the same species to create identities for neurons. Frameworks for systematic connectomics analysis in tractable model systems such as C. elegans are critical in laying a foundation for future analyses in other organisms with up to a billion-fold increase in neurons (Toga et al., 2012).’

Comment

Lack of surface smoothing in NeuroSC leads to processes sometimes appearing to have gaps, which could be remedied by smoothing with a surface mesh. 

We thank the reviewer for the suggestion, and understand the visibility of gaps in certain neuron processes can be distracting. But this was an intentional choice, with our main goal being to show the most accurate representation of the available data segmentation and avoid any rendering interpretations. In this way, we render the data with the highest fidelity we can and as close as possible to the ground truth of the EM segmentation. We have added language to describe this in the methods, lines 490-491, and in Figure legend 5b.

Comment

Toggling between time points while maintaining the same neurons and contact area in NeuroSC is a really valuable feature. The tool would be improved even more by extending this feature to synapses, specifically by allowing the user to add an entire group of synapses to the viewer at once (e.g. "all synapses between AIM and PVQ"), and to keep this synapse group invariant when toggling between developmental stages.

We thank the reviewer for this suggestion. In response we have now implemented a new feature to ‘clone’ a rendered scene across time while preserving the original elements to ease comparisons. Once the user has rendered a scene, they can use the in-viewer developmental slider to clone the renderings and assigned colors, but display the renderings of the newly selected timepoint. These renderings populate a new window tab which can be dragged to align developmental stage windows side by side. We have added a sentence to account for this in lines 315-317 and to the legend of supplemental Figure S11. 

Reviewer #2 (Public review)

Comment

The ability to visualize the data from both a connectomics and contactomics perspective across developmental time has significant power. The original C. elegans connectome (White et al., 1986) presented their circuits as line drawings with chemical and electrical synapses indicated through arrows and bars. While these line drawings remain incredibly useful, they were also necessary simplifications for a 2D publication and they lack details of the complex architecture seen within each EM image. Koonce et al take advantage of segmented image data of each neuronal process within the nerve ring to create a web interface where users can visualize 3D models for their neuron of choice. The C-PHATE visualization allows users to explore similarities among different neurons in terms of adjacency and then go directly to the 3D model for these neurons. The 3D models it generates are beautiful and will likely be showing up in many future presentations and publications. The tool doesn't require any additional downloading and is open source.

We thank that reviewer for this positive assessment of our work.

Comment

While it's impossible to create one tool that will satisfy all potential users, I found myself wanting to have numbers associated with the data. For example, knowing the number of connections or the total surface area of contacts between individual neurons wasn't possible through the viewer, which limits the utility of taking deep analytical dives. While connectivity data is readily accessible through other interfaces such as Nemanode and WormWiring, a more thorough integration may be helpful to some users.

We thank the reviewer for this feedback and in response have now implemented displays with quantitative information in NeuroSC. Now, upon hovering over a contact patch or synapse, the user will see the quantitative data of the relationship. For contact patches, you will see the total area shared between two neurons in that dataset. On hovering over a synapse, you will see how many synapses there are in total with the same members and throughout the dataset. We agree that this improves user analyses, (see also R1.3 response).

Comment

There were several issues with the user interface that made it a bit clunky to use. For example, as I added additional neurons to the filter search box, the loading time got longer and longer. I ran an experiment uploading all of the amphid neurons, one pair at a time. Each additional neuron pair added an additional 5-10 seconds to the loading. By the time I got to the last pair, it took over a minute to load. Issues like these, some of which may be unavoidable given the size of the data, could be conveyed through better documentation. I did not find the tutorial very helpful and the supplementary movies lacked any voiceover, so it wasn't always clear what they were trying to show.

We appreciate that some of the more complex models can take a while to load. One of our core goals is to keep the high resolution of our models to most accurately represent the EM data, so we had to compromise between resolution and loading times. But to address this concern we have now added a ‘loading’ prompt that reassures the user when there is a wait. We also added, as suggested, text guidance throughout all of the supplemental videos (Supplemental Videos 1-4).

Reviewer #3 (Public review)

Comment

A web-based app, NeuroSC, that individual researchers can use to interrogate the structure and organization of the C. elegans nerve ring across development In the opinion of this reviewer, only minor revisions are required.

We thank that reviewer for this positive assessment of our work.

Comment

Contact is defined by length, why not contact area? How are these normalized for changes in the overall dimensions of neurons during development?

To clarify our methodology: the adjacency algorithm that we use generates a 2D adjacency profile by summing the number of adjacent boundary points per EM section, which are then summed across all EM z slices.

Contact area can be derived by multiplying the adjacency length in each slice by pixel resolution and z-thickness. Prompted by the reviewer we have now also calculated and display contact surface areas, along with their ranks among all contact relationships for a given neuron. These can be inspected directly via the interface by clicking on a rendered cell or contact patch (Figure S5 and lines 308-312). We believe these additional surface area metrics enhance the interpretability and utility of the viewer.

We apply normalization at the level of the adjacency threshold to account for dataset-specific differences such as contrast, boundary definition, and age-related changes in neuropil packing density. This normalization is applied before running the adjacency algorithm. We do not normalize by individual neuron size, as the contact data are intended to reflect relational differences between neurons, rather than absolute morphological scaling. In fact, our addition of a scale-spheroid within each rendered model emphasizes the large increase in spatial scale that the nerve ring experiences during larval growth.  

Comment

Figure 1, C&D, explanation unclear for how the adjacency matrix is correlated with C-Phate schematic in D.

We thank the reviewer for the comment and have clarified this section by adding greater detail to the explanation of how an adjacency matrix is computed (lines 149-155), as well as a description now in the figure legend 1C. Additionally, we revised Figure 1C and D to simplify neuron representations/colors and to simplify the adjacency heat map gradient. We also extended the area of contact between neurons on Figure 1C to better reflect what would be considered a “contact”. Lastly, in the figure, we changed the color and placement for the z plane arrow and label from black to white, to make it more visible, to highlight the method of computing adjacency for each z slice. 

Comment

Figure 4, panels F & G, unclear why AVF is shown in panel G (L3) but not panel F (L1). Explanation (see below) should be provided earlier, i.e., AVF is not generated until the end of the L1.

We have now clarified this important point by adding labels to Figure 4 panels F and G, ‘Pre-AVF outgrowth’ and ‘Post-AVF outgrowth’ respectively. Briefly, the point is that AVF grows into the nerve ring after the L2 stage, and that is why it is absent in panel F (L1 stage, now with the label ‘Pre-AVF outgrowth’).  

Comment

Line 146 What is the justification for the statement: "By end of Larval Stage 1 (L1), neuronal differentiation has concluded...."? This statement is confusing since this sentence also states that "90% of neurons in the neuropil...have entered the nerve ring..." which would suggest that at least 10% additional NR neurons have NOT fully differentiated.

We have fixed this sentence in the text. Now the sentence reads ‘By Larval stage 1 (L1) 90% of the neurons in the neuropil (161 neurons out of the 181 neurons) have grown into the nerve ring and adopted characteristic morphologies and positions. 

Lines 171-175 What is meant by the statement that "degree of these changes mapped onto...plasticity? What are examples of "behavioral plasticity?"

We have added the following new lines of text (lines 200-204) and now additionally cite a review discussing C. elegans behaviors to clarify and give context to behavioral plasticity. ‘C. elegans exhibit tractable behaviors which can adapt due to changing environmental conditions  (Flavell et. al. Genetics 2020). Strata 3 and 4 contain most neurons belonging to circuits associated with such learned behaviors, including chemo, mechano and thermo sensation. This is seemingly reflected by strata 3 and 4 harboring the most readily recognized set of changes in neuronal relationships across postembryonic development.’  

Comment

Lines 189-190 The meaning of this sentence is unclear, "The logic in....merge events."

This sentence has been deleted and we have instead refocused our descriptions of C-PHATES comparisons by neuronal clustering trajectories and cluster members (rather than iterations).

Comment

Lines 193-208 This section reports varying levels of convergence across larval development in C-Phate maps for the interneurons AIML and PVQL. Iterations leading to convergence varied: 16 (L1), 14 (L2), 22 (L3), 20 (l4), 14 (adult). The authors suggest that these differences are biologically significant and reflect the reorganization of AIML and PVQL contact relationships especially between the L4 and adult. Are these differences in iterations significant?

We agree this could be confusing and instead of focusing on comparing the iteration at which each merging event occurs, we now focus on examining the differences in members of clusters, before and after the merge event. Cluster membership is easier to interpret than the differences in the number of DC iterations (lines 224-229).

Lines 240-241 States that AVF neurons "terminally differentiate in the embryo" which is not correct. AVF neurons are generated from neuronal precursors (P0 and P1) at the end of the L1 stage which accounts for their outgrowth into the NR during the L2 stage. 

We thank the reviewer for the correction and have edited the text to read: ‘AVF neurons are generated from neuronal precursors (P0 and P1) at the end of the L1 stage (Sulston et al. (1983); Sun and Hobert (2023); Poole et al. (2024); Hall and Altun (2008); Sulston and Horvitz (1977). AVF neurons do not grow into the nerve ring until the L2 stage, and continue to grow until the Adult stage (lines 261-266).’

Comment

Lines 289-315. A detailed and highly technical description of website architecture would seem more appropriate for the Methods section.

We agree and have moved this section to the methods as suggested (lines 663-690).

Comment

Line 307 "source data is" should be "source data are"

Thank you- we have fixed this grammatical error.

Comment

Line 324 "circuits identities" should be "circuit identity".

Thank you- we have fixed this grammatical error.

Comment

Trademark/copyright conflict with these sites? https://compumedicsneuroscan.com/about/ https://www.neuroscanai.com/

We thank the reviewer for drawing our attention to this. To avoid potential conflicts, we have proactively altered the name to NeuroSC throughout the paper.