Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Because the "source" and "target" tasks are merely parameter variations of the same paradigm, it is unclear whether EIDT achieves true crosstask transfer. The manuscript provides no measure of how consistent each participant's behaviour is across these variants (e.g., two- vs threestep MDP; easy vs difficult MNIST). Without this measure, the transfer results are hard to interpret. In fact, Figure 5 shows a notable drop in accuracy when transferring between the easy and difficult MNIST conditions, compared to transfers between accuracy-focused and speedfocused conditions. Does this discrepancy simply reflect larger withinparticipant behavioural differences between the easy and difficult settings? A direct analysis of intra-individual similarity for each task pair and how that similarity is related to EIDT's transfer performance is needed.

Thank you for your insightful comment. We agree that the tasks used in our study are variations of the same paradigm. Accordingly, we have revised the manuscript to consistently frame our findings as demonstrating individuality transfer "across task conditions" rather than "across distinct tasks."

In response to your suggestion, we have conducted a new analysis to directly investigate the relationship between individual behavioural patterns and transfer performance. As show in the new Figures 4, 11, S8, and S9, we found a clear relationship between the distance in the space of individual latent representation (called individuality index in the previous manuscript) and prediction performance. Specifically, prediction accuracy for a given individual's behaviour degrades as the latent representation of the model's source individual becomes more distant. This result directly demonstrates that our framework captures meaningful individual differences that are predictive of transfer performance across conditions.

We have also expanded the Discussion (Lines 332--343) to address the potential for applying this framework to more structurally distinct tasks, hypothesizing that this would rely on shared underlying cognitive functions.

Related to the previous comment, the individuality index is central to the framework, yet remains hard to interpret. It shows much greater within-participant variability in the MNIST experiment (Figure S1) than in the MDP experiment (Figure 3). Is such a difference meaningful? It is hard to know whether it reflects noisier data, greater behavioural flexibility, or limitations of the model.

Thank you for raising this important point about interpretability. To enhance the interpretability of the individual latent representation, we have added a new analysis for the MDP task (see Figures 6 and S4). By applying our trained encoder to data from simulated Q-learning agents with known parameters, we demonstrate that the dimensions of the latent space systematically map onto the agents' underlying cognitive parameters (learning rate and inverse temperature). This analysis provides a clearer interpretation by linking our model's data-driven representation to established theoretical constructs.

Regarding the greater within-participant variability observed in the MNIST task (visualized now in Figure S7), this could be attributed to several factors, such as greater behavioural flexibility in the perceptual task. However, disentangling these potential factors is complex and falls outside the primary scope of the current study, which prioritizes demonstrating robust prediction accuracy across different task conditions.

The authors suggests that the model's ability to generalize to new participants "likely relies on the fact that individuality indices form clusters and individuals similar to new participants exist in the training participant pool". It would be helpful to directly test this hypothesis by quantifying the similarity (or distance) of each test participant's individuality index to the individuals or identified clusters within the training set, and assessing whether greater similarity (or closer proximity) to the clusters in the training set is associated with higher prediction accuracy for those individuals in the test set.

Thank you for this excellent suggestion. We have performed the analysis you proposed to directly test this hypothesis. Our new results, presented in Figures 4, 11, S5, S8, and S9, quantify the distance between the latent representation of a test participant and that of the source participant used to generate the prediction model.

The results show a significant negative correlation: prediction accuracy consistently decreases as the distance in the latent space increases. This confirms that generalization performance is directly tied to the similarity of behavioural patterns as captured by our latent representation, strongly supporting our hypothesis.

Reviewer #2 (Public review):

The MNIST SX baseline appears weak. RTNet isn't directly comparable in structure or training. A stronger baseline would involve training the GRU directly on the task without using the individuality index-e.g., by fixing the decoder head. This would provide a clearer picture of what the index contributes.

We agree that a more direct baseline is crucial for evaluating the contribution of our transfer mechanism. For the Within-Condition Prediction scenario, the comparison with RTNet was intended only to validate that our task solver architecture could achieve average humanlevel task performance (Figure 7).

For the critical Cross-Condition Transfer scenario, we have now implemented a stronger and more appropriate baseline, which we call ``task solver (source).'' This model has the same architecture as our EIDT task solver but is trained directly on the source task data of the specific test participant. As shown in revised Figure 9, our EIDT framework significantly outperforms this direct-training approach, clearly demonstrating the benefit of the individuality transfer mechanism.

Although the focus is on prediction, the framework could offer more insight into how behaviour in one task generalizes to another. For example, simulating predicted behaviours while varying the individuality index might help reveal what behavioural traits it encodes.

Thank you for this valuable suggestion. To provide more insight into the encoded behavioural traits, we have conducted a new analysis linking the individual latent representation to a theoretical cognitive model. As detailed in the revised manuscript (Figures 6 and S4), we applied our encoder to simulated data from Q-learning agents with varying parameters. The results show a systematic relationship between the latent space coordinates and the agents' learning rates and inverse temperatures, providing a clearer interpretation of what the representation captures.

It's not clear whether the model can reproduce human behaviour when acting on-policy. Simulating behaviour using the trained task solver and comparing it with actual participant data would help assess how well the model captures individual decision tendencies.

We have added the suggested on-policy evaluation (Lines 195--207). In the revised manuscript (Figure 5), we present results from simulations where the trained task solvers performed the MDP task. We compared their performance (total reward and rate of the highly-rewarding action selected) against their corresponding human participants. The strong correlations observed demonstrate that our model successfully captures and reproduces individual-specific behavioural tendencies in an onpolicy setting.

Figures 3 and S1 aim to show that individuality indices from the same participant are closer together than those from different participants. However, this isn't fully convincing from the visualizations alone. Including a quantitative presentation would help support the claim.

We agree that the original visualizations of inter- and intraparticipant distances was not sufficiently convincing. We have therefore removed that analysis. In its place, we have introduced a more direct and quantitative analysis that explicitly links the individual latent representation to prediction performance (see Figures 4, 11, S5, S8, and S9). This new analysis demonstrates that prediction error for an individual is a function of distance in the latent space, providing stronger evidence that the representation captures meaningful, individual-specific information.

The transfer scenarios are often between very similar task conditions (e.g., different versions of MNIST or two-step vs three-step MDP). This limits the strength of the generalization claims. In particular, the effects in the MNIST experiment appear relatively modest, and the transfer is between experimental conditions within the same perceptual task. To better support the idea of generalizing behavioural traits across tasks, it would be valuable to include transfers across more structurally distinct tasks.

We agree with this limitation and have revised the manuscript to be more precise. We now frame our contribution as "individuality transfer across task conditions" rather than "across tasks" to accurately reflect the scope of our experiments. We have also expanded the Discussion section (Line 332-343) to address the potential and challenges of applying this framework to more structurally distinct tasks, noting that it would likely depend on shared underlying cognitive functions.

For both experiments, it would help to show basic summaries of participants' behavioural performance. For example, in the MDP task, first-stage choice proportions based on transition types are commonly reported. These kinds of benchmarks provide useful context.

We have added behavioral performance summaries as requested. For the MDP task, Figure 5 now compares the total reward and rate of highlyrewarding action selected between humans and our model. For the MNIST task, Figure 7 shows the rate of correct responses for humans, RTNet, and our task solver across all conditions. These additions provide better context for the model's performance.

For the MDP task, consider reporting the number or proportion of correct choices in addition to negative log-likelihood. This would make the results more interpretable.

Thank you for the suggestion. To make the results more interpretable, we have added a new prediction performance metric: the rate for behaviour matched. This metric measures the proportion of trials where the model's predicted action matches the human's actual choice. This is now included alongside the negative log-likelihood in Figures 2, 3, 4, 8, 9, and 11.

In Figure 5, what is the difference between the "% correct" and "% match to behaviour"? If so, it would help to clarify the distinction in the text or figure captions.

We have clarified these terms in the revised manuscript. As defined in the Result section (Lines 116--122, 231), "%correct" (now "rate of correct responses") is a measure of task performance, whereas "%match to behaviour" (now "rate for behaviour matched") is a measure of prediction accuracy.

For the cognitive model, it would be useful to report the fitted parameters (e.g., learning rate, inverse temperature) per individual. This can offer insight into what kinds of behavioural variability the individual latent representation might be capturing.

We have added histograms of the fitted Q-learning parameters for the human participants in Supplementary Materials (Figure S1). This analysis revealed which parameters varied most across the population and directly informed the design of our subsequent simulation study with Q-learning agents (see response to Comment 2-2), where we linked these parameters to the individual latent representation (Lines 208--223).

A few of the terms and labels in the paper could be made more intuitive. For example, the name "individuality index" might give the impression of a scalar value rather than a latent vector, and the labels "SX" and "SY" are somewhat arbitrary. You might consider whether clearer or more descriptive alternatives would help readers follow the paper more easily.

We have adopted the suggested changes for clarity.

"Individuality index" has been changed to "individual latent representation".

"Situation SX" and "Situation SY" have been renamed to the more descriptive "Within-Condition Prediction" and "Cross-Condition Transfer", respectively.

We have also added a table in Figure 7 to clarify the MNIST condition acronyms (EA/ES/DA/DS).

Please consider including training and validation curves for your models. These would help readers assess convergence, overfitting, and general training stability, especially given the complexity of the encoder-decoder architecture.

Training and validation curves for both the MDP and MNIST tasks have been added to Supplementary Materials (Figure S2 and S6) to show model convergence and stability.

Reviewer #3 (Public review):

To demonstrate the effectiveness of the approach, the authors compare a Q-learning cognitive model (for the MDP task) and RTNet (for the MNIST task) against the proposed framework. However, as I understand it, neither the cognitive model nor RTNet is designed to fit or account for individual variability. If that is the case, it is unclear why these models serve as appropriate baselines. Isn't it expected that a model explicitly fitted to individual data would outperform models that do not? If so, does the observed superiority of the proposed framework simply reflect the unsurprising benefit of fitting individual variability? I think the authors should either clarify why these models constitute fair control or validate the proposed approach against stronger and more appropriate baselines.

Thank you for raising this critical point. We wish to clarify the nature of our baselines:

For the MDP task, the cognitive model baseline was indeed designed to account for individual variability. We estimated its parameters (e.g., learning rate) from each individual's source task behaviour and then used those specific parameters to predict their behaviour in the target task. This makes it a direct, parameter-based transfer model and thus a fair and appropriate baseline for individuality transfer.

For the MNIST task, we agree that the RTNet baseline was insufficient for evaluating individual-level transfer in the "Cross-Condition Transfer" scenario. We have now introduced a much stronger baseline, the "task solver (source)," which is trained specifically on the source task data of each test participant. Our results (Figure 9) show that the EIDT framework significantly outperforms this more appropriate, individualized baseline, highlighting the value of our transfer method over direct, within-condition fitting.

It's not very clear in the results section what it means by having a shorter within-individual distance than between-individual distances. Related to the comment above, is there any control analysis performed for this? Also, this analysis appears to have nothing to do with predicting individual behavior. Is this evidence toward successfully parameterizing individual differences? Could this be task-dependent, especially since the transfer is evaluated on exceedingly similar tasks in both experiments? I think a bit more discussion of the motivation and implications of these results will help the reader in making sense of this analysis.

We agree that the previous analysis on inter- and intra-participant distances was not sufficiently clear or directly linked to the model's predictive power. We have removed this analysis from the manuscript. In its place, we have introduced a new, more direct analysis (Figures 4, 11, S5, S8, and S9) that demonstrates a quantitative relationship between the distance in the latent space and prediction accuracy. This new analysis shows that prediction error for an individual increases as a function of this distance, providing much stronger and clearer evidence that our framework successfully parameterizes meaningful individual differences.

The authors have to better define what exactly he meant by transferring across different "tasks" and testing the framework in "more distinctive tasks". All presented evidence, taken at face value, demonstrated transferring across different "conditions" of the same task within the same experiment. It is unclear to me how generalizable the framework will be when applied to different tasks.

Conceptually, it is also unclear to me how plausible it is that the framework could generalize across tasks spanning multiple cognitive domains (if that's what is meant by more distinctive). For instance, how can an individual's task performance on a Posner task predict task performance on the Cambridge face memory test? Which part of the framework could have enabled such a cross-domain prediction of task performance? I think these have to be at least discussed to some extent, since without it the future direction is meaningless.

We agree with your assessment and have corrected our terminology throughout the manuscript. We now consistently refer to the transfer as being "across task conditions" to accurately describe the scope of our findings.

We have also expanded our Discussion (Line 332-343) to address the important conceptual point about cross-domain transfer. We hypothesize that such transfer would be possible if the tasks, even if structurally different, rely on partially shared underlying cognitive functions (e.g., working memory). In such a scenario, the individual latent representation would capture an individual's specific characteristics related to that shared function, enabling transfer. Conversely, we state that transfer between tasks with no shared cognitive basis would not be expected to succeed with our current framework.

How is the negative log-likelihood, which seems to be the main metric for comparison, computed? Is this based on trial-by-trial response prediction or probability of responses, as what usually performed in cognitive modelling?

The negative log-likelihood is computed on a trial-by-trial basis. It is based on the probability the model assigned to the specific action that the human participant actually took on that trial. This calculation is applied consistently across all models (cognitive models, RTNet, and EIDT). We have added sentences to the Results section to clarify this point (Lines 116--122).

None of the presented evidence is cross-validated. The authors should consider performing K-fold cross-validation on the train, test, and evaluation split of subjects to ensure robustness of the findings.

All prediction performance results reported in the revised manuscript are now based on a rigorous leave-one-participant-out cross-validation procedure to ensure the robustness of our findings. We have updated the

Methods section to reflect this (Lines 127--129 and 229).

For some purely illustrative visualizations (e.g., plotting the entire latent space in Figures S3 and S7), we used a model trained on all participants' data to provide a single, representative example and avoid clutter. We have explicitly noted this in the relevant figure captions.

The authors excluded 25 subjects (20% of the data) for different reasons. This is a substantial proportion, especially by the standards of what is typically observed in behavioral experiments. The authors should provide a clear justification for these exclusion criteria and, if possible, cite relevant studies that support the use of such stringent thresholds.

We acknowledge the concern regarding the exclusion rate. The previous criteria were indeed empirical. We have now implemented more systematic exclusion procedure based on the interquartile range of performance metrics, which is detailed in Section 4.2.2 (Lines 489--498). This revised, objective criterion resulted in the exclusion of 42 participants (34% of the initial sample). While this rate is high, we attribute it to the online nature of the data collection, where participant engagement can be more variable. We believe applying these strict criteria was necessary to ensure the quality and reliability of the behavioural data used for modeling.

The authors should do a better job of creating the figures and writing the figure captions. It is unclear which specific claim the authors are addressing with the figure. For example, what is the key message of Figure 2C regarding transfer within and across participants? Why are the stats presentation different between the Cognitive model and the EIDT framework plots? In Figure 3, it's unclear what these dots and clusters represent and how they support the authors' claim that the same individual forms clusters. And isn't this experiment have 98 subjects after exclusion, this plot has way less than 98 dots as far as I can tell. Furthermore, I find Figure 5 particularly confusing, as the underlying claim it is meant to illustrate is unclear. Clearer figures and more informative captions are needed to guide the reader effectively.

We agree that several figures and analyses in the original manuscript were unclear, and we have thoroughly revised our figures and their captions to improve clarity.

The confusing analysis in the old Figures 2C and 5 (Original/Others comparison) have been completely removed. The unclear visualization of the latent space for the test pool (old Figure 3 showing representations only from test participants) has also been removed to avoid confusion. For visualization of the overall latent space, we now use models trained on all data (Figures S3 and S7) and have clarified this in the captions. In place of these removed analyses, we have introduced a new, more intuitive "cross-individual" analysis (presented in Figures 4, 11, S5, S8, and S9). As explained in the new, more detailed captions, this analysis directly plots prediction performance as a function of the distance in latent space, providing a much clearer demonstration of how the latent representation relates to predictive accuracy.

I also find the writing somewhat difficult to follow. The subheadings are confusing, and it's often unclear which specific claim the authors are addressing. The presentation of results feels disorganized, making it hard to trace the evidence supporting each claim. Also, the excessive use of acronyms (e.g., SX, SY, CG, EA, ES, DA, DS) makes the text harder to parse. I recommend restructuring the results section to be clearer and significantly reducing the use of unnecessary acronyms.

Thank you for this feedback. We have made significant revisions to improve the clarity and organization of the manuscript. We have renamed confusing acronyms: "Situation SX" is now "Within- Condition Prediction," and "Situation SY" is now "Cross-Condition Transfer." We also added a table to clarify the MNIST condition acronyms (EA/ES/DA/DS) in Figure 7.

The Results section has been substantially restructured with clearer subheadings.

Duplicate of reference 27.

Regarding the decline in age-standardized incidence rates, we expect that as diagnostic tools improve and early detection advances, more cases will be identified, which may lead to an increase in this indicator. I think it might be better to relate this factor to the improvement of preventive strategies.

Results currently under-discussed. - State outliers in Table 1 not discussed; New York largest declines; New Mexico's significant male prevalence increase; briefly discuss why these states merit targeted evaluation. - Non-fatal burden (YLDs) is absent from the discussion.

This study does not quantify risk-factor contributions. I think it might be better to shorten this paragraph to 1–2 key examples and remove non-essential details.

eLife Assessment

This study presents compelling new data that combine two FTD-tau mutations, P301L/S320F (PL-SF), that reliably induce spontaneous full-length tau aggregation across multiple cellular systems. The findings are important for the field of neurodegenerative disease. The strength of evidence is solid; however, several conclusions would benefit from more validation.

Reviewer #1 (Public review):

Summary:

This study presents compelling new data that combine two FTD-tau mutations P301L/S320F (PL-SF), that reliably induce spontaneous full-length tau aggregation across multiple cellular systems. However, several conclusions would benefit from more validation. Key findings rely on quantification of overexposed immunoblot, and in some experiments, the tau bands shift in molecular weight that are not explained (and in some instances vary between experiments). The effect seems to be driven by the S320F mutation, with the P301L mutation enhancing the effect observed with S320F alone. Although the observation that Hsp70, but not the related Hsc70, enhances aggregation is intriguing, the mechanistic basis for these differences remains unclear despite both Hsp70 and Hsc70 binding to tau. Additional experiments clarifying which PL-SF tau species Hsp70 engages, how this interaction alters tau conformational landscapes, and whether other chaperones or cofactors contribute to this effect would help solidify the conclusions and build a mechanistic picture. Overexpression of Hsp70 in the context of PL tau did not increase tau aggregation, which raises questions about whether the observed effects are specific to the SF mutation. Hsp70 functions in the context of a larger network of chaperones and has been proposed to cooperate with other proteins/machinery to disassemble tau amyloids, perhaps to produce more seeds. This would be consistent with the presented observations. For example, co-IP experiments using Hsp70 as bait combined with proteomics could really help build a more complete picture of what tau species Hsp70 binds and what other factors cooperate to yield the observed increases in aggregation. As it stands, the Hsp70 component of the paper is not fully developed, and additional experiments to address these questions would strengthen this manuscript beyond simply presenting a new tool to study spontaneous tau aggregation.

Strengths:

(1) The PL-SF FL tau mutant aggregates spontaneously in different cellular systems and shows hallmarks of tau pathology linked to disease.

(2) PL-SF 4delta mutant reverses the spontaneous aggregation phenotype, consistent with these residues being critical for tau aggregation.

(3) PL-SF 4delta also loses the ability to recruit Hsp70/Hsc70, consistent with these residues also being critical for chaperone recruitment.

(4) The PL-SF tau mutant establishes a new system to study spontaneous tau assembly and to begin to compare it to seeded tau aggregation processes.

Weaknesses:

(1) Mechanistic insight into how Hsp70 but not Hsc70 increase PL-SF FL tau aggregation/pathology is missing. This is despite both chaperones binding to PL-SF FL tau. What species of tau does Hsp70 bind, and what cofactors are important in this process?

(2) The study relies heavily on densitometry of bands to draw conclusions; in several instances, the blots are overexposed to accurately quantify the signal.

Reviewer #2 (Public review):

Summary:

This study developed a novel tauopathy model combining two mutations, P301L and S320F, termed the PL-SF model. This model shows rapid tau protein aggregation.

Strengths:

The authors demonstrated pathogenicity through solid in vivo and in vitro experiments. Simultaneously, they used this model to investigate the role of the heat shock protein Hsp70 in tau protein aggregation, finding that Hsp70 promotes rather than inhibits tau pathology, which differs from previous findings.

Weaknesses:

(1) Although the PL-SF model can accelerate tau aggregation, it is crucial to determine whether this aligns with the temporal progression and spatial distribution of tau pathology in the brains of patients with tauopathies.

(2) The authors did not elucidate the specific molecular mechanism by which Hsp70 promotes tau aggregation.

(3) Some figures in this study show large error bars in the quantitative data (some statistical analysis figures, MEA recordings, etc.), indicating significant inter-sample variability. It is recommended to label individual data points in all quantitative figures and clearly indicate them in figure legends.

Author response:

Reviewer #1

(1) Mechanistic insight into how Hsp70 but not Hsc70 increase PL-SF FL tau aggregation/pathology is missing. This is despite both chaperones binding to PL-SF FL tau. What species of tau does Hsp70 bind, and what cofactors are important in this process?

We agree that explaining why Hsp70, but not Hsc70, promotes tau aggregation would strengthen the study. Although both chaperones bind tau, they diverge slightly in 1) protein sequence, 2) biochemical activity, and 3) co-chaperone engagement.

Sequence: Hsp70 has an extra cysteine residue (Cys306) that is highly reactive to oxidation and a glycine residue that is critical for cysteine oxidation (Gly557). Both residues are specific to Hsp70 (not present in Hsc70) and may alter Hsp70 conformation or client handling (Hong et al., 2022).

Biochemical activity: Prior studies indicate that Hsp70’s ATPase domain (NBD) is critical for tau interactions (Jinwal et al., 2009; Fontaine et al., 2015; Young et al., 2016) and can be disrupted with point mutations including K71E and E175S for ATPase and A406G/V438G for substrate binding (Fontaine et al., 2015).

Co-chaperone engagement: Hsp70 recruits the co-chaperone and E3 ubiquitin ligase CHIP/Stub1 more strongly than Hsc70, suggesting co-chaperone engagement could lead to differences in tau processing (Jinwal et al., 2013).

To directly test how the two closely related chaperones could differentially impact tau, we plan to perform the following experiments:

(a) We will mutate residues responsible for cysteine reactivity in Hsp70 including the cysteine itself (Cys306) and the critical glycine that facilitates cysteine reactivity (Gly557). These residues will be deleted from Hsp70 or alternatively inserted into Hsc70 to determine whether cysteine reactivity is the reason for Hsp70’s ability to drive tau aggregation.

(b) We will generate Hsp70 mutants lacking ATPase- or substrate-binding mutants to determine which Hsp70 domains are responsible for driving tau aggregation.

(c) We will perform seeding assays in stable tau-expressing cell lines to determine whether Hsp70/Hsc70 overexpression or depletion alters seeded tau aggregation.

(d) We will perform confocal microscopy to determine the extent of co-localization of Hsp70 or Hsc70 with phospho-tau, oligomeric tau, or Thioflavin-S (ThioS) to identify which tau species are engaged by Hsp70/Hsc70.

(e) We will perform immunoprecipitation pull-downs followed by mass spectrometry to globally identify any relevant Hsp70/Hsc70 interacting factors that might account for the differences in tau aggregation.

(2) The study relies heavily on densitometry of bands to draw conclusions; in several instances, the blots are overexposed to accurately quantify the signal.

All immunoblots were acquired as 16-bit TIFFs with exposure settings chosen to prevent pixel saturation, and quantification was performed on raw, unsaturated images. Brightness and contrast adjustments were applied only for visualization and did not alter pixel values used for analysis. All quantified bands fell within the linear range of the detector, with one exception in Figure 7B, which we removed from quantification. We will add both low- and high-exposure versions of immunoblots to the revised figures to demonstrate signal linearity and dynamic range.

Reviewer #2

(1) Although the PL-SF model can accelerate tau aggregation, it is crucial to determine whether this aligns with the temporal progression and spatial distribution of tau pathology in the brains of patients with tauopathies.

No single tauopathy model fully recapitulates the temporal and spatial progression of human tauopathies. The PL-SF system is not intended to model the disease course. Rather, it is an excellent model for mechanistic studies of mature tau aggregation, which is otherwise challenging to study. We note that prior studies showed that PL-SF tau expression in transgenic mice (Xia et al., 2022 and Smith et al., 2025) and rhesus monkeys (Beckman et al., 2021) led to prion-like tau seeding and aggregation in hippocampal and cortical regions. Indeed, the spatial and temporal tau aggregation patterns aligned with features of human tauopathies. So far, these findings all support PL-SF as a valid accelerated model of tauopathy than can be used to interrogate pathogenic mechanisms that impact tau processing, degradation, and/or aggregation.

(2) The authors did not elucidate the specific molecular mechanism by which Hsp70 promotes tau aggregation.

We agree that a deeper understanding of the molecular mechanism is needed. The revision experiments outlined above (Reviewer #1, point #1) will define how Hsp70 promotes tau aggregation by testing sequence contributions, dissecting ATPase and substrate-binding domain requirements, and mapping Hsp70/Hsc70 interactors to directly address this mechanistic question.

(3) Some figures in this study show large error bars in the quantitative data (some statistical analysis figures, MEA recordings, etc.), indicating significant inter-sample variability. It is recommended to label individual data points in all quantitative figures and clearly indicate them in figure legends.

We acknowledge the inter-sample variability in some of the quantitative datasets. This level of variability can occur in primary neuronal cultures (e.g., MEA recordings) that are sensitive to growth and surface adhesion conditions, leading to many technical considerations. To improve transparency and interpretation, we will revise all quantitative figures to display individual data points overlaid on summary statistics and will update figure legends to clearly indicate sample sizes and statistical tests used.

References

Hong Z, Gong W, Yang J, Li S, Liu Z, Perrett S, Zhang H. Exploration of the cysteine reactivity of human inducible Hsp70 and cognate Hsc70. J Biol Chem. 2023 Jan;299(1):102723. doi: 10.1016/j.jbc.2022.102723. Epub 2022 Nov 19. PMID: 36410435; PMCID: PMC9800336.

Jinwal UK, Miyata Y, Koren J 3rd, Jones JR, Trotter JH, Chang L, O'Leary J, Morgan D, Lee DC, Shults CL, Rousaki A, Weeber EJ, Zuiderweg ER, Gestwicki JE, Dickey CA. Chemical manipulation of hsp70 ATPase activity regulates tau stability. J Neurosci. 2009 Sep 30;29(39):12079-88. doi: 10.1523/JNEUROSCI.3345-09.2009. PMID: 19793966; PMCID: PMC2775811.

Fontaine SN, Rauch JN, Nordhues BA, Assimon VA, Stothert AR, Jinwal UK, Sabbagh JJ, Chang L, Stevens SM Jr, Zuiderweg ER, Gestwicki JE, Dickey CA. Isoform-selective Genetic Inhibition of Constitutive Cytosolic Hsp70 Activity Promotes Client Tau Degradation Using an Altered Co-chaperone Complement. J Biol Chem. 2015 May 22;290(21):13115-27. doi: 10.1074/jbc.M115.637595. Epub 2015 Apr 11. PMID: 25864199; PMCID: PMC4505567

Young ZT, Rauch JN, Assimon VA, Jinwal UK, Ahn M, Li X, Dunyak BM, Ahmad A, Carlson G, Srinivasan SR, Zuiderweg ERP, Dickey CA, Gestwicki JE. Stabilizing the Hsp70‑Tau Complex Promotes Turnover in Models of Tauopathy. Cell Chem Biol. 2016 Aug 4;23(8):992–1001. doi:10.1016/j.chembiol.2016.04.014.

Jinwal UK, Akoury E, Abisambra JF, O'Leary JC 3rd, Thompson AD, Blair LJ, Jin Y, Bacon J, Nordhues BA, Cockman M, Zhang J, Li P, Zhang B, Borysov S, Uversky VN, Biernat J, Mandelkow E, Gestwicki JE, Zweckstetter M, Dickey CA. Imbalance of Hsp70 family variants fosters tau accumulation. FASEB J. 2013 Apr;27(4):1450-9. doi: 10.1096/fj.12-220889. Epub 2012 Dec 27. PMID: 23271055; PMCID: PMC3606536.

Xia, Y., Prokop, S., Bell, B.M. et al. Pathogenic tau recruits wild-type tau into brain inclusions and induces gut degeneration in transgenic SPAM mice. Commun Biol 5, 446 (2022). https://doi.org/10.1038/s42003-022-03373-1.

Smith ED, Paterno G, Bell BM, Gorion KM, Prokop S, Giasson BI. Tau from SPAM Transgenic Mice Exhibit Potent Strain-Specific Prion-Like Seeding Properties Characteristic of Human Neurodegenerative Diseases. Neuromolecular Med. 2025 May 30;27(1):44. doi: 10.1007/s12017-025-08850-4. PMID: 40447946; PMCID: PMC12125038.

Beckman D, Chakrabarty P, Ott S, Dao A, Zhou E, Janssen WG, Donis-Cox K, Muller S, Kordower JH, Morrison JH. A novel tau-based rhesus monkey model of Alzheimer's pathogenesis. Alzheimers Dement. 2021 Jun;17(6):933-945. doi: 10.1002/alz.12318. Epub 2021 Mar 18. PMID: 33734581; PMCID: PMC8252011.

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eLife Assessment

This study investigates the role of developmental oligodendrocytes in synchronising spontaneous activity in neuronal circuits and influencing cerebellar-dependent behaviour. The authors use advanced viral targeting techniques to deplete oligodendrocytes in a cell-specific manner, paired with in vivo calcium imaging of Purkinje cells, to establish a relationship between oligodendrocyte-mediated neuronal synchrony and complex brain function. The authors present compelling evidence of oligodendrocyte-regulated neuronal synchrony. Overall, this manuscript holds promise as an important contribution to neurodevelopment research.

Reviewer #1 (Public review):

Summary:

This study presents convincing findings that oligodendrocytes play a regulatory role in spontaneous neural activity synchronization during early postnatal development, with implications for adult brain function. Utilizing targeted genetic approaches, the authors demonstrate how oligodendrocyte depletion impacts Purkinje cell activity and behaviors dependent on cerebellar function. Delayed myelination during critical developmental windows is linked to persistent alterations in neural circuit function, underscoring the lasting impact of oligodendrocyte activity.

Strengths:

(1) The research leverages the anatomically distinct olivocerebellar circuit, a well-characterized system with known developmental timelines and inputs, strengthening the link between oligodendrocyte function and neural synchronization.

(2) Functional assessments, supported by behavioral tests, validate the findings of in vivo calcium imaging, enhancing the study's credibility.

(3) Extending the study to assess long-term effects of early life myelination disruptions adds depth to the implications for both circuit function and behavior.

Weaknesses:

(1) The study would benefit from a closer analysis of myelination during the periods when synchrony is recorded. Direct correlations between myelination and synchronized activity would substantiate the mechanistic link and clarify if observed behavioral deficits stem from altered myelination timing.

(2) Although the study focuses on Purkinje cells in the cerebellum, neural synchrony typically involves cross-regional interactions. Expanding the discussion on how localized Purkinje synchrony affects broader behaviors-such as anxiety, motor function, and sociality - would enhance the findings' functional significance.

(3) The authors discuss the possibility of oligodendrocyte-mediated synapse elimination as a possible mechanism behind their findings, drawing from relevant recent literature on oligodendrocyte precursor cells. However, there are no data presented supporting these assumptions. The authors should explain why they think the mechanism behind their observation extends beyond the contribution of myelination or remove this point from the discussion entirely.

Comment for resubmission: Although the argument on synaptic elimination has been removed, it has been replaced with similarly unclear speculation about roles for oligodendrocytes outside of conventional myelination or metabolic support, again without clear evidence. The authors measured MBP area but have not performed detailed analysis of oligodendrocyte biology to support the claims made in the discussion. Please consider removing this section or rephrasing it to align with the data presented.

(4) It would be valuable to investigate secondary effects of oligodendrocyte depletion on other glial cells, particularly astrocytes or microglia, which could influence long-term behavioral outcomes. Identifying whether the lasting effects stem from developmental oligodendrocyte function alone or also involve myelination could deepen the study's insights.

(5) The authors should explore the use of different methods to disturb myelin production for a longer time, in order to further determine if the observed effects are transient or if they could have longer-lasting effects.

(6) Throughout the paper, there are concerns about statistical analyses, particularly on the use of the Mann-Whitney test or using fields of view as biological replicates.

Author response:

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

Public Reviews:

Reviewer #1 (Public review): 

Summary: 

This study presents convincing findings that oligodendrocytes play a regulatory role in spontaneous neural activity synchronisation during early postnatal development, with implications for adult brain function. Utilising targeted genetic approaches, the authors demonstrate how oligodendrocyte depletion impacts Purkinje cell activity and behaviours dependent on cerebellar function. Delayed myelination during critical developmental windows is linked to persistent alterations in neural circuit function, underscoring the lasting impact of oligodendrocyte activity. 

Strengths: 

(1) The research leverages the anatomically distinct olivocerebellar circuit, a well-characterized system with known developmental timelines and inputs, strengthening the link between oligodendrocyte function and neural synchronization. 

(2) Functional assessments, supported by behavioral tests, validate the findings of in vivo calcium imaging, enhancing the study's credibility. 

(3) Extending the study to assess the long-term effects of early-life myelination disruptions adds depth to the implications for both circuit function and behavior.

We appreciate these positive evaluation.

Weaknesses: 

(1) The study would benefit from a closer analysis of myelination during the periods when synchrony is recorded. Direct correlations between myelination and synchronized activity would substantiate the mechanistic link and clarify if observed behavioral deficits stem from altered myelination timing. 

We appreciate the reviewer’s thoughtful suggestion and have expanded the manuscript to clarify how oligodendrocyte maturation relates to the development of Purkinje-cell synchrony. The developmental trajectory of Purkinje-cell synchrony has already been comprehensively characterized by Good et al. (2017, Cell Reports 21: 2066–2073): synchrony drops from a high level at P3–P5 to adult-like values by P8. We found that the myelination in the cerebellum starts to appear from P5-P7 (Figure S1A, B), indicating that the timing of Purkinje cell desynchronization coincides with the initial appearance of oligodendrocytes and myelin in the cerebellum. To determine whether myelin growth could nevertheless modulate this process, we quantified ASPA-positive oligodendrocyte density and MBP-positive bundle thickness and area at P10, P14, P21 and adulthood (Fig. 1J, K, Fig. S1E). Both metrics increase monotonically and clearly lag behind the rapid drop in synchrony, indicating that myelination could be not the primary trigger for the desynchronization. When oligodendrocytes were ablated during the second postnatal week, the synchrony was reduced (new Fig. 2). Thus, once myelination is underway, oligodendrocytes become critical for maintaining the synchrony, acting not as the initiators but as the stabilizers and refiners of the mature network state.

We have added the new subsection in discussion (lines 451–467) now in which we propose a two-phase model. Phase I (P3–P8): High early synchrony is generated by non-myelin mechanisms (e.g. transient gap junctions, shared climbing-fiber input). Phase II (P8-). As oligodendrocytes proliferate and ensheath axons, they fine-tune conduction velocity and stabilize the mature, low-synchrony network state.

We believe these additions fully address the reviewer’s concerns.

(2) Although the study focuses on Purkinje cells in the cerebellum, neural synchrony typically involves cross-regional interactions. Expanding the discussion on how localized Purkinje synchrony affects broader behaviors - such as anxiety, motor function, and sociality - would enhance the findings' functional significance.

We appreciate the reviewer’s helpful suggestion and have expanded the Discussion (lines 543–564) to clarify how localized Purkinje-cell synchrony can influence broader behavioral domains. In the revised text we note that changes in PC synchrony propagate  into thalamic, prefrontal, limbic, and parietal targets, thereby impacting distributed networks involved in motor coordination, affect, and social interaction. Our optogenetic rescue experiments further support this framework, as transient resynchronization of PCs normalized sociability and motor coordination while leaving anxiety-like behavior impaired. This dissociation highlights that different behavioral domains rely to varying degrees on precise cerebellar synchrony and underscores how even localized perturbations in Purkinje timing can acquire system-level significance.

(3) The authors discuss the possibility of oligodendrocyte-mediated synapse elimination as a possible mechanism behind their findings, drawing from relevant recent literature on oligodendrocyte precursor cells. However, there are no data presented supporting this assumption. The authors should explain why they think the mechanism behind their observation extends beyond the contribution of myelination or remove this point from the discussion entirely.

We thank the reviewer for pointing out that our original discussion of oligodendrocyte-mediated synapse elimination was not directly supported by data in the present manuscript. Because we are actively analyzing this question in a separate, follow-up study, we have deleted the speculative passage to keep the current paper focused on the demonstrated, myelination-dependent effects. We believe this change sharpens the mechanistic narrative and fully addresses the reviewer’s concern.

(4) It would be valuable to investigate the secondary effects of oligodendrocyte depletion on other glial cells, particularly astrocytes or microglia, which could influence long-term behavioral outcomes. Identifying whether the lasting effects stem from developmental oligodendrocyte function alone or also involve myelination could deepen the study's insights. 

We thank the reviewer for raising this point and have performed the requested analyses. Using IBA1 immunostaining for microglia and S100b for Bergmann glia, we quantified cell density and these marker signal intensity at P14 and P21. Neither microglial or Bergmann-glial differed between control and oligodendrocyte-ablated mice at either time‐point (new Figure S2). These results indicate that the behavioral phenotypes we report are unlikely to arise from secondary activation or loss of other glial populations.

We now added results (lines 275–286) and also discuss myelination and other oligodendrocyte function (lines 443–450). It remains difficult to disentangle conduction-related effects from myelination-independent trophic roles of oligodendrocytes. We therefore note explicitly that future work employing stage-specific genetic tools or acute metabolic manipulations will be required to parse these contributions more definitively.

(5) The authors should explore the use of different methods to disturb myelin production for a longer time, in order to further determine if the observed effects are transient or if they could have longer-lasting effects.

We agree that distinguishing transient from enduring effects is critical. Importantly, our original submission already included data demonstrating a persistent deficit of PC population synchrony (Fig. 4, previous Fig. 3): (i) at P14—the early age after oligodendrocyte ablation—population synchrony is reduced, and (ii) the same deficit is still present in adults (P60–P70) despite full recovery of ASPA-positive cell density and MBP-area and -thickness (Fig. 2H-K, Fig. S1E, and Fig. 4). We also performed the ablation of oligodendrocytes after the third postnatal week. Despite a similar acute drop in ASPA-positive cells, neither population synchrony nor anxiety-, motor-, or social behaviors differed from littermate controls. Thus, extending myelin disruption beyond the developmental window does not exacerbate or prolong the phenotype, whereas a short perturbation within that window leaves a permanent timing defect. These findings strengthen our conclusion that it is the developmental oligodendrocyte/myelination program itself—rather than ongoing adult myelin production—that is essential for establishing stable network synchrony. We now highlight this point explicitly in the revised Discussion (lines 507–522).

(6) Throughout the paper, there are concerns about statistical analyses, particularly on the use of the Mann-Whitney test or using fields of view as biological replicates.

We appreciate the reviewer’s guidance on appropriate statistical treatment. To address these concerns we have re-analyzed all datasets that contained multiple measurements per animal (e.g., fields of view, lobules, or trials) using nested statistics with animal as the higher-order unit. Specifically, we applied a two-level nested ANOVA when more than two groups were compared and a nested t-test when two conditions were present. The re-analysis confirmed all original conclusions. Because the nested models yielded comparable effect sizes to the Mann–Whitney tests, we have retained the mean ± SEM for ease of comparison with prior literature but now also report all values for each mouse in Table 1. In cases where a single measurement per mouse was compared between two groups, we used the Mann–Whitney test and present the results in the graphs as median values.

Major

(1) The authors present compelling evidence that early loss of myelination disrupts synchronous firing prematurely. However, synchronous neuronal firing does not equate to circuit synchronization. It is improbable that myelination directly generates synchronous firing in Purkinje cells (PCs). For instance, Foran et al. (1992) identified that cerebellar myelination begins around postnatal day 6 (P6), while Good et al. (2017) recorded a developmental decline in PC activity correlation from P5-P11. To clarify myelin's role, we recommend detailed myelin imaging through light microscopy (MBP staining at higher magnification) to assess the extent of myelin removal accurately. Myelin sheaths, as shown by Snaidero et al. (2020), can persist after oligodendrocyte (OL) death, particularly following DTA induction (Pohl et al. 2011). Quantification of MBP+ area, rather than mean MBP intensity, is necessary to accurately measure myelin coverage.

We appreciate the reviewer’s concern that residual sheaths might remain after oligodendrocyte ablation and have therefore re-examined myelin at higher spatial resolution. Then, two independent metrics were extracted: MBP⁺ area fraction in the white matter and MBP⁺ bundle thickness (new Figure 1J, K, and Fig. S1E). We confirm a robust, transient loss of myelin at P10 and P14 as shown by the reduction of MBP⁺ area and MBP⁺ bundle thickness. Both parameters recovered to control values by P21 and adulthood, indicating effective remyelination. These data demonstrate that, in our paradigm, oligodendrocyte ablation is accompanied by substantial sheath loss rather than the persistent myelin reported after acute toxin exposure. We have added them in Result (lines 266–271).

The results reinforce the view that myelin removal and/or loss of trophic support during a narrow developmental window drive the long-term hyposynchrony and behavioral phenotypes we report. We have added the new subsection in discussion (lines 443–450) now in which we propose a two-phase model. Phase I (P3–P8): High early synchrony is generated by non-myelin mechanisms (e.g. transient gap junctions, shared climbing-fiber input). Phase II (P8-). As oligodendrocytes proliferate and ensheath axons, they fine-tune conduction velocity and stabilize the mature, low-synchrony network state. We believe these additions fully address the reviewer’s concerns.

(2) Surprisingly, the authors speculate about oligodendrocyte-mediated synaptic pruning without supportive data, shifting the focus away from the potential impact of myelination. Even if OLs perform synaptic pruning, OL depletion would likely maintain synchrony, yet the opposite was observed. Further characterisation of the model and the potential source of the effect is needed. 

We thank the reviewer for pointing out that our original discussion of oligodendrocyte-mediated synapse elimination was not directly supported by data in the present manuscript. Because we are actively analyzing this question in a separate, follow-up study, we have deleted the speculative passage to keep the current paper focused on the demonstrated, myelination-dependent effects. We believe this change sharpens the mechanistic narrative and fully addresses the reviewer’s concern.

(3) Improved characterization of the DTA model would add clarity. Although almost all infected cells are reported as OLs, quantification of infected OL-lineage cells (e.g., via Olig2 staining) would verify this. It remains possible that observed activity changes are driven by OL-independent demyelination effects. We suggest cross-staining with Iba1 and GFAP to rule out inflammation or gliosis. 

We thank the reviewer for this important suggestion and have expanded our histological characterization accordingly. First, to verify that DTA expression is confined to mature oligodendrocytes, we co-stained cerebellar sections collected 7 days after AAV-hMAG-mCherry injection with Olig2 (pan-OL lineage) and ASPA (mature OL marker) as shown in Figure S1C-D. Quantitative analysis revealed that 100 % of mCherry⁺ cells were Olig2⁺/ASPA⁺, whereas mCherry signal was virtually absent in Olig2⁺/ASPA⁻ immature oligodendrocytes. These data confirm that our DTA manipulation targets mature myelinating OLs rather than earlier lineage stages. We have added them in Result (lines 260–262).

Second, to examine indirect effects mediated by other glia, we performed cross-staining with IBA1 (microglia) and S100β (Bergmann). Cell density and fluorescence intensity for each marker were indistinguishable between control and DTA groups at P14 and P21 (Figure S2A-H). Thus, neither inflammation nor astro-/microgliosis accompanies OL ablation. We have added them in Result (lines 275–286).

Collectively, these results demonstrate that the observed desynchronization and behavioral phenotypes arise from specific loss of mature oligodendrocytes and their myelin, rather than from off-target viral expression or secondary glial responses.

(4) The use of an independent model of myelin loss, such as the inducible Myrf knockout mouse with a MAG promoter, to assess if oligodendrocyte loss causes temporary or sustained impacts, employing an extended knockout model like Myrf cKO with MAG-Cre viral methods would be advantageous.

We agree that distinguishing transient from enduring effects is critical. Importantly, our original submission already included data demonstrating a persistent deficit of PC population synchrony (Fig. 4, previous Fig. 3): (i) at P13-15—the early age after oligodendrocyte ablation—population synchrony is reduced, and (ii) the same deficit is still present in adults (P60–P70) despite full recovery of ASPA-positive cell density and MBP-area and -thickness (Fig. 2H-K, Fig. S1E, and Fig. 4). We also performed the ablation of oligodendrocytes after the third postnatal week. Despite a similar acute drop in ASPA-positive cells, neither population synchrony nor anxiety-, motor-, or social behaviors differed from littermate controls. Thus, extending myelin disruption beyond the developmental window does not exacerbate or prolong the phenotype, whereas a short perturbation within that window leaves a permanent timing defect. These findings strengthen our conclusion that it is the developmental oligodendrocyte/myelination program itself—rather than ongoing adult myelin production—that is essential for establishing stable network synchrony. We now highlight this point explicitly in the revised Discussion (lines 507–522).

(5) For statistical robustness, the use of non-parametric tests (Mann-Whitney) necessitates reporting the median instead of the mean as the authors do. Furthermore, as repeated measurements within the same animal are not independent, the authors should ideally use nested ANOVA (or nested t-test comparing two conditions) to validate their findings (Aarts et al., Nat. Neuroscience 2014). Alternatively use one-way ANOVA with each animal as a biological replicate, although this means that the distribution in the data sets per animal is lost.

We appreciate the reviewer’s guidance on appropriate statistical treatment. To address these concerns we have re-analyzed all datasets that contained multiple measurements per animal (e.g., fields of view, lobules, or trials) using nested statistics with animal as the higher-order unit. Specifically, we applied a two-level nested ANOVA when more than two groups were compared and a nested t-test when two conditions were present. The re-analysis confirmed all original conclusions. Because the nested models yielded comparable effect sizes to the Mann–Whitney tests, we have retained the mean ± SEM for ease of comparison with prior literature but now also report all values for each mouse in Table 1. In cases where a single measurement per mouse was compared between two groups, we used the Mann–Whitney test and present the results in the graphs as median values.

Minor Points 

(1) In all figures, please specify the ages at which each procedure was conducted, as demonstrated in Figure 2A.

All main and supplementary figures now specify the exact postnatal age.

(2) Clarify the selection criteria for regions of interest (ROI) in calcium imaging, and provide representative ROIs.

We appreciate the reviewer’s guidance. We have clarified that our ROI detection followed the protocol reported by our previous paper (Tanigawa et al., 2024, Communications Biology) (lines 177-178) and representative Purkinje cell ROIs are now shown in Fig. 2B.

(3) Include data on the proportion of climbing fiber or inferior olive neurons expressing Kir and the total number of neurons transfected, which would help contextualize the observed effects on PC synchronization and its broader implications for cerebellar circuit function.

We appreciate the reviewer’s guidance. New Fig. 7C summarizes the efficiency of AAV-GFP and AAV-Kir2.1-GFP injections into the inferior olive. Across 4 mice PCs with GFP-labeled CFs was detected in 19.3 ± 11.9 (mean ± S.D.) % for control and 26.2 ± 11.8 (mean ± S.D.) % for Kir2.1 of PCs. These numbers are reported in the Results (lines 373–375).

(4) Higher magnification images in Figures 1 and S3 would improve visual clarity. 

We have addressed the request for higher-magnification images in two ways. First, all panels in Figure S3 were placed on a larger canvas. Second, in Figure 1 we adjusted panel sizes to emphasize fine structure: panel 1C already represents an enlargement of the RFP positive cells shown in 1B, and panel 1H and 1J now occupies a wider span so that every ASPA-positive cell body can be distinguished. Should the reviewer still require an even closer view, we have additional ready for upload.

(5) Consider language editing to enhance overall clarity and readability.

The entire manuscript was edited to improve flow, consistency, and readability.

(6) Refine the discussion to align with the presented data.

We have refined the discussion.

Thank you once again for your constructive suggestions and comments. We believe these changes have improved the clarity and readability of our manuscript.

Reviewer #2 (Public review):

We appreciate Reviewer #2’s positive evaluation of our work and thank him/her for the constructive suggestions and comments. We followed these suggestions and comments and have conducted additional experiments. We have rewritten the manuscript and revised the figures according to the points Reviewer #1 mentioned. Our point-by-point responses to the comments are as follows.

Summary:

In this manuscript, the authors use genetic tools to ablate oligodendrocytes in the cerebellum during postnatal development. They show that the oligodendrocyte numbers return to normal post-weaning. Yet, the loss of oligodendrocytes during development seems to result in decreased synchrony of calcium transients in Purkinje neurons across the cerebellum. Further, there were deficits in social behaviors and motor coordination. Finally, they suppress activity in a subset of climbing fibers to show that it results in similar phenotypes in the calcium signaling and behavioral assays. They conclude that the behavioral deficits in the oligodendrocyte ablation experiments must result from loss of synchrony. 

Strengths:

Use of genetic tools to induce perturbations in a spatiotemporally specific manner.

We appreciate these positive evaluation.

Weaknesses: 

The main weakness in this manuscript is the lack of a cohesive causal connection between the experimental manipulation performed and the phenotypes observed. Though they have taken great care to induce oligodendrocyte loss specifically in the cerebellum and at specific time windows, the subsequent experiments do not address specific questions regarding the effect of this manipulation.

Calcium transients in Purkinje neurons are caused to a large extent by climbing fibers, but there is evidence for simple spikes to also underlie the dF/F signatures (Ramirez and Stell, Cell Reports, 2016).

We thank the reviewer for drawing attention to the work of Ramirez & Stell (2016), which showed that simple-spike bursts can elicit Ca²⁺ rises, but only in the soma and proximal dendrites of adult Purkinje cells. In our study, Regions of Interest were restricted to the dendritic arbor, where SS-evoked signals are essentially undetectable (Ramirez & Stell, 2016), whereas climbing-fiber complex spikes generate large, all-or-none transients (Good et al., 2017). Accordingly, even if a rare SS-driven event reached threshold it would likely fall outside our ROIs.

In addition, we directly imaged CF population activity by expressing GCaMP7f in inferior-olive neurons. Correlation analysis of CF boutons revealed that DTA ablation lowers CF–CF synchrony at P14 (new Fig. 3A–D). Because CF synchrony is a principal driver of Purkinje-cell co-activation, this reduction provides a mechanistic link between oligodendrocyte loss and the hyposynchrony we observe among Purkinje cells. Consistent with this interpretation, electrophysiological recordings showed that parallel-fiber EPSCs and inhibitory synaptic inputs onto Purkinje cells were unchanged by DTA treatment (Fig. 3E-H) , which makes it less likely that the reduced synchrony simply reflects changes in other excitatory or inhibitory synaptic drive.

That said, SS-dependent somatic Ca²⁺ signals could still influence downstream plasticity and long-term cerebellar function. In future work we therefore plan to combine somatic imaging with stage-specific oligodendrocyte manipulations to test whether SS-evoked Ca²⁺ dynamics are themselves modulated by oligodendrocyte support. We have added these descriptions in the Results (lines 288–294) and Discussion (lines 423–434).

Also, it is erroneous to categorize these calcium signals as signatures of "spontaneous activity" of Purkinje neurons as they can have dual origins.

Thank you for pointing out the potential ambiguity. In the revised manuscript we have clarified how we use the term “spontaneous activity” in the context of our measurements (lines 289-290). Our calcium imaging was restricted to the dendritic arbor of Purkinje cells, where calcium transients are dominated by climbing-fiber (CF) inputs (Ramirez & Stell, 2016; Good et al., 2017). Thus, the synchrony values reported here primarily reflect CF-driven complex spikes rather than mixed signals of dual origin. We have revised the Results section accordingly (lines 289–293) to make this measurement-specific limitation explicit.

Further, the effect of developmental oligodendrocyte ablation on the cerebellum has been previously reported by Mathis et al., Development, 2003. They report very severe effects such as the loss of molecular layer interneurons, stunted Purkinje neuron dendritic arbors, abnormal foliations, etc. In this context, it is hardly surprising that one would observe a reduction of synchrony in Purkinje neurons (perhaps due to loss of synaptic contacts, not only from CFs but also from granule cells).

We appreciate the reviewer’s comparison to Mathis et al. (2003). Mathis et al. used MBP–HSV-TK transgenic mice in which systemic FIAU treatment eliminates oligodendrocytes. When ablation began at P1, they observed severe dysmorphology—loss of molecular-layer interneurons, Purkinje-cell (PC) dendritic stunting, and abnormal foliation. Crucially, however, the same study reports that starting the ablation later (FIAU from P6-P20) left cerebellar cyto-architecture entirely normal.

Our AAV MAG-DTA paradigm resembles this later window. Our temporally restricted DTA protocol produces the same ‘late-onset’ profile—robust yet reversible hypomyelination with no loss of Purkinje cells, interneurons, dendritic length, or synaptic input (new Fig. S1–S2, Fig. 3E-H). The enduring hyposynchrony we report therefore cannot be attributed to the dramatic anatomical defects seen after prenatal ablation, but instead reveals a specific requirement for early-postnatal myelin in stabilizing PC synchrony, especially affecting CF-CF synchrony.

This clarification shows that we have carefully considered the Mathis model and that our findings not only replicate, but also extend the earlier work. We have added these description in Result (lines 273-286)

The last experiment with the expression of Kir2.1 in the inferior olive is hardly convincing.

We appreciate the reviewer’s concern and have reinforced the causal link between Purkinje-cell synchrony and behavior. To test whether restoring PC synchrony is sufficient to rescue behavior, we introduced a red-shifted opsin (AAV-L7-rsChrimine) into PCs of DTA mice raised to adulthood. During testing we delivered 590-nm light pulses (10 ms, 1 Hz) to the vermis, driving brief, population-wide spiking (new Fig. 8). This periodic re-synchronization left anxiety measures unchanged (open-field center time remained low) but rescued both motor coordination (rotarod latency normalized to control levels) and sociability (time spent with a novel mouse restored). The dissociation implies that distinct behavioral domains differ in their sensitivity to PC timing precision and confirms that reduced synchrony—not cell loss or gross circuit damage (Fig. S1F, S2)—is the primary driver of the motor and social deficits. Together, the optogenetic rescue establishes a bidirectional, mechanistic link between PC synchrony and behavior, addressing the reviewer’s reservations about the original experiment. We have added these descriptions in Result (lines 394-415)

In summary, while the authors used a specific tool to probe the role of developmental oligodendrocytes in cerebellar physiology and function, they failed to answer specific questions regarding this role, which they could have done with more fine-grained experimental analysis.

Thank you once again for your constructive suggestions and comments. We believe these changes have improved the clarity and readability of our manuscript.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Show that ODC loss is specific to the cerebellum.

We thank the reviewer for requesting additional evidence. To verify that oligodendrocyte ablation was confined to the cerebellum, we injected an AAV carrying mCherry under the human MAG promoter (AAV-hMAG-mCherry) into the cerebellum, and screened the whole brain one week later. As shown in the new Figure 1E–G, mCherry positive cells were present throughout the injected cerebellar cortex (Fig. 1E), but no fluorescent cells were detected in extracerebellar regions—including cerebral cortex, medulla, pons, midbrain. These data demonstrate that our viral approach are specific to the cerebellum, ruling out off-target demyelination elsewhere in the CNS as a contributor to the behavioral and synchrony phenotypes. We have added these descriptions in Result (lines 262-264)

(2) Characterize the gross morphology of the cerebellum at different developmental stages. Are major cell types all present? Major pathways preserved? 

We thank the reviewer for requesting additional evidence. To ensure that the developmental loss of oligodendrocytes did not globally disturb cerebellar architecture, we performed a comprehensive histological and electrophysiological survey during development. New data are presented (new Fig. S1–S2, Fig. 3E-H).

(1) Overall morphology. Low-magnification parvalbumin counterstaining revealed similar cerebellar area in DTA versus control mice at every age (Fig. S1F, G).

(2) Major neuronal classes. Quantification of parvalbumin-positive Purkinje cells and interneurons showed no differences in density between control and DTA (Fig. S2E, H, M, N, P). Purkinje dendritic arbors were not different between control and DTA (Fig. S2G, O).

(3) Excitatory and inhibitory synapse inputs. Miniature IPSCs and Parallel-fiber-EPSCs onto Purkinje cells were quantified; neither was differed between groups (Fig. 3E-G).

(4) Glial populations. IBA1-positive microglia and S100β-positive astrocytes exhibited normal density and marker intensity (Fig. S2).

Taken together, these analyses show that all major cell types are present at normal density, synaptic inputs from excitatory and inhibitory neurons are preserved, and gross cerebellar morphology is intact after DTA-mediated oligodendrocyte ablation.

(3) Recording of PNs to see whether the lack of synchrony is due to CFs or simple spikes.

We thank the reviewer for drawing attention to the work of Ramirez & Stell (2016), which showed that simple-spike bursts can elicit Ca²⁺ rises, but only in the soma and proximal dendrites of adult Purkinje cells. In our study, Regions of Interest were restricted to the dendritic arbor, where SS-evoked signals are essentially undetectable (Ramirez & Stell, 2016), whereas climbing-fiber complex spikes generate large, all-or-none transients (Good et al., 2017). Accordingly, even if a rare SS-driven event reached threshold it would likely fall outside our ROIs.

In addition, we directly imaged CF population activity by expressing GCaMP7f in inferior-olive neurons. Correlation analysis of CF boutons revealed that DTA ablation lowers CF–CF synchrony at P14 (new Fig. 3A–D). Because CF synchrony is a principal driver of Purkinje-cell co-activation, this reduction provides a mechanistic link between oligodendrocyte loss and the hyposynchrony we observe among Purkinje cells. Consistent with this interpretation, electrophysiological recordings showed that parallel-fiber EPSCs and inhibitory synaptic inputs onto Purkinje cells were unchanged by DTA treatment (Fig. 3E-H) , which makes it less likely that the reduced synchrony simply reflects changes in other excitatory or inhibitory synaptic drive.

That said, SS-dependent somatic Ca²⁺ signals could still influence downstream plasticity and long-term cerebellar function. In future work we therefore plan to combine somatic imaging with stage-specific oligodendrocyte manipulations to test whether SS-evoked Ca²⁺ dynamics are themselves modulated by oligodendrocyte support. We have added these descriptions in the Results (lines 301–312) and Discussion (lines 423–434).

(4) Is CF synapse elimination altered? Test using evoked EPSCs or staining methods.

We agree that directly testing whether oligodendrocyte loss disturbs climbing-fiber synapse elimination would provide a full mechanistic picture. We are already quantifying climbing fiber terminal number with vGluT2 immunostaining and recording evoked CF-EPSCs in the same DTA model; these data, together with an analysis of how population synchrony is involved in synapse elimination, will form the basis of a separate manuscript now in preparation. To keep the present paper focused on the phenomena we have rigorously documented—transient oligodendrocyte loss and the resulting long-lasting hyposynchrony and abnormal behaviors—we have removed the speculative sentence on oligodendrocyte-mediated synapse elimination. We believe this revision meets the reviewer’s request without over-extending the current dataset.

Thank you once again for your constructive suggestions and comments. We believe these changes have improved the clarity and readability of our manuscript.

Source? Notice we can't just focus on all the intersectionalities during an analysis. I for sure would love to only recommend Open Source games made by minoritised people through a local research citizen science exchange, in paid working labour condtions, without stolen content, no washing marketing campaigns, with accessibility features, with a proven social impact, and made using devices without rare exploitative materials... but this ain't possible.

We pick our fights, for me it's biases, because they influence most of our daily acts, but activism has many other sides. I just don't think jumping into activism without awareness of bias is a safe avenue, as it can lead to radical violence as a means of change.

Yes, but don't synonimise Plato's world of ideas to the Web's Internet of things. What I mean by this is that both are erroneous dichotomies, but they are different dichotomies. Believing in free will and a soul doesn't mean you separate the influences of the virtual-online, and the day-to-day physical space. They may be both real, but this conceptualisation can be a useful communicative tool to put into perspective that before globalisation you couldn't simply receive an email in 1 second from someone 10k miles away.

I hope it's not just American soldiers... and wydm just soldiers? If This War of Mine showed us something, it's that soldiers are not the only victims of war.

Beware, Brianna Wu is a TERF anti-peace "activist". She was not in favour of Palestine, and although she has post-traumatic stress disorder from GamerGate, she has once mostly switched sides.

Synonym of celebrate. https://www.merriam-webster.com/dictionary/praise#synonyms

The derringer is a type of handgun.

Here the reference is to the Black Panther Party: do you remember which singer (or which song) among the previous ones mentioned it?

Lamar compares the civil war between two African ethnic groups (Zulu and Xhosa) to the street fighting between the Crips and the Bloods. Indeed, street fighting between local gangs is read by the singer as a form of civil war since it involves people that live in the same area. A bitter ending awaits those who kill each other ("only death settle(s) the score").

Lamar refuses to accept that Blacks are "doomed from the start": making use of a sport metaphor, he speaks in terms of a "race", in which everyone begins from the same starting point.

Double consciousness is a key concept to interpret this line: Lamar feels like a prisoner in his own head, enchained by his own thoughts. This occurs because he has internalized a way of perceiving and judging reality which pertains to the oppressor (in this case,whites).

According to you and to what we have been examined in the explanatory part, what does this line refer to? Clue: focus on the word "penitentiary".

These lines pose a critique towards societal impositions: Lamar feels irrelevant and deprived of any importance in American society. However, this feeling entirely depends on what white people have been telling him. Once again, double consciousness dominates the self.

As you may have gathered by now, Lamar's song has no filters: although he acknowledges the hierarchy that forces his community to remain "at the bottom of mankind", he does not feel inferior. On the contrary, he is proud of his identity and his African ancestry, so much as he does not hesitate in judging the oppressors.

Lamar's viewpoint is crystal clear: not only is there a social hierarchy in America, but also he identifies black as the ones "at the bottom". There is no possible equality in this scenario.

This sentence functions as an explanation of the previous one: Lamar claims that he may be experiencing life in a schizophrenic way but blames whites (the ideal interlocutors in this scenario) for it.

"l'attribut" serait plus approprié non ?

Nombre

eLife Assessment

By investigating spine nanostructure and dynamics across multiple genetic mouse models for neurodevelopmental disorders, this important study has the potential to uncover convergent or divergent synaptic phenotypes that may be specifically associated with autism versus schizophrenia risk. While the imaging and breadth are impressive, there are potential methodological concerns, especially around statistical analyses, which render the evidence incomplete and should be addressed. The purely in vitro nature of the study also slightly limits the generalisability of the findings.

Reviewer #1 (Public review):

Summary:

Kashiwagi et al. undertook a population analysis of dendritic spine nanostructure applied to the objective grouping of 8 mouse models of neuropsychiatric disorders. They report that spine morphology in cultured hippocampal neurons shows a higher similarity among schizophrenia mouse models (compared with autism spectrum disorder (ASD) mouse models), and identify an effect of Ecrg4 (encoding small secretory peptides) on spine dynamics and shape in these models.

Strengths:

The study developed a method for objectively comparing spine properties in primary hippocampal neuron cultures from 8 mouse models of psychiatric disorders at the population level using high-resolution structured illumination microscopy (SIM) imaging. This novel technique identified two distinct groups of mouse models according to the population-level spine properties: those with ASD-related gene mutations and those with schizophrenia-related gene mutations. Functional studies, including gene knockdown and overexpression experiments, identified an effect of Ecrg4 on the spine phenotype of the schizophrenia model mice.

Weaknesses:

The main weakness is that the study is wholly in vitro, using cultured hippocampal neurons. The authors present this as an advantage, however, arguing that spine morphology as measured in a reduced culture system can demonstrate direct effects of gene mutations on neuronal phenotypes in the absence of indirect influences from nonneuronal cells or specific environments.

Another weakness is that CaMKIIαK42R/K42R mutant mice are presented as a schizophrenia model, the authors justifying this by saying that "CaMKII-related signaling pathway disruption has been implicated in the working memory deficits found in schizophrenia patients". Since mutations in CAMK2A cause autosomal dominant intellectual developmental disorder-53 (OMIM 617798) and autosomal recessive intellectual developmental disorder-63 (OMIM 618095), and mice carrying the CAMK2A E183V mutation exhibit ASD-related synaptic and behavioral phenotypes (PMID: 28130356), I think it's stretching credibility to refer to the CaMKIIαK42R/K42R mice as a schizophrenia model.

Although the manuscript is largely well written, there are some instances of ambiguous/unspecific language. This extends to the title (Decoding Spine Nanostructure in Mental Disorders Reveals a Schizophrenia-1 Linked Role for Ecrg4), which gives no indication that the work was in vitro on cultured neurons derived from mouse models.

Reviewer #2 (Public review):

Okabe and colleagues build on a super-resolution-based technique that they have previously developed in cultured hippocampal neurons, improving the pipeline and using it to analyze spine nanostructure differences across 8 different mouse lines with mutations in autism or schizophrenia (Sz) risk genes/pathways. It is a worthy goal to try to use multiple models to examine potential convergent (or not) phenotypes, and the authors have made a good selection of models. They identify some key differences between the autism versus the Sz risk gene models, primarily that dendritic spines are smaller in Sz models and (mostly) larger in autism risk gene models. They then focus on three models (2 Sz - 22q11.2 deletion, Setd1a; 1 ASD - Nlgn3) for time-lapse imaging of spine dynamics, and together with computational modelling provide a mechanistic rationale for the smaller spines in Sz risk models. Bulk RNA sequencing of all 8 model cultures identifies several differentially expressed genes, which they go on to test in cultures, finding that ecgr4 is upregulated in several Sz models and its misexpression recapitulates spine dynamics changes seen in the Sz mutants, while knockdown rescues spine dynamics changes in the Sz mutants. Overall, these have the potential to be very interesting findings and useful for the field. However, I do have a number of major concerns.

(1) The main finding of spine nanostructure changes is done by carrying out a PCA on various structural parameters, creating spine density plots across PC1 and PC2, and then subtracting the WT density plot from the mutant. Then, spines in the areas with obvious differences only are analyzed, from which they derive the finding that, for example, spine sizes are smaller. However, this seems a circular approach. It is like first identifying where there might be a difference in the data, then only analyzing that part of the data. I welcome input from a statistician, but to me, this is at best unconventional and potentially misleading. I assume the overall means are not different (although this should be included), but could they look at the distribution of sizes and see if these are shifted?

(2) Despite extracting 64 parameters describing spine structure, only 5 of these seemed to be used for the PCA. It should be possible to use all parameters and show the same results. More information on PC1 and PC2 would be helpful, given that the rest of the paper is based on these - what features are they related to? These specific features could then be analyzed in the full dataset, without doing the cherry picking above. It would also be helpful to demonstrate whether PC1 and 2 differ across groups - for example, the authors could break their WT data into 2 subsets and repeat the analysis.

(3) Throughout the paper, the 'n' used for statistical analysis is often spine, which is not appropriate. At a minimum, cell should be used, but ideally a nested mixed model, which would take into account factors like cell, culture, and animal, would be preferable. Also, all of these factors should be listed, with sufficient independent cultures.

(4) The authors should confirm that all mutants are also on the C57BL/6J background, and clarify whether control cultures are from littermates (this would be important). Also, are control versus mutant cultures done simultaneously? There can be significant batch effects with cultures.

(5) The spine analysis uses cultures from 18-22 DIV - this is quite a large range. It would be worth checking whether age is a confounder or correlated with any parameters / principal components.

(6) The computational modelling is interesting, but again, I am concerned about some circularity. Parameter optimization was used to identify the best fit model that replicated the spine turnover rates, so it is somewhat circular to say that this matched the observations when one of these is the turnover rate. It is more convincing for spine density and size, but why not go back and test whether parameter differences are actually seen - for example, it would be possible to extract the probability of nascent spine loss, etc. More compelling would be to repeat the experiments and see if the model still fits the data. In the interpretation (line 314-318) it is stated that '... reduced spine maturation rate can account for the three key properties of schizophrenia-related spines...', which is interesting if true, but it has just been stated that the probability of spine destabilization is also higher in mutants (line 303) - the authors should test whether if the latter is set to be the same as controls whether all the findings are replicated.

(7) No validation for overexpression or knockdown is shown, although it is mentioned in the methods - please include. Also, for the knockdown, a scrambled shRNA control would be preferable.

(8) The finding regarding ecgr4 is interesting, but showing that some ecgr4 is expressed at boutons and spines and some in DCVs is not enough evidence to suggest that actively involved in the regulation of synapse formation and maturation (line 356).

(9) The same caveats that apply to the analysis also apply to the ecgr4 rescue. In addition, while for 22q the control shRNA mutant vs WT looks vaguely like Figure 2, setd1a looks completely different. And if rescued, surely shRNA in the mutant should now resemble control in WT, so there shouldn't be big differences, but in fact, there are just as many differences as comparing mutant vs wildtype? Plus, for spine features, they only compare mutant rescue with mutant control, but this is not ideal - something more like a 2-way ANOVA is really needed. Maybe input from a statistician might be useful here?

(10) Although this is a study entirely focused on spine changes in mouse models for Sz, there is no discussion (or citation) of the various studies that have examined this in the literature. For example, for Setd1a, smaller spines or reduced spine densities have been described in various papers (Mukai et al, Neuron 2019; Chen et al, Sci Adv 2022; Nagahama et al, Cell Rep 2020).

(11) There is a conceptual problem with the models if being used to differentiate autism risk from Sz risk genes. It is difficult to find good mouse models for Sz, so the choice of 22q11.2del and Setd1a haploinsufficiency is completely reasonable. However, these are both syndromic. 22qdel syndrome involves multiple issues, including hearing loss, delayed development, and learning disabilities, and is associated with autism (20% have autism, as compared to 25% with Sz). Similarly, Setd1a is also strongly associated with autism as well as Sz (and also involves global developmental delay and intellectual disability). While I think this is still the best we can do, and it is reasonable to say that these models show biased risk for these developmental disorders, it definitely can't be used as an explanation for the higher variability seen in the autism risk models.

(12) I am not convinced that using dissociated cultures is 'more likely to reflect the direct impact of schizophrenia-related gene mutations on synaptic properties' - first, cultures do have non-neuronal cells, although here glial proliferation was arrested at 2 days, glia will be present with the protocol used (or if not, this needs demonstrating). Second, activity levels will affect spine size, and activity patterns are very abnormal in dissociated cultures, so it is very possible that spine changes may not translate into in vivo scenarios. Overall, it is a weakness that the dissociated culture system has been used, which is not to say that it is not useful, and from a technical and practical perspective, there are good justifications.

(13) As a minor comment, the spine time-lapse imaging is a strength of the paper. I wonder about the interpretation of Figure 5. For example, the results in Figure 5G and J look as if they may be more that the spines grow to a smaller size and start from a smaller size, rather than necessarily the rate of growth.

Author response:

Reviewer #1

(1) The main weakness is that the study is wholly in vitro, using cultured hippocampal neurons.

We appreciate this reviewer's concern about the limitation of cultured hippocampal neurons in extracting disease-related spine phenotypes. While we fully recognize this limitation, we consider that this in vitro system has several advantages that contribute to translational research on mental disorders.

First, our culture system has been shown to support the development of spine morphology similar to that of the hippocampal CA1 excitatory synapse in vivo. High-resolution imaging techniques confirmed that the in vitro spine structure was highly preserved compared with in vivo preparations (Kashiwagi et al., Nature Communications, 2019). The present study used the same culture system and SIM imaging. Therefore, the difference we detected in samples derived from disease models is likely to reflect impairment of molecular mechanisms underlying native structural development in vivo.

Second, super-resolution imaging of thousands of spines in tissue preparations under precisely controlled conditions cannot be practically applied using currently available techniques. The advantage of our imaging and analytical pipeline is its reproducibility, which enabled us to compare the spine population data from eight different mouse models without normalization.

Third, a reduced culture system can demonstrate the direct effects of gene mutations on synapse phenotypes, independent of environmental influences. This property is highly advantageous for screening chemical compounds that rescue spine phenotypes. Neuronal firing patterns and receptor functions can also be easily controlled in a culture system. The difference in spine structure between ASD and schizophrenia mouse models is valuable information to establish a drug screening system.

Fourth, establishing an in vitro system for evaluating synapse phenotypes could reduce the need for animal experiments. Researchers should be aware of the 3Rs principles. In the future, combined with differentiation techniques for human iPS cells, our in vitro approach will enable the evaluation of disease-related spine phenotypes without the need for animal experiments. The effort to establish a reliable culture system should not be eliminated.

(2) Another weakness is that CaMKIIαK42R/K42R mutant mice are presented as a schizophrenia model.

We agree with this reviewer that CAMK2A mutations in humans are linked to multiple mental disorders, including developmental disorders, ASD, and schizophrenia. Association of gene mutations with the categories of mental disorders is not straightforward, as the symptoms of these disorders also overlap with each other. For the CaMKIIα K42R/K42R mutant, we considered the following points in its characterization as a model of mental disorder. Analysis of CaMKIIα +/- mice in Dr. Tsuyoshi Miyakawa's lab has provided evidence for the reduced CaMKIIα in schizophrenia-related phenotypes (Yamasaki et al., Mol Brain 2008; Frankland et al., Mol Brain Editorial 2008). It is also known that the CaMKIIα R8H mutation in the kinase domain is linked to schizophrenia (Brown et al., 2021). Both CaMKIIα R8H and CaMKIIα K42R mutations are located in the N-terminal domain and eliminate kinase activity. On the other hand, the representative CaMKIIα E183V mutation identified in ASD patients exhibits unique characteristics, including reduced kinase activity, decreased protein stability and expression levels, and disrupted interactions with ASD-associated proteins such as Shank3 (Stephenson et al., 2017). Importantly, reduced dendritic spines in neurons expressing CaMKIIα E183V is a property opposite to that of the CaMKIIα K42R/K42R mutant, which showed increased spine density (Koeberle et al. 2017).

Different CAMK2A mutations likely cause distinct phenotypes observed in the broad spectrum of mental disorders. In the revised manuscript, we will include a discussion of the relevant literature to categorize this mouse model appropriately.

References related to this discussion.

(1) Yamasaki et al., Mol Brain. 2008 DOI: 10.1186/1756-6606-1-6

(2) Frankland et al. Mol Brain. 2008 DOI: 10.1186/1756-6606-1-5

(3) Stephenson et al., J Neurosci. 2017 DOI: 10.1523/JNEUROSCI.2068-16.2017

(4) Koeberle et al. Sci Rep. 2017 DOI: 10.1038/s41598-017-13728-y

(5) Brown et al., iScience. 2021 DOI: 10.1016/j.isci.2021.103184

Reviewer #2

We recognize the reviewer's comments as important for improving our manuscript. We outline our general approach to addressing major concerns. Detailed responses to each point, along with additional data, will be provided in a formal revised manuscript.

(1) Demonstrating the robustness of statistical analyses

We appreciate this reviewer's concern about our strategies for the quantitative analysis of the large spine population. For the PCA analysis (Point 2), our preliminary results indicated that including all parameters or the selected five parameters did not make a significant difference in the relative placement of spines with specific morphologies in the feature space defined by the principal components. This point will be discussed in the revised manuscript. The potential problem of selecting a particular region within a feature space for spine shape analysis (Point 1) can be addressed by using alternative simulation-based approaches, such as bootstrap or permutation tests. These analyses will be included in the revised manuscript. The use of sample numbers in statistical analyses should align with the analysis's purpose (Point 3). When analyzing the distribution of samples in the feature space, it is necessary to use spine numbers for statistical assessment. We will recheck the statistical methods and apply the appropriate method for each analysis. The spine population data in Figures 2 and 8 cannot be directly compared, as the spine visualization methods differ (Figure 2 with membrane DiI labeling; Figure 8 with cytoplasmic GFP labeling) (Point 9). Spine populations of the same size are inevitably plotted in different feature spaces. This point will be discussed more clearly in the revised manuscript.

(2) Clarification of experimental conditions and data reliability

Per this reviewer's suggestion, we will provide more information on the genetic background of mice and the differences in spine structure from DIV 18-22 (Points 4 and 5). We will also provide additional validation data for the functional analyses using knockdown and overexpression methods, for which we already have preliminary data (Point 7). Concerns about the interpretation of data obtained from in vitro culture (Point 12), raised by this reviewer, are also noted by reviewer #1. As explained in the response to reviewer #1, we intentionally selected an in vitro culture system to analyze multiple samples derived from mouse models of mental disorders for several reasons. Nevertheless, we will revise the discussion and incorporate the points this reviewer raised regarding the disadvantages of in vitro systems.

(3) Validation of biological mechanisms and interpretation

In the computational modeling (Point 6), we started from the data of spine turnover (excluding the data of spine volume increase/decrease), fitted the model with the data, and found that the best-fit model showed three features: fast spine turnover, lower spine density, and smaller size of transient spines in schizophrenia mouse models. As the reviewer noted, information about spine turnover is already present in the input data. However, the other two properties are generated independently of the input data, indicating the value of this model. We plan to add additional confirmatory analyses to this model in the revised manuscript.

In response to Point 8, we will provide supporting data on the functional role of Ecgr4 in synapse regulation. We will also refine our discussion on the ASD and Schizophrenia phenotypes based on the suggested literature (Points 10 and 11). Quantification of the initial growth of spines is technically demanding, as it requires higher imaging frequency and longer time-lapse recordings to capture rare events. It is difficult to conclude which of the two possibilities, slow spine growth or initial size differences, is correct, based on our available data. This point will be discussed in the revised manuscript (Point 13).

eLife Assessment

This useful study provides a systematic and solid comparison of sex-biased enteroendocrine peptide expression, including AstC and Tk, to show that these peptides contribute to female-biased fat storage. The major research question of this study is based on the authors' previous papers, and therefore, the presented results are incremental. This study serves as a foundation for future investigation of regulatory mechanisms for the sex-biased fat content by AstC and Tk.

Reviewer #1 (Public review):

Summary of goals:

The authors' stated goal (line 226) was to compare gene expression levels for gut hormones between males and females. As female flies contain more fat than males, they also sought to identify hormones that control this sex difference. Finally, they attempted to place their findings in the broader context of what is already known about established underlying mechanisms.

Strengths:

(1) The core research question of this work is interesting. The authors provide a reasonable hypothesis (neuro/entero-peptides may be involved) and well-designed experiments to address it.

(2) Some of the data are compelling, especially positive results that clearly implicate enteropeptides in sex-biased fat contents (Figures 1 and 3).

Weaknesses:

(1) The greatest weakness of this work is that it falls short of providing a clear mechanism for the regulation of sex-biased fat content by AstC and Tk. By and large, feminization of neurons or enteroendocrine cells with UAS-traF did not increase fat in males (Figure 2). The authors mention that ecdysone, juvenile hormone or Sex-lethal may instead play a role (lines 258-270), but this is speculative, making this study incomplete.

(2) Related to the above point, the cellular mechanisms by which AstC and Tk regulate fat content in males and females are only partially characterized. For example, knockdown of TkR99D in insulin-producing neurons (Figure 4E) but not pan-neuronally (Figure 4B) increases fat in males, but Tk itself only shows a tendency (Figure 3B). In females, the situation is even less clear: again, Tk only shows a tendency (Figure 3B), and pan-neuronal, but not IPC-specific knockdown of TkR99D decreases fat.

(3) The text sometimes misrepresents or contradicts the Results shown in the figures. UAS-traF expression in neurons or enteroendocrine cells did sometimes alter fat contents (Figure 2H, S), but the authors report that sex differences were unaffected (lines 164-166). On the other hand, although knockdown of Tk in enteroendocrine cells caused no significant effect (Figure 3B), the authors report this as a trend towards reduction (lines 182-183). This biased representation raises concerns about the interpretation of the data and the authors' conclusions.

(4) The authors find that in males, neuropeptide expression in the head is higher (Figure 1F-J). This may also play an important role in maintaining lower levels of fat in males, but this finding is not explored in the manuscript.

Appraisal of goal achievement & conclusions:

The authors were successful in identifying hormones that show sex bias in their expression and also control the male vs. female difference in fat content. However, elucidation of the relevant cellular pathways is incomplete. Additionally, some of their conclusions are not supported by the data (see Weaknesses, point 3).

Impact:

It is difficult to evaluate the impact of this study. This is in great part because the authors do not attempt to systematically place their findings about AstC/Tk in the broader context of their previous studies, which investigated the same phenomenon (Wat et al., 2021, eLife and Biswas et al., 2025, Cell Reports). As the underlying mechanisms are complex and likely redundant, it is necessary to generate a visual model to explain the pathways which regulate fat content in males and females.

Reviewer #2 (Public review):

Summary:

This manuscript by Biswas and Rideout investigates sex differences in the expression and function of hormones derived from Drosophila enteroendocrine cells (EE). The authors report that while whole-body and head expression of several EE hormones (AstA, AstC, Tk, NPF, Dh31) is male-biased, gut-specific expression of AstC, Tk, and NPF is female-biased. Intriguingly, this sex-specific effect is not dependent on Tra - a surprising and important result. The authors then used an RNAi-based approach to demonstrate that gut-derived AstC and Tk promote fat storage specifically in females. Similar effects are observed when their receptors are knocked down in neurons. In addition, the authors were able to demonstrate that while Tk promotes female body fat via the insulin-producing cells. Together, these findings suggest that EE cell-derived hormones contribute to sex-specific fat storage regulation.

Strengths:

Overall, I find the paper quite interesting. While the findings are brief, they reveal novel aspects of the sex-specific lipid storage program that I believe are important. As noted by the authors in the discussion, there are many open questions, including how these neuronal effects translate into systemic sex-specific regulation of lipid storage. Regardless, I find the results to be convincing - this paper will serve as the launching point of many future studies.

Weaknesses:

My main criticisms are focused on two points:

(1) If the sex specific differences are eliminated by tra overexpression, what else might be responsible? As the authors note, the differences in 20E titers might be responsible. I would encourage the authors to simply feed adult flies with food containing 20E and determine if this alters sex-specific 20E expression.

(2) I'm quite intrigued by the discovery that Tra does not eliminate the sex-specific differences. There are quite a few recent studies demonstrating that fruitless influences sex-specific neuronal function - here to I would encourage the authors to examine whether this aspect of the sex-determination pathway is involved in the lipid accumulation phenotype.

TaskGroup を利用していること自体はコードから読み取れるため、 コメントとしては 「複数のタスクを TaskGroup に登録して並行実行する」 といった 処理の意図がわかる内容にした方がよいかなと思いました。

print しているのは自明なので、コメントとして記載しなくてもよいかと思いました。（ノイズになりそうなので）

ちょっと意味をとらえずらかったので、 「イベントループを生成するファクトリー関数」とするのはどうでしょう？

ここから下のインデントが2つになっています

インデントがずれている

引数が増えているので確認してください

捕捉

コードを書き換えたのでこのコメントは正しくない

https://docs.python.org/ja/3/library/asyncio-runner.html#asyncio.run

3.12からloop_factory引数が増えてる

インデントが3文字になっている

「実行できる リクエスト（タスク）数を」 とした方がわかりやすですかね？

このprint文の意味がよくわからず...

このサンプルで一度に全ての例を盛り込んでいるので、 まずはTaskGroupの使い方の例と例外と例とコードサンプルを分けるか、TaskGroupの使い方をコメントで書くか？した方がわかりやすいかなと思いました。

nits: 「まとめて送出されます。」とした方が伝わりやすそうですかね？

こちらも同様に、引数が追加されている？

「#」 をつけてコメントにする、ですかね。 下の行も同様

https://docs.python.org/ja/3/library/asyncio-task.html#asyncio.create_task

これをみると3.11以降で引数が増えているようです。

thinking_order()はsecを引数に取るので、この実行でエラーになります。

TypeError: thinking_order() missing 1 required positional argument: 'sec'

what does this mean?

above, they included the % when they defined the format specifier. But here, they did not add the - to the "beginning of the format specifier (before the %).

Note that these classes are special because: 1. instead of storing variables, they store constants 2. there are no static subroutines 3. the constants are stored into variables of type Season. Therefore, the static constants behave like objects. We can conclude that classes are not limited to storing variables; they can also store constants. The definition of an enum must integrate the constants with subroutines to create objects.

https://cassyni.com/embed/events/34c8XAi2zKHe8EE9ASzyfj

Das US-Anerikanische Hardland-Institut, lobbyiert inzwischen durch Vermittlung der FPÖ auch bei der EU in Brüssel you Benedikt Naudov-Slaski zeigt in einem Artikel im Falter auf, dass das Institut selbst bei der Darstellung der eigenen angeblichen Erfolge unseres Arbeiter. Herzlandvertreter behaupten, kommen ein EU-Klimagesetz gestoppt zu haben. Und nennen dafür ein Datum, kann man an dem tatsächlich nur eine ungarische Ministerin gegen die EU-Renaturierungsverordnung argumentiert hat, kann man die dann aber schließlich angenommen worden. https://www.derstandard.at/story/3000000254766/wie-us-klimawandelleugner-fake-news-ueber-die-eu-verbreiten-und-die-fpoe-ihnen-dabei-hilft

https://cassyni.com/embed/events/FXtmbYFNs3VJKVwJVP9nGd

神经网络中的数据转换遵循一个清晰、线性的路径，从接收原始数据开始，通过处理阶段，最终产生一个预测。这三个主要层协同工作： 1. 输入层 (Input Layer) 作用： 这是网络的入口点。它不执行任何计算或转换，只是接收原始的外部数据。数据形式： 数据以数字向量或矩阵的形式进入网络（例如，在您提供的图像中，输入是 [1.0, 5.0, 9.0] 这个向量）。数据流向： 将原始输入信号直接传递到下一个隐藏层。 2. 隐藏层 (Hidden Layer) 作用： 隐藏层是神经网络的“大脑”，负责执行大部分复杂的计算和模式识别。数据形式： 数据在这里被转换。每个神经元接收来自上一层的加权输入，加上偏置，并通过非线性激活函数进行处理。数据流向： 隐藏层提取并转换原始输入数据为更抽象、更有意义的特征表示，并将这些新表示传递给下一层（另一个隐藏层或输出层）。网络的深度（隐藏层的数量）决定了它可以学习的复杂程度。 3. 输出层 (Output Layer) 作用： 这是网络的出口点，负责生成最终的预测结果或决策。数据形式： 它接收来自最后一个隐藏层的信号，并将其格式化为用户需要的输出形式（例如，一个概率值、一个类别标签或一个连续的数值）。数据流向： 输出层将网络的最终答案传递给外部世界。 数据转换顺序总结 数据从左向右（如您图像所示）按顺序转换： 原始数据 \(\rightarrow \) 输入层 (接收) \(\rightarrow \) 隐藏层 (特征提取/转换) \(\rightarrow \) 输出层 (最终预测) 这三个层的结合使网络能够从简单的数据点构建复杂的决策。

1. 为什么 ReLU 比 Sigmoid 计算效率更高？ 计算效率的差异主要源于它们底层的数学运算： ReLU (\(\max (0,x)\)):ReLU 的计算只涉及一个简单的比较和条件判断（输入值是否大于 0），然后返回输入值或 0。这个操作在现代 CPU 和 GPU 硬件上执行速度极快。Sigmoid (\(\frac{1}{1+e^{-x}}\)):Sigmoid 的计算涉及指数运算 (\(e^{-x}\))、加法和除法。指数和除法运算在计算机硬件上比简单的条件判断昂贵得多（需要更多的时钟周期）。 在大规模深度学习模型中，激活函数需要在数十亿个神经元上执行数万亿次，这种微小的计算差异累积起来，使得使用 ReLU 的模型训练和推理速度快得多。 2. 为什么非线性是必不可少的？ 非线性是神经网络能够学习复杂模式和表示的关键原因。 线性限制： 如果所有激活函数都是线性的，那么整个神经网络（无论有多少层）都可以被数学上简化为一个单一的线性变换（一个大的矩阵乘法）。线性模型只能拟合直线或平面，无法捕捉现实世界数据（例如图像中的曲线边缘、语言的复杂语义）中固有的非线性关系。捕捉复杂性： 非线性激活函数引入了表达能力。它们允许网络创建弯曲的决策边界，使网络能够学习任意复杂的函数映射（根据通用逼近定理，一个具有足够神经元的单隐藏层非线性网络可以逼近任何连续函数）。 总结来说，线性层负责组合信息，而非线性激活函数负责使网络能够处理现实世界数据的复杂性。

一个神经元的输出方程可以总结为一个简洁的数学表达式，结合了加权和、偏置项和激活函数。 神经元的总输入（也称为加权和加上偏置）通常表示为 \(z\)。 \(z=\left(\sum {i=1}^{n}w{i}x_{i}\right)+b\)其中： \(x_{i}\) 代表第 \(i\) 个输入信号。\(w_{i}\) 代表与第 \(i\) 个输入信号相对应的权重。\(b\) 代表偏置项 (bias)。\(\sum \) 代表对所有输入进行求和。 然后，这个总输入 \(z\) 会通过一个激活函数 \(\sigma \)（例如 Sigmoid、ReLU、GELU 等）来产生神经元的最终输出 \(a\)： \(a=\sigma (z)\)将这两个步骤结合起来，一个神经元的完整输出方程通常写作： \(a=\sigma \left(\left(\sum {i=1}^{n}w{i}x_{i}\right)+b\right)\)这个方程是所有现代深度学习模型的基本构建块。

Die FPÖ-Politiker Harald Vilimsky und Roman Haider haben dem Chef des Heartland Institutes, James Taylor, den Weg als Lobbyist in das EU Parlament gebahnt. Heartland Institute und FPÖ-Abgeordnete bemühen sich intensiv, mit Desinformation gegen den Green Deal vorzugehen, u.a. duch Beeinflussung ungarischer Abgeordneter. Auch auf der Llimaleugner-Konferenz in Maria Enzersdorf im Juni ist Taylor aufgetreten. Die Heartland-Thesen finden sich im Wahlprogramm der FPÖ. Hintergrundbericht von Benedikt Narodoslawsky. https://www.derstandard.at/story/3000000237376/mein-freund-harald-fpoe-ebnete-klimaleugner-lobby-den-weg-ins-eu-parlament

ここも書き換えてください

1つの文で「以下」が連続しているので、こっちはトルでも意味が通じるかな。

電子版はこれで良さそうですが、紙の書籍の場合だと、リンクは文字で起こしてあったほうがよいと思いました。 （richも同様です。）

Klaus Taschwe im Standard zur Arbeit des Hartland Instituts punkt das Institut hat jetzt in London eine Diplonore aufgemacht. Komma die von einer früheren Vorsitzenden der Antibrexipathal-Yukip geleitet wird. Es versucht in enger Kooperation mit der FPÖ den Europien Drain Deal abzusprechen bzw. zu blockieren. Das Institut gehört zum Aufdraggebern des sogenannten Project 2025-Kommar, dem Drehbuch für das Vorgehen der Tram-Administration gegen bisherige Regierungsinstitutionen vor allem In Bereichen wie Wissenschaft und Diversität https://www.derstandard.at/story/3000000254264/us-lobby-der-klimawandelleugner-dank-fpoe-weiter-auf-dem-vormarsch-in-europa

单元名称默认自动与计划名称保持一致

• 比如本计划是:致铂+<广告类型>+互点+<笔记作者>+<定向包名>+<日期>
• 单元名称缺省:致铂+<广告类型>+互点+<笔记作者>+<定向包名>+<日期>

这部分是区域全选,而不是单点,单点不方便用户操作,详细交互体验见创量

去掉,模仿创量笔记板块的笔记组设计. 这一部分操作,创量已经更新优化到笔记板块中的笔记组概念了.

这部分可以去掉

需要由下往上傳遞兩個資訊

需要由下往上傳遞一個資訊 也需要由上往下傳遞一個資訊

需要由下往上傳遞一個資訊

使用docker compose，相当于在yaml中一次启动多个容器，但除此之外还可以解决依赖问题（容器启动顺序）

https://proquest.syndetics.com/news/2025/12/16/top-syndetics-unbound-titles-of-2025/

https://annoying.one/

https://cassyni.com/embed/events/Efqq7EQsJaJNBEDLbmmM4Z

Wow this comment read 10 years later really has me thinking.

What did "Moldbug was Controlled Opposition" mean in 2015

These days we just call him by his real name "Curtis Yarbin" and he had a dating advise column called Uncle Yarv

Hmmm I wonder who would have that much energy and time to defame this sort of character

If they only knew how bad Twitter would become, dam 2015 is spiritually so far away. That was before the Culture Wars took hold. Twitter was "based" in 2015. Wow

Well it's been a decade since this article came out, the JQ is quite popular via Nick Fuentes and on Elon's X (formally twitter). I wonder why this author mentions it here void of context.

That's a strong sentence

This is something mememaps.net ought to be able to help with

Hmmm what would this consist of?

The Leviathan and its enemies is pretty dam scary book, spiritually.

The Oppression under a stupid King and defective Court would also be similarly depressing.

I think the core of the issue is how these Democratic-Monarchy cycle (cyclones) occur in history and how we are now aware of them.

The way the "Capitalist Caste" are easily able to subvert the democracy.

If only Democracy's participants were limited to people who can write essays, using pen and paper in a single sitting, stating what they believe in to be published for the world to see

If someone randomly asked me what the difference between Monarchy and Fascism is, I would invent and answer on the spot. It's nice to finally have an answer I can use as reference in the future.

I want a Taxonomy of these values

I agree that Inequality is not itself a problem, but the Inflation we are experiencing in the mid 2020's is bullshit. Defective Aristocrats are getting a free ride. Index funds for index funds sake create weird market conditions

Yes but "Liberal Democracies" have this "Elite Overproduction" problem producing over educated faggots.

Bloom's 2 Sigma Problem shows that proper aristocratic education can produce proper Genuises.

I guess we will see how this plays out as we reach further into the 21st centuary

I mean the "Liberal Institutions" are incentivized to indoctrinate "Liberal Values" into the "Dumb Youths" right?!?!?

I wonder what they mean by "Aryan" here

The experiment, "Let people make their own life decisions" seems seems to be turning in some results

"Gatekeeping is based" and "Democracy" "Rule by LCD(Lowest Common Denominator)" can't maintain the gates.

So "inegalitarian" and "monarchy".

I thought "neoreacitonary" meant new-reactionary or as I like to think of it as "meta reactionary". Neoreactionaries interpret Hegel's Dialectic as "Fake and Gay".

To get out of the "modernist" frame imagine being judged by your great great great grandparents.

To really get out of the "Overton Window" ask what kind of civilization these Ancient Megalithic Structures and why they went extinct.

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wow the breakup trauma,they didn't end well

wow,tom told the truth to george,poor guy,but george doesn't seem to accept the truth

george start to accuse gastby as the murderer of his wife,i think hisa poor guy,everyone has been hiding secret from him.

Nick cares about Gatsby. He wants to make sure he is not alone. It also shows how abandoned Gatsby is at the end.

Gatsby’s hope was almost gone. Although he waited for the phone call, he likely knew it would never come. The call maybe symbolize the end of Gatsby’s dream.

Daisy’s decision was shaped by fear and pressure than by a lack of love. She need reassurance and stability, but Gatsby couldn’t give her at the time, this is the reason why she turned to Tom.

Is this a reference to new bonds being issued to cover future investment, now that costs are growing beyond the ability to be covered with free cash flow from even the biggest players?

Quadruple Star System is unique.

mobile ipfs

implementations

This is a critical improvment. Allows publishing permanent pointers to mutable information

Me parece una afirmación con poca evidencia, es mejor decir, should, no must.

https://www.reddit.com/r/Libraries/comments/1p2p7a7/not_here_to_judge_here_to_help/

Anglicans are Episcopalians

STUPNE ODKÁZANOSTI

Stupeň odkázanosti sa určí na základe počtu základných životných potrieb, ktoré fyzická osoba nie je schopná samostatne uspokojovať. Základné životné potreby, ktoré fyzická osoba nie je schopná samostatne uspokojovať, a ich počet sa určí na základe dotazníka k určeniu odkázanosti fyzickej osoby na pomoc inej fyzickej osoby. Stupeň odkázanosti Počet základných životných potrieb, ktoré fyzická osoba nad 15 rokov veku nie je schopná samostatne uspokojovať Počet základných životných potrieb, ktoré fyzická osoba do 15 rokov veku nie je schopná samostatne uspokojovať I. ľahká odkázanosť 1 – 2 1 II. stredne ľahká odkázanosť 3 – 4 2 – 3

C. POSTIHNUTIA VNÚTORNÝCH ORGÁNOV, NÁSLEDKY INÝCH OCHORENÍ k) cukrovka kompenzovaná inzulínom alebo cukrovka s rozvinutými komplikáciami, s obmedzením mobility alebo sebaobsluhy (diabetická mikroangiopatia, makroangiopatia, retinopatia, neuropatia, nefropatia alebo diabetická noha),

1. DOLNÉ KONČATINY b) ťažké obmedzenie funkcie aspoň jednej dolnej končatiny, ťažké obmedzenie pohyblivosti v oblasti aspoň jedného bedrového kĺbu alebo ťažké obmedzenie pohyblivosti v oblasti aspoň kolenného kĺbu,

e) stavy spojené s často sa opakujúcimi poruchami vedomia alebo závratmi, nereagujúce na liečbu,

What does Wicked mean in this context?

The Wicked exist in opposition to the people of lived experience. The Cynic is a derivative experience from those who actually live life and try and do things.

Okay I don't understand this level. Is this some port modern explanation, there is no objective truth or morals idea? Can you go another level deeper, symbols are more real than the world we live in.

Here's an example, Aristocrats deal with a Simulacrum of reality. Their "Reality" is the social pressures via the competing lifestyles of status created by other Elites. The Symbols of status are what is real and valuable to the Aristocrat.

A Coal Miner deal with the very "real" reality of mining actual energy out of the ground. The Physical Mine is what is real to the coal miner.

Both the Coal Miner and Aristocrat deal with Money which is some sort of Simulacrum game theory trust relationship contract thing.

I never realized a simple statement can be viewed from so many perspectives, I wonder of a AI Prompt Ecology can do a similar level of Analysis from various perspectives and then Synthesize action items

The idea of saying stuff people will object to in conversation or debate to check if they are engaging is a pretty great strategy.

Taking strong opinions is required for the Thesis, Antithesis, Synthesis cognitive pattern

The claim is that ker(ε) ≠ {0}, meaning there exists a non-zero polynomial that annihilates φ.

i.e. $$w \in \text{Ker}\varphi^m$$

Consider $$φ(x, y) = (x, 0)$$

We analyzed wage and rent data for 400 German independent cities and districts from 2014 to 2024. The rent burden compares the median net income (tax class I, single) with the average monthly rent for a typical 50 m² unit. Net income was calculated using a simplified progressive tax model: deduction rates of 30% (under €30,000), 35% (€30,000–€60,000), and 40% (over €60,000) capture income tax and social security contributions typical for employment relationships. Wage data comes from the Federal Employment Agency and shows median gross monthly earnings for full-time employees. For national wage trends, we use Destatis earnings data (Table 81000-0008). Inflation adjustment is done using the Consumer Price Index (2016–2024: 25.58%). Real wages are calculated using geometric linking rather than simple subtraction to avoid overstating the effect over the eight-year period. Rent data is sourced from the Empirica real estate price index, based on the VALUE market database—a collection of prepared real estate market data from more than 100 sources. The rents shown are calculated using a hedonic model to factor out qualitative differences (age, amenities, condition) and reveal pure price trends. The database uses a random sample independent of any specific date, with professional data-cleaning methods. Rents include a 25% flat surcharge to estimate 'warm' rent (including utilities/heating). All values refer to asking rents for new contracts, not existing rents, which are typically lower due to tenant protection laws. The 30 percent threshold follows common economic principles; German law does not prescribe a fixed income-to-rent ratio. For the living space analysis, profession-specific salaries are only available at the state level. Cities like Frankfurt use the Hesse averages, Munich the Bavarian ones; for the city-states of Berlin and Hamburg, exact values are available. The four professions shown (Geriatric Care, Hospitality, IT, and Electrical Engineering) represent the two biggest winners and two biggest losers in wage growth from 2016 to 2024, thus spanning the spectrum of wage development in Germany. Data Limitations: This simplified model is for comparative analysis, not individual financial planning. Regional tax differences, household composition, and existing rental agreements may yield different results.

Why these four professions? We chose contrasting examples across the spectrum of real purchasing power (2016–2024): Geriatric Care (+24%) and Hospitality (+14.3%) represent the biggest winners, while Software Development (+3%) and Electrical Engineering (−3.3%) show how even highly skilled professions failed to keep pace with inflation. Apartment size calculated as 30% of net disposable professional income divided by the local rent per m². Net income calculated using a progressive German tax model (deductions of 30%, 35%, and 40% depending on income level). Data Note: Professional salary data is available at the state level. For non-city-states, the average salaries of the respective state were used (e.g., Frankfurt = Hesse, Munich = Bavaria). Rent data is city-specific.

In 2016, a geriatric caregiver in Berlin could afford a 44-square-meter apartment. Today, the same professional can only afford 38 square meters—a loss of 6 square meters in less than a decade. In comparison, a software developer in Berlin lost as much as 14 square meters (78 m² → 64 m²). But while Berlin professionals lost space, geriatric caregivers in Dresden actually gained 17 square meters. The same salary now buys completely different standards of living depending on the place of work.

Nationwide, the ability to keep up with rent depends, to some extent, on wage growth and geography. In affordable cities, rising wages in essential professions have actually improved financial stability. In expensive metropolitan regions, even strong wage growth cannot keep up with housing inflation.

Rising rents affect everyone, but not everyone faces the housing market with the same level of financial stability. A detailed analysis of wage development by profession reveals a surprising pattern and challenges old certainties about who is moving up and who is falling behind.

We have compiled all the data in this interactive table. Here, you can view the rent burden for each district and independent city, along with its development over the last 10 years.

While these metropolitan effects raise housing costs, other districts, especially in eastern Germany and industrial centers, remain comparatively affordable. In 2024, the lowest rent burdens are found in regions like Salzgitter, where a single-person household spends only 14.7% of their net income on a 50 m² apartment. Other areas in the lower group include Chemnitz (15.4%), Holzminden (16.0%), and Wolfsburg (16.3%), all well below the 20% threshold. Many of these more affordable regions are former industrial centers like Gelsenkirchen, Hagen, Salzgitter, or Wolfsburg, or rural and semi-rural eastern German districts such as Chemnitz, Zwickau, Vogtlandkreis, and Salzlandkreis. These are not classic commuter belts of large cities or places with strong population growth. These regions tend to have slow or negative population growth, limited rental pressure, and only a moderate increase in housing demand. Rents here have remained relatively stable, and even with lower average incomes, households in these districts can maintain a comfortably low rent-to-income ratio—a rare form of financial freedom in today's market.

It is striking that not only have the inner cities become more expensive, but also the surrounding suburbs. Many people have moved to the outskirts in search of cheaper rents and more space, but the increased demand has also driven up prices there. As a result, commuters in the Munich region now have some of the highest rent-to-income ratios in Germany. At the top of the list of districts with the highest rent burden in 2024 is Fürstenfeldbruck, where tenants spend almost 40% of their net income on rent. The city of Munich follows with 39%, and the surrounding districts of Dachau (38%), Ebersberg (38%), and Miesbach (37%) are only slightly behind and well above the 30 percent mark.

German cities are recording one of the sharpest increases in rent burden. Even significant salary increases in these metropolitan areas are often not enough to keep pace with rising rents. For example, in Berlin, rents have risen by 91% since 2014, while nominal wages have increased by only 45%. In Munich, the situation is only slightly better: rents climbed by 53%, while wages over the same period rose by only 38%. A similar trend can be seen in Frankfurt and Düsseldorf: rent increases of 42% and 44%, respectively, are set against wage gains of 32% and 29%. These cities illustrate where the real pressure in the housing market lies: in metropolitan areas with the strongest labor markets, where rent inflation is outpacing income growth. Some cities show a more balanced relationship. In Hamburg, rents rose by 38%, while wages increased by 31%. Dresden shows a similar pattern: rents +41%, wages +38%. And then there are cities like Leipzig: still comparatively affordable, but rapidly changing. In Leipzig, rents have risen by 74% in the last ten years, while wages have increased by 49%. The gap is smaller than in Berlin or Munich, but the dynamic is remarkable.

A look at the 30 percent mark—the point at which housing costs begin to undermine financial stability—shows just how much the burden has intensified. In 2014, only six districts crossed this critical threshold, all near Munich. Ten years later, this number has more than quadrupled: in 2024, 26 regions are now among the particularly burdened. This increase shows how the housing crisis has spread far beyond Germany's traditional hotspots.

One of the most reliable metrics for housing affordability is the rent-to-income ratio, which is the share of net salary spent on rent. A standard guideline is: anyone who spends more than 30% of their net income on housing has little financial cushion. When this threshold is exceeded, the scope for savings and unforeseen expenses shrinks—even when nominal wages are rising.

For years, the nominal wage growth of 27 percent was often presented as proof of a strong labor market. But this "pay bump" tells only part of the story. At the same time, prices rose due to pandemic-related bottlenecks, the energy crisis, and permanently rising living costs. In the end, only about one percent of the wage increase remained in real terms. The following chart shows how much Germany's purchasing power has actually declined since 2016.

German wages have risen by 27 percent over the past eight years, but a good 25 percent of those gains have been wiped out by inflation. What remains is real wage growth of just 1.3 percent. This minimal progress evaporates almost completely because rents in many places are rising even faster than incomes. In Berlin, for example, rents increased by 91 percent, in Leipzig by 74 percent, and in Munich by 53 percent. For comparison: In 2014, only six districts and independent cities in Germany exceeded the critical rent burden threshold of 30 percent. Ten years later, there are already 26. The pressure is no longer limited to major hubs; it has become a nationwide phenomenon.

In 2016, software developers in Berlin earned a median net monthly income of about €2,802 and could afford to rent around 78 m². By 2024, the salary for the same position had risen to about €3,956—yet their rental budget stretched to only 64 m². Despite over €1,100 more in income, about 14 m² of living space were lost. This is not an isolated case. In most cities and professions, rising wages are being outpaced by even faster-growing rents.

Rosetta Stone (RS)

Useful Links

1. Rosetta Stone_ Wikipedia
2. Explore the Rosetta Stone_ British Museum
3. Rosetta Stone_ Britannica
4. What is Rosetta Stone and why is it important?
5. Rosetta Stone- Smithsonian

On July 19, 1799, Pierre- Francois Bouchard's men discovered an ancient "basalt" slab in Rosetta (local name Rashid), Egypt. It was covered with 3 types of writing- Demotic, Hieroglyphics and ancient Greek. Scholars traced origin of the RS to 196 BCE in Egypt's Ptolemaic era

Click map of the Ptolemaic dynasty

<center>The Rosetta Stone decoded by AI</center> Click this YouTube Link

it seems that here shouldn't be '+' but product (same problem below)

hello! if you see this on everythingness.net it means you're on hypothes.is... this has such a lot potential!

if you find something interesting on the internet please @everythingness

very much yes. Scaling is useless metaphor. Spread, evolution much more. Effective behaviour is contagious. Invisible hand of networks / communities [[Of Scaling TV Salons and the Invisible Hand of Networks – Interdependent Thoughts 20250803205329]]

Systems convening, social learning landscapes [[Systems convening Wenger Trayner 20230825170358]]

The likelihood of it increases with social distance (no community) and with places where the underlying logic is different (vgl [[Waarheid en kennis kent historische periodes 20250914161603]] Foucault's periods of epistemic assumptions), at a smaller scale), clashes of differently positions 'Overton' type of windows of accepted discourse, and Rorty being forced wording the new in the language of the old. It's a language came underneath.

again yes. (Same is true for e.g. the EU. What others see as it weaknesses, endless talk and no swift action, is precisely why it endures and has more resilience and robustness than acknowledged)

By def, we're not talking about community then. The behaviour mentioned is that of those who do not think they're part of a bigger whole here. Then by def whatever output is there to just use, as there is no social contract involved. Social asymmetry then is a given, and thus a breakdown of commons.

Not logical, though still true in a sense. It's only a community if a group cares enough to keep it relevant and thus viable. You can't 'start a community' and then complain people don't contribute. It only becomes a community when people do.

Reviewer #1 (Public review):

Summary:

This study investigates how the brain processes facial expressions across development by analyzing intracranial EEG (iEEG) data from children (ages 5-10) and post-childhood individuals (ages 13-55). The researchers used a short film containing emotional facial expressions and applied AI-based models to decode brain responses to facial emotions. They found that in children, facial emotion information is represented primarily in the posterior superior temporal cortex (pSTC)-a sensory processing area-but not in the dorsolateral prefrontal cortex (DLPFC), which is involved in higher-level social cognition. In contrast, post-childhood individuals showed emotion encoding in both regions. Importantly, the complexity of emotions encoded in the pSTC increased with age, particularly for socially nuanced emotions like embarrassment, guilt, and pride.The authors claim that these findings suggest that emotion recognition matures through increasing involvement of the prefrontal cortex, supporting a developmental trajectory where top-down modulation enhances understanding of complex emotions as children grow older.

Strengths:

(1) The inclusion of pediatric iEEG makes this study uniquely positioned to offer high-resolution temporal and spatial insights into neural development compared to non-invasive approaches, e.g., fMRI, scalp EEG, etc.

(2) Using a naturalistic film paradigm enhances ecological validity compared to static image tasks often used in emotion studies.

(3) The idea of using state-of-the-art AI models to extract facial emotion features allows for high-dimensional and dynamic emotion labeling in real time.

Weaknesses:

(1) The study has notable limitations that constrain the generalizability and depth of its conclusions. The sample size was very small, with only nine children included and just two having sufficient electrode coverage in the posterior superior temporal cortex (pSTC), which weakens the reliability and statistical power of the findings, especially for analyses involving age. Authors pointed out that a similar sample size has been used in previous iEEG studies, but the cited works focus on adults and do not look at the developmental perspectives. Similar work looking at developmental changes in iEEG signals usually includes many more subjects (e.g., n = 101 children from Cross ZR et al., Nature Human Behavior, 2025) to account for inter-subject variabilities.

(2) Electrode coverage was also uneven across brain regions, with not all participants having electrodes in both the dorsolateral prefrontal cortex (DLPFC) and pSTC, making the conclusion regarding the different developmental changes between DLPFC and pSTC hard to interpret (related to point 3 below). It is understood that it is rare to have such iEEG data collected in this age group, and the electrode location is only determined by clinical needs. However, the scientific rigor should not be compromised by the limited data access. It's the authors' decision whether such an approach is valid and appropriate to address the scientific questions, here the developmental changes in the brain, given all the advantages and constraints of the data modality.

(3) The developmental differences observed were based on cross-sectional comparisons rather than longitudinal data, reducing the ability to draw causal conclusions about developmental trajectories. Also, see comments in point 2.

(4) Moreover, the analysis focused narrowly on DLPFC, neglecting other relevant prefrontal areas such as the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), which play key roles in emotion and social processing. Agree that this might be beyond the scope of this paper, but a discussion section might be insightful.

(5) Although the use of a naturalistic film stimulus enhances ecological validity, it comes at the cost of experimental control, with no behavioral confirmation of the emotions perceived by participants and uncertain model validity for complex emotional expressions in children. A non-facial music block that could have served as a control was available but not analyzed. The validation of AI model's emotional output needs to be tested. It is understood that we cannot collect these behavioral data retrospectively within the recorded subjects. Maybe potential post-hoc experiments and analyses could be done, e.g., collect behavioral, emotional perception data from age-matched healthy subjects.

(6) Generalizability is further limited by the fact that all participants were neurosurgical patients, potentially with neurological conditions such as epilepsy that may influence brain responses. At least some behavioral measures between the patient population and the healthy groups should be done to ensure the perception of emotions is similar.

(7) Additionally, the high temporal resolution of intracranial EEG was not fully utilized, as data were downsampled and averaged in 500-ms windows. It seems like the authors are trying to compromise the iEEG data analyses to match up with the AI's output resolution, which is 2Hz. It is not clear then why not directly use fMRI, which is non-invasive and seems to meet the needs here already. The advantages of using iEEG in this study are missing here.

(8) Finally, the absence of behavioral measures or eye-tracking data makes it difficult to directly link neural activity to emotional understanding or determine which facial features participants attended to. Related to point 5 as well.

Comments on revisions:

A behavioral measurement will help address a lot of these questions. If the data continues collecting, additional subjects with iEEG recording and also behavioral measurements would be valuable.

Reviewer #2 (Public review):

Summary:

In this paper, Fan et al. aim to characterize how neural representations of facial emotions evolve from childhood to adulthood. Using intracranial EEG recordings from participants aged 5 to 55, the authors assess the encoding of emotional content in high-level cortical regions. They report that while both the posterior superior temporal cortex (pSTC) and dorsolateral prefrontal cortex (DLPFC) are involved in representing facial emotions in older individuals, only the pSTC shows significant encoding in children. Moreover, the encoding of complex emotions in the pSTC appears to strengthen with age. These findings lead the authors to suggest that young children rely more on low-level sensory areas and propose a developmental shift from reliance on lower-level sensory areas in early childhood to increased top-down modulation by the prefrontal cortex as individuals mature.

Strengths:

(1) Rare and valuable dataset: The use of intracranial EEG recordings in a developmental sample is highly unusual and provides a unique opportunity to investigate neural dynamics with both high spatial and temporal resolution.

(2 ) Developmentally relevant design: The broad age range and cross-sectional design are well-suited to explore age-related changes in neural representations.

(3) Ecological validity: The use of naturalistic stimuli (movie clips) increases the ecological relevance of the findings.

(4) Feature-based analysis: The authors employ AI-based tools to extract emotion-related features from naturalistic stimuli, which enables a data-driven approach to decoding neural representations of emotional content. This method allows for a more fine-grained analysis of emotion processing beyond traditional categorical labels.

Weaknesses:

(1) While the authors leverage Hume AI, a tool pre-trained on a large dataset, its specific performance on the stimuli used in this study remains unverified. To strengthen the foundation of the analysis, it would be important to confirm that Hume AI's emotional classifications align with human perception for these particular videos. A straightforward way to address this would be to recruit human raters to evaluate the emotional content of the stimuli and compare their ratings to the model's outputs.

(2) Although the study includes data from four children with pSTC coverage-an increase from the initial submission-the sample size remains modest compared to recent iEEG studies in the field.

(3) The "post-childhood" group (ages 13-55) conflates several distinct neurodevelopmental periods, including adolescence, young adulthood, and middle adulthood. As a finer age stratification is likely not feasible with the current sample size, I would suggest authors temper their developmental conclusions.

(4) The analysis of DLPFC-pSTC directional connectivity would be significantly strengthened by modeling it as a continuous function of age across all participants, rather than relying on an unbalanced comparison between a single child and a (N=7) post-childhood group. This continuous approach would provide a more powerful and nuanced view of the developmental trajectory. I would also suggest including the result in the main text.

Author response:

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

eLife Assessment

This study examines a valuable question regarding the developmental trajectory of neural mechanisms supporting facial expression processing. Leveraging a rare intracranial EEG (iEEG) dataset including both children and adults, the authors reported that facial expression recognition mainly engaged the posterior superior temporal cortex (pSTC) among children, while both pSTC and the prefrontal cortex were engaged among adults. However, the sample size is relatively small, with analyses appearing incomplete to fully support the primary claims. 

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study investigates how the brain processes facial expressions across development by analyzing intracranial EEG (iEEG) data from children (ages 5-10) and post-childhood individuals (ages 13-55). The researchers used a short film containing emotional facial expressions and applied AI-based models to decode brain responses to facial emotions. They found that in children, facial emotion information is represented primarily in the posterior superior temporal cortex (pSTC) - a sensory processing area - but not in the dorsolateral prefrontal cortex (DLPFC), which is involved in higher-level social cognition. In contrast, post-childhood individuals showed emotion encoding in both regions. Importantly, the complexity of emotions encoded in the pSTC increased with age, particularly for socially nuanced emotions like embarrassment, guilt, and pride. The authors claim that these findings suggest that emotion recognition matures through increasing involvement of the prefrontal cortex, supporting a developmental trajectory where top-down modulation enhances understanding of complex emotions as children grow older.

Strengths:

(1) The inclusion of pediatric iEEG makes this study uniquely positioned to offer high-resolution temporal and spatial insights into neural development compared to non-invasive approaches, e.g., fMRI, scalp EEG, etc.

(2) Using a naturalistic film paradigm enhances ecological validity compared to static image tasks often used in emotion studies.

(3) The idea of using state-of-the-art AI models to extract facial emotion features allows for high-dimensional and dynamic emotion labeling in real time

Weaknesses:

(1) The study has notable limitations that constrain the generalizability and depth of its conclusions. The sample size was very small, with only nine children included and just two having sufficient electrode coverage in the posterior superior temporal cortex (pSTC), which weakens the reliability and statistical power of the findings, especially for analyses involving age

We appreciated the reviewer’s point regarding the constrained sample size.

As an invasive method, iEEG recordings can only be obtained from patients undergoing electrode implantation for clinical purposes. Thus, iEEG data from young children are extremely rare,  and rapidly increasing the sample size within a few years is not feasible. However, we are confident in the reliability of our main conclusions. Specifically, 8 children (53 recording contacts in total) and 13 control participants (99 recording contacts in total) with electrode coverage in the DLPFC are included in our DLPFC analysis. This sample size is comparable to other iEEG studies with similar experiment designs [1-3]. 

For pSTC, we returned to the data set and found another two children who had pSTC coverage. After involving these children’s data, the group-level analysis using permutation test showed that children’s pSTC significantly encode facial emotion in naturalistic contexts (Figure 3B). Notably, the two new children’s (S33 and S49) responses were highly consistent with our previous observations. Moreover, the averaged prediction accuracy in children’s pSTC (r_speech=0.1565) was highly comparable to that in post-childhood group (r_speech=0.1515).

(1) Zheng, J. et al. Multiplexing of Theta and Alpha Rhythms in the Amygdala-Hippocampal Circuit Supports Pafern Separation of Emotional Information. Neuron 102, 887-898.e5 (2019).

(2) Diamond, J. M. et al. Focal seizures induce spatiotemporally organized spiking activity in the human cortex. Nat. Commun. 15, 7075 (2024).

(3) Schrouff, J. et al. Fast temporal dynamics and causal relevance of face processing in the human temporal cortex. Nat. Commun. 11, 656 (2020).

(2) Electrode coverage was also uneven across brain regions, with not all participants having electrodes in both the dorsolateral prefrontal cortex (DLPFC) and pSTC, and most coverage limited to the left hemisphere-hindering within-subject comparisons and limiting insights into lateralization.

The electrode coverage in each patient is determined entirely by the clinical needs. Only a few patients have electrodes in both DLPFC and pSTC because these two regions are far apart, so it’s rare for a single patient’s suspected seizure network to span such a large territory. However, it does not affect our results, as most iEEG studies combine data from multiple patients to achieve sufficient electrode coverage in each target brain area. As our data are mainly from left hemisphere (due to the clinical needs), this study was not designed to examine whether there is a difference between hemispheres in emotion encoding. Nevertheless, lateralization remains an interesting question that should be addressed in future research, and we have noted this limitation in the Discussion (Page 8, in the last paragraph of the Discussion).

(3) The developmental differences observed were based on cross-sectional comparisons rather than longitudinal data, reducing the ability to draw causal conclusions about developmental trajectories.  

In the context of pediatric intracranial EEG, longitudinal data collection is not feasible due to the invasive nature of electrode implantation. We have added this point to the Discussion to acknowledge that while our results reveal robust age-related differences in the cortical encoding of facial emotions, longitudinal studies using non-invasive methods will be essential to directly track developmental trajectories (Page 8, in the last paragraph of Discussion). In addition, we revised our manuscript to avoid emphasis causal conclusions about developmental trajectories in the current study (For example, we use “imply” instead of “suggest” in the fifth paragraph of Discussion).

(4) Moreover, the analysis focused narrowly on DLPFC, neglecting other relevant prefrontal areas such as the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), which play key roles in emotion and social processing.

We agree that both OFC and ACC are critically involved in emotion and social processing. However, we have no recordings from these areas because ECoG rarely covers the ACC or OFC due to technical constraints. We have noted this limitation in the Discussion(Page 8, in the last paragraph of Discussion). Future follow-up studies using sEEG or non-invasive imaging methods could be used to examine developmental patterns in these regions.

(5) Although the use of a naturalistic film stimulus enhances ecological validity, it comes at the cost of experimental control, with no behavioral confirmation of the emotions perceived by participants and uncertain model validity for complex emotional expressions in children. A nonfacial music block that could have served as a control was available but not analyzed. 

The facial emotion features used in our encoding models were extracted by Hume AI models, which were trained on human intensity ratings of large-scale, experimentally controlled emotional expression data[1-2]. Thus, the outputs of Hume AI model reflect what typical facial expressions convey, that is, the presented facial emotion. Our goal of the present study was to examine how facial emotions presented in the videos are encoded in the human brain at different developmental stages. We agree that children’s interpretation of complex emotions may differ from that of adults, resulting in different perceived emotion (i.e., the emotion that the observer subjectively interprets). Behavioral ratings are necessary to study the encoding of subjectively perceived emotion, which is a very interesting direction but beyond the scope of the present work. We have added a paragraph in the Discussion (see Page 8) to explicitly note that our study focused on the encoding of presented emotion.

We appreciated the reviewer’s point regarding the value of non-facial music blocks. However,  although there are segments in music condition that have no faces presented, these cannot be used as a control condition to test whether the encoding model’s prediction accuracy in pSTC or DLPFC drops to chance when no facial emotion is present. This is because, in the absence of faces, no extracted emotion features are available to be used for the construction of encoding model (see Author response image 1 below).  Thus, we chose to use a different control analysis for the present work. For children’s pSTC, we shuffled facial emotion feature in time to generate a null distribution, which was then used to test the statistical significance of the encoding models (see Methods/Encoding model fitting for details).

(1) Brooks, J. A. et al. Deep learning reveals what facial expressions mean to people in different cultures. iScience 27, 109175 (2024).

(2) Brooks, J. A. et al. Deep learning reveals what vocal bursts express in different cultures. Nat. Hum. Behav. 7, 240–250 (2023).

Author response image 1.

Time courses of Hume AI extracted facial expression features for the first block of music condition. Only top 5 facial expressions were shown here to due to space limitation.

(6) Generalizability is further limited by the fact that all participants were neurosurgical patients, potentially with neurological conditions such as epilepsy that may influence brain responses. 

We appreciated the reviewer’s point. However, iEEG data can only be obtained from clinical populations (usually epilepsy patients) who have electrodes implantation.  Given current knowledge about focal epilepsy and its potential effects on brain activity, researchers believe that epilepsy-affected brains can serve as a reasonable proxy for normal human brains when confounding influences are minimized through rigorous procedures[1]. In our study, we took several steps to ensure data quality: (1) all data segments containing epileptiform discharges were identified and removed at the very beginning of preprocessing, (2) patients were asked to participate the experiment several hours outside the window of seizures. Please see Method for data quality check description (Page 9/ Experimental procedures and iEEG data processing). 

(1) Parvizi J, Kastner S. 2018. Promises and limitations of human intracranial electroencephalography. Nat Neurosci 21:474–483. doi:10.1038/s41593-018-0108-2

(7) Additionally, the high temporal resolution of intracranial EEG was not fully utilized, as data were down-sampled and averaged in 500-ms windows.  

We agree that one of the major advantages of iEEG is its millisecond-level temporal resolution. In our case, the main reason for down-sampling was that the time series of facial emotion features extracted from the videos had a temporal resolution of 2 Hz, which were used for the modelling neural responses. In naturalistic contexts, facial emotion features do not change on a millisecond timescale, so a 500 ms window is sufficient to capture the relevant dynamics. Another advantage of iEEG is its tolerance to motion, which is excessive in young children (e.g., 5-year-olds). This makes our dataset uniquely valuable, suggesting robust representation in the pSTC but not in the DLPFC in young children. Moreover, since our method framework (Figure 1) does not rely on high temporal resolution method, so it can be transferred to non-invasive modalities such as fMRI, enabling future studies to test these developmental patterns in larger populations.

(8) Finally, the absence of behavioral measures or eye-tracking data makes it difficult to directly link neural activity to emotional understanding or determine which facial features participants afended to.  

We appreciated this point. Part of our rationale is presented in our response to (5) for the absence of behavioral measures. Following the same rationale, identifying which facial features participants attended to is not necessary for testing our main hypotheses because our analyses examined responses to the overall emotional content of the faces. However, we agree and recommend future studies use eye-tracking and corresponding behavioral measures in studies of subjective emotional understanding. 

Reviewer #2 (Public review):

Summary:

In this paper, Fan et al. aim to characterize how neural representations of facial emotions evolve from childhood to adulthood. Using intracranial EEG recordings from participants aged 5 to 55, the authors assess the encoding of emotional content in high-level cortical regions. They report that while both the posterior superior temporal cortex (pSTC) and dorsolateral prefrontal cortex (DLPFC) are involved in representing facial emotions in older individuals, only the pSTC shows significant encoding in children. Moreover, the encoding of complex emotions in the pSTC appears to strengthen with age. These findings lead the authors to suggest that young children rely more on low-level sensory areas and propose a developmental shiZ from reliance on lower-level sensory areas in early childhood to increased top-down modulation by the prefrontal cortex as individuals mature.

Strengths: 

(1) Rare and valuable dataset: The use of intracranial EEG recordings in a developmental sample is highly unusual and provides a unique opportunity to investigate neural dynamics with both high spatial and temporal resolution. 

(2) Developmentally relevant design: The broad age range and cross-sectional design are well-suited to explore age-related changes in neural representations. 

(3) Ecological validity: The use of naturalistic stimuli (movie clips) increases the ecological relevance of the findings. 

(4) Feature-based analysis: The authors employ AIbased tools to extract emotion-related features from naturalistic stimuli, which enables a data-driven approach to decoding neural representations of emotional content. This method allows for a more fine-grained analysis of emotion processing beyond traditional categorical labels. 

Weaknesses: 

(1) The emotional stimuli included facial expressions embedded in speech or music, making it difficult to isolate neural responses to facial emotion per se from those related to speech content or music-induced emotion. 

We thank the reviewer for their raising this important point. We agree that in naturalistic settings, face often co-occur with speech, and that these sources of emotion can overlap. However, background music induced emotions have distinct temporal dynamics which are separable from facial emotion (See the Author response image 2 (A) and (B) below). In addition, face can convey a wide range of emotions (48 categories in Hume AI model), whereas music conveys far fewer (13 categories reported by a recent study [1]). Thus, when using facial emotion feature time series as regressors (with 48 emotion categories and rapid temporal dynamics), the model performance will reflect neural encoding of facial emotion in the music condition, rather than the slower and lower-dimensional emotion from music. 

For the speech condition, we acknowledge that it is difficult to fully isolate neural responses to facial emotion from those to speech when the emotional content from faces and speech highly overlaps. However, in our study, (1) the time courses of emotion features from face and voice are still different (Author response image 2 (C) and (D)), (2) our main finding that DLPFC encodes facial expression information in postchildhood individuals but not in young children was found in both speech and music condition (Figure 2B and 2C). In music condition, neural responses to facial emotion are not affected by speech. Thus, we have included the DLPFC results from the music condition in the revised manuscript (Figure 2C), and we acknowledge that this issue should be carefully considered in future studies using videos with speech, as we have indicated in the future directions in the last paragraph of Discussion.

(1) Cowen, A. S., Fang, X., Sauter, D. & Keltner, D. What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures. Proc Natl Acad Sci USA 117, 1924–1934 (2020).

Author response image 2.

Time courses of the amusement. (A) and (B) Amusement conveyed by face or music in a 30-s music block. Facial emotion features are extracted by Hume AI. For emotion from music, we approximated the amusement time course using a weighted combination of low-level acoustic features (RMS energy, spectral centroid, MFCCs), which capture intensity, brightness, and timbre cues linked to amusement. Notice that music continues when there are no faces presented. (C) and (D) Amusement conveyed by face or voice in a 30-s speech block. From 0 to 5 seconds, a girl is introducing her friend to a stranger. The camera focuses on the friend, who appears nervous, while the girl’s voice sounds cheerful. This mismatch explains why the shapes of the two time series differ at the beginning. Such situations occur frequently in naturalistic movies

(2) While the authors leveraged Hume AI to extract facial expression features from the video stimuli, they did not provide any validation of the tool's accuracy or reliability in the context of their dataset. It remains unclear how well the AI-derived emotion ratings align with human perception, particularly given the complexity and variability of naturalistic stimuli. Without such validation, it is difficult to assess the interpretability and robustness of the decoding results based on these features.  

Hume AI models were trained and validated by human intensity ratings of large-scale, experimentally controlled emotional expression data [1-2]. The training process used both manual annotations from human raters and deep neural networks. Over 3000 human raters categorized facial expressions into emotion categories and rated on a 1-100 intensity scale. Thus, the outputs of Hume AI model reflect what typical facial expressions convey (based on how people actually interpret them), that is, the presented facial emotion. Our goal of the present study was to examine how facial emotions presented in the videos are encoded in the human brain at different developmental stages. We agree that the interpretation of facial emotions may be different in individual participants, resulting in different perceived emotion (i.e., the emotion that the observer subjectively interprets). Behavioral ratings are necessary to study the encoding of subjectively perceived emotion, which is a very interesting direction but beyond the scope of the present work. We have added text in the Discussion to explicitly note that our study focused on the encoding of presented emotion (second paragraph in Page 8).

(1) Brooks, J. A. et al. Deep learning reveals what facial expressions mean to people in different cultures. iScience 27, 109175 (2024).

(2) Brooks, J. A. et al. Deep learning reveals what vocal bursts express in different cultures. Nat. Hum. Behav. 7, 240–250 (2023).

(3) Only two children had relevant pSTC coverage, severely limiting the reliability and generalizability of results.  

We appreciated this point and agreed with both reviewers who raised it as a significant concern. As described in response to reviewer 1 (comment 1), we have added data from another two children who have pSTC coverage. Group-level analysis using permutation test showed that children’s pSTC significantly encode facial emotion in naturalistic contexts (Figure 3B). Because iEEG data from young children are extremely rare, rapidly increasing the sample size within a few years is not feasible. However, we are confident in the reliability of our conclusion that children’s pSTC can encode facial emotion. First,  the two new children’s responses (S33 and S49) from pSTC were highly consistent with our previous observations (see individual data in Figure 3B). Second, the averaged prediction accuracy in children’s pSTC (r_speech=0.1565) was highly comparable to that in post-childhood group (r_speech=0.1515).

(4) The rationale for focusing exclusively on high-frequency activity for decoding emotion representations is not provided, nor are results from other frequency bands explored.   

We focused on high-frequency broadband (HFB) activity because it is widely considered to reflect the responses of local neuronal populations near the recording electrode, whereas low-frequency oscillations in the theta, alpha, and beta ranges are thought to serve as carrier frequencies for long-range communication across distributed networks[1-2]. Since our study aimed to examine the representation of facial emotion in localized cortical regions (DLPFC and pSTC), HFB activity provides the most direct measure of the relevant neural responses. We have added this rationale to the manuscript (Page 3).

(1) Parvizi, J. & Kastner, S. Promises and limitations of human intracranial electroencephalography. Nat. Neurosci. 21, 474–483 (2018).

(2) Buzsaki, G. Rhythms of the Brain. (Oxford University Press, Oxford, 200ti).

(5) The hypothesis of developmental emergence of top-down prefrontal modulation is not directly tested. No connectivity or co-activation analyses are reported, and the number of participants with simultaneous coverage of pSTC and DLPFC is not specified.  

Directional connectivity analysis results were not shown because only one child has simultaneous coverage of pSTC and DLPFC. However, the  Granger Causality results from post-childhood group (N=7) clearly showed that the influence in the alpha/beta band from DLPFC to pSTC (top-down) is gradually increased above the onset of face presentation (Author response image 3, below left, plotted in red). By comparison, the influence in the alpha/beta band from pSTC to DLPFC (bottom-up) is gradually decreased after the onset of face presentation (Author response image 3, below left, blue curve). The influence in alpha/beta band from DLPFC to pSTC was significantly increased at 750 and 1250 ms after the face presentation (face vs nonface, paired t-test, Bonferroni  corrected P=0.005, 0.006), suggesting an enhanced top-down modulation in the post-childhood group during watching emotional faces. Interestingly, this top-down influence appears very different in the 8-year-old child at 1250 ms after the face presentation (Author response image 3, below left, black curve).

As we cannot draw direct conclusions from the single-subject sample presented here, the top-down hypothesis is introduced only as a possible explanation for our current results. We have removed potentially misleading statements, and we plan to test this hypothesis directly using MEG in the future.

Author response image 3.

Difference of Granger causality indices (face – nonface) in alpha/beta and gamma band for both directions. We identified a series of face onset in the movie that paticipant watched. Each trial was defined as -0.1 to 1.5 s relative to the onset. For the non-face control trials, we used houses, animals and scenes. Granger causality was calculated for 0-0.5 s, 0.5-1 s and 1-1.5 s time window. For the post-childhood group, GC indices were averaged across participants. Error bar is sem.

(6) The "post-childhood" group spans ages 13-55, conflating adolescence, young adulthood, and middle age. Developmental conclusions would benefit from finer age stratification.  

We appreciate this insightful comment. Our current sample size does not allow such stratification. But we plan to address this important issue in future MEG studies with larger cohorts.

(7) The so-called "complex emotions" (e.g., embarrassment, pride, guilt, interest) used in the study often require contextual information, such as speech or narrative cues, for accurate interpretation, and are not typically discernible from facial expressions alone. As such, the observed age-related increase in neural encoding of these emotions may reflect not solely the maturation of facial emotion perception, but rather the development of integrative processing that combines facial, linguistic, and contextual cues. This raises the possibility that the reported effects are driven in part by language comprehension or broader social-cognitive integration, rather than by changes in facial expression processing per se.  

We agree with this interpretation. Indeed, our results already show that speech influences the encoding of facial emotion in the DLPFC differently in the childhood and post-childhood groups (Figure 2D), suggesting that children’s ability to integrate multiple cues is still developing. Future studies are needed to systematically examine how linguistic cues and prior experiences contribute to the understanding of complex emotions from faces, which we have added to our future directions section (last paragraph in Discussion, Page 8-9 ).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

In the introduction: "These neuroimaging data imply that social and emotional experiences shape the prefrontal cortex's involvement in processing the emotional meaning of faces throughout development, probably through top-down modulation of early sensory areas." Aren't these supposed to be iEEG data instead of neuroimaging? 

Corrected.

Reviewer #2 (Recommendations for the authors):

This manuscript would benefit from several improvements to strengthen the validity and interpretability of the findings:

(1) Increase the sample size, especially for children with pSTC coverage. 

We added data from another two children who have pSTC coverage. Please see our response to reviewer 2’s comment 3 and reviewer 1’s comment 1.

(2) Include directional connectivity analyses to test the proposed top-down modulation from DLPFC to pSTC. 

Thanks for the suggestion. Please see our response to reviewer 2’s comment 5.

(3) Use controlled stimuli in an additional experiment to separate the effects of facial expression, speech, and music. 

This is an excellent point. However, iEEG data collection from children is an exceptionally rare opportunity and typically requires many years, so we are unable to add a controlled-stimulus experiment to the current study. We plan to consider using controlled stimuli to study the processing of complex emotion using non-invasive method in the future. In addition, please see our response to reviewer 2’s comment 1 for a description of how neural responses to facial expression and music are separated in our study.

These are incorrect pricing for US. And I don't think this is yet thought or supported for US. My suggestion is to hide this page for US. Shubham should confirm

Notable

バッククォートで囲んでほしい

Meaning fanbases. Get together and talk about this gay character, to feel like you belong somewhere, to be socially validated.

Yet, the character is the excuse to meet for the first time. Even if as time goes on this can change, the initial pulse comes from some media exposure, or someone sharing theirs.

Menu mobile : "Changer lien du bouton nous contacter", il renvoi vers le formulaire newsletter et non pas le formulaire de contact

Bouton billetterie inacessible en version mobile (Google Chrome iPhone 13 Pro + emulateur)

lllll

mmmm

hgjyg

Vụ 3

CỤc phòng chống tội phạm kinh tế, buôn lậu (C03)

Cục phòng chống tn (Cục 4)

ソートされていると思うけどなんでここで指摘されているのかな？ 実行していないので勘違いかな。

5秒ちょっとになるかと思ったけど、他にもっと時間がかかるサイトがあるのかな？ 短くはなっているけど、効果が思ったより出ていなくてなんでだろうって思った。

uv sync ではpytestがインストールされてないと思います。

uv sync --group dev でインストールするのが正しいようです。

「開発」ということばは取ったほうが良いかもです。 いろいろと調べていって知ったのですが、 --extra dev は非推奨のようです。

https://docs.astral.sh/uv/concepts/projects/dependencies/ https://peps.python.org/pep-0735/

uv sync --group dev を使うのが正しいそうです。

あえて、アンインストールするために uv pip でインストールしているのかな。 uv add と uv pip installを混ぜて使うことを推奨しているように見えたけど注意のためにこれをやっているのかな？ なくても良いがしました。

「依存関係のあるPythonパッケージが書き込まれる」とあり、別の場所にあったコメントがハイライトをしているので、後半だけにハイライトしてコメントします。

ここは、「依存関係にある」ではなく、「インストールしたPythonパッケージ」だと思います。

「uv vunvで作った仮想環境と同様に仮想環境を作り、その仮想環境を使うため、」ってことですかね？ 「必ず使用するため」っていうところがピンとこなかった。

「Pythonの実行や開発」としたほうが、開発以外でも使うかもしれないので良いかなって思いました。

Create a note @Jeremy_Dow hypothes.is

يتكوّن موقفي التفاوضي كمحامٍ من قوة الأساس القانوني للملف، ووضوح الوقائع والأدلة، مع تقدير واقعي لاحتمالات الربح والخسارة. كما أعتمد على معرفة بدائل التفاوض المتاحة في حال عدم الوصول إلى اتفاق، وفهم مصلحة الموكل الحقيقية وأولوياته. ويكتمل ذلك بحسن اختيار توقيت التفاوض، والقدرة على إدارة الحوار باحتراف، مع مراعاة طبيعة العلاقة والظروف المحيطة بالنزاع.

Sidebar watchers season 2026 is coming

better diet, get good sleep and exercise

Negative self talk and fear of failure

Poor diet and cluttered environment

Headaches and less sleep

Because many things can cause stress even the good things that happen in your life.

Esteem

🔧 Guía Completa para la Reparación y Mantenimiento de Aire Acondicionado

¿Problemas técnicos con tu sistema de climatización?

Soluciones que debes conocer:

• Mantenimiento preventivo: Evita costosas fallas con acciones simples.
• Diagnóstico sencillo: Identifica fugas, bajo rendimiento o ruidos anormales.
• Optimización energética: Reduce el consumo eléctrico ajustando el termostato.

📌 Enlace útil<br /> Guía Completa para la Reparación y Mantenimiento de Aire Acondicionado<br /> (Obtén detalles completos sobre mantenimiento, diagnóstico y soluciones para mejorar el rendimiento de tu aire acondicionado.)

Incluye:

✔️ Problemas comunes y soluciones rápidas<br /> ✔️ Tips de mantenimiento preventivo<br /> ✔️ ¿Cuándo llamar a un profesional?<br /> ✔️ Preguntas Frecuentes

#Climatización #MantenimientoAC #EficienciaEnergética #AireAcondicionado

I mentioned Rhesus ladders in another comment (https://hypothes.is/a/gvP9DmJfEeyj-zfV0Z4Zsw) and also the relationship to Chesterton's fence in reply to a comment from someone else (https://hypothes.is/a/r7YFemJgEeymEnOBlFNH5A), but this captures the spirit of my comments elsewhere about false diagnoses perfectly.

Excellent point

Nicely put.

Les Algorithmes Contre la Société : Synthèse des Analyses d'Hubert Guillaud

Résumé Exécutif

Ce document de synthèse expose les arguments principaux développés par Hubert Guillaud, journaliste et essayiste, concernant l'impact sociétal des systèmes algorithmiques.

L'analyse révèle que loin d'être des outils neutres, les algorithmes constituent une nouvelle logique systémique qui transforme en profondeur les services publics et les rapports sociaux.

Leur fonction première est de calculer, trier et appareiller, traduisant le fait social en une simple "combinaison de chiffres".

Les points critiques à retenir sont les suivants :

• La discrimination comme fonctionnalité : Par nature, le calcul est une machine à différencier.

Des systèmes comme Parcoursup ou le "score de risque" de la Caisse d'Allocations Familiales (CAF) génèrent des distinctions souvent aberrantes et fictionnelles pour classer les individus, ce qui institutionnalise la discrimination sous couvert d'objectivité mathématique.

• Ciblage des populations précaires : L'automatisation des services publics cible et surveille de manière disproportionnée les populations les plus vulnérables.

La CAF, par exemple, ne chasse pas tant la fraude que les "indus" (trop-perçus), affectant principalement les personnes aux revenus morcelés et complexes comme les mères isolées.

• Menace sur les principes démocratiques :

L'interconnexion croissante des données entre les administrations (CAF, Impôts, France Travail, Police) menace la séparation des pouvoirs en créant un système de surveillance généralisée où les faiblesses d'un individu dans un domaine peuvent avoir des répercussions dans tous les autres.

• La massification déguisée : Contrairement à l'idée d'une personnalisation poussée, les algorithmes opèrent une massification des individus.

Ils ne ciblent pas des personnes uniques mais les regroupent en permanence dans des catégories larges et standardisées à des fins de contrôle ou de publicité.

• Un risque de dérive fasciste : En systématisant la discrimination et en la rendant opaque et invisible, ces technologies créent un terrain propice à des dérives autoritaires, un risque qualifié par Hubert Guillaud de "fasciste".

En conclusion, bien que ces technologies posent une menace sérieuse, Hubert Guillaud les replace dans un contexte plus large, arguant que les enjeux primordiaux demeurent le réchauffement climatique et les logiques du capitalisme financier, dont les algorithmes ne sont qu'un outil d'amplification.

--------------------------------------------------------------------------------

1. Introduction : La Logique Algorithmique et ses Enjeux Sociétaux

La discussion, introduite par Marine Placa, doctorante en droit public, s'articule autour de l'ouvrage d'Hubert Guillaud, Les algorithmes contre la société.

L'enjeu central est "l'immixtion d'une nouvelle logique algorithmique plus insidieuse et plus systémique à la délivrance des prestations de services publics".

Cette logique, qui "traduit le fait social comme une combinaison de chiffres", gouverne de plus en plus l'environnement des individus avec des conséquences tangibles.

Plusieurs critiques majeures sont soulevées dès l'introduction :

• Opacité et injustice : Les systèmes d'IA sont souvent trop opaques, discriminants et il est impossible d'expliciter les décisions qui en résultent.

• Déconnexion des réalités : Alors que les investissements massifs se poursuivent (109 milliards d'euros débloqués par le gouvernement français), les retours d'expérience alertent sur les "dégâts sociaux, démocratiques et écologiques".

• Technologie privée : La technologie est privée, développée par des capitaux privés et dictée par les "mastodontes économiques de la Silicon Valley".

Son usage est ainsi largement influencé par des intérêts de profit plutôt que par le bien commun.

• L'IA n'est pas autonome : L'IA "ne décide de rien.

Elle ne raisonne pas." Elle est le résultat d'une conception humaine, et son impact dépend moins de son essence que de son usage.

2. Définition et Fonctionnement des Algorithmes

Selon Hubert Guillaud, les systèmes algorithmiques, de l'algorithme simple à l'IA complexe, doivent être compris comme une "continuité technologique" de systèmes de calcul appliqués à la société. Leur fonctionnement repose sur trois fonctions fondamentales :

| Fonction | Description | Exemple | | --- | --- | --- | | 1\. Produire des scores | Transformer des informations qualitatives (mots, comportements) en données quantitatives (chiffres, notes). | Un profil sur une application de rencontre est "scoré", une demande d'aide sociale reçoit une note de risque. |

| 2\. Trier | Classer les individus ou les informations en fonction des scores produits. | Les candidats sur Parcoursup sont classés du premier au dernier. |

| 3\. Apparier (Le "mariage") | Faire correspondre une demande à une offre sur la base du tri effectué. | Un étudiant est appareillé à une formation, un demandeur d'emploi à un poste, un bénéficiaire à l'obtention (ou non) d'une aide sociale. |

Cette mécanique simple est au cœur de tous les systèmes, des réseaux sociaux aux plateformes de services publics, avec pour enjeu principal de classer, trier et faire correspondre.

3. La Modification des Rapports de Force Sociétaux

3.1. Le Calcul comme Machine à Discriminer : l'Exemple de Parcoursup

Hubert Guillaud utilise l'exemple de Parcoursup pour illustrer comment le calcul génère une discrimination systémique.

• Contexte : Une plateforme nationale orientant 900 000 élèves de terminale vers plus de 25 000 formations.

• Mécanisme : Chaque formation doit classer tous ses candidats du premier au dernier, sans aucun ex-æquo.

• Le critère principal : les notes. Le système se base quasi exclusivement sur les bulletins scolaires, ignorant des critères essentiels comme la motivation, qui est pourtant un facteur clé de la réussite dans le supérieur.

• La création de distinctions aberrantes : Pour départager la masse d'élèves aux dossiers homogènes (par exemple, avec une moyenne de 14/20), le système génère des calculs complexes pour créer des micro-différences.

Les scores finaux sont calculés à trois chiffres après la virgule (ex: 14,001 contre 14,003). Guillaud souligne l'absurdité de cette distinction :

"Je ne peux pas faire de différence académique même entre eux. [...] Mais en fait pour le calcul par le calcul on va générer en fait des différences entre ces élèves."

• Équivalence au tirage au sort : Pour 80 % des candidats, ce système d'attribution basé sur des différences insignifiantes est "pleinement équivalent au tirage au sort", mais il est camouflé par l'apparence scientifique des chiffres.

3.2. La Normalisation d'une Sélection Élitaire

Contrairement à un simple tirage au sort, Parcoursup n'introduit pas d'aléa.

Au contraire, il diffuse et normalise les méthodes de sélection des formations d'élite (grandes écoles, Sciences Po) à l'ensemble du système éducatif, y compris à des formations techniques (BTS) où ce type de sélection est inadapté.

Cette standardisation interdit les méthodes d'évaluation alternatives (entretiens, projets) et renforce les biais sociaux.

Le résultat est un taux d'insatisfaction élevé :

• 2 % des candidats ne reçoivent aucune proposition.

• 20 % reçoivent une seule proposition qu'ils refusent.

• 20 % retentent leur chance l'année suivante.

Au total, environ 45-46 % des élèves sont insatisfaits chaque année par la plateforme.

4. L'Automatisation de la Vie et la Neutralité Illusoire de la Technologie

4.1. Le "Score de Risque" de la CAF : Surveillance des Plus Précaires

Hubert Guillaud réfute l'idée que la technologie est neutre. L'exemple de la Caisse d'Allocations Familiales (CAF) est emblématique de cette non-neutralité.