This might be a good topic to talk about just because someone's life could be ruined for doing what they think or know is morally right in there head.

citation needed

Author Response

Reviewer #1 (Public Review):

Due complicated and often unpredictable idiosyncratic differences, comparing fMRI topography between subjects typically would require extra expensive scan time and extra laborious analyzing steps to examine with specific functional localizer scan runs that contrast fMRI responses of every subject to different stimulus categories. To overcome this challenge, hyperaligning tools have recently been developed (e.g., Guntupalli et al., 2016; Haxby et al., 2011) based on aligning in a high-dimensional space of voxels of subjects' fMRI responses to watching a given movie. In the present study, Jiahui and colleagues propose a significantly improved version of hyperaligning functional brain topography between individuals. This new version, based on fMRI connectivity, works robustly on datasets when subjects watched different movies and were scanned with different parameters/scanners at different MRI centers.

Robustness is the major strength of this study. Despite the fact that datasets from different subjects watching different movies at different MRI centers with different scan parameters were used, the results of functional brain topography from between-subject hyperalignment based on fMRI connectivity were comparable to the golden standard of within-subject functional localizations, and significantly better than regular surface anatomical alignments. These results also support the claim that the present approach is a useful improvement from previous hyperalignments based on time-locked fMRI voxel responses, which would require normative samples of subjects watching a same movie.

We thank the reviewer for the appreciation of our work.

Given the robustness, this new version of hyperalignment would provide much stronger statistical power for group-level comparisons with less costs of time and efforts to collect and analyze data from large sample size according to the current stringent standard, likely being useful to the whole research community of functional neuroimaging. That said, more discussions of the limit of the present hyperalignment approach would be helpful to potential eLife readers. For example, to what extend the present hyperalignment approach would be applicable to individuals with atypical functional brain topography such as brain lesion patients with e.g., acquired prosopagnosia? Even in typical populations, while bilateral fusiform face areas can be identified in the majority through functional localizer scans, the left fusiform face area sometimes cannot be found. Moreover, many top-down factors are known to modulate functional brain topography. Due to these factors, brain responses and functional connectivity may be different even when a same subject watched a same movie twice (e.g., Cui et al., 2021).

We thank the reviewer for the suggestion and agree that it would be fascinating if the predictions can be made with high fidelity in neuropsychological populations. Although we are optimistic that our algorithm is able to generalize across diverse populations, to date, no previous literature has provided empirical evidence to illustrate the effectiveness, including optimizations and special applications beyond typical brains. Besides the neuropsychological population, it would also be valuable to study the generalization across a broad age range, for example, from infants to the elderly. The brain changes across age both anatomically and functionally, so it is a challenge to predict functional topographies based on a normative group that only includes young participants. With all these potential applications in mind, future research is needed to illustrate the efficacy, build the pipeline, and construct the representative normative groups to meet the requirements of accurate individualized predictions in diverse populations.

In typical populations, although participants have great individual variabilities in their functional topographies, for instance, some participants have distinguishable patches of activations in their left ventral temporal cortex while some participants don’t, our algorithms successfully captured these individualized differences in the prediction. The figure below shows, as an example, the face-selective topographies of two individuals that have markedly different face-selective topographies on the left ventral temporal cortex. The left participant has prominent face-selective areas on the left ventral temporal cortex that are in similar sizes as the right side, while the right participant only has a few scattered small face-selective spots on the left side. No matter what their face-selective areas look like, our algorithm accurately recovered the individualized locations, shapes, and sizes, retaining the individual variability in the functional topographies.

Functional connectivity profiles based on naturalistic stimuli are very stable across the cortex, even when participants watch different movies. In Figure 4-figure supplement 9, the mean correlations of fine-scaled connectome for most searchlights (r = 15mm) when participants watched The Grand Budapest Hotel and the Raiders of the Lost Ark were generally around 0.8. The mean correlations were about 0.9 between the first and second half of the same movie although the stimuli contents were different between the two halves. Thus, the fine-grained functional connectivity profiles remain highly stable and reliable across movie contents, which contributes to the robustness of cross-movie, time, and other parameters (e.g., scanner models, scanning parameter) predictions using our algorithms.

We added a paragraph in the discuss section to address the concerns (page 18-19):

“This study successfully illustrated that accurate individualized predictions are both robust and applicable across a variety of conditions, including movie types, languages, scanning parameters, and scanner models. Importantly, the intricate connectivity profiles remain consistent even when participants view entirely different movies, as evidenced by Figure 4-figure supplement 9, reinforcing the prediction's stability in various scenarios. However, all four datasets in this study only included typical participants with anatomically intact brains. An unanswered question is whether individualized topographies of neuropsychological populations with atypical cortical function (e.g., developmental prosopagnosics) or with lesioned brains (e.g., acquired prosopagnosics) could also be accurately predicted using the hyperalignment-based methods. Up to now, as far as we know, no previous literature has investigated this question. Beyond neuropsychological groups, it is also valuable to investigate how well the predictions will be across a wide range of age, from infants to the elderly. Future research is essential to adapt our algorithms to diverse populations.”

Reviewer #2 (Public Review):

Guo and her colleagues develop a new approach to map the category-selective functional topographies in individual participants based on their movie-viewing fMRI data and functional localizer data from a normative sample. The connectivity hyperalignment are used to derived the transformation matrices between the participants according to their functional connectomes during movies watching. The transformation matrices are then used to project the localizer data from the normative sample into the new participant and create the idiosyncratic cortical topography for the participant. The authors demonstrate that a target participant's individualized category-selective topography can be accurately estimated using connectivity hyperalignment, regardless of whether different movies are used to calculate the connectome and regardless of other data collection parameters. The new approach allows researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate datasets from laboratories worldwide to map functional areas for individuals. The topic is of broad interest for neuroimaging community; the rationale of the study is straightforward and the experiments were well designed; the results are comprehensive. I have some concerns that the authors may want to address, particularly on the details of the pipeline used to map individual category-selective functional topographies.

We thank the reviewer for the encouragement.

1) How does the length of the scan-length of movie-viewing fMRI affect the accuracy in predicting the idiosyncratic cortical topography? Similarly, how does the number of participants in the normative database affect the prediction of the category-selective topography? This information is important for the researchers who are interested in using the approach in their studies.

To investigate the influence of movie-viewing data length and the number of participants in the normative database on prediction performance, we systematically varied these parameters. Specifically, we altered the number of runs utilized in the analysis for both the normative and target data and experimented with varying the number of participants in the normative dataset using the Budapest and the Sraiders datasets. We have included a new Figure 4-figure supplement 5 to present a summary of these findings.

The results reveal that both within-dataset and between-dataset prediction performances are positively correlated with the length of movie-viewing fMRI data used for both the normative and target groups. A similar trend was observed with respect to the number of participants included in the normative dataset. It is important to highlight, though, that, even when analyzing as little as one run of movie-viewing data—roughly 10-15 minutes, our hyperalignment-based prediction performance was significantly higher than that achieved using traditional surface alignment. This held true even when the normative dataset included as few as five participants.

In summary, our results show that prediction performance generally improves with longer movie-viewing sessions and larger normative datasets. However, it is noteworthy that even with minimal data—10 minutes of movie-viewing and a small number of participants in the normative dataset—our algorithm still outperforms traditional surface alignment methods significantly.

We also added sentences in the discussion section (page 15):

“We investigated the influence of naturalistic movie length and the size of the training group on the prediction accuracy of individualized functional topographies. By incrementally increasing both the number of movie runs in the training and target dataset and the participants in the training group in the Budapest and Sraiders dataset, we observed enhanced prediction accuracy (Figure 4-figure supplement 5). Notably, even with just one movie run in the training or target dataset, or with a mere five participants in the training group, our prediction performance (Pearson r) ranged from about 0.6 to 0.7. This accuracy significantly outperformed results obtained using surface-based alignment.”

2) The data show that category-selective topography can be accurately estimated using connectivity hyperalignment, regardless of whether different movies are used to calculate the connectome and regardless of other data collection parameters. I'm wondering whether the functional connectome from resting state fMRI can do the same job as the movie-watching fMRI. If it is yes, it will expand the approach to broader data.

We agree with the reviewer that demonstrating the applicability of the resting state data will expand the application scenarios of this approach. Most previous findings on resting state connectivity, including the comparison between the naturalistic and the resting state paradigms, focused on the macro-scale similarities and differences (e.g., Samara et al., 2023). Very few studies have investigated the fine-scaled connectome based on resting state data. The study on connectivity hyperalignment (Guntupalli et al., 2018) demonstrated a shared fine-scale connectivity structure among individuals that co-exists with the common coarse-scale structure and built the algorithm to successfully hyperalign individuals to the shared fine-scaled space. Another study from our lab (Feilong et al., 2021) revealed that the fine-scaled connectivity profiles in both resting and task states are highly predictive of general intelligence, indicating reliable and biologically relevant fine-scaled resting state connectome structures. Thus, it is highly plausible that our approach is able to be generalized to the resting state data, generating significantly better predictions of individualized functional topographies than traditional surface alignment. However, due to the limitations of the current datasets, we do not have resting state data available in the current datasets to perform this analysis. We are in the process of collecting new data to explore this hypothesis in future work.

We added sentences to the discussion section to discuss this idea (page 18):

“Studies comparing movie-viewing and resting state functional connectivity have shown that both paradigms yield overlapping macroscale cortical organizations (29), though naturalistic viewing introduces unique modality-specific hierarchical gradients. However, there remains a gap in research comparing the fine-scaled connectomes of naturalistic and resting state paradigms. Guntupalli and colleagues (14) revealed a shared fine-scale structure that coexists with the coarse-scale structure, and connectivity hyperalignment successfully improved intersubject correlations across a wide variety of tasks. Feilong et al. (13) noted that the fine-scaled connectivity profiles in both resting and task states are highly predictive of general intelligence. This suggests a reliable and biologically relevant fine-scale resting state connectivity structure among individuals. Therefore, it is plausible that individualized functional topography could be effectively estimated using resting state functional connectivity, expanding the applicability of our approach. Future studies are needed to explore this direction.”

3) The authors averaged the hyper-aligned functional localizer data from all of subjects to predict individual category-selective topographies. As there are large spatial variability in the functional areas across subjects, averaging the data from many subjects may blur boundaries of the functional areas. A better solution might be to average those subjects who show highly similar connectome to the target subjects.

We appreciate the reviewer’s insightful question about optimizing prediction performance by selecting participants most similar in functional connectivity to the target individuals. This is a promising direction and difficult problem as well. Our approach is based on fine-scale connectome to hyperalign participants, thus different groups of participants may be similar to the target participant in different searchlights. In addition, based on results discussed in the response to Q2, the more participants included in the normative dataset, the better the prediction performance. Thus, there is a trade-off between the number of participants included in the normative dataset for the prediction and the overall similarity of those participants to the target participant.

To quantitatively explore this idea, we used a searchlight in the right ventral temporal cortex, roughly at the location of posterior fusiform face area (pFFA).We sorted participants by their connectome similarity to each target participant and then examined prediction performance based on either the top nine most similar participants or the bottom nine least similar participants. Our results, presented in Figure 4-figure supplement 8, reveal that hyperalignment consistently outperforms surface alignment regardless of the subset of participants used. Notably, using the nine most similar participants did not significantly alter prediction performance (Tukey Test, z = -0.09, p = 0.996), while using the least similar participants did negatively impact it (Tukey Test, z = 2.492, p = 0.034). Interestingly, the stability of hyperalignment-based predictions remained high even when only a subset of participants was used, contrasting with the variability observed in surface-alignment-based predictions.

Overall, these findings suggest that while selecting functionally similar participants is a promising avenue for future optimization, the process will require nuanced, searchlight-specific criteria. Each searchlight may necessitate its own set of optimal participants to balance between the performance boost from having more participants and the fidelity gained from participant similarity.

We added the following to the discussion in the manuscript (page 16):

“In our study, we used fine-scale connectomes, noting that some participants are more similar to the target participant in specific searchlights. It is an interesting question whether predictions could be enhanced by exclusively selecting those more similar participants for the target participant. To explore this option, we examined a searchlight in the right ventral temporal cortex that was roughly at the location of the posterior fusiform area (pFFA) using the top and bottom nine participants similar to each target participant measured by their fine-scale connectome similarities in the budapest dataset. Generally, using all or part of the participants for the prediction generated similar results (Figure 4-figure supplement 8). Compared to using all the participants, using only the top nine participants who are the most similar to the target participants did not significantly improve the prediction (Tukey Test, z = -0.09, p = 0.996), but using only the bottom nine participants generated significantly lower prediction accuracies (Tukey Test, z = 2.492, p = 0.034). This suggests a trade-off between the number of participants included in the prediction and the similarity of the participants. Future studies are needed to explore the optimal threshold for the number of participants included for each searchlight to refine the algorithm.”

4) It is good to see that predictions made with hyperalignment were close to and sometimes even exceeded the reliability values measured by Cronbach's alpha. But, please clarify how the Cronbach's alpha is calculated.

Cronbach’s alpha calculates the correlation score between localizer-based maps across the runs, and it reflects the amount of noise in maps based on individual localizer runs. Traditionally, the reliability was estimated based on split-half correlations. For example, Guntupalli et al. (2016) used correlations of category-selectivity maps between odd and even localizer runs as the measure of reliability. The odd/even split measure underestimated reliability and necessitated recalculation of correlations between maps for only half the data to provide valid comparisons. In contrast, Cronbach’s alpha involves all localizer runs and provides a more accurate statistical estimate of the reliability of the topographies estimated with localizer runs.

Cronbach’s alpha has been used in many previously published works from our lab (e.g., Feilong et al., 2021; Jiahui et al., 2020, 2023). The code for implementing this metric is publicly accessible on the first author’s Github repository (https://github.com/GUOJiahui/face_DCNN/blob/main/code/cronbach_alpha.py).

We added the detailed explanation above to the Material and Methods section (page 24):

“Cronbach’s alpha calculates the correlation score between localizer-based maps across the runs, and it reflects the amount of noise in maps based on individual localizer runs. Traditionally, the reliability was estimated based on split-half correlations. The common odd/even split measure underestimated reliability and necessitated recalculation of correlations between maps for only half the data to provide valid comparisons. In contrast, Cronbach’s alpha involves all localizer runs and provides a more accurate statistical estimate of the reliability of the topographies estimated with localizer runs.”

5) Which algorithm was used to perform surface-based anatomical alignment? Can the state-ofthe-art Multimodal Surface Matching (MSM) algorithm from HCP achieve better performance?

We preprocessed our datasets using fMRIPrep, which employs algorithms from FreeSurfer’s recon-all for surface-based anatomical alignment. It is worth noting that different alignment methods can yield varying degrees of performance. For instance, a study by Coalson et al. (2018) compared the localization performance of multiple surface-based alignment methods, including Multimodal Surface Matching (MSM) and FreeSurfer. The study found that MSM outperformed FreeSurfer in terms of peak probabilities and spatial clustering, suggesting better overall localization.

Additionally, Guntupalli et al. (2018) evaluated intersubject correlations (ISC) of functional connectivity from movie-viewing data using both Connectivity Hyperalignment (CHA) and MSM-All with the Human Connectome Project (HCP) dataset. The study showed that although MSM-All yielded marginally better ISC than traditional surface alignment, CHA’s performance was significantly superior.

In summary, while using a more advanced alignment algorithm like MSM could marginally improve prediction performance, its advantages may not be substantial when compared to our CHA-based predictions. The combination of MSM and CHA represents an intriguing direction for future research, although it falls outside the scope of our current study.

6) Is it necessary to project to the time course of the functional localizer from the normative sample into the new participants? Does it work if we just project the contrast maps from the normative samples to the new subjects?

It is an interesting question and a practical alternative to researchers to know whether time series of the localizer runs are required to obtain reasonable predictions, as in some scenarios, contrast maps may be the only accessible data in the analysis. To quantitatively explore this possibility, we applied transformation matrices derived from the movie data to training participants’s individual pre-calculated contrast maps of all four categories, and evaluated the predictions. We found nearly similar prediction performance between the two flavors within and across datasets (Figure 4-figure supplement 7). However, it is worth noting that applying transformation matrices directly to contrast maps did not get as much improvement in the interactive steps as the other flavor in the advanced CHA, perhaps due to the scale changes when multiple iterations were implemented and the difficulty to properly normalize the t-maps compared to the regular time series.

Overall, although our algorithm is originally designed to be used on the time course of the functional localizer runs, relatively comparable results can be generated even when the contrast maps are directly projected from the normative group to the target participant. However, to derive the best results with our approach, time series are recommended when the situation permits.

We have also added the contents into the Discussion section (page 16):

“Our original algorithm is designed to apply transformation matrices to the time series of localizer data of training participants before generating contrast maps. To explore whether directly applying these matrices to pre-calculated contrast maps yields comparable results, we conducted an additional analysis across the four categories. Our findings indicate that the prediction outcomes were indeed quite similar between the two approaches for both the within- and across-datasets predictions (Figure 4-figure supplement 7). However, it is worth noting that the improvements observed with enhanced CHA were not as pronounced when applied directly to the contrast maps as opposed to the time series.”

7) Saygin and her colleagues have demonstrated that structural connectivity fingerprints can predict cortical selectivity for multiple visual categories across cortex (Osher DE et al, 2016, Cerebral Cortex; Saygin et al, 2011, Nat. Neurosci). I think there's a connection between those studies and the current study. If the author can discuss the connection between them, it may help us understand why CHA work so well.

We thank the reviewer for raising this point that provides us with the chance of clarifying how our approach differs with methods previously reported in the literature. The computational logic underlying our approach is that we derived the transformation matrices between the training and the target participants in the high-dimensional space based on functional connectivity calculated from the movie data. Then, we applied these transformation matrices to the training participant’s localizer data to accomplish the prediction. On the other hand, Saygin and colleagues directly used diffusion-weighted imaging (DWI) data and predicted participants’ functional responses based on the anatomical-functional correspondence. They evaluated the prediction by calculating the mean absolute errors (AE) of the difference between the actual and predicted contrast responses. Although AE linearly increases with the quality of the prediction, it is difficult to measure the prediction performance of the shape, size, and location of the functional areas precisely using this mean value. With our algorithm, we were able to predict the general location and size of the areas and recover the individualized shapes, generating more powerful predictions. We also used the searchlight analysis to evaluate the performance across the cortex systematically. In addition, Osher et al. (2016) and Saygin et al. (2012) always have a few participants failing to show better predictions based on the connectivity than the group averaged method. Our algorithm is more stable, as all participants across all four datasets had better predicted performance using our algorithm than using the group average. However, although we did not directly use the anatomical-functional correspondence with DWI, the relationships between individual structural connectivity and cortical visual category selectivity could be one of the biological underpinnings that contribute to this robust and accurate prediction.

The Connectivity-Based Shared Response Model (cSRM, Nastase et al., 2020) offers an alternative framework for aligning individuals through functional connectivity. While the overarching aim of cSRM and our methodology converges, substantial differences emerge in the respective implementation and application between the two methods that make our approach the more suitable for predicting individualized topographies. The most significant difference between the two is that, instead of focusing on within-individual connectivity profiles, cSRM used inter-subject functional connectivity (ISFC) in the initial step. This design requires that all participants must have time-locked time series, making the algorithm unusable for cross-content prediction and making it incompatible with resting-state data. Our approach, on the other hand, does not require time-locked stimuli, thereby offering a more flexible framework that permits generalization across different types of stimuli and experimental settings and enables bringing data across laboratories across the world together. Secondly, cSRM predominantly focuses on Region of Interest (ROI) analyses, whereas our model employs searchlight-based analyses designed to comprehensively cover the entire cortical sheet. Whole-brain coverage is needed to generate the topography that reflects the patterns across the cortex. Finally, with the optimized 1step method, our approach directly hyeraligns the training and target participants together, avoiding the accumulation of errors from the intermediate common space. cSRM, with an implementation similar to the classic connectivity hyperalignment, creates and hyperaligns all participants to a shared information space. In summary, while our approach and cSRM share a similar theoretical foundation, our approach has been specifically optimized to address the challenges and complexities in predicting individualized whole-brain functional topographies. Moreover, our approach demonstrates a remarkable ability to generalize across a variety of contexts and stimuli, offering a significant advantage in dealing with diverse experimental settings and datasets.

We have added the contents to the discussion section (page 16-17):

“By leveraging transformation matrices obtained from hyperaligning participants based on movie-viewing data, we successfully mapped these relationships to the training participants’ localizer data, enabling robust predictions. Prior work employing diffusion-weighted imaging (DWI) has underscored the link between anatomical connectivity and category selectivity across diverse visual fields (22, 23) and has established a notable congruence between structural and functional connectivities (24). These findings suggest that the unique anatomical connectivity patterns of individuals may serve as a foundational mechanism, contributing to the stable finescale functional connectome that underpins our approach. The connectivity-based Shared Response Model (cSRM) proposed by Nastase and colleagues (25) used connectivity to functionally align individuals similar to the connectivity hyperalignment algorithm. While both approaches share overarching goals, they diverge considerably in implementation and application. First and most important, cSRM used inter-subject functional connectivity (ISFC) rather than within-subject functional connectivity to initially estimate the connectome. As a result, cSRM requires participants to have time-locked fMRI time series. Therefore, unlike our algorithm, the cSRM approach does not support cross-content applications and also is not suitable for use with resting-state data. Second, cSRM is implemented based on a predefined cortical parcellation rather than the overlapping, regularly-spaced cortical searchlights applied in our method which are not constrained by areal borders. For the application, cSRM has mainly been used to do ROI analysis rather than the estimation of the whole-brain topography that requires broader coverage of the cortex with a searchlight analysis. Third, our method is specifically designed to work in each individual’s space, while cSRM decomposes data across subjects into shared and subjectspecific transformations, focusing on a communal connectivity space. In summary, although cSRM presents a promising alternative for similar aims, its current implementation precludes it from fulfilling the range of applications for which our method is optimized.”

Reviewer #3 (Public Review):

In this paper, Jiahui and colleagues propose a new method for learning individual-specific functional resonance imaging (fMRI) patterns from naturalistic stimuli, extending existing hyperalignment methods. They evaluate this method - enhanced connectivity hyperalignment (CHA) - across four datasets, each comprising between nine (Raiders) and twenty (Budapest, Sraiders) participants.

The work promises to address a significant need in existing functional alignment methods: while hyperalignment and related methods have been increasingly used in the field to compare participants scanned with overlapping stimuli (or lack thereof, in the case of resting state data), their use remains largely tied to naturalistic stimuli. In this case, having non-overlapping stimuli is a significant constraint on application, as many researchers may have access to only partially overlapping stimuli or wish to compare stimuli acquired under different protocols and at different sites.

It is surprising, however, that the authors do not cite a paper that has already successfully demonstrated a functional alignment method that can address exactly this need: a connectivitybased Shared Response Model (cSRM; Nastase et al., 2020, NeuroImage). It would be relevant for the authors to consider the cSRM method in relation to their enhanced CHA method in detail. In particular, both the relative predictive performance as well as associated computational costs would be useful for researchers to understand in considering enhanced CHA for their applications.

We thank the reviewer for raising this point that provides us with the chance of clarifying how our approach differs with methods previously reported in the literature. The computational logic underlying our approach is that we derived the transformation matrices between the training and the target participants in the high-dimensional space based on functional connectivity calculated from the movie data. Then, we applied these transformation matrices to the training participant’s localizer data to accomplish the prediction. On the other hand, Saygin and colleagues directly used diffusion-weighted imaging (DWI) data and predicted participants’ functional responses based on the anatomical-functional correspondence. They evaluated the prediction by calculating the mean absolute errors (AE) of the difference between the actual and predicted contrast responses. Although AE linearly increases with the quality of the prediction, it is difficult to measure the prediction performance of the shape, size, and location of the functional areas precisely using this mean value. With our algorithm, we were able to predict the general location and size of the areas and recover the individualized shapes, generating more powerful predictions. We also used the searchlight analysis to evaluate the performance across the cortex systematically. In addition, Osher et al. (2016) and Saygin et al. (2012) always have a few participants failing to show better predictions based on the connectivity than the group averaged method. Our algorithm is more stable, as all participants across all four datasets had better predicted performance using our algorithm than using the group average. However, although we did not directly use the anatomical-functional correspondence with DWI, the relationships between individual structural connectivity and cortical visual category selectivity could be one of the biological underpinnings that contribute to this robust and accurate prediction.

The Connectivity-Based Shared Response Model (cSRM, Nastase et al., 2020) offers an alternative framework for aligning individuals through functional connectivity. While the overarching aim of cSRM and our methodology converges, substantial differences emerge in the respective implementation and application between the two methods that make our approach the more suitable for predicting individualized topographies. The most significant difference between the two is that, instead of focusing on within-individual connectivity profiles, cSRM used inter-subject functional connectivity (ISFC) in the initial step. This design requires that all participants must have time-locked time series, making the algorithm unusable for cross-content prediction and making it incompatible with resting-state data. Our approach, on the other hand, does not require time-locked stimuli, thereby offering a more flexible framework that permits generalization across different types of stimuli and experimental settings and enables bringing data across laboratories across the world together. Secondly, cSRM predominantly focuses on Region of Interest (ROI) analyses, whereas our model employs searchlight-based analyses designed to comprehensively cover the entire cortical sheet. Whole-brain coverage is needed to generate the topography that reflects the patterns across the cortex. Finally, with the optimized 1step method, our approach directly hyeraligns the training and target participants together, avoiding the accumulation of errors from the intermediate common space. cSRM, with an implementation similar to the classic connectivity hyperalignment, creates and hyperaligns all participants to a shared information space. In summary, while our approach and cSRM share a similar theoretical foundation, our approach has been specifically optimized to address the challenges and complexities in predicting individualized whole-brain functional topographies. Moreover, our approach demonstrates a remarkable ability to generalize across a variety of contexts and stimuli, offering a significant advantage in dealing with diverse experimental settings and datasets.

We have added the contents to the discussion section (page 16-17):

“By leveraging transformation matrices obtained from hyperaligning participants based on movie-viewing data, we successfully mapped these relationships to the training participants’ localizer data, enabling robust predictions. Prior work employing diffusion-weighted imaging (DWI) has underscored the link between anatomical connectivity and category selectivity across diverse visual fields (22, 23) and has established a notable congruence between structural and functional connectivities (24). These findings suggest that the unique anatomical connectivity patterns of individuals may serve as a foundational mechanism, contributing to the stable finescale functional connectome that underpins our approach. The connectivity-based Shared Response Model (cSRM) proposed by Nastase and colleagues (25) used connectivity to functionally align individuals similar to the connectivity hyperalignment algorithm. While both approaches share overarching goals, they diverge considerably in implementation and application. First and most important, cSRM used inter-subject functional connectivity (ISFC) rather than within-subject functional connectivity to initially estimate the connectome. As a result, cSRM requires participants to have time-locked fMRI time series. Therefore, unlike our algorithm, the cSRM approach does not support cross-content applications and also is not suitable for use with resting-state data. Second, cSRM is implemented based on a predefined cortical parcellation rather than the overlapping, regularly-spaced cortical searchlights applied in our method which are not constrained by areal borders. For the application, cSRM has mainly been used to do ROI analysis rather than the estimation of the whole-brain topography that requires broader coverage of the cortex with a searchlight analysis. Third, our method is specifically designed to work in each individual’s space, while cSRM decomposes data across subjects into shared and subjectspecific transformations, focusing on a communal connectivity space. In summary, although cSRM presents a promising alternative for similar aims, its current implementation precludes it from fulfilling the range of applications for which our method is optimized.”

With this in mind, I noted several current weaknesses in the paper:

First, while the enhanced CHA method is a promising update on existing CHA techniques, it is unclear why this particular six step, iterative approach was adopted. That is: why was six steps chosen over any other number? At present, it is not clear if there is an explicit loss function that the authors are minimizing over their iterations. The relative computational cost of six iterations is also likely significant, particularly compared to previous hyperalignment algorithms. A more detailed theoretical understanding of why six iterations are necessary-or if other researchers could adopt a variable number according to the characteristics of their data-would significantly improve the transferability of this method.

In the advanced connectivity hyperalignment implementation, we gradually increased the number of targets. The six steps were not intentionally chosen but were the result of the increase to the maximum number of fine-grained targets, namely single cortical vertices.

Our datasets were resampled to the cortical mesh with 18,742 vertices across both hemispheres (approximately 3 mm vertex spacing; icoorder 5; 20,484 vertices before removing non-cortical vertices). Step 1 was the classic standard connectivity hyperalignment implementation based on the anatomically-aligned data. Since using dense connectivity targets (e.g., using all 18742 vertices on the surface) with anatomically-aligned data generates poor functional correspondence across participants (Busch et al., 2021), we used 1,284 vertices (icoorder 3, before removing the medial wall) as connectivity targets in step 1. However, it is beneficial to include more targets for calculating connectivity patterns after the first iteration of connectivity hyperalignment and repeated iterations to lead to a better solution by gradually aligning the information at finer scales. To better align across participants, we iterated the alignment for another two times (step 2 and step 3) with the same number of 1,284 coarse connectivity targets to ensure improved alignment before increasing the number of targets in the later steps. In step 4, we increased the number of targets to 5,124 (icoorder 4, before removing the medial wall), and iterated with this number of vertices for two times in total (step 4 & step 5) before using all vertices as targets. In the final step (step 6), all vertices were used as connectivity targets.

It is true that the multiple iteration steps largely increased the computational complexity compared to the classic connectivity hyperalignment, but the prediction increase was steady across all datasets and became comparable to response hyperalignment performance which requires time-locked stimuli. We did not use an explicit loss function in the algorithm, but followed the natural progression of the number of potential connectivity targets in the implementation. On the other hand, the difference between the performance of the improved and the classic connectivity hyperalignment was relatively small (difference of r < 0.05), which indicates the effectiveness of our classic algorithm. It is up to the researchers’ own options to adopt the number of iterations and the pace of increasing the number of targets in each step. If computational resources are limited or if a shorter total computational time is the primary priority, using the classic connectivity hyperalignment may be the best option to balance the trade-offs.

The Materials and Methods section had the details of the implementation (page 22-23):

“Using dense connectivity targets (e.g., using all 18742 vertices on the surface) with anatomically-aligned data usually generates poor functional correspondence across participants (33). It is, however, beneficial to include more targets for calculating connectivity patterns after the first iteration of connectivity hyperalignment and repeated iterations to lead to a better solution by gradually aligning the information at finer scales.

We used six steps to further improve the connectivity hyperalignment method. Step 1 was the initial connectivity hyperalignment step as described above that was based on the raw anatomically aligned movie data. The resultant transformation matrices were applied to those movie runs, and the hyperaligned data were then used in step 2 to calculate new connectivity patterns and calculate new transformation matrices. We repeated this procedure iteratively six times and derived transformation matrices for each step. In steps 1, 2, and 3, 642 × 2 (icoorder3, before removing the medial wall) connectivity targets were defined with 13 mm searchlights. In step 4 and 5, 2562 × 2 (icoorder 4, before removing the medial wall) connectivity targets were used with 7 mm searchlights to calculate target mean time series. In the final step 6, all 18742 vertices were included as separate connectivity targets, using each vertex’s time series rather than calculating the mean in a searchlight. Each step of this advanced connectivity hyperalignment algorithm increased the prediction performance (Figure 4-figure supplement 2).”

But to help the readers understand the logic of the advanced connectivity hyperalignment algorithm used in this study, we expanded the discussion section (page 15):

“Because using dense connectivity targets (e.g., using all vertices as connectivity targets) with anatomically-alignment data often leads to suboptimal alignment across participants (33), we started with coarse connectivity targets and gradually increased the number of connectivity targets to form a denser representation of connectivity profiles. The iterations improved the prediction performance step by step, and at the final step (step 6, all vertices were used as connectivity targets) in this analysis, the enhanced CHA generated comparable performance with RHA (Figure 4-figure supplement 4).”

Second, the existing evaluations for enhanced CHA appear to be entirely based on imagederived correlations. That is, the authors compare the predicted image from CHA with the ground-truth image using correlation. While this provides promising initial evidence, correlation-based measures are often difficult to interpret given their sensitivity to image characteristics such as smoothness. Including Cronbach's alpha reliability as a baseline does not address this concern, as it is similarly an image-based statistic. It would be useful to see additional predictive experiments using frameworks such as time-segment classification, intersubject decoding, or encoding models.

We appreciate the reviewer’s concern regarding the stability of local correlations in relation to image characteristics. To address this, we conducted additional analysis using different searchlight sizes (with radii of 10 mm, 15 mm, and 20 mm) to evaluate the predicted categoryselective maps, focusing specifically on the Budapest dataset. The local correlations between the predicted category-selective maps (obtained using enhanced CHA) and participants’ own maps based on classic localizer runs were calculated for each searchlight. We averaged these correlations across participants and plotted the resulting maps, as shown in Figure 4-figure supplement 10. Although using a larger searchlight radius is similar to employing a larger smoothing kernel, the results remained relatively stable across different searchlight sizes, particularly in regions selectively responsive to the specific category. This stability suggests that while the evaluation may be influenced by image-related features, the conclusion would remain consistent under varying parameters.

As for the use of enhanced CHA, it serves as an optimized version of the classic CHA, specifically designed for predicting individualized functional topographies. Evaluating prediction performance in our study is based on t-value contrast maps for each participant. Given this, it's unclear how time-segment classification or other decoding/encoding models could be appropriately implemented for performance evaluation. However, prior research from our lab has already established the effectiveness of classic CHA. Specifically, Guntupalli et al. (2018) showed that classic CHA significantly improved intersubject correlations (ISC) of connectivity profiles across the cortex. They also revealed that CHA captured fine-scale variations in connectivity profiles for nearby cortical nodes across participants and led to improved betweensubject multivariate pattern classification accuracies (bsMVPC) of movie segments. These findings serve as robust evidence for the effectiveness of classic CHA, laying the groundwork for our enhanced CHA approach.

We added Figure 4-figure supplement 10 to the supplementary material:

Addressing these concerns and considering cSRM as a comparison model would significantly strengthen the paper. There are also notable strengths that I would encourage the authors to further pursue. In particular, the authors have access to a unique dataset in which the same Raiders of the Lost Ark stimulus was scanned for participants within the Budapest (SRaiders) dataset as well as non-overlapping participants in the Raiders dataset. Exploring the relative performance for cross-movie prediction within a dataset as compared to a shared movie prediction across datasets is particularly interesting for methods development. I would encourage the authors to explicitly report results in this framework to highlight both this unique testing structure as well as the performance of their enhanced CHA method.

We appreciate the reviewer's suggestion to examine a shared time-series but non-overlapping participants scenario using the Sraiders and Raiders datasets. However, there are significant differences between the two datasets that preclude such direct comparison. These differences include varying scanning parameters, MRI scanners, localizer types, and data collection procedures. Due to these methodological divergences, the datasets cannot be treated as identical time-series.

Firstly, the scanning parameters vary considerably. Sraiders were scanned with TR = 1 s (TR/TE = 1000/33 ms, flip angle = 59 °, resolution = 2.5 mm3 isotropic voxels, matrix size = 96 × 96, FoV = 240 × 240 mm, multiband acceleration factor = 4, and no in-plane acceleration), and Raiders were scanned with TR = 2.5 s (TR = 2.5 s, TE = 35 ms, Flip angle = 90°, 80 × 80 matrix, FOV = 240 mm × 240 mm, resolution = 0.938 mm × 0.938 mm × 1.0 mm).

Secondly, participants in the Sraiders were scanned with a 3 T S Magnetom Prisma MRI scanner with a 32 channel head coil and the Raiders dataset, collected more than 10 years ago, used a 3T Philips Intera Achieva scanner with an eight-channel head coil.

Thirdly, the stimuli presentations were different. In the Sraiders dataset, the movie Raiders of the Lost Ark was split into eight parts (~15 min each), and the first four parts were watched outside of the scanner prior to the scanning (~56 min). The later four parts were watched in the scanner (57 min) with audio. And in the Raiders dataset, the audio-visual movie was split into eight parts (~15 min each). Participants watched all eight parts in the scanner with audio (one part / per run).

Fourthly and critically, the two datasets included two types of localizers. The Sraiders dataset included dynamic localizer runs, and the Raiders dataset only contained a static localizer that was similarly designed as in the Forrest dataset.

With all four points, it is not suitable to treat the two datasets as identical time-series. The difference in the localizer type is a further issue. The topographies generated from the two types of localizers are dissimilar in many ways. For all categories, the dynamic localizer elicited stronger and broader category-selective activations than the static localizer, and the searchlight analysis showed that the dynamic localizer had higher reliabilities across the cortex, especially in regions that were selectively responsive to the target category. Due to these differences, crossdataset predictions yielded lower correlations than within-dataset predictions. This is not indicative of methodological failure but reflects diverging topographies activated by different localizers.

In the manuscript, we have extensively analyzed cross-dataset predictions (Figure 2-figure supplement 1-Figure 4-figure supplement 4 & 6).

● Figure 2-figure supplement 1 demonstrates that, despite the limitations of cross-localizertype evaluation, both R-to-S (Raiders to Sraiders) and S-to-R (Sraiders to Raiders) predictions significantly outperformed surface alignment methods across categories.

● Figure Figure 2-figure supplement 2 confirms that the prediction performance remained stable across individual participants, underscoring the robustness of our methodology.

● Figure 3-figure supplement 1 & Figure 3-figure supplement 2 display contrast maps generated from both native and alternate localizers, revealing that the maps share similar topographies irrespective of the dataset origin.

● Figure 4-figure supplement 1 presents a correlation analysis of local similarities in R-to-S and S-to-R predictions, highlighting particularly strong correlations in the ventral face regions.

● Figure 4-figure supplement 2 employs histograms to showcase performance across major cortices and furnishes additional evidence regarding the influence of localizer types on the results.

● Figure 4-figure supplement 3 offers a searchlight analysis for other categories, enriching the scope of our investigation.

● Figure 4-figure supplement 4 affirms that the advanced CHA is effective in both R-to-S and S-to-R predictions.

● Figure 4-figure supplement 6 compares the efficacy of 1-step vs. 2-step prediction methods for R-to-S and S-to-R, showing a clear advantage for the 1-step approach.

These analyses affirmed that our approach outperforms surface alignment methods. But the inherent limitations in data collection and localizer types preclude a direct exploration of the reviewer’s hypothesis. These complexities necessitate further research to fully validate the proposed scenario.

Overall, I share the authors' enthusiasm for the potential of cross-movie, cross-dataset prediction, and I believe that methods such as enhanced CHA are likely to significantly improve our ability to make these comparisons in the near future. At present, however, I find that the theoretical and experimental support for enhanced CHA is incomplete. It is therefore difficult to assess how enhanced CHA meets its goals or how successfully other researchers would be able to adopt this method in their own experiments.

We hope our new analysis and replies addressed the reviewer’s concerns.

we can run through writing in our head to better prepare our brains to write on the topic.

Author Response

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

Reviewer #1 (Public Review):

Summary:

This important study from Godneeva et al. establishes a Drosophila model system for understanding how the activity of Tif1 proteins is modified by SUMO. The authors nicely show that Bonus, like homologous mammalian Tif1 proteins, is a repressor, and that it interacts with other co-repressors Mi-2/NuRD and setdb1 in Drosophila ovaries and S2 cells. They also show that Bonus is SUMOylated by Su(var)2-10 on at least one lysine at its N-terminus to promote its interaction with setdb1. By combining nice biochemistry with an elegant reporter gene approach, they show that SUMOylation is important for Bonus interaction with setdb1, and that this SUMO-dependent interaction triggers high levels of H3K9me3 deposition and gene silencing. While there are still major questions of how SUMO molecularly promotes this process, this study is a valuable first step that opens the door for interesting future experimentation.

Major Point:

The RNAseq and ChIPseq data is not available. This is critical for the review of the paper and would help the readers and reviewers interpret the Bonus mutant phenotype and its mechanism of repressing genes.

The sequencing data have been deposited to the NCBI GEO archive. The accession number for all other RNA-seq and ChIP-seq data reported in this paper is GEO: GSE241375.

1) The author's conclusion that Bonus SUMOylation is "essential for its chromatin localization" is not supported by the data. Figure 5F shows less 3KR mutant in the chromatin fraction but there is still significant signal.

We appreciate the reviewer's feedback and agree that the term "essential" was not appropriate in this context. We have revised the manuscript to replace "essential" with "contributes to" to accurately reflect our findings.

2) The author's conclusion that Bonus is SUMOylated at a single site close to its N-terminus is not necessarily true. In several SUMO and Bonus blots throughout the paper (5B, 6C, S4A), there are >2 differentially migrating species that could represent more than one SUMO added to Bonus. While the single K20R mutation eliminates all of these species in Fig 5C, it is possible that K20R SUMOylation is required for additional SUMOylation events on other residues. One way to determine if Bonus is SUMOylated on multiple sites is to add recombinant SUMO protease to the extract and see if multiple higher molecular weight bands collapse into a single migrating species (implying multiple SUMOs) or multiple migrating species (implying something else is altering gel migration).

We appreciate the suggestion made by the reviewer. While we acknowledge the presence of occasional multiple bands in SUMO Western blots, the predominant pattern is the presence of unmodified Bon and a single additional band corresponding to SUMO-modified Bon. To investigate the possibility of multi-site SUMOylation, we performed requested experiment where we added SENP2 SUMO protease to the extract and checked Bon's SUMOylation. In the presence of NEM, we observed the unmodified form of Bon, as well as a single additional band representing a SUMO-modified form of Bon. Following SENP2 SUMO protease treatment, SUMOylation form of Bon was completely abolished in all samples, leaving only the unmodified Bon band (Extended Data Fig. 4D). This indicates that Bon is not SUMOylated on multiple sites and that the observed differential migration species likely result from other factors affecting gel migration.

3) The authors state that most upregulated genes in BonusGLKD are not highly enriched in H3K9me3. The heatmap in figure 3D is not an ideal presentation of this argument. The authors should show an example of what the signal on a highly enriched gene looks like for comparison. The authors also argue that because most upregulated genes in BonusGLKD are not highly enriched in H3K9me3, they must be indirectly repressed. Another possibility is that bonus-mediated H3K9me3 is only important (and present) during early nurse cell differentiation and is later lost and dispensable during the rapid endocycles. After bonus establishes repression though H3K9me3, it might be maintained through bonus-Mi2/Nurd, something else, or nothing at all. The authors could discuss this possibility or perform H3K9me3 ChIP during cyst formation and early nurse cell differentiation rather than in whole ovaries, which are enriched for later stages.

We thank the reviewer for their thoughtful comments and suggestions. In our revised manuscript we have included the tracks of gene that is highly enriched in H3K9me3 but remain unchanged upon Bon GLKD (Extended Data Fig. 3B). This addition allows for a visual comparison and better supports our argument that majority of genes upregulated in Bon GLKD are not enriched in H3K9me3 mark. We also appreciate the reviewer's suggestion regarding the potential temporal dynamics of Bon-mediated H3K9me3. It is indeed possible that Bon's role in establishing H3K9me3 might be more prominent during early nurse cell differentiation and less critical in later stages. We included discussion of this possibility in revised manuscript. To further explore it would be valuable to perform H3K9me3 ChIP during cyst formation and early nurse cell differentiation. However, given the limitations of our current resources and time limitations, we were unable to perform these experiments for the revised manuscript.

4) The BonusGLKD RNAseq analysis is underwhelming. The conclusion that "Bonus represses tissue-specific genes" has limited value. Every gene that is not expressed in ovaries is "tissue-specific." What subset of tissue-specific genes does Bonus repress? What common features do these genes have and how do they compare to other sets of tissue-specific genes, such as those reportedly repressed by setdb1, Polycomb proteins, small ovary, l(3)mbt, and stonewall (among others in female germ cells). Comparing these available data sets could help the authors understand the mechanism of Bonus repression and how BonusGLKD leads to sterility. The authors could also further analyze the differences between nos-Gal4 and MT-Gal4 to better understand why nos- but not MT-driven knockdown is sterile.

We appreciate the reviewer's feedback regarding the RNA-seq analysis and acknowledge the importance of identifying the specific subset of tissue-specific genes. The Figure 2C shows specific tissues where genes derepressed upon Bon GLKD are normally expressed. These are tissues/organs such as the head, digestive system, and nervous system. The reviewer's suggestion to compare our findings with existing datasets are valid and could indeed provide a more comprehensive understanding of Bon repression and its implications in female germ cells. However, many of the published datasets are based on mutant fly lines or use different GAL4 drivers to induce knockdowns, making direct comparisons challenging. We have conducted a preliminary analysis of available data, specifically nos-Gal4>SetDB1KD (GSE109852), and identified an overlap of 135 genes out of the 464 genes upregulated upon nos-Gal4>BonusKD with those affected by SetDB1 knockdown. We have included this result in the revised manuscript.

Main Study Limitations:

1) It is unclear which genes are directly vs indirectly regulated by bonus, which makes it difficult to understand Bonus's repressive mechanism. Several lines of experiments could help resolve this issue. 1) Bonus ChIPseq, which the authors mentioned was difficult. 2) RNAseq of BonusGLKD rescued with KR3 mutation. This would help separate SUMO/setdb1-dependent regulation from Mi-2 dependent regulation. Similarly, comparing differentially expressed genes in Su(var)2-10GLKD, setdb1GLKD, 3KR rescue, and MI-2 GLKD could identify overlapping targets and help refine how bonus represses subsets of genes through these different corepressors.

We appreciate the reviewer's suggestions and agree that discrimination between direct and indirect Bon targets should be the next step in understanding Bon repressive mechanism. We have previously attempted to determine Bon direct targets using ChIP-seq approach. However, despite our multiple efforts using both native Bon antibodies and GFP-tagged Bon fly lines, analysis of ChIP-seq data did not reveal specific enrichment indicating that Bon – similar to many other chromatin-bound proteins – are not amenable to ChIP. The recommendation for RNA-seq analysis of Bon GLKD rescued with the 3KR mutation is valuable, and we will certainly consider it for future investigations.

We compared differentially expressed genes in Su(var)2-10 GLKD and Mi-2 GLKD and found limited overlap: out of the 231 genes affected by Bon GLKD, 39 genes were affected in Mi-2 GLKD and 42 in Su(var)2-10 GLKD. We acknowledge the importance of understanding which genes are directly or indirectly regulated by Bon and the potential for further experiments to address this question.

2) The paper falls short in discussing how SUMO might promote repression. This is important when considering the conservation (of lack thereof) of SUMOylation sites in Tif1 proteins in distantly related animals. One piece of data that was not discussed is the apparent localization of SUMOylated bonus in the cytoplasmic fraction of the blot in Figure 5F. Su(var)2-10 is mostly a nuclear protein, so is bonus SUMOylated in the nucleus and then exported to the cytoplasm? Also, setdb1 is a nuclear protein, so it is unlikely that the SUMOylated bonus directly interacts with setdb1 on target genes. Together with Fig 5E (unSUMOylatable Bonus aggregates in the nucleus), one could make a model where SUMO solubilizes bonus (perhaps by disassembling aggregates) and indirectly allows it to associate with setdb1 and chromatin. It is also important to note that in Figure 5I, the K3R mutation appears to lessen but not eliminate Bonus interaction with setdb1. This data again disfavors a model where SUMO establishes an interaction interface between setdb1 and Bonus. To determine which form of Bonus interacts with setdb1, the authors could perform a setdb1 pulldown and monitor the SUMOylation state of coIPed Bonus through mobility shift. If mostly unSUMOylated bonus interacts with setdb1, and SUMO indirectly promotes Bonus interaction with setdb1 (perhaps by disassembling Bonus aggregates), then the precise locations of Bonus SUMOylation sites could more easily shift during evolution, disfavoring the author's convergent evolution hypothesis.

We appreciate the reviewer's valuable feedback. Regarding the observation of SUMOylated Bon in the cytoplasmic fraction in Figure 5F, we recognize its significance. This finding has prompted us to consider a model in which SUMOylation may play a role in translocating Bon from the nucleus to the cytoplasm, potentially influencing interactions with SetDB1 and chromatin indirectly. Furthermore, Figure 5I which shows only a partial reduction in Bon-SetDB1 interaction with the 3KR mutation, suggests that SUMO may not be the primary mediator of this interaction. We recognize the need for further investigations to clarify SUMO's exact role in this context. In response to the reviewer's suggestion, we conducted SetDB1 pulldown experiments in S2 cells. The results reveal that indeed SetDB1 primarily interacts with unmodified Bon which is by far more abundant compared to SUMOylated form (Extended Data Fig. 5C). We think this experiment presents certain technical challenges, as the signal for Bon, when used as prey in co-IP experiments, is relatively faint, making it inherently difficult to detect the lower levels of SUMO-modified Bon. Additionally, in revised manuscript we have added new result of determining Bon interactors in ovary using mass-spec analysis, which showed that SetDB1 associates with wild-type, but not SUMO-deficient Bon. While our data support the idea that SUMO may contribute to Bon solubilization, possibly by disassembling aggregates, thereby indirectly facilitating its association with SetDB1 and chromatin, we acknowledge that the precise mechanism remains unclear.

Reviewer #2 (Public Review):

Summary:

The authors analyze the functions and regulation of Bon, the sole Drosophila ortholog of the TIF1 family of mammalian transcriptional regulators. Bon has been implicated in several developmental programs; however, the molecular details of its regulation have not been well understood. Here, the authors reveal the requirement of Bon in oogenesis, thus establishing a previously unknown biological function for this protein. Furthermore, careful molecular analysis convincingly established the role of Bon in transcriptional repression. This repressor function requires interactions with the NuRD complex and histone methyltransferase SetDB1, as well as sumoylation of Bon by the E3 SUMO ligase Su(var)2-10. Overall, this work represents a significant advance in our understanding of the functions and regulation of Bon and, more generally, the TIF1 family. Since Bon is the only TIF1 family member in Drosophila, the regulatory mechanisms delineated in this study may represent the prototypical and important modes of regulation of this protein family. The presented data are rigorous and convincing. As discussed below, this study can be strengthened by a demonstration of a direct association of Bon with its target genes, and by analysis of the biological consequences of the K20R mutation.

Strengths:

1. This study identified the requirement for Bon in oogenesis, a previously unknown function for this protein.
2. Identified Bon target genes that are normally repressed in the ovary, and showed that the repression mechanism involves the repressive histone modification mark H3K9me3 deposition on at least some targets.
3. Showed that Bon physically interacts with the components of the NuRD complex and SetDB1. These protein complexes are likely mediating Bon-dependent repression.
4. Identified Bon sumoylation site (K20) that is conserved in insects. This site is required for repression in a tethering transcriptional reporter assay, and SUMO itself is required for repression and interaction with SetDB1. Interestingly, the K20-mutant Bon is mislocalized in the nucleus in distinct puncta.
5. Showed that Su(var)2-10 is a SUMO E3 ligase for Bon and that Su(var)2-10 is required for Bon-mediated repression.

Weaknesses:

The study would be strengthened by demonstrating a direct recruitment of Bon to the target genes identified by RNA-seq. Given that the global ChIP-seq was not successful, a few possibilities could be explored. First, Bon ChIP-qPCR could be performed on the individual targets that were functionally confirmed (e.g. rbp6, pst). Second, a global Bon ChIP-seq has been reported in PMID: 21430782 - these data could be used to see if Bon is associated with specific targets identified in this study. In addition, it would be interesting to see if there is any overlap with the repressed target genes identified in Bon overexpression conditions in PMID: 36868234.

We greatly appreciate the reviewer's suggestion to demonstrate the direct recruitment of Bon to the target genes. As described in our answer to reviewer #1, we attempted to determine Bon direct targets using ChIP-seq approach using both native Bon antibodies and GFP-tagged Bon fly lines. However, analysis of ChIP-seq data did not reveal specific enrichment. Similarly, Bon ChIP-qPCR on individual targets showed the same results suggesting that Bon – similar to many other chromatin-bound proteins – are not amenable to ChIP protocol, at least in standard conditions. To further explore this issue, we have analyzed results of a global Bon ChIP-seq reported in PMID: 21430782. We did not find Bon binding to individual targets, but even more importantly, we did not see clear Bon enrichment elsewhere in the genome confirming a conclusion that Bon targets on chromatin cannot be determined by ChIP. Additionally, we explored the possibility of overlap between target genes repressed by Bon in our study and those observed under Bon overexpression conditions in PMID: 36868234. While we did identify 41 genes in common, it's important to note that the datasets are derived from different tissues (pupal eyes vs. ovaries), making direct comparison problematic.

The second area where the manuscript can be improved is to analyze the biological function of the K20R mutant Bonus protein. The molecular data suggest that this residue is important for function, and it would be important to confirm this in vivo.

We appreciate the reviewer's suggestion to analyze the biological function of the K20R mutant Bon protein. While we acknowledge that we did not use single-site K20R mutant for in vivo experiments, we demonstrated that the mutant with the three-residue substitution (3KR) is incapable of inducing repression (Figure 5G). Given that other experiments consistently showed that K20 is the primarily SUMOylation site, this result supports the conclusion that K20 SUMOylation plays an important role in Bon-mediated transcriptional silencing.

Reviewer #1 (Recommendations for The Authors):

Make the RNAseq and ChIPseq data publicly available!

The sequencing data have been deposited to the NCBI GEO archive. The accession number for all other RNA-seq and ChIP-seq data reported in this paper is GEO: GSE241375.

Reviewer #2 (Recommendations for The Authors):

It would be interesting to identify the biological basis of aberrant ovary development in Bon depletion conditions. Previous studies (e.g. PMID: 11336699) suggested that Bon loss of function clones are cell lethal, and the developmental defects in oogenesis presented in the current study offer an opportunity to delve more into the causes of cell loss, e.g. by showing that the cells die via apoptosis.

Thank you for your valuable suggestion. In response to your comment, we performed a TUNEL assay to investigate whether germ cells in nos-Gal4>BonusKD ovaries undergo apoptosis. Our results indeed indicate that germ cells in these ovaries exhibit apoptosis, as evidenced by the TUNEL signal (Extended Data Fig. 1C). This information has been included in the revised manuscript to provide insights into the biological basis of aberrant ovary development in Bon depletion conditions.

The K20 residue could also be ubiquitinated. This possibility could at least be discussed, particularly given the presence of the RING Ub ligase domain in Bon that might potentially perform self-ubiquitination.

Indeed, the possibility that Bon can be ubiquitinated is a valid consideration. We have explored this possibility. We did not detect any signals with the Ubiquitin antibody in both wild-type Bon immunoprecipitant and triple-mutant [3KR] ovaries (in which K20 is also mutated) (Extended Data Fig. 4C). This suggests that K20 is more likely responsible for Bon SUMOylation rather than ubiquitination. We appreciate the reviewer's suggestion and have included this information into the revised manuscript.

Author Response

We thank the reviewers for their suggestions in improving the manuscript. We are currently working on a formal revision and plan to submit a revised manuscript in the near future. However, we would be remiss, if we did not address concerns regarding the conceptual merits of the paper. Below we speak to major points of note that address select reviewer comments and the eLife assessment of our manuscript.

eLife assessment:

However, the strength of evidence is incomplete due to the concern that larval contraction is a result of chilling the nervous system and muscles, which causes spreading depolarization and mechanical contraction of the body, rather than an active sensorimotor response to cold.

Reviewer #3:

The scientific premise is that a full body contraction in larvae that are exposed to noxious cold is a sensorimotor behavioral pathway. This premise is, to start with, questionable. A common definition of behavior is a set of "orderly movements with recognizable and repeatable patterns of activity produced by members of a species (Baker et al., 2001)." In the case of nociception behaviors, the patterns of movement are typically thought to play a protective role and to protect from potential tissue damage.

Does noxious cold elicit a set of orderly movements with a recognizable and repeatable pattern in larvae? Can the patterns of movement that are stimulated by noxious cold allow the larvae to escape harm? Based on the available evidence, the answer to both questions is seemingly no.

We thank the reviewer for their questions and clarify, here. Exposure to cold temperatures does elicit a recognizable and repeatable pattern of behavior across multiple strains, including both wildtype and genetic control strains (w1118, Oregon R) and numerous control conditions that have been previously published (Himmel et al., 2021, Himmel et al., 2023, Patel et al., 2022, Turner et al., 2016, Turner et al., 2018, Tenedini et al., 2019). Our initial publication on Drosophila cold nociception demonstrated a variety of cold-evoked behavior responses including head and/or tail raising of the larva as well as contraction behavior. These behaviors were repeatedly observed in assays involving either local cold stimulation with a cold probe or global cold stimulation on a cold plate. Head and/or tail raise behaviors are consistent with behavior that displaces the larval body from the cold surface, however, exposure to increasingly colder temperatures leads to an increasing level of cold-evoked contraction (CT) responses which result in a reduction of larval area (Turner et al., 2016). Presumably, increasing the level of CIII md neuron activation leads to greater activation of downstream circuitry. We previously performed optogenetic dose response assays to further clarify the increased prevalence CT response to strong noxious cold stimuli and investigated how CIII md neurons discriminate between innocuous touch and noxious cold stimuli. Here, we found that lower-level activation of CIII md neurons lead to predominantly touch-evoked behaviors whereas high-level activation led predominantly to cold-evoked responses (Turner et al., 2016). These analyses were coupled with stimulus-evoked calcium imaging, which revealed that touch-evoked Ca2+ levels were significantly lower than cold-evoked Ca2+ levels (Turner et al., 2016).

In this manuscript, we confirm our previously published findings that neural silencing of CIII md neurons with either tetanus toxin expression or impairing action potential propagation results impaired cold-evoked CT responses (Turner et al., 2016, Turner et al., 2018). However, neural silencing of CIII md neurons did not eliminate cold-evoked CT responses. We interpret this finding as evidence that some component of cold-evoked CT response may be due to cold-induced muscle contraction. Furthermore, in this manuscript, we implicate the requirement of chordotonal (Ch) neurons in cold-evoked CT and demonstrate cold-evoked Ca2+ increases in Ch neurons. Furthermore, neural silencing of multiple sensory neuron types (CIII + Ch or CIII + CII) resulted in greater deficits in cold-evoked behaviors (Turner et al., 2016). Thus, the noxious cold stimulus is detected by multiple peripheral sensory neurons and inhibiting neural activity in CIII md neurons alone cannot eliminate cold-evoked CT responses.

In this manuscript and in several other publications, studies have shown that optogenetic activation of CIII md neurons, or CIII neurons plus CII neurons or Ch neurons elicits CT-like responses (Hwang et al., 2007, Shearin et al., 2013, Turner et al., 2016). Conversely, optogenetic stimulation of CIII md neurons knocked down for paralytic, the α-subunit of voltage-gated sodium channel, did not elicit blue light-evoked CT responses due to impaired action potential propagation. These analyses collectively indicate that CIII md neuron activation is sufficient for eliciting CT-like responses. Additionally, we have previously published electrophysiological recordings of CIII md neurons under cold exposure. To address potential confounds of cold-induced muscle contraction on cold-induced electrical activity of CIII md neurons, we performed these analyses on de-muscled fillets revealing that CIII neural activity is not dependent upon muscles in response to cold. Exposure to noxious cold stimuli results in temperature-dependent increases in CIII neuron firing pattern consisting of both bursting and tonic firing (Himmel et al., 2021, Himmel et al., 2023, Maksymchuk et al., 2022, Patel et al., 2022, Himmel et al., 2022, Maksymchuk et al., 2023).

Reviewer #3:

Can the patterns of movement that are stimulated by noxious cold allow the larvae to escape harm?

We were similarly curious about the neuroethological and/or protective implications of cold-evoked behaviors. In Drosophila larvae, noxious mechanical stimuli-evoked body rolling allows for lateral escape from predatory wasp (Hwang et al., 2007). Reducing the overall surface area that is exposed to cold (e.g., huddling behavior) serves as a protective strategy in many species (Canals et al., 1997, Contreras, 1984, Gilbert et al., 2006, Vickery and Millar, 1984, Hayes et al., 1992). Low temperatures can be fatal to poikilotherms (e.g., insects), however, many species have evolved the ability to cold acclimate thereby increasing their cold tolerance. To explore the potential evolutionary benefit of CIII-mediated contraction response to cold, we previously published work revealing a neural basis for cold acclimation in Drosophila larvae implicating these neurons (Himmel et al., 2021). We demonstrated that cold-evoked CT behavior is evolutionarily conserved across 11 different drosophilid species and that other cold-induced behaviors (e.g., tail raise) were also observed. Furthermore, drosophilid species adapted to rapid temperature swings were more likely to retain the ability to locomote even at lower temperatures (Himmel et al., 2021). Next, we elucidated the role of CIII md neurons in cold acclimation. Silencing CIII md neurons resulted in the inability to cold acclimate. We additionally investigated roles of Ch or CII md neurons, which alone did not inhibit the ability of larvae to cold acclimate. However, combinatorial silencing of CIII with CII or Ch neurons resulted in an inability to cold acclimate but did not obviously increase baseline cold tolerance. We explored how developmental exposure to noxious cold temperature impacts CIII md neuron cold-evoked firing pattern. Electrophysiological analyses revealed that cold acclimation results in hypersensitization in CIII md neurons (Himmel et al., 2021). Lastly, developmental optogenetic activation of CIII md neurons led to increased cold tolerance. Therefore, CIII md neurons are necessary and sufficient for cold tolerance and our collective evidence demonstrate that CIII-mediated cold nociception constitutes a peripheral neural basis for Drosophila larval cold acclimation (Himmel et al., 2021).

Reviewer #3:

It should be noted that this actuator drives very strong activation, and other studies with milder optogenetic stimulation of Class III neurons have shown that these cells produce behavioral responses that resemble gentle touch responses (Tsubouchi et al 2012 and Yan et al 2013)…The latter makes the reported Calcium responses to cold difficult to interpret in light of the fact that the strong muscle contractions driven by cold may actually be driving mechanosensory responses in these cells (ie through deformation of the mechanosensitive dendrites)…. Are the cIII calcium signals still observed in a preparation where cold induced muscle contractions are prevented?”

We agree with the reviewer that mild activation of CIII md neurons results in gentle touch-like responses. In this manuscript, and other previously published work, it has been shown that optogenetic activation of CIII neurons, or CIII neurons and other sensory neurons, using a variety of optogenetic actuators (ChR2, ChETA, and CsChrimson) promotes bilateral contraction of the larval body along the anterior-posterior axis (Shearin et al., 2013, Hwang et al., 2007, Meloni et al., 2020, Turner et al., 2016, Patel and Cox, 2017, Patel et al., 2022, Himmel et al., 2023).

As described above, in our initial publication documenting larval cold nociception in Drosophila, we investigated how CIII md neurons discriminate multimodal stimuli to elicit stimulus relevant behavioral responses. We reported that increased activation of CIII md neurons results in cold-evoked behaviors, where lower activation results in touch-evoked behaviors. Subsequent, calcium analyses revealed greater stimulus-evoked calcium response to noxious cold and milder calcium response to gentle touch (Turner et al., 2016).

Though we have not performed cold-evoked Ca2+ imaging of CIII md neurons in larval preparations without muscles, we have recorded electrical responses of CIII md neurons in the absence of muscle contractions using de-muscled larvae fillets to analyze cold-evoked firing patterns of CIII md neurons (Himmel et al., 2021, Himmel et al., 2022, Himmel et al., 2023, Patel et al., 2022, Maksymchuk et al., 2022, Maksymchuk et al., 2023). These studies demonstrate the cold-evoked CIII neural activity is not dependent upon muscles.

Reviewer #3:

A major weakness of the study is that none of the second or third order neurons (that are downstream of CIII neurons) are found to trigger the CT behavioral responses even when strongly activated with the ChETA actuator (Figure 2 Supplement 2). These findings raise major concerns for this and prior studies and it does not support the hypothesis that the CIII neurons drive the CT behaviors.”

We conducted extensive screening of interneuron populations post-synaptically connected to CIII neurons in an effort to identify post-synaptic partners that were sufficient to trigger CT response. Much to our surprise, we were unable to find any individual neuron type or driver line that was sufficient to elicit a CT response. However, we provide substantial supporting evidence for our co-activation experiments including neural silencing, EM connectivity and calcium imaging. We also report necessity for the reported second/third order neurons in cold-evoked behavioral responses, where inhibiting neural activity resulted in reduced cold-evoked behavior. Second/third order neurons also exhibit cold-evoked calcium responses. Lastly, we also report CIII-evoked (using optogenetics) increases in calcium response in downstream post-synaptic neurons.

Previously published literature investigating CIV md neuron circuitry has implicated downstream neurons that are not sufficient to elicit rolling behavior upon activation. In CIV md neuron circuit dissection, select neurons are reported as acting downstream of CIV md neurons that require additional circuit components in order to execute rolling behavior. For example, A00c neuron activation alone does not lead to rolling behavior, however, co-activation of A00c and Basin-4 neurons facilitates rolling response (Ohyama et al., 2015). Similarly, co-activation of Basin-1 and Basin-4 neurons significantly enhance rolling probability relative to Basin-4 alone (Ohyama et al., 2015). Further, DnB neurons require Goro command neuron activity to promote rolling behavior (Burgos et al., 2018). Thus, there is precedent for co-activation requirements to elicit robust behavioral output in sensorimotor circuits and we employed a similar strategy after we discovered that activation of second or third order neurons alone did not elicit CT response.

Reviewer #3:

Later experiments in the paper that investigate strong CIII activation (with ChETA) in combination with other second and third order neurons does support the idea activating those neurons can facilitate body-wide muscle contractions. But many of the co-activated cells in question are either repeated in each abdominal neuromere or they project to cells that are found all along the ventral nerve cord, so it is therefore unsurprising that their activation would contribute to what appears to be a non-specific body-wide activation of muscles along the AP axis. Also, if these neurons are already downstream of the CIII neurons the logic of this co-activation approach is not particularly clear.”

We agree with the reviewer’s comment that various cell-types that were investigated are repeated in every abdominal neuromere, however, only select post-synaptic neurons (Basin 1-4, DnB, mCSI, and Chair neurons) are segmentally repeated in every abdominal segment. Conversely, other projection and ascending neurons we investigated (A09e, A00c, A05q, Goro, TePn04/05, and A08n) are not segmentally repeated in every section. We used connectome evidence to guide our experiments on populations of neurons to explore in cold-evoked behavior and as alluded to above our co-activation approach was driven by the observation that an individual subpopulation of connected interneurons was not found to be sufficient to elicit CT behavior. That said, it does not change the findings that inhibition of neural activity in these subpopulations impairs cold-evoked behavior, nor does it change the observation that connected interneurons exhibit cold-evoked Ca2+ responses that can also be observed with optogenetic activation of CIII neurons. Reviewer #3: “The authors argument that the co-activation studies support "a population code" for cold nociception is a very optimistic interpretation of a brute force optogenetics approach that ultimately results in an enhancement of a relatively non-specific body-wide muscle convulsion.” Many studies exploring circuit bases of behavior have applied large-scale optogenetic, including co-activation strategies, or silencing screens to identify circuit components involved in specific behaviors under investigation. We employed similar methods in our circuit-based dissection and our conclusions are not solely based upon optogenetic analyses.

References: BURGOS, A., HONJO, K., OHYAMA, T., QIAN, C. S., SHIN, G. J.-E., GOHL, D. M., SILIES, M., TRACEY, W. D., ZLATIC, M., CARDONA, A. & GRUEBER, W. B. 2018. Nociceptive interneurons control modular motor pathways to promote escape behavior in Drosophila. eLife, 7:e26016.

CANALS, M., ROSENMANN, M. & BOZINOVIC, F. 1997. Geometrical aspects of the energetic effectivenes of huddling in small mammals. Acta Theriologica 42(3):321-328..

CONTRERAS, L. C. 1984. Bioenergetics of Huddling: Test of a Psycho-Physiological Hypothesis. Journal of Mammalogy, 65, 256-262.

GILBERT, C., ROBERTSON, G., LE MAHO, Y., NAITO, Y. & ANCEL, A. 2006. Huddling behavior in emperor penguins: Dynamics of huddling. Physiol Behav, 88, 479-88.

HAYES, J. P., SPEAKMAN, J. R. & RACEY, P. A. 1992. The Contributions of Local Heating and Reducing Exposed Surface Area to the Energetic Benefits of Huddling by Short-Tailed Field Voles (Microtus agrestis). Physiological Zoology, 65, 742-762.

HIMMEL, N. J., LETCHER, J. M., SAKURAI, A., GRAY, T. R., BENSON, M. N., DONALDSON, K. J. & COX, D. N. 2021. Identification of a neural basis for cold acclimation in Drosophila larvae. iScience, 24, 102657.

HIMMEL, N. J., SAKURAI, A., DONALDSON, K. J. & COX, D. N. 2022. Protocols for measuring cold-evoked neural activity and cold tolerance in Drosophila larvae following fictive cold acclimation. STAR Protoc, 3, 101510.

HIMMEL, N. J., SAKURAI, A., PATEL, A. A., BHATTACHARJEE, S., LETCHER, J. M., BENSON, M. N., GRAY, T. R., CYMBALYUK, G. S. & COX, D. N. 2023. Chloride-dependent mechanisms of multimodal sensory discrimination and nociceptive sensitization in Drosophila. elife, 12:e76863.

HWANG, R. Y., ZHONG, L., XU, Y., JOHNSON, T., ZHANG, F., DEISSEROTH, K. & TRACEY, W. D. 2007. Nociceptive Neurons Protect Drosophila Larvae from Parasitoid Wasps. Current Biology, 17, 2105-2116.

MAKSYMCHUK, N., SAKURAI, A., COX, D. N. & CYMBALYUK, G. 2022. Transient and Steady-State Properties of Drosophila Sensory Neurons Coding Noxious Cold Temperature. Frontiers in Cellular Neuroscience, 16:831803.

MAKSYMCHUK, N., SAKURAI, A., COX, D. N. & CYMBALYUK, G. S. 2023. Cold-Temperature Coding with Bursting and Spiking Based on TRP Channel Dynamics in Drosophila Larva Sensory Neurons. Int J Mol Sci, 24(19):14638.

MELONI, I., SACHIDANANDAN, D., THUM, A. S., KITTEL, R. J. & MURAWSKI, C. 2020. Controlling the behaviour of Drosophila melanogaster via smartphone optogenetics. Scientific Reports, 10, 17614.

OHYAMA, T., SCHNEIDER-MIZELL, C. M., FETTER, R. D., ALEMAN, J. V., FRANCONVILLE, R., RIVERA-ALBA, M., MENSH, B. D., BRANSON, K. M., SIMPSON, J. H., TRUMAN, J. W., CARDONA, A. & ZLATIC, M. 2015. A multilevel multimodal circuit enhances action selection in Drosophila. Nature, 520, 633-639.

PATEL, A. & COX, D. 2017. Behavioral and Functional Assays for Investigating Mechanisms of Noxious Cold Detection and Multimodal Sensory Processing in Drosophila Larvae. BIO-PROTOCOL, 7(13):e2388.

PATEL, A. A., SAKURAI, A., HIMMEL, N. J. & COX, D. N. 2022. Modality specific roles for metabotropic GABAergic signaling and calcium induced calcium release mechanisms in regulating cold nociception. Front Mol Neurosci 15:942548.

SHEARIN, H. K., DVARISHKIS, A. R., KOZELUH, C. D. & STOWERS, R. S. 2013. Expansion of the Gateway MultiSite Recombination Cloning Toolkit. PLoS ONE, 8, e77724-e77724.

TENEDINI, F. M., SÁEZ GONZÁLEZ, M., HU, C., PEDERSEN, L. H., PETRUZZI, M. M., SPITZWECK, B., WANG, D., RICHTER, M., PETERSEN, M., SZPOTOWICZ, E., SCHWEIZER, M., SIGRIST, S. J., CALDERON DE ANDA, F. & SOBA, P. 2019. Maintenance of cell type-specific connectivity and circuit function requires Tao kinase. Nature Communications, 10, 3506.

TURNER, H. N., ARMENGOL, K., PATEL, A. A., HIMMEL, N. J., SULLIVAN, L., IYER, S. C., BHATTACHARYA, S., IYER, E. P. R., LANDRY, C., GALKO, M. J. & COX, D. N. 2016. The TRP Channels Pkd2, NompC, and Trpm Act in Cold-Sensing Neurons to Mediate Unique Aversive Behaviors to Noxious Cold in Drosophila. Curr Biol, 26, 3116-3128.

TURNER, H. N., PATEL, A. A., COX, D. N. & GALKO, M. J. 2018. Injury-induced cold sensitization in Drosophila larvae involves behavioral shifts that require the TRP channel Brv1. PLoS One, 13, e0209577.

VICKERY, W. L. & MILLAR, J. S. 1984. The Energetics of Huddling by Endotherms. Oikos, 43, 88-93.

Adverb phrases consist of adverbs together with elements which complement or modify them in different ways. Although adverb phrases are complex, in practice most of them have a fairly simple structure. A typical adverb phrase consists of a head in the form of an adverb sometimes accompanied by modifiers. For example: Aluminum burns comparatively slowly.

Here, the adverb head slowly is modified by the adverb (phrase) ‘comparatively’.

The examples provided to show exceptions for adjective phrases occurring before the head noun sound weird. Is "Let's do an activity fun" a correct sentence?

Reviewer #2 (Public Review):

Summary:

This paper nicely introduces WormPsyQi, an imaging analysis pipeline that effectively quantifies synaptically localized fluorescent signals in C. elegans through high-throughput automation. This toolkit is particularly valuable for the analysis of densely packed regions in 3D space, such as the nerve ring. The authors applied WormPsyQi to various aspects, including the examination of sexually dimorphic synaptic connectivity, presynaptic markers in eight head neurons, five GRASP reporters, electrical synapses, the enteric nervous system, and developmental synapse comparisons. Furthermore, they validated WormPsyQi's accuracy by comparing its results to manual analysis.

Strengths:

Overall, the experiments are well done, and their toolkit demonstrates significant potential and offers a valuable resource to the C. elegans community. This will expand the range of possibilities for studying synapses in the central nervous system in C. elegans.

Weaknesses:

1. The authors effectively validated sexually dimorphic synaptic connectivity by comparing the synapse puncta numbers of PHB>AVA, PHA>AVG, PHB>AVG, and ADL>AVA. However, these differences appear to be quite robust. Knowing how well WormPsyQi can detect more subtle changes at the synapses, such as 10-20% changes in puncta number and fluorescence intensity, will require further study.

2. The authors mentioned that having a cytoplasmic reporter in the background of the synaptic reporter enhanced performance. However, comparative results with and without cytoplasmic reporters, particularly for scenarios involving dim signals or densely distributed signals, are not provided, making it difficult to rigorously assess the importance of this step.

3. In some cases, the authors note discrepancies between WormPsyQi and human quantification. While they provide some potential explanations for these, the areas of discrepancy are not always highlighted in the images. This may make it difficult for users to know which types of signals are or are not well-suited for analysis by WormPsyQi.

Togan-Sessu Kogaku-ji lineage of Daruma paintings

• for: statistic - how little we know each other

• statistic: how little we know each other

  • the average person when they meet a stranger and start a conversation with him they accurately understand what's going on in that person's head 20% of the time
  • with friends and family it goes up to 35% of the time
  • some people are pretty good they're 55% of the time
  • some people are zero% of the time but think they're 100% of the time
  • we're often strangers to each other

I have dread in my head every time I use cloud service that there is an issue about to happen. There will be a data breach, data loss, service becoming unavailable (e.g., they don't like you anymor, e.g., Github can block you from your private repos at their will), it's just a matter of time.

So I need to get my data out, back it up, if service been kind to allow for that. But that's a burden. It's a hidden cost you learn to recognize over time. May not be apparent from the start to everybody.

Reviewer #2 (Public Review):

Summary:<br /> In this study, Ghafari et al. explored the correlation between hemispheric asymmetry in the volume of various subcortical regions and lateralization of posterior alpha-band oscillations in a spatial attention task with varying cognitive demands. To this end, they combined structural MRI and task MEG to investigate the relationship between hemispheric differences in the volume of basal ganglia, thalamus, hippocampus, and amygdala and hemisphere-specific modulation of alpha-band power. The authors report that differences in the thalamus, caudate nucleus, and globus pallidus volume are linked to the attention-related changes in alpha band oscillations with differential correlations for different regions in different conditions of the design (depending on the salience of the distractor and/or the target).

Strengths:<br /> The manuscript contributes to filling an important gap in current research on attention allocation which commonly focuses exclusively on cortical structures. Because it is not possible to reliably measure subcortical activity with non-invasive electrophysiological methods, they correlate volumetric measurements of the relevant subcortical regions with cortical measurements of alpha band power. Specifically, they build on their own previous finding showing a correlation between hemispheric asymmetry of basal ganglia volumes and alpha lateralization by assessing a task without an explicit reward component. Furthermore, the authors use differences in saliency and perceptual load to disentangle the individual contributions of the subcortical regions.

Weaknesses:<br /> The theoretical bases of several aspects of the design and analyses remain unclear. Specifically, we missed statements in the introduction about why it is reasonable, from a theoretical perspective, to expect:<br /> (i) a link between volumetric measurements and task activity;<br /> (ii) a specific link with hemispheric asymmetry in subcortical structures (While focusing on hemispheric lateralization might circumvent the problem of differences in head size, it would be better to justify this focus theoretically, which requires for example a short review of evidence showing ipsilateral vs contralateral connections between the relevant subcortical and cortical structures);<br /> (iii) effects not only in basal ganglia and thalamus, but also hippocampus and amygdala (a justification of selection of all ROIs);<br /> (iv) effects that depend on distractor versus target salience (a rationale for the specific two-factor design is missing);<br /> (v) effects in the absence of reward (why it is important to show that the effect seen previously in a task with reward is seen also in a task without reward);<br /> (vi) effects on rapid frequency tagging.

Second, the results are not fully reported. The model space and the results from the model comparison are omitted. Behavioral data and rapid frequency tagging results are not shown. Without having access to the data or the results of the analyses, the reader cannot evaluate whether the null effect corresponds to the absence of evidence or (as claimed in the discussion) evidence of absence.

Third, it remains unclear whether the MMS is the best approach to analyzing effects as a function of target and distractor salience. To address the question of whether the effects of subcortical volumes on alpha lateralization vary with task demands (which we assume is the primary research question of interest, given the factorial design), we would like to evaluate some sort of omnibus interaction effect, e.g., by having target and distractor saliency interact with the subcortical volume factors to predict alpha lateralization. Without such analyses, the results are very hard to interpret. What are the implications of finding the differential effects of the different volumes for the different task conditions without directly assessing the effect of the task manipulation? Moreover, the report would benefit from a further breakdown of the effects into simple effects on unattended and attended alpha, to evaluate whether effects as a function of distractor (vs target) salience are indeed accompanied by effects on unattended (vs attended) alpha.

The fourth concern is that the discussion section is not quite ready to help the reader appreciate the implications of key aspects of the findings. What are the implications for our understanding of the roles of different subcortical structures in the various psychological component processes of spatial attention? Why does the volumetric asymmetry of different subcortical structures have diametrically opposite effects on alpha lateralization? Instead, the discussion section highlights that the different subcortical structures are connected in circuits: "Globus pallidus also has wide projections to the thalamus and can thereby impact the dorsal attentional networks by modulating prefrontal activities." If this is true, then why does the effect of the GP dissociate from that of the thalamus? Also, what is it about the current behavioural paradigm that makes the behavioral readout insensitive to variation in subcortical volume (or alpha lateralization?)?

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, the authors use anatomical tracing and slice physiology to investigate the integration of thalamic (ATN) and retrosplenial cortical (RSC) signals in the dorsal presubiculum (PrS). This work will be of interest to the field, as the postsubiculum is thought to be a key region for integrating internal head direction representations with external landmarks. The main result is that ATN and RSC inputs drive the same L3 PrS neurons, which exhibit superlinear summation to near-coincident inputs. Moreover, this activity can induce bursting in L4 PrS neurons, which can pass the signals LMN (perhaps gated by cholinergic input).

Strengths:<br /> The slice physiology experiments are carefully done. The analyses are clear and convincing, and the figures and results are well-composed. Overall, these results will be a welcome addition to the field.

Weaknesses:<br /> The conclusions about the circuit-level function of L3 PrS neurons sometimes outstrip the data, and their model of the integration of these inputs is unclear. I would recommend some revision of the introduction and discussion. I also had some minor comments about the experimental details and analysis.

Specific major comments:<br /> 1) I found that the authors' claims sometimes outstrip their data, given that there were no in vivo recordings during behavior. For example, in the abstract, their results indicate "that layer 3 neurons can transmit a visually matched HD signal to medial entorhinal cortex", and in the conclusion they state "[...] cortical RSC projections that carry visual landmark information converge on layer 3 pyramidal cells of the dorsal presubiculum". However, they never measured the nature of the signals coming from ATN and RSC to L3 PrS (or signals sent to downstream regions). Their claim is somewhat reasonable with respect to ATN, where the majority of neurons encode HD, but neurons in RSC encode a vast array of spatial and non-spatial variables other than landmark information (e.g., head direction, egocentric boundaries, allocentric position, spatial context, task history to name a few), so making strong claims about the nature of the incoming signals is unwarranted.

2) Related to the first point, the authors hint at, but never explain, how coincident firing of ATN and RSC inputs would help anchor HD signals to visual landmarks. Although the lesion data (Yoder et al. 2011 and 2015) support their claims, it would be helpful if the proposed circuit mechanism was stated explicitly (a schematic of their model would be helpful in understanding the logic). For example, how do neurons integrate the "right" sets of landmarks and HD signals to ensure stable anchoring? Moreover, it would be helpful to discuss alternative models of HD-to-landmark anchoring, including several studies that have proposed that the integration may (also?) occur in RSC (Page & Jeffrey, 2018; Yan, Burgess, Bicanski, 2021; Sit & Goard, 2023). Currently, much of the Discussion simply summarizes the results of the study, this space could be better used in mapping the findings to the existing literature on the overarching question of how HD signals are anchored to landmarks.

The Sheriff's office is still looking for the person(s) who wacked the man in the head.

Chain letters still exist in some form today. On various social media platforms, mainly TikTok, people will make posts saying if you skip the video, if you don't interact with the video in some way, or if you don't use the sound under the video then something good or bad will happen. This comes from the rise in practicing manifestation. However, people have flipped it on its head to gain followers, views, and likes. While this trend doesn't require paying for envelopes or stamps, it does cost some people a lot of unnecessary anxiety and stress if they don't interact with those types of videos

After reading this definition of "meme" I'm surprised that the definition of "meme" I had in my head is much different. I thought a meme was most of the time related to mocking or in a joking sense.

Shows how frowned upon being a thief is in this age

What a crazy story

not sure why, but this name is in my head for some reason tonight.

This is the first Google search hit and nothing else looks familiar....

Nowadays, many of our kids no longer look like kids. Social media plays a big part in this. The makeup they wear and the interactions they have with others are all virtually.

This is a very important idea that the healing process may bring up a lot of trauma and pain for the person, but it will be worth it down the road. Essentially Kaba says you must face it head on and come to terms with it or else that trauma will be pent up inside you for the rest of your life. She also explains that if the harm was inflicted by someone you know the process is much harder. You may still come in contact and see that person on a daily basis. Kaba says " the fear, the anger, the vengeance" you must come to terms with but eventually you will come to terms with it and find peace.

I am glad to relate to this tory as well. It has been a tough time being unemployed but still have my head up, continue working on my self and attend college.

Reviewer #3 (Public Review):

This paper seeks to characterize finger enslavement impairment after stroke-"the unwanted coactivation of non-intended fingers in individuated finger movements." In the past, three possible neuromuscular mechanisms contributing to finger enslavement were suggested: passive musculotendon properties, an intrusion of flexor bias, and a loss of complexity in finger control repertoire. To tease apart these factors, the authors simultaneously recorded all five fingertip forces using a sensitive isometric force measurement device, which allowed characterizing patterns of enslavement for all fingers in a variety of instructed tasks. This novel experimental design opened new opportunities to study finger enslavement in more detail. To analyze this multi-dimensional dataset, new metrics were introduced, and many detailed analyses were conducted. Here is a brief account of the important results as best as I can summarize them.

1. Gross finger individuation ability is lower in the paretic hand of stroke patients than in non-paretic or healthy hands. Enslavement worsens with the severity of overall stroke impairment.<br /> 2. The enslavement patterns - unintended finger forces as functions of an instructed force in a different finger - show smaller "complexity" in paretic than nonparetic hands. I.e., the directions of unintended finger forces in the paretic hand remain similar across various instructed tasks. This reduced complexity also correlates with the severity of stroke.<br /> 3. The enslavement patterns show larger magnitude differences in the paretic than non-paretic hands; i.e., the unintended fingers' forces show a larger shift when comparing two instructed force directions in a paretic finger.<br /> 4. Finger force biases exist in paretic and non-paretic hands and correlate with the severity of stroke. Biases are more pronounced in flexion than ab/adduction direction.<br /> 5. The resting hand posture does not correlate with finger force bias or enslavement patterns.<br /> 6. Finger force biases correlate with enslavement patterns in the paretic hand, but not in the non-paretic hand.<br /> 7. Flexor bias (force biases in flexor direction) does not correlate with gross individuation ability in the ab/adduction direction in the non-paretic hand, but it correlates with the ab/adduction individuation ability in the paretic hand.<br /> 8. Finger force biases do not correlate with directional differences in enslavement patterns on either hand. However, biases correlate with the magnitude of force shift in the enslavement pattern.<br /> 9. The intrusion of flexor bias (difference of finger force biases in paretic and non-paretic hands) does not correlate with directional differences in enslavement patterns in either hand, but it correlates with force shifts in enslavement patterns in both hands.<br /> 10. More principal components (in principal component analysis, PCA) are required to explain similar levels of variance of enslavement patterns in paretic than non-paretic hands.

Taken together, the authors use these results to claim that: 1) enslavement impairment is unrelated to passive biomechanical properties, and 2) loss of complexity and flexor bias both contribute to enslavement, but possibly via different mechanisms.

The first argument is supported by the result that resting hand posture does not explain gross individuation ability or enslavement patterns. Although these results are valid, biomechanical contributions are not ruled out altogether in my opinion. The experiment starts from the optimal posture in which minimal finger forces are recorded in a relaxed state, essentially an "equilibrium" posture where all forces from muscles, ligaments, and other soft tissues are balanced. However, this equilibrium posture alone does not represent potential asymmetry in passive biomechanical properties (e.g., at equilibrium, flexion may face less stiffness than extension), nor does it take into account complex interactions between muscles of the hand. A simple finger force requires the co-activation of several intrinsic and extrinsic hand muscles as well as those of the wrist, some of which may be weak, shortened, stiff, painful, etc. Even if neural activity is present, compensation from other muscles may be needed, which may lead to unintended forces in other fingers. Although my "hunch" agrees with the author's claim that neural contributions outweigh biomechanical factors in enslavement, I believe resting posture on its own cannot account for "all" biomechanical factors. Additionally, the results comparing biases in paretic and non-paretic hands (line 389) are unrelated to biomechanics. It is reasonable to believe that the passive biomechanical properties of the paretic hand are different from those of the non-paretic hand if long enough time has passed since the stroke. So biomechanical of one hand is not representative of the other hand. Even if biases in the non-paretic hand could explain those in the paretic hand, I find it hard to extend the conclusion that biomechanics is a factor.

The authors further presented detailed analyses to tease apart contributions of flexor bias and loss of complexity to enslavement. The flexor bias is straightforward to define, and its correlation with enslavement (or the absence of correlation in the non-paretic hand) is supported by the results. However, the arguments about complexity are less straightforward. Two separate definitions of complexity are used: one is the directional differences between enslavement patterns, and the other is based on the number of principal components. This is one source of confusion as to which definition is used when referring to "loss of complexity". Nonetheless, both complexities are shown to decrease with the severity of stroke. The first type of complexity is also shown to be uncorrelated to flexor bias. However, I did not find evidence among the results that directly linked complexity to enslavement. Could complexity, similar to biomechanical properties, be ruled out? This paper provides no evidence for or against the contribution of complexity to enslavement.

My last point is about the neural correlates of these characteristics. The authors frequently use the terms "low-level", "subcortical", "top-down cortical", etc. throughout the paper, while the results are exclusively at a behavioral level. This issue is also present in the abstract where the authors state that: "we aim to tease apart the contributions of lower biomechanical, subcortical constraints, and top-down cortical control to these patterns in both healthy and stroke hands"; however, the methods and the results are unrelated to neural aspects of control, and the authors only refer to other studied to link these behavioral effects to "potential" neural causes. Further, the intrusion of flexor bias is usually associated with "subcortical" neural pathways in Results. The authors have properly discussed these possible neural correlates in the Discussion, but mentioning these terms in the Results is unjustified and unsupported by the results or the methods. This paper does not provide any standalone evidence to directly link complexity or bias to their neural correlates.

Comments on the representational distance matrices (RDM):<br /> Two types of RDM were defined: "by-Finger" RDM (Fig 4A), and "by-Target Direction" RDM (Fig 4D). While I understand the by-Finger RDM and it physically makes sense to me, I cannot fully wrap my head around the by-Target RDM. I leave the interpretation of these results to the reader.

The distinction between the Euclidean and Angular distance is also vague to me. Angular distance is a valid similarity measure for the directions of two vectors and it is unrelated to the norms of vectors. However, Euclidean distance is not fully independent of the Angular distance as the authors claim; it changes with both the norms of the two vectors and the angle between them. If the angular distance is small, then Euclidian distance mostly represents norm differences, but the statement "Euclidean distances are sensitive to the length difference between two force vectors but insensitive to direction differences" is not generally correct. This issue is particularly important because the averaging of distances (see my next point) masks details of individual distance values, which hinders the interpretation of the results.

The enslavement patterns and RDMs are potentially valuable metrics, however, the way they are condensed in the final statistical analyses reduces their value. The way I understand it, all elements of the RDM matrix are averaged into a single value. This averaging masks the details of individual pairs of comparisons, which not only reduces the information resolution but also seriously hinders rigorous analysis and interpretation of the results.

Labeling can be quite harmful when it comes to mental illness because there is a lot of stigma attached to labels. This leads to others treating them different based on this label. By having this label "hanging over a person's head", a lot of damage can be done to the person's self esteem especially if we are not taking the right steps to combat this label.

Love the way the narration is meted out here... the description of the violence. The "I didn't even know you could do this, and the cowboy, he didn't seem to know either." Then the final two short sentences.

Connection: Both these women share so much of themselves with like minded groups and individuals eager to engage and learn. I have had conversations with both of them separately and I am in awe of their generosity of time with how busy they both are in their careers and lives. This whole paragraph made me reflect on importance of community and how differently that can be defined. I am thinking specifically about the readings that focussed on black women building in the art world here and the emotional labour alone to get it done without any support. BAND, The State of Blackness database, “Black Wimmin: When and Where We Enter” https://canadianart.ca/essays/why-have-there-been-no-great-black-%20canadian-women-artists/ https://www.artsy.net/article/artsy-editorial-overlooked-black-women-altered-course-feminist-art

Why was this method developed?

eLife assessment

This study presents a valuable finding on the mechanism of glucocorticoid-induced osteonecrosis of the femoral head. The data were collected and analyzed using solid and validated methodology and can be used as a starting point for functional studies of the development of glucocorticoid-induced osteonecrosis. This paper would be of interest to cell biologists and biophysicists working on the potential pharmacological treatments for glucocorticoid-induced osteonecrosis.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript by Xia et al. investigated the mechanisms underlying Glucocorticoid-induced osteonecrosis of the femoral head (GONFH). The authors observed that abnormal osteogenesis and adipogenesis are associated with decreased β-catenin in the necrotic femoral head of GONFH patients, and that the inhibition of β-catenin signalling leads to abnormal osteogenesis and adipogenesis in GONFH rats. Of interest, the deletion of β-catenin in Col2-expressing cells rather than in osx-expressing cells leads to a GONFH-like phenotype in the femoral head of mice.

Strengths:<br /> A strength of the study is that it sets up a Col2-expressing cell-specific β-catenin knockout mouse model that mimics the full spectrum of osteonecrosis phenotype of GONFH. This is interesting and provides new insights into the understanding of GONFH. Overall, the data are solid and support their conclusions.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors reported a study to uncover that β-catenin inhibition disrupting the homeostasis of osteogenic/adipogenic differentiation contributes to the development of Glucocorticoid-induced osteonecrosis of the femoral head (GONFH). In this study, they first observed abnormal osteogenesis and adipogenesis associated with decreased β-catenin in the necrotic femoral head of GONFH patients, but the exact pathological mechanisms of GONFH remain unknown. They then performed in vivo and in vitro studies to further reveal that glucocorticoid exposure disrupted osteogenic/adipogenic differentiation bone marrow stromal cells (BMSCs) by inhibiting β-catenin signaling in glucocorticoid-induced GONFH rats, and specific deletion of β-catenin in Col2+ cells shifted BMSCs commitment from osteoblasts to adipocytes, leading to a full spectrum of disease phenotype of GONFH in adult mice.

Strengths:<br /> This innovative study provides strong evidence supporting that β-catenin inhibition disrupts the homeostasis of osteogenic/adipogenic differentiation that contributes to the development of GONFH. This study also identifies an ideal genetically modified mouse model of GONFH. Overall, the experiment is logically designed, the figures are clear, and the data generated from humans and animals is abundant supporting their conclusions.

Weaknesses:<br /> There is a lack of discussion to explain how the Wnt agonist 1 works. There are several types of Wnt ligands. It is not clear if this agonist only targets Wnt1 or other Wnts as well. Also, why Wnt agonist 1 couldn't rescue the GONFH-like phenotype in β-cateninCol2ER mice needs to be discussed.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, the authors are trying to delineate the mechanism underlying the osteonecrosis of the femoral head.

Strengths:<br /> The authors provided compelling in vivo and in vitro data to demonstrate Col2+ cells and Osx+ cells were differentially expressed in the femoral head. Moreover, inducible knockout of β-catenin in Col2+ cells but not Osx+ cells lead to a GONFH-like phenotype including fat accumulation, subchondral bone destruction, and femoral head collapse, indicating that imbalance of osteogenic/adipogenic differentiation of Col2+ cells plays an important role in GONFH pathogenesis. Therefore, this manuscript provided mechanistic insights into osteonecrosis as well as potential therapeutic targets for disease treatment.

Weaknesses:<br /> However, additional in-depth discussion regarding the phenotype observed in mice is highly encouraged.

what web component libraries are available around the web today - to get a head start on the future.

I did not know what modifiers was mean but now I know.

It tells how adjective phrases are not parallel to prepositional phrases and how it goes as a modifiers to noun phrases.

Author Response

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

Thank you for reviewing and assessing our paper. Reviewer2 had only posive comments. Reviewer 1 also had posive comments but included a list of suggesons. The revised version includes text edits to address the suggesons.

Reviewer 1:

… First, it is unclear whether the experiments and analyses were set up to be able to rule out more specific candidate funcons of the ZI.

The list of possible funcons performed by the ZI is broad. Nevertheless, our study considers a rather long list of neural processes related to the behaviors listed below.

Second, many important details of the experiments and their results are hard to decipher given the current descripons and presentaons of the data.

The procedures used in the present study have all been used and described in our previous studies (cited). We used the same descripons and presentaons as in the prior studies. We have gone over the Methods and figures to ensure that all details required to understand the experiments are provided, but we also added further details following the suggesons noted below.

The paper could be significantly strengthened by including more details from each experiment, stronger jusficaons for the limited behaviors and experimental analyses performed, and, finally, a broader analysis of how the recorded acvity in the ZI relates to behavioral parameters.

The paper studied several behaviors including: 1) spontaneous movement of head-fixed mice on a spherical treadmill, 2) tacle (whisker, and body parts) and auditory (tones and white noise) smuli applied to head fixed mice, 3) spontaneous movement iniaon, change, and turns in freely moving mice, 4) auditory tone (frequency and SPL) mapping in freely behaving mice, 5) auditory-evoked orienng head movements (responses) in the context of several behavioral tasks, 6) signaled acve avoidance responses and escapes (AA1), 7) unsignaled/signaled passive avoidance responses (AA2ITI/AA3-CS2), 8) sensory discriminaon (AA3), 9) CS-US interval ming discriminaon (AA4), and 10) USevoked unsignaled escape responses.

In freely moving experiments, the behavior is connuously tracked and decomposed into translaonal and rotaonal movement components. Discrete responses are also evaluated (e.g., acve avoids, escapes, passive avoids, errors, intertrial crossings, latencies, etc.). These behavioral procedures evaluate many neural processes, including decision making (Go/NoGo in AA1-3), response control/inhibion (unsignaled and signaled passive avoidance in AA2/3), and smulus discriminaon (AA3). The applied smuli, discrete responses, and tracked movement are always related to the recorded ZI acvity using a variety of techniques (e.g., cross-correlaons, PSTHs, event-triggered me extracons, etc.), which relate the discrete and me-series parameters to the neural acvity. We do not think all this qualifies as, “limited behaviors”.

(1) Anatomical specificaon: The ZI contains many disnct subdivisions--each with its own topographically organized inputs/outputs and putave funcons. The current manuscript doesn't reference these known divisions or their behavioral disncons, and one cannot tell exactly which poron(s) of the ZI was included in the current study. Moreover, the elongated structure of the ZI makes it very difficult to specifically or completely infect virally. The data could be beter interpreted if the paper included basic informaon on the locaons of recordings, the extent of the AAV spread in the ZI in each viral experiment, and what fracon of infected neurons were inside versus outside ZI.

Our experiments employed Vgat-Cre mice to target ZI neurons. In this line, GABAergic neurons from the enre ZI express Cre, including the dorsal and ventral subdivisions (see (Vong et al., 2011; Hormigo et al., 2020)). Consequently, AAV injecons in Vgat-Cre mice produce restricted expression in the ZI that can fully delineate the nucleus as shown in the papers referenced above (including ours). There is nil expression in structures above or below ZI because they do not express Cre in these mice (e.g., thalamus and subthalamic nucleus), which allows for selecve targeng of ZI. Our optogenec manipulaons and photometry recordings were not aimed at specific ZI subdivisions. We targeted the area of ZI indicated by the stereotaxic coordinates (see Methods), which are aimed at the center of the structure to maximize success in recording/manipulang neurons within ZI. While all the animals included in the study expressed opsins and GCaMP within ZI that in many animals fully delineated the nucleus, there was normal variability in the locaon of opcal fibers, but we did not detect any differences in the results related to these variaons.

Fiber photometry and optogenecs experiments are performed with rather large diameter opcal probes, which record/manipulate relavely large areas of the targeted structure. This is useful because our goal was to idenfy funconal roles of the enre ZI, which could then be parsed. In the present study, we did not perform experiments to target specific ZI populaons (e.g., retrograde Cre expression from target areas), which may have revealed differences atributed to their projecon sites. However, in the last experiment, we selecvely excited ZI fibers targeng three different areas (midbrain tegmentum, superior colliculus, and posterior thalamus), which revealed clear differences on movement. Thus, future experiments should explore these different populaons (e.g., using retrograde/anterograde expression systems), which may be in different subdivisions.

We have enhanced the Methods secon to clarify these points, including the addion of these references.

(2) Electrophysiological recording on the treadmill: The authors are commended for this technically very difficult experiment. The authors do not specify, however, how they knew when they were recording in ZI rather than surrounding structures, parcularly given that recording site lesions were only performed during the last recording session. A map of the locaons of the different classes of units would be valuable data to relate to the literature.

We have added details about this procedure in the Methods secon. These recordings are performed based on coordinates, and categorizing neurons as belonging to ZI is obviously an esmate based on the final histological verificaon. Nevertheless, the marking lesions revealed that the electrodes were on target, which likely resulted from the care taken during the surgical procedure to define reference points used later during the recording sessions (see Methods). Regarding a map of the unit locaons, we performed several analyses that did not reveal clear differences based on site. For example, we compared depth vs cell class, “There was no difference in recording depth between the four classes of neurons (ANOVA F(3,337)= 1.06 p=0.3676)”. Future experiments that employ addional methods (labelling, opto-tagging, etc.) would be more appropriate to address mapping quesons. Finally, as we state in the paper, “However, these recordings do not target GABAergic neurons and may sample some neurons in the tissue surrounding the zona incerta. Therefore, we used calcium imaging fiber photometry to target GABAergic neurons in the zona incerta”.

(3) The raonale of the analysis of acvity with respect to “movement peak”: It is unclear why the authors did not assess how ZI acvity correlates with a broad set of movement parameters, but rather grouped heterogeneous behavioral epochs to analyze firing with respect to “movement peaks”.

The reviewer is referring to movement peaks on the spherical treadmill. On the treadmill, we used the forward locomotor movement of the animal because this is the main acvity of the mice on the treadmill. We considered “all peaks” (or movements) and “>4 sec peaks”, which select for movement onsets. Compared to the treadmill, in freely movement condions during various behavioral tasks, there is a richer behavioral repertoire, which was analyzed in more detail (i.e., translaonal, and rotaonal components during spontaneous ongoing movement and movement onsets, movement related to various behaviors such as orienng, acve and passive avoidance, escape, sensory smulaon, discriminaon, etc.). Thus, we focused on a broader set of movement parameters in the Cre-defined ZI cells of freely behaving mice.

(4) The display of mean categorical data in various figures is interesng, however, the reader cannot gather a very detailed view of ZI firing responses or potenal heterogeneity with so litle informaon about their distribuons.

The PCA performs the heterogeneity classificaon in an unbiased manner, which we feel is a thoughul approach. The firing rates and correlaons with movement for each category of neurons are detailed in the results. Furthermore, the sensory responses for these neurons are also detailed. Together, we think this provides a detailed view of the units we recorded in awake/head-fixed mice. As already stated, further study would benefit from an addional level of cell site verificaon.

(5) Somatosensory firing responses in ZI: It is unclear why the authors chose the specific smuli used in the study. How oen did they evoke reflexive motor responses? What was the latency of sensory-evoked responses in ZI acvity and the latency of the reflexive movement?

These are broad quesons, and we assume that the reviewer is asking about somatosensory evoked responses on the spherical treadmill. We used air-puffs applied to the whiskers and on the back (le vs right) because the whiskers represent an important sensory representaon for mice, and the back is a part of the body (trunk), which we oen use to movate the animals to move forward on the treadmill. Regarding the latency of the somatosensory evoked responses, in this case, we did not correct them based on the me it takes the air-puff to travel to the whiskers or body part, and therefore we did not provide latencies. Moreover, air-puffs are not a very good method to quanfy whisker-evoked latencies, which are beter measured using other methods (whisker deflecons of single/mulple whiskers using piezo-devices or other mechanical devices, as we and others have done in many studies). We are not sure what the reviewer means by “reflexive behavior”; we did not measure any reflexive behavior under these condions. We have gone over the Methods and Results to ensure that sufficient details are provided about these experiments.

(6) It would be valuable to see example traces in Figure 3 to get a beter sense of the me course and contexts under which Ca signals in ZI tracks movement. What is the typical latency? What is the typical range of magnitudes of responses? Does the Ca signal track both fast and slow movements? How are the authors sure that there are no movement arfacts contribung to the calcium imaging? It seems there is more informaon in the dataset that could be valuable.

As is well known, fiber photometry calcium imaging is a slow populaon signal. We do not think it would be valuable to get into ming issues beyond what is already detailed in the study (i.e., magnitudes measured as areas or peaks, and ming as me-to-peaks). Regarding “movement arfacts”, these signals are absent (flat) in animals that do not express GCAMP. We agree that there must be addional valuable informaon in our datasets (as in most me-series). However, the current paper is already rather extensive. We will connue to peruse our datasets and report addional findings in new papers.

(7) Figure 4: The raonale for quanfying the F/Fo responses over a 6-second window, rather than with respect to discrete movement parameters, is not well explained. What types of movement are binned in this approach and might this broad binning hinder the ability to detect more specific relaonships between acvity and movement?

Figure 4 is focused on characterizing the relaonship between turns (ipsiversive and contraversive) during movement and ZI acvity. We tested different binning windows to find differences, including the 6 sec window in figure 4 for populaon measures (-3 to 3 sec around the turns). This binning approach is effecve at revealing differences where they exist (e.g., superior colliculus) as shown in our previous studies (e.g. (Zhou et al., 2023)). Moreover, the turns in the different direcons can be considered discrete responses at their peak, and the ming of the related acvaons (e.g., me to peaks), which we evaluated, are rather sensive and would have revealed differences, but we did not find them.

(8) Separaon of sensory and motor responses in Figure 5: The current data do not adequately differenate whether the responses are sensory or motor given the high correlaon of the sensory inputs driving motor responses. Because isoflurane can diminish auditory responses early in the auditory pathway, this reviewer is not convinced the isoflurane experiments are interpretable.

The reviewer is referring to Fig. 5C,D. Indeed, the point of this experiment was to show that it is difficult to differenate whether neural responses are sensory or motor in awake and freely moving condions. As we stated in the Results secon, “Although arousal and movement were not dissected in the present experiment (this would likely require paralyzing and ventilating the animal), the results indicate that activation of zona incerta neurons by sensory stimulation is primarily associated with states when sensory-evoked movement is also present”. This is followed in the Discussion by, “…as already noted, the suppression of sensory responses may be due to changes in arousal (Castro-Alamancos, 2004; Lee and Dan, 2012) and not caused by the abolishment of the movements per se”.

(9) Given the broad duraon of the mean avoidance response (Fig. 6 C, botom), it would be useful to know to what extent this plot reflects a prolonged behavior or is the result of averaging different animals/trials with different latencies. Given that the shapes of the F/Fo responses in ZI appear similar across avoids and escapes (Fig. 6D), despite their apparent different speeds and movement duraons (Fig 6C), it would be valuable to know how the ming of the F/Fo relates to movement on a trial-by-trial basis.

The duraon of the avoidance response cannot be ascertained from CS onset (panel 6C botom) and avoids are not wide but rather sharp. We have now made this clearer when Fig. 6C is first menoned (“note that since avoids occur at different latencies after CS onset they are best measured from their occurrence as in Fig. 6D”). Like other related condioned and uncondioned responses, avoids and escapes are similar, varying in the noted parameters. Regarding ming, as already menoned above, we think that the characteriscs of the populaon calcium signal make it unsuitable for further ming consideraons than what we included, parcularly for movements occurring at the fast speeds of avoids and escapes.

(10) Lesion quanficaon: One cannot tell what rostral-caudal extent of ZI was lesioned and quanfied in this experiment. It would be easier to interpret if also ploted for each animal, so the reader can tell how reliable the method is. The mean ablaon would be beter shown as a normalized fracon of cells. Although the authors claim the lesions have litle impact on behavior, it appears the incompleteness of the lesions could warrant a more conservave interpretaon.

The lesion experiment was a complement to the optogenecs inacvaon experiments we performed in our preceding ZI paper and in the present paper. Thus, the finding that the lesions had litle impact on behavior is supporve of the optogenecs findings. Regarding cell counts, we did not select any parts of the ZI to quanfy the number of neurons in either control or lesion mice. We considered the full rostrocaudal extent in our measurements. We are not sure what “fracon” the reviewer is suggesng, considering that these counts are from two different groups of mice (control vs lesion). Note that the red-marked neurons, as shown in Fig. 8A, reveal healthy non-Vgat-Cre neurons outside ZI that mark the extent of the AAV diffusion, which as shown spanned the full extent of the ZI in the coronal plane (and in other planes as the AAV spreads in all direcons).

(11) Optogenecs: the locaon of infected neurons is poorly described, including the rostral-caudal extent and the fracon of neurons inside and outside of ZI. Moreover, it is unclear how strongly the optogenec manipulaons in this study are expected to affect neuronal acvity in ZI.

We discussed the first point in (1) above. Regarding, how optogenec manipulaons are expected to affect neuronal acvity in ZI and its targets, we have conducted extensive electrophysiological recordings in slices and in vivo to detail the effects of our manipulaons on GABAergic neurons (e.g. (Hormigo et al., 2016; Hormigo et al., 2019; Hormigo et al., 2021a; Hormigo et al., 2021b), including ZI neurons (Hormigo et al., 2020). In fact, we never use an opsin we have not validated ourselves using electrophysiology. Moreover, our experiments employ a spectrum of optogenec light paterns (including trains/cont at different powers) that trate the optogenec effects within each session/animal. As shown in fig. 11 and 12, these paterns produce different behavioral effects related to the different levels of neural firing they induce. For ChR2-expressing neurons in ZI, firing is frequency dependent and maximal during Cont blue light (at the same power). For Arch-expressing neurons only Cont is used, and inhibion is a funcon of the green light power. When blue light is applied in ZI fibers targeng different areas, this relaonship changes. Blue light trains (1-ms pulses) at 40-66 Hz become the most effecve means of inducing sustained postsynapc inhibion compared to Cont or low frequencies.

References

Castro-Alamancos MA (2004) Dynamics of sensory thalamocorcal synapc networks during informaon processing states. Progress in Neurobiology 74:213-247.

Hormigo S, Vega-Flores G, Castro-Alamancos MA (2016) Basal Ganglia Output Controls Acve Avoidance Behavior. J Neurosci 36:10274-10284.

Hormigo S, Zhou J, Castro-Alamancos MA (2020) Zona Incerta GABAergic Output Controls a Signaled Locomotor Acon in the Midbrain Tegmentum. eNeuro 7.

Hormigo S, Zhou J, Castro-Alamancos MA (2021a) Bidireconal control of orienng behavior by the substana nigra pars reculata: disnct significance of head and whisker movements. eNeuro. Hormigo S, Vega-Flores G, Rovira V, Castro-Alamancos MA (2019) Circuits That Mediate Expression of Signaled Acve Avoidance Converge in the Pedunculoponne Tegmentum. J Neurosci 39:45764594.

Hormigo S, Zhou J, Chabbert D, Shanmugasundaram B, Castro-Alamancos MA (2021b) Basal Ganglia Output Has a Permissive Non-Driving Role in a Signaled Locomotor Acon Mediated by the Midbrain. J Neurosci 41:1529-1552.

Lee SH, Dan Y (2012) Neuromodulaon of brain states. Neuron 76:209-222.

Vong L, Ye C, Yang Z, Choi B, Chua S, Jr., Lowell BB (2011) Lepn acon on GABAergic neurons prevents obesity and reduces inhibitory tone to POMC neurons. Neuron 71:142-154.

Zhou J, Hormigo S, Busel N, Castro-Alamancos MA (2023) The Orienng Reflex Reveals Behavioral States Set by Demanding Contexts: Role of the Superior Colliculus. J Neurosci 43:1778-1796.

This was an initial disadvantage the British faced because they were not ready to fight that type of war

Good leader ship by Washington realizing that his men aren't trained in the same ways the Red Coats are. He realized that his men were basically marksmen who can hit farther and harder shots. He used this knowledge to use a different strategy.

!

There is talk of generalize specific patterns in the sentence structure. Is their a way to make sentences more difficult?

So it’s another term for adverbs and adjectives?

C: Hunter’s fear about being taken out of context or misread reminds me of Kobena Mercer’s text, “Skin Head Sex Thing: Racial Difference and the Homoerotic Imaginary,” that we read for Art Methods; because Mercer explains that he is altering his position on Mapplethorpe in order to avoid being appropriated by the alt-Right (13). Mercer returns to his own writing, embracing unknowingness and the learning process but also as an attempt control how his work is interpreted and engaged with. I think the concept of looping has the potential to address this fear, as it points to a continuous dialogue in which one can respond to their own writing but also to those who have sampled/cited it. I understand the desire to avoid being misread, especially when thinking about the vulnerability of curiosity, but I also think being taken out of context can indicate a conversation that needs to be had.

This paragraph explains that the Mi'kmaq people were very open to the French settling in Port Royal, however they felt that the french are now starting to take over and it is effect the way of living for the Mi'Kmaq people people, they did not like that they wee being forced to take refuge on their own land.

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Referee #1

Evidence, reproducibility and clarity

Summary

Elucidating the cellular and molecular mechanisms underlying age-related neurodegeneration remains a key challenge for neurobiologists. In this manuscript, Mariana Tsap and colleagues in the team of Halyna Shcherbata focus on the function of the neuropathy target esterase NTE/Swiss Cheese (Sws) in the Drosophila brain. The authors use an elegant combination of genetics, light and electron microscopy, RT-qPCR and GS-MS mass spectrometry to determine the complex role of Sws in cellular blood brain barrier (BBB) integrity, the brain inflammatory response and fatty acid metabolism. The study provides a detailed characterisation as to how the loss of sws affects glial cell morphology in the BBB revealing abnormal membrane accumulations and tight junctions, and in consequence causing permeability issues. Importantly, they observed the upregulation of antimicrobial peptides in the brain, indicative of neuroinflammation, as well as of fatty acids, equally connected with the inflammatory response.

Major comments

The study provides a detailed and comprehensive characterization of the sws mutant phenotype, and in particular the role of this gene in blood-brain barrier forming glia. - The study connects neurodegeneration and inflammation, but also makes a particular point about "inflammaging". However, the age contribution has not been studied in detail. Indeed, the flies analyzed are 15 days old (according to the Material and Methods section, with the exception of Figure 1 where flies are 30 days old), and hence have not been compared with younger or older flies to make a point of age as evoked in the abstract, introduction or discussion. The authors should either add experiments comparing differently aged flies or de-emphasize this point to a brief consideration in the discussion. Instead, it would be very helpful to provide concise information about the current knowledge concerning the inflammatory response in the Drosophila brain. - Related to this point, the authors convincingly show that sws is required in surface glia using rescue experiments. Nevertheless, all experiments rely on drivers and mutants that could cause the emergence of phenotypes during development. Thus, to strengthen the causative link between the breakdown of the BBB and the neuroinflammatory response, it would be helpful to consider an acute knock-down in adults after BBB formation has been completed. - To test the brain permeability barrier, the study uses a 10 KDa dextran permeability assay. Almost 25% of brain in controls show a leaky barrier. It would be helpful to describe the causes for this relatively high occurrence. - An important point in the study concerns the increase of free fatty acids as cause of the inflammatory response. The measurements were based on measurements of whole heads, which could include the hemolymph and fat body within the head in addition to brain. However, the causative relationship remains unclear and the question why a leaky blood brain barrier would increase the free fatty acid levels in the body or brain remains mainly an observation at the descriptive level. Here, it would be helpful to design an experiment, which could test the causative links or to modify the interpretation in scheme 6D and adjust the wording in the text. - Related to this, how do the levels of AMP caused by a leaky BBB would compare to an elicited neuroinflammation by the presence of bacteria? The neuroinflammatory response can be accompanied by macrophage entry into the brain following AMP induction. Could the authors detect this response (which could be envisioned as manipulations include pupal development, provided macrophages would persist into adulthood)? This would make a strong point regardless of the outcome. - Expression of sws is determined using sws-Gal4 driving membrane-tethered GFP. As sws is expressed very widely and classical Gal4 lines tend to be active in the BBB, it is important to provide the exact information about the nature of this driver. - The Material and Methods section should contain a proper Quantification and Statistical analysis section. In the Figures, it would be helpful to refer to the Table reporting sample numbers. - In Figure 5, it would be important to indicate sample numbers, the nature of the error bar, and show data points together with columns.

Minor comments

• On page 8, cell death is visualized using "the apoptotic marker Cas3". It should be Caspase-3. Moreover, it is not clear whether this antibody (directed against vertebrate Caspase-3) recognizes indeed Caspase-3 in Drosophila? This should be formulated more carefully.
• On Page 9 (3rd paragraph), the authors report that they "want to understand what signaling pathway is activated." However, the described experiments do not lead to a signaling pathway, but conclude that an antiflammatory response is evoked. This should thus be reworded.
• Figure 1 reports the expression pattern and phenotype of sws; thus, the title of the figure should be extended.
• Concerning the description of phenotypes, the authors use the term "clumps", but it is not clear what this entails (e.g., Page 6, or Figure 6). For the reader, it is also necessary to refer to original studies of moody to understand the septate junction phenotype represented in the figure.

Referees cross-commenting

I fully agree with the comments of the other two reviewers, as they were complementary and overlapping with mine (e.g. the contribution of age).

Significance

This study provides a detailed cellular and functional characterization of the swiss cheese phenotype in the blood-brain barrier so far not reported in previous studies, including the team's own earlier publications (e.g., Kretzschmar et al., 1997; Melentev et al., 2021 and Ryabova et al., 2021). Furthermore, it uses cutting-edge technology to provide links to neuroinflammation and neurodegeneration, Previous studies explored neuroinflammation in the brain of Drosophila by challenging the organism with bacteria to mount an inflammatory response (Winkler et al., 2021). Intriguingly, this current study provides evidence, that a leaky blood brain barrier alone could lead to an inflammatory response, and that in turn, treatment with anti-inflammatory agents could reduce the cellular defects in glia and in consequence neurodegeneration. This represents an important conceptual advance that will be of wide interest to neurobiologists interested in glial biology, neuroinflammation and neurodegeneration in Drosophila and in vertebrates. One possible limitation of the study may be that while complex cellular processes have been pinpointed, some of the causative links of the BBB with neuroinflammation remain unexplored, in particular the aspect of elevated free fatty acids/antimicrobial peptides.

College and Graduate schools are meant to be period of time where young adults experiment, find themselves, and grow into their adult values as well as determine the direction they want to head in for the rest of their lives. This is no easy feat but, what makes it more stressful?

I'm not familiar with Aragon, but the shout out to Borges I think help's capture LaSalle's point of some non-corporeal plane at work; some "metaphysical" as he says, or perhaps ontological draw back through the streets to a specific place that haunts him on a level he is unaware of until he finds himself returned to that place - physically or mentally.

Reviewer #1 (Public Review):

Summary:<br /> Herein, Blaeser et al. explored the impact of migraine-related cortical spreading depression (CSD) on the calcium dynamics of meningeal afferents that are considered the putative source of migraine-related pain. Critically previous studies have identified widespread activation of these meningeal afferents following CSD; however, most studies of this kind have been performed in anesthetized rodents. By conducting a series of technically challenging calcium imaging experiments in conscious head fixed mice they find in contrast that a much smaller proportion of meningeal afferents are persistently activated following CSD. Instead, they identify that post-CSD responses are differentially altered across a wide array of afferents, including increased and decreased responses to mechanical meningeal deformations and activation of previously non-responsive afferents following CSD. Given that migraine is characterized by worsening head pain in response to movement, the findings offer a potential mechanism that may explain this clinical phenomenon.

Strengths:<br /> Using head fixed conscious mice overcomes the limitations of anesthetized preps and the potential impact of anaesthesia on meningeal afferent function which facilitated novel results when compared to previous anesthetized studies. Further, the authors used a closed cranial window preparation to maximize normal physiological states during recording, although the introduction of a needle prick to induce CSD will have generated a small opening in the cranial preparation, rendering it not fully closed as suggested.

Weaknesses:<br /> Although this is a well conducted technically challenging study that has added valuable knowledge on the response of meningeal afferents the study would have benefited from the inclusion of more female mice. Migraine is a female dominant condition and an attempt to compare potential sex-differences in afferent responses would undoubtedly have improved the outcome.

The authors imply that the current method shows clear differences when compared to older anaesthetized studies; however, many of these were conducted in rats and relied on recording from the trigeminal ganglion. Inclusion of a subgroup of anesthetized mice in the current preparation may have helped to answer these outstanding questions, being is this species dependent or as a result of the different technical approaches.

The authors discuss meningeal deformations as a result of locomotion; however, despite referring to their previous work (Blaeser et al., 2022), the exact method of how these deformations were measured could be clearer. It is challenging to imaging that simple locomotion would induce such deformations and the one reference in the introduction refers to straining, such as cough that may induce intracranial hypertension, which is likely a more powerful stimulus than locomotion.

Reviewer #1 (Public Review):

Summary:<br /> The authors use a combination of biochemistry and cryo-EM studies to explore a complex between the cap-binding complex and an RNA binding protein, ALYREF, that coordinates mRNA processing and export.

Strengths:<br /> The biochemistry and structural biology are supported by mutagenesis which tests the model in vitro. The structure provides new insight into how key events in RNA processing and export are likely to be coordinated.

Weaknesses:<br /> The authors provide biochemical studies to confirm the interactions that they identify; however, they do not perform any studies to test these models in cells or explore the consequences of mRNA export from the nucleus. In fact, several of the amino acids that they identified in ALYREF that are critical for the interaction, as determined by their own biochemical studies, are conserved in budding yeast Yra1 (residues E124/E128 are E/Q in budding yeast and residues Y135/V138/P139 are F/S/P), where the impact on poly(A) RNA export from the nucleus could be readily evaluated. The authors could at least mention this point as part of the implications and the need for future studies. No one seems to have yet targeted any of these conserved residues, so this would be a logical extension of the current work.

Specific suggestions:<br /> The authors could put their work in context by speculating how some of the amino acids that they identify as being critical for the interactions they identify could contribute to cancer. For example, they mention mutations of interacting residues in NCBP2 are associated with human cancers, pointing out that NCBP2 R105C amino acid substitution has been reported in colorectal cancer and the NCBP2 I110M mutation has been found in head and neck cancer. Do the authors speculate that these changes would decrease the interaction between NCBP2 and ALYREF and, if so, how would this contribute to cancer? They also mention that a K330N mutation in NCBP1 in human uterine corpus endometrial carcinoma, where Y135 on the α2 helix of mALYREF2 makes a hydrogen bond with K330 of NCBP1. How do they speculate loss of this interaction would contribute to cancer?

It is awful to think about the negative impacts that this has on all ecosystems, and scary to wrap your head around the fact that it has been happening for so long.

Everyone processes information in different ways. Some people may take notes some might highlight while other people might be able to grasp things in their head.

Author is stating how recless we drive now like we dont have a care in the world.

Author Response

Reviewer #1 (Pulic Review):

The authors aimed to understand whether the superficial, retinorecipient layers of the mouse superior colliculus (sSC) participate in figure-ground segregation and object recognition. To address this question, they use a combination of optogenetic perturbations of sSC and recordings. These data are consistent with SC being causally involved in object recognition. This would be useful information for the field and likely to be cited.

Thank you for your positive evaluation.

However, I have several concerns regarding their conclusions.

A significant limitation of this study is methodological. The major novelty is the effect of optogenetic silencing, because the recordings are largely correlative, but the optogenetic silencing approach lacks appropriate controls for the effects of the optogenetic excitation light. The authors acknowledge that the optogenetic light is a potential confound, but attempt to address this by shielding the fiber to eliminate light leak and strobing a blue led in the arena. The former does not account for the effects of excitation light scattering intracerebrally--during optogenetic experiments, intracerebral scattering causes the eyes to light up--and for the latter, there is no way to compare the intensity or qualia of the externally strobed LED and the intracerebral light. The proper control would be a cohort of mice lacking channelrhodopsin expression in sSC. Regardless, it is essential to acknowledge this potential confound.

This is a good point. We have added discussion of this in lines 90-95. The proposed experiment was done in Kirchberger et al. (Sci Adv 2021, Suppl Figure 3). In mice without expression of channelrhodopsin trained on the same task as in our study, blue laser light in the cortex did not affect accuracy. Although the exact location of these fibers is different from ours, the distance from the fiber to the eye is very similar. Furthermore, in answer to this comment, we have done a new set of experiments with 4 wild type mice, in which we recorded neural activity in the sSC while delivering optogenetic light stimulation. The procedure was similar to our previous experimental animals except that they did not receive a virus injection. In these mice, we did not see any response in the superior colliculus to the laser light, but we noticed a 5% reduction in response to the visual stimuli (new Figure 1—figure supplement 3). This small reduction could be a small reduction of contrast of the visual stimulus due to the laser light hitting the retina, but given that we did not see any response to the laser alone, it is more likely to come from the known inhibiting effects of light on neural activity (e.g. through heat, see Owen et al. Nat Neurosci 2019). Because our aim was to silence sSC, this particular effect is not a strong confound for our study.

Relatedly, as the authors note, there are GABAergic projection neurons in sSC that may be driving these effects via gain of function. This is a significant concern that has limited the widespread adoption of this approach in sSC despite its popularity in studies in cortex. Indeed, one recently published study of behavioral functions of deep SC found that activating inhibitory neurons actually caused paradoxical behavioral effects consistent with gain of function in the targeted hemisphere, due to the effects of long-range inhibitory projections on the other SC hemisphere. Given the presence of inhibitory projections in sSC, it would be preferable to use an orthogonal method for silencing and at least to thoroughly acknowledge these concerns and cite these recent studies.

This is a valid point. When we started our study, we had some experience with inhibitory opsin (archaerhodopsin and halorhodopsin) and were not confident that we could widely inhibit the sSC reversibly, repeatedly and consistently for an extended period. Other labs have now shown this is feasible with improved inhibitory opsins, so this would now be our preferred option too. The method of silencing sSC by inhibition of GABAergic neurons, however, is still the most common optogenetic method to silence sSC for an extended period (e.g. Hu et al. Neuron 2019, Brenner et al. Neuron 2023) .

We thank the reviewer pointing us to recently published paradoxical behavioral effects. These effects, that we found in Essig et al. (Comm. Biol. 2021) are very interesting, but are not really a concern for the interpretation of our results, partially because as the reviewer pointed out, the GABAergic neurons activated there were in the deep and intermediate layers of the SC, below the sSC that we targeted. The paradoxical effects in that manuscript were attributed to direct inhibition of the contralateral superior colliculus. In our case, we activated the inhibitory neurons bilaterally, and this interhemispheric GABAergic connectivity, if it extends to sSC, only strengthened the bilateral silencing of the sSC. However, we have now discussed the possibility of our transfection of these deeper GABAergic neurons (lines 272-278). The more general point that activating GABAergic neurons in the sSC may also cause inhibition in other structures is indeed a concern. GABAergic neurons in the sSC project to the PBG and the LGN (in particular the vLGN) (Gale & Murphy, 2014; Whyland et al., 2019; Li et al., 2023). Although the primary effect of our manipulation is silencing of the superior colliculus, including the GABAergic neurons (see our answer further below), we cannot exclude the possibility that activating these extracollicular GABAergic projections has an effect. We have edited our discussion of this and updated the references (lines 268-272). However, our measurements in anesthetized (previous submission) and in awake mice (new Figure 1—figure supplement 2) show that apart from a short period directly after the onset of the laser, also almost all putative GABAergic neurons are reduced in their response (see also our answer to the next comment).

A minor point is that although activation of GABAergic neurons in sSC is expected to cause inhibition of neighboring neurons, I would expect channelrhodopsin-expressing GABAergic cells to show an increase in firing during optogenetic excitation. However, it seems that none of the cells plotted (assuming each point in Supplementary Fig 4D is a cell, which the legend does not specify) had such an increase. Do these extracellular recordings not detect inhibitory neurons well?

This is indeed an intriguing observation. The data in the original figure (Supp Fig 1D) was spiking data from 15 recording sites and not from sorted units. This was mentioned in panel C, but not in the caption. For the purpose of the amount of silencing, there was no need to sort single units. Still, it is surprising to see the reduction on almost all channels. The data of Supp Fig 1D came from experiments in anesthetized mice. Prompted by a question from another reviewer, we have now redone these experiments in head-fixed awake mice. The new Figure 1—figure supplement 2 shows these results, for single- and multi-unit clusters. In response to a short laser pulse (50 ms), we find that many units significantly increase their firing rate (Figure 1—figure supplement 2A-B). However, almost all activated then reduce there firing rate and again, we see an overall reduction of responses to visual stimuli. Only one unit fires significantly more when the laser is on during the period of visual stimulation compared to when the laser is off, and the overall firing rate is strongly reduced (Figure 1—figure supplement 2C-E). It appears that optogenetically activating the inhibitory neurons in the sSC for a longer period also reduces the activity of these neurons. The effect that we are seeing might be similar to the paradoxical effects that may occur in visual cortex, where additional excitation of inhibitory neurons leads also leads to their reduced activity due to network dynamics (see e.g. Sadeh & Clopath, Nat Neurosci Rev 2021). However, the effect may also be due to a few inhibitory neurons having a strong inhibitory effect on other inhibitory neurons. This is an interesting point worthy of more investigation, but it falls out to scope of this manuscript.

Finally, the relationship between these stimuli and objects is not entirely clear. The authors acknowledge this but it would be worthwhile to devote more attention to this point. In effect, as the authors note, the gray screen and sinuisoidal grating do not have any sharp edges on the screen, whereas each of the behaviorally relevant stimuli will create a sharp, step-like edge on the screen. Whether edge detection is truly object detection or simply a variant of more general visual detection is unclear.

Indeed, the task can be solved by detection of texture edges, and it is not necessary to integrate the edge components into an object to successfully perform the task. A linear decoder fed with simple cell-like inputs is able to do the orientation task (Luongo et al., 2023). The same network failed to learn the phase task, but also the image of a phase-defined figure contains features that are not present in the background image, and could be solved by learning only local features. Even the texture-defined figures used in Kirchberger et al. (2021) and in earlier monkey studies (Lamme, 1995) which do not contain any sharp stimulus edges can be detected without integrating the local edges into objects and segregation the figure from the background. Several monkey studies show that late neuronal responses in V1 are enhanced for neurons with receptive fields on what we, humans, perceive as the figure. This effect has also been seen in mouse V1, even in the case where there are no local features distinguishing the figure from the background (Fig 7. in Kirchberger et al. 2021). Interfering with activity in V1 in this late phase reduces the ability to detect the figure in human (by TMS) and mouse (by optogenetics). This is suggestive that this figure-ground modulation is used in solving the task, but not a proof. To understand if mice solve the tasks by detecting a figure or by detecting specific features, we can look at generalization. Mice were previously shown to generalize to some degree for size, position and spatial phase of the figure grating patch (Schnabel et al., 2018), suggesting that the mice did not train to detect specific features at specific locations. Rats trained on a similar task had difficulty generalizing from a luminance-defined object to an orientation-defined object (De Keyser et al., 2015), as do mice (Khastkhodaei et al., 2016), but once the rats were acquainted with one set of oriented figures, they immediately generalized to other texture-orientations above chance. On a slightly different figure-detection task mice also showed generalization for different orientations once the initial task was learned (Luongo et al. 2023). This suggests that at least some generalization to object detection occurs in this task. We have added these observation to the discussion (line 301-305).

Reviewer #2 (Public Review):

The goal of this study is to show that the superficial superior colliculus (sSC) of mouse signals figure-ground differences defined by contrast, orientation, and phase, and that these signals are necessary for the animal to detect such figure-ground differences. By inhibiting sSC while the animals perform a figure-ground detection task, the study shows that detection performance decreases when sSC activity is suppressed during the onset of the visual stimulus. The study then intends to show that sSC neurons exhibit surround suppression based on orientation differences, and that surround suppression is stronger when the animal detects the correct location of the figure on the background.

The major strength of this study is the use of a behavioural paradigm to test detection performance of figure-ground stimuli while manipulating neural activity in the sSC during different times after stimulus onset. This paradigm would show whether activity in the sSC is relevant for performing the task. Secondly, the study collected data to confirm previous findings: sSC neurons exhibit orientation specific surround suppression. Additionally, it is impressive that the authors were able to train mice to generalize their task performance across different stimulus categories (figure-ground differences in orientation and phase). This should be highlighted as it may inform future studies.

Thank you for your positive evaluation. We have extended our discussion on the generalization in object detection tasks in mice.

The study has, however, methodological and analytical weaknesses so that the stated conclusions are not supported by the presented results.

1) Optogenetic inhibition is not limited to sSC (even expression may not be limited) About 30% of inhibitory neurons in the sSC project to other areas, e.g. ventral LGN, parabigeminal nucleus and pretectum (Whyland et al, 2019, see ref in manuscript). This means that these areas receive direct inhibition when inhibitory sSC neurons are optogenetically stimulated. This fact is mentioned in the discussion but the consequences and implications for the results are ignored. This is a major flaw of the optogenetic experiments of this study. Additionally, no evidence is given that opsin expression was limited to the superficial layers (except for one histological slice), which the authors acknowledge in line 285. Deeper layers may have other inhibitory neurons with long-range projections.

The finding that sSC neurons show no figure-ground modulation for phase while the optogenetic manipulation has behavioural effects may be an indication for other areas being affected by the optogenetic manipulation.

This is a valid point, also raised by reviewer 1. Although the primary effect of activating the GABAergic neurons in the sSC is a strong reduction of activity in the sSC (see also new figure S1), we cannot rule out that we also activate GABAergic neurons below the sSC and that there are some effects of activating GABAergic connections to the LGN and PBG. We have extended our discussion of this point in lines 269-277. However, as shown in new Figure 1—figure supplement 2, the effect of optogenetically activating Gad2-positive neurons appears to lead to a counter-intuitive reduction of their activity. This effect has previously been observed in cortex.

2) Could other behavioural variables explain the results?

a) Are there any task events other than the visual stimuli that the mice could use to make their decisions? The authors state the use of a custom made lick spout but it is not clear how this spout works, i.e. how do mechanics of the spout deliver water to the right versus the left output and could the mouse perceive these mechanics?

We believe there were no task events besides the visual stimuli that the mice could use to make their decisions. The lick spout was Y-shaped (see Figure 1B) to facilitate the two-alternative forced choice task. Each side of the lick spout was connected to a separate water tube. The water flow in each tube was controlled using a valve. Also, each side of the lick spout was connected to its own lick detector wire. The two valves and the two detector wires were connected to an Arduino which was controlled by our MATLAB task script. The task script was coded such that, when the lick of the mouse had been on the correct side, the valve controlling the water flow on the correct side would briefly open to deliver the water reward. To summarize, the water would only flow after the mouse had licked and if the first lick had been on the correct side. Hence, the water reward did not produce additional cues. We have edited the description of the lick spout in the Methods section to make the functioning of the lick spout more clear (lines 511-513).

b) Could the different neural responses to figure versus ground shown in Fig 2I-J and Fig 3B be explained by behaviours varying between the trial types, e.g. by early lick movements (which are conceivable even if the spout is not present), eye movements or changes in pupil-linked arousal? A behavioural difference seems even more likely to occur between hit and error/miss trials (Fig 4). If these behaviours were not measured, the possibility of behavioural modulation should be discussed.

In the awake behaving electrophysiology experiments, the lick spout was not present until 500 ms after stimulus onset, so the mouse could not lick the spout. We did not record whisking or other face and jaw movements, hence we cannot say for sure whether the mice performed early ‘licks’ in the absence of the lick spout. We did, however, add a supplementary figure showing the licking behavior of the mice in the optogenetic interference experiments (see Figure 1—figure supplement 5). In this experiment, the lick spout was present at all times so all early licks would be recorded. Any licks before 200 ms after stimulus onset were disregarded as this would be too early for the decision to include knowledge about the stimulus. Figure 1—figure supplement 5B shows that the mice indeed only performed very few early licks as they probably knew this would not yield reward. The mice that performed the awake electrophysiology experiments were trained on the same task as these mice before introducing the lick spout delay of 500 ms. So although we cannot rule out early licks during electrophysiology, we think early licks would be an unlikely explanation for the neural response differences.

We have added a new supplementary figure (Figure 2—figure supplement 2) showing data for eye movements and pupil dilation during the tasks. We had excluded all trials where the mice performed eye movements between 0-450 ms after stimulus onset, and indeed we saw no eye movements during the peak of the visual response (0-250 ms). Furthermore, the pupil dilation of the mice also did not change in this period.

All in all, we view it as unlikely that the differences in neural activity in sSc were caused by either licking, eye movements or pupil-linked arousal.

3) What is the behavioural strategy of the animals? Only licks beyond 200 ms after stimulus onset determine the choice of the animal because "mice made early random licks" from 0 to 200 ms. To better understand the behavioural strategies of the animals we need to see their behavioural data, i.e. left and right licks aligned to stimulus onset. It would be particularly interesting to see how number and latency of licks changes during optogenetic manipulation.

Based on these suggestions, we investigated the licking behavior of the mice during the optogenetic experiments in more detail. Our new Figure 1—figure supplement 5 taught us several things:

1) The fully trained mice hardly perform any early licks; they seem to understand that early licks cannot yield reward.

2) The mice typically only lick one side of the lick spout during one trial. In correct trials the fluid reward is given directly after a correct lick, which causes the mouse to lick the correct side of the spout even more. However, even if the first lick is incorrect (bottom rows), the mouse generally does not lick the other (correct) side afterward. They seem to know that correct licks after an incorrect lick do not yield reward.

3) The maximum licking rates were not significantly affected by laser onset.

4) The latency of the first lick (reaction time) was not significantly affected by laser onset. (Please also see our response to question 2b).

4) Data relating to misses should be included in analyses to provide a complete picture of behaviour and neural responses

a) In the optogenetic manipulations, an increase in misses seems to dominate the decreased accuracy (please, explain when a response was counted as a miss). A separate analysis of miss trials may be more robust than of error trials and also offers a different interpretation of the data, namely that the mouse did not see the stimulus rather than perceiving the figure on the opposite side. However, if the mice reduced their lick rate in general during optogenetic stimulation, this begs the question whether their motor performance was affected by optogenetic manipulation. Can this possibility be excluded?

Trials were counted as follows: A trial was counted as a hit when the first lick after 200 ms after stimulus onset was on the correct side. A trial was counted as an error, when the first lick after 200 ms after stimulus onset was on the incorrect side. A trial was counted as a miss, when the mouse did not lick in the window between 200 and 2000 ms after stimulus onset. We have clarified this in the methods section (line 517-526).

Our previous text may not have been sufficiently clear but the decrease in accuracy during optogenetic trials is not dominated by an increase in missed trials. As we have now indicated explicitly in its caption, in figure 1, missed trials are excluded from the analysis. Hence, the significant effects shown in figure 1 are not driven by an increase in missed trials but rather by an increase in erroneous licks. When comparing figure 1 vs figure S3, where the missed trials are added to the analysis as if they were error trials, we can see an overall downward shift of the performances. Indeed, mice miss more trials when the laser is on. The increase in number of missed trials is lower than the increase in number of wrong choices. Furthermore, the range between the performances at early laser onset and late laser onset is still very similar. This indicates that the mice on average do not have higher miss rates when laser onset is early.

Finally, nor maximum licking rate, nor the reaction time is affected by the laser onset (see the new figure S2)

Related to Fig 4, it would be equally interesting to see how FGM changes during misses. Do the changes support the observations for error trials?

We are not convinced that the neural data from missed trials can be interpreted in a simple way. Mice may have various reasons to miss a trial: they may be tired or not paying attention, they may not have seen the stimulus well, they may not feel thirsty enough, they might be distracted by some sensory input that humans might not be aware of, etc. This is why we specifically opted to not use a go-no/go task but instead opted to use a 2-alternative forced choice task.

5) Statistical tests do not support the conclusions, are missing or inadequate

a) In Fig 1E, accuracy is significantly affected at only 1-2 time points in each task, specifically either the 1st and 3rd or the 2nd time point. How do the authors interpret these results? If inhibition starting at the 2nd time point has no significant effects, why would it be significant when inhibition starts later (at the 3rd time)? Furthermore, given that all other starting points of laser stimulation have no significant effects, there is no reason to trust the latency of inhibition effects based on mostly insignificant data points. This analysis in its current form should be removed, including a comparison of latencies between tasks, which was not tested for significance. It may be more meaningful to analyse accuracy for each animal separately. This may reduce variability.

We can understand that the reviewer may have concerns regarding the post-hoc analysis of Fig 1E, but we feel these concerns stem from a misinterpretation of our goal with this analysis. In Figure 1E, we use a 1-way repeated-measures ANOVA. By using this test, we ask whether the performance of the animals is affected by the laser onset. More specifically “does the performance increase or decrease with increasing laser onset?” The test is significant, so indeed the performance goes up as laser onset goes up. This indicates that the performance of the mice is affected by the inhibition of sSC. For the sake of completeness we had included the post-hoc tests for each latency in the statistics table. Indeed, some individual latencies are not significantly different to the no-laser condition. However, this does not invalidate the conclusion of the main test: a repeated measures ANOVA can only be performed on data with 3 or more groups, so the conclusion of the repeated measures ANOVA could not have been drawn from simply those laser onset(s) that is/are significantly different from the no-laser condition. The main effect of higher performance with higher latencies is significant, even if some individual comparisons are non-significant. The difference in significance of the post-hoc tests does not indicate a significant difference between the groups, but insufficient power to do six individual tests.

We have changed the wording in the reporting of the statistics of Figure 1E to hopefully more precisely indicate the conclusions we drew from the statistics. We do not draw conclusions from the post hoc tests. We have considered removing them from the statistics table 1, but believe that some readers might be interested. We can remove them if the reviewer believes that would be better.

b) Analyses regarding the difference in neural response to figure and ground (Fig 2I-J, Fig 3B, Fig 4B, Fig 5C) would be more convincing and informative if the differences were analysed on the level of single neurons in response to the same orientation within their RF (or at the location where the figure is presented, for edge-RF neurons). A histogram of these differences would show how many neurons are affected and how large the effect is in single neurons.

We fully appreciate this idea, but the way we set up the behavioural task does not quite allow for this type of statistical analysis. This is because we tested all three of the tasks during single sessions (contrast/orientation/phase), and on top of that, we varied the orientations of the stimuli (0/90deg), as well as the phase of the gratings (60 different phases). This all was done with the idea that it would prevent the mice from memorizing the individual stimuli of the task. This also had the effect that only very few trials per session contained the exact same stimulus type, figure-ground condition, orientation and phase. For example, if a mouse would perform around 120 trials in a session. 25% of those were contrast-stimulus-trials, 37.5% of those were orientation-stimulus-trials and 37,5% were phase trials. If we look into 120*0.375 = 45 orientation-stimulus-trials, half of those were figure trials, half were ground trials: 22 trials each. If we split these trials up by their individual orientations, we are left with only about 11 trials per condition to analyse for figure-ground effects, each of which would probably have a different grating phase. Given the firing rate variations that the individual neurons show in awake mice, this amount of trials would not provide enough statistical power to test the significance of modulation in single neurons.

Although we feel the study design would not allow analysis of individual neurons in response to the same orientation within their RF, we did perform an aggregated analysis on orientation selectivity. For this analysis, we included all the trials where the RF of the recorded neurons was on the background-half of the screen. We then computed the responses of each neuron to the trials where the background orientation was 0 and 90, respectively. This analysis showed that most neurons had no preference for either of the two tested orientations of the other. Only 4 out of 64 (6%) neurons showed a significant preference. We therefore believe that splitting the data by orientation preference would not be very informative.

c) All statistical tests performed across neurons should account for dependencies due to simultaneous recordings (dependency on session) and due to recordings in the same animal (dependency on animal). This can be done in most cases by using linear mixed-effects models.

We agree with the reviewer and have changed the analysis for figure 2I, 3B and 3E to an LME analysis (see also Table 1).

d) There was no significant difference between model weights (Fig 3D), so the statement in line 210 (RF-edge neurons had higher weights) should be removed.

In answer to previous we question changed the analysis for what is now Figure 3E to an LME. This shows that relative weights were significantly higher for the orientation compared to the phase task. We have adapted our conclusion accordingly (line 214-218).

e) Fig 4B compares FGM during correct and error trials. This comparison has to be performed with the same set of neurons in correct and error trials (not the case for orientation). Again, the most compelling and informative comparison would be on the level of single neurons: response difference between figure and ground (same visual features at figure position) during hits versus errors.

As described above, we feel the study design does not allow analysis on the level of individual neurons. The analysis in 4B was actually performed using the same set of neurons, we have removed the typo.

f) There is no evidence that FGM for phase was different between hit and error trials as stated in line 234.

Indeed, we had phrased this incorrectly. Since we recorded all task during single recording sessions, we have data for each task for most neurons. We were therefore able to pool the results from the different tasks, and the main d-prime difference between hit vs. error was significant. Post-hoc tests showed that this is mainly driven by the difference in the orientation task. We have edited the wording to be more accurate (line 239-242).

g) It is not clear why and how the mixed linear effects model was used pooling data across tasks (Fig 4C and Fig 5D). Different neurons were recorded for each task, so the sample points (neurons) are not affected by both task effects (orientation and phase). Each task should be analysed separately.

Since we recorded all three task versions during single behavioral sessions, we have data for multiple tasks from each neuron. This is why the linear mixed effects model pools the data across the tasks. We have added a note in the main text for clarity (line 238-242)

h) Bonferroni correction in Fig 1E should correct multiple comparisons across time points, not across tasks (see Table 1).

The multiple time points all belong to the same one-way repeated measures ANOVA, so there’s no need to correct the post-hoc analysis. We did run the ANOVA for three tasks, which is why we corrected the p-values of each task. We think that this is best way, but can also present uncorrected p-values if needed.

i) What is the reason to perform some tests one-tailed, others two-tailed?

Following the reviewer comments, we changed some analyses to LME models. The remaining tests that require definition of the tails are all two-tailed.

6) The results relating to "multisensory neurons" are ambiguous regarding their interpretation (if significant at all) and seem unrelated to the goal of the study. It is particularly likely that behaviours like licking or other movements cause the response differences between figure and ground.

We agree with the reviewer that finding these neurons was not the aim of the study. We did not include enough type of tests in our paradigm to fully determine the properties of these neurons. Furthermore, we note that we have recorded too few of these neurons to draw strong conclusions. The data shown in new Figure 2—figure supplement 1H suggest that the responses of these neurons or not as strongly time-locked to the first lick as they are to the trial onset. We presented the behavior of these neurons in our manuscript, because, whatever their exact behavior, they are clearly distinct from the visually responsive cells that show a short latency response to the visual stimulus (Figure 2—figure supplement 1). We still feel that it is useful for the reader to know there are cells in the sSC that show such a distinct behavior, but we have moved the figure and the accompanying text to a figure supplement to avoid distraction from the main message of the manuscript.

7) What depth were neurons recorded from (Fig 3 and 4)?

The depths of the recorded visually responsive neurons is now shown in Figure 2—figure supplement 1E.

Reviewer #3 (Public Review):

The authors used optogenetic manipulations and electrophysiology recordings to study a causal role and the coding of superficial part of the mouse Superior Colliculus (SCs) during figure detection tasks.

Authors previously reported that figure-ground perception relies on V1 activity (Kirchberger et al. 2021) and pointed out that silencing of V1 reduced the accuracy of the mice but still the performance was above the chance level. Therefore, visual information necessary in this task, could be processed via alternative pathways. In this study, authors investigated specifically SCs and used similar approach and analysis as in Kirchberger et al. 2021. Optogenetic silencing of the activity of visual neurons in SCs impaired the accuracy in all 3 versions of the figure detection task: contrast, orientation, and phase. Electrophysiology recordings revealed that SCs neurons are figure-ground modulated, but only by contrast- and orientation-based figures. They show SCs visually responsive neurons reflect behavioral performance in orientation-based figure task. The authors conclusion is that SCs is involved in figure detection task.

Overall, this study provides evidence that mouse SCs is involved in a figure detection task, and codes for task-related events. Authors heroically compared results between 3 different versions of the figure-based detection task. The logic of the study flows through the manuscript and authors prepared a detailed description of methods.

Thank you for your positive comments.

However, my main concern is with 1) the amount of data used to make the key arguments, and 2) the interpretation of results. The key findings of this study (figure-ground modulations in SCs) could be a result of the visual cortical feedback in SCs during the task, or pupil diameter changes. Unfortunately, the authors did not rule out these possibilities.

Still, this study can be relevant to a general neuroscience audience, and results could be more convincing if the authors could clarify:

1) Optogenetic inactivation

a) The impact of laser stimulation on neural activity is not satisfactory (Supplementary Figure 1). The method seems to be insufficient to fully salience neurons. Electrophysiology control recordings of inactivation are performed in anesthetized mice, which is not a fair estimation of the effect in awake state. Therefore, it rises a major question how effective the inactivation is during the task?

We have conducted new control experiments for the impact of laser stimulation on neural activity, now in awake animals (see Figure 1—figure supplement 2). The reviewer was right to ask for these experiments. We had not expected much difference in the effect of silencing in the awake and anesthetized state. To minimize the animal discomfort, we had therefore done these control experiments in terminal experiments under anesthesia. However, these new set of experiments showed that the impact of laser stimulation was much stronger in awake mice than anesthetized mice. We see an average spike rate reduction of 90% when the laser is on. Although it is not full silencing, we think this reduction is sufficient to draw some conclusions on the role of sSC in the behavioral tasks.

b) Could authors provide more details if laser stimulation has an effect only on visual, or all sampled units? How many of units were recorded, and how many show positive and negative laser modulation?

We defined visually responsive units as units that have an evoked rate of at least 2 spikes/s. In the new figure 1—figure supplement 2D from the new set of control experiments, we plotted, for every unit, the mean rate in laser ON and OFF trials - also including the non-visually responsive units. It is evident that the spiking activity of most units – including those that were not classified as ‘visual’ – is reduced in the laser ON compared to OFF trials. We observed 1 unit that showed strong positive laser modulation over the entire duration (figure 1—figure supplement 1D). Many units were activated by shorter laser pulses directly after laser onset (Figure 1—figure supplement 2A-B), but these also reduced in activity as the stimulation continued.

c) How local the inactivation effect is? Where was the silicon probe placed in relation to AAV expression and optical fiber position?

The AAV was injected at 0.3 mm anterior and 0.5 mm lateral to the lambda cranial landmark. With this injection location we aimed to focus the expression at low/nasal receptive fields, in front of the mouse, because that is where the visual stimulation would take place. From there, the expression did spread laterally across sSC (see Figure 1C). The silicon probe was placed roughly in the same location as the viral injection. The optical fiber was positioned such that the tip would shine on the surface of the sSC at a slight angle, from a lateral distance of ~200 µm from the silicon probe. We have edited the methods section to make this more clear (line 583-585). This procedure allowed us to record only relatively local effects of the inactivation. Although we did not record neural activity across the entirety of sSC, we did record from multiple electrode penetrations per mouse, each time slightly varying the recording location with up to ~300µm and ~500µm in the anterior and lateral directions, respectively. In these variations of recording location the optogenetic effect was always present (see new Figure 1—figure supplement 2G). Moreover, the suppressive effect of optogenetic stimulation of GAD2+ neurons was observed across the entire depth of the sSC (new Figure 1—figure supplement 2H).

2) Number of sessions and units

a) The inactivation effect on behavior (Figure 1E) during phase-task has a significantly larger effect at 66ms after stimulus onset. How can authors explain this? Could this result be biased by one animal/session, or low number of trials for this condition? There is no information about number of trials, or sessions from individual animals. Adding a single example of animal's performance, and sessions for individual mice could clarify results in Figure 1.

The criterium for each mouse to be included in the analysis for one of the tasks was to have 100 trials where optogenetics were used (aggregated across the latencies). So at minimum, we would have about 100 trials/6 latencies = 17 trials per latency per mouse. For most mice though, the number of trials per latency was closer to about 40. We have added more information about this to the methods section (lines 567-570). Despite these inclusion criteria, the 66 ms effect is present for multiple mice (we have now added data visualizations for the individual mice in Figure 1—figure supplement 4). To address the reviewer’s concerns, we can only speculate as to why this happens. It might be random variation. A more speculative conclusion would be that perhaps this 66ms laser onset is particularly disturbing to the visual processing and/or decision-making of the mouse. But we feel that we do not have enough evidence to conclude this.

b) Figure 2H shows an example of neuron with an effect in the figure detection task based on phase difference, but Figure 2I/J (population response) shows there is no effect. Overall, the conclusion is that SCs neurons are not modulated by a phase-defined object. It seems that number of mice and hence units are smaller in phase-detection task comparing to two other tasks. How many of single units are modulated in each version of the task? How big is the FGM effect on single neuron response (could authors provide values in spikes/s)? One task is dropped from analysis which it is one of the main points of the paper: to compare responses across different versions of the figure detection task in SCs. But Figures 3-5 only focuses on two tasks, because there is not enough of data for figure-based contrast task.

We have updated Figure 2H to show spikes/s of the example single neuron response. For the population responses, we explicitly normalized the individual neurons because they all have different baseline and peak firing rates. This normalization was important for the decoding, so we decided to print the data such that the data from Figures 2I and 3B went into the decoding as printed. If we look at the non-normalized values, the maximum amplitude of the average FGM effect is 22.3, 5.9 and 2.9 sp/s respectively for the three tasks (for neurons with RF on stimulus center).

We have furthermore updated the FGM analysis such that the clustered statistic is now based on linear mixed effects statistics instead of T-test statistics. The results based on this new analysis are largely the same (see statistics table T1). We checked the significance of individual neurons in the time window where the grouped LME analysis was significant. For the phase task (n.s. in grouped analysis), we used the significant window from the orientation task. For this analysis, we want to stress that the number of trials for each version of the task for each individual neurons is quite limited as we recorded all three of the tasks during each recording session. Individually, 7/23 neurons were significant for the contrast task, 1/49 were significant for the orientation task, 0/32 were significant for the phase task (after Bonferroni-holm correction).

To address the final part of this comment on dropping the contrast task: we indeed have recorded too few data points to draw conclusions on decoding (Fig. 3) and discriminability (Fig. 4) for the contrast task. However, we do not see the contrast detection task as the main point of the paper. As earlier work had already shown involvement of the sSC in visually-evoked behaviours based on objects that are clearly isolated from the background, the main focus in this work is to show involvement of sSC in complex object detection, where the visual contrast and luminance is the same across object and background.

3) Figure-ground modulation in SCs

a) How is neural activity correlated with pupil size, movement (eg. whisking, or face), or jaw movement (preparation to lick)? Can activity of FGM neurons in SCs be explained by these behavioral variables?

We did not record whisking or other face and jaw movements. We did record the eye of the mice, so have included a new Figure 2—figure supplement 2 which shows eye position and pupil dilation during the task. For the analysis in the originally submitted paper, trials with substantial eye movement (Z-score of eye speed > 2.5) between 0 and 450 ms had already been removed from the analysis. This way, we could exclude effects of eye movements (but not pupil dilation) on the visual responses in sSC. The additional figures and analyses have been done using the same inclusion criteria. Indeed, in the included trials mice did not move their eyes during the peak of the visual response (0-250 ms). The pupil dilation also did not change in this period.

b) Could authors describe in more detail how they measure a pupil position and diameter, by showing raw data, pupil size aligned to task events?

We have added a new Figure 2—figure supplement 2 to show the pupil position and diameter aligned to task onset.

c) How does pupil diameter change between tasks? Small pupil changes can affect responses of visual neurons, and this could be an explanation of FGM effect in SCs. Can authors rule out this possibility, by for example showing pupil size and changes in position at stimulus onset in different tasks?

Our new Figure 2—figure supplement 2B shows that pupil dilation changes and differences in pupil dilation between figure/ground trials do occur, but only after ~300 ms, so after the peak of the visual response and after the FGM is present in sSC.

d) Authors in discussion mentioned that the modulation of V1 could be transferred to SCs through the direct projection. Moreover, animals perform above chance in both inactivation experiments (V1 and SC), which could be also an effect of geniculate projections to HVAs (eg. Sincich et al. 2004). Could authors discuss different possibilities?

The direct geniculate projection to HVAs is an interesting possibility that we had not considered yet. The dLGN in the mouse projects (apart from V1) mostly to the medial HVAs (Bienkowski et al. 2018). The lateral extrastriate regions receive only very sparse input from the dLGN. The medial HVAs, however, could be silenced without drop in performance in a simple visual detection task (Goldback et al., 2020). Therefore, it does not seem likely that this geniculate to HVAs projections would be important in the figure detection task.

4) Interpretation of multisensory neurons is not clear. In Figure 5B, there is an example of neuron with two peaks of response. Authors speculate about the activity (pre-motor) but there is lack of clear measurement showing "multisensory" response of these neurons. Could these responses be related to the movement of the lick spout towards the mouth of the mouse (500 ms after the presentation of the stimulus)? Moreover, the number of "multisensory" units is very low (5 units, and 8 units).

We have not done definitive test to show what these putative multisensory neurons exactly respond to. Because of their response was after the appearance of the lick and time locking to the trial start, rather than to the licking response, we think that is likely that these neurons responded to the appearance of the spout. There might have been visual, auditory, vibrational or touch clues to which these neurons respond. We believe it is interesting for the reader to know that there is class of neurons in the sSC that did not show a visual stimulus but was time locked to the trial. This was the reason that we had included this figure in the manuscript. However, given the reviewers comments we have decided to move the figure and accompanying text to a figure supplement (Figure 2—figure supplement 1) in order to not distract from the main message of the manuscript.

I agree with this but at the same time, I would like to share my own opinions on this. Yes, you should fight for what's right, but at the same time, one must always have a plan for the battle that they're stepping into, instead of diving in head first.

When looking at all kinds of religions, no matter what their beliefs, values, and traditions are. People who believe and worship a specific religion believe that the most honorable acts one can do then to give themselves to the religion and carry its values and traditions with their head held high.

Which one is more common for old people ?

It is one of the most common forms of permanent hearing loss and is often referred to as nerve-related or inner ear hearing loss

New paragraph maybe? Not sure the hard pivot from film to family/college is the smoothest option

The repetition of "what is" and "who are" in this stanza illuminates a fragmented series of lines, as if working through the an internalized thought process in someone's head. Also the hood imagery includes this obfuscated image that separates the internal from the external, as if the hood is an extension of an individuals cloudy perspective.

ZOMBIE: The fixation on the feet of each man shows that each each man connects to my motif because they are showing signs of a disembodied spirit, regular people would have their eyes/head up to see what's ahead of them but now these people doesn't have to worry about whats ahead of them.

Has this entire poem been the conversation of the speaker receiving a taro card reading?

When it comes to the students in my class, I like to make weekly goals on something I am observing, then come up with a solution on how to reach the goal. It could be as simple as "A is having difficulty at lunch. It could possibly be the noise. We will try noise eliminating head phones." Sometimes the solutions don't work, but it is all trial and error.

The study demonstrated the phenomenon of stimulus generalization, showing that the conditioned fear response could transfer to similar stimuli (in this case, a rabbit and a dog).

The paper begins by highlighting the lack of direct experimental evidence concerning the conditioning of emotional responses, a crucial topic in the history of psychology. The authors acknowledge the importance of understanding how emotions, particularly fear, can be conditioned and their impact on later life.

Author Response

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

We are grateful for the helpful comments of both reviewers and have revised our manuscript with them in mind.

One of the main issues raised was that readers may by default assume that our models are correct. We in fact made it very clear in our discussion that the models are merely hypotheses that will need testing by “wet” experiments and we do not therefore agree that even readers unfamiliar with AF would assume that the models must be correct. It was also suggested that readers could be reassured by including extensive confidence estimates such as PAE plots. As it happens, every single model described in the manuscript had reasonably high PAE scores and more crucially the entire collection of output files, including PAE data, are readily accessible on Figshare at https://doi.org/10.6084/m9.figshare.22567318.v2, a fact that the reviewers appear to have overlooked. The Figshare link is mentioned three times in the manuscript. Embedding these data within the manuscript itself would in our view add even more details and we have therefore not included them in our revised manuscript. Likewise, it is rather simple for any reader to work out which part of a PAE matrix corresponds to an interaction observed in the corresponding pdb prediction. Besides which, it is our view that the biological plausibility and explanatory power of models is just as important as AF metrics in judging whether they may be correct, as is indeed also the case for most experimental work.

Another important point was that the manuscript was too long and not readable. Yes, it is long and it could well be argued that we could have written a different type of manuscript, focusing entirely on what is possibly the simplest and most important finding, namely that our AF models suggest that in animal cells Wapl appears to form a quarternary complex with SA, Pds5, and Scc1 in a manner suggesting that a key function of Wapl’s conserved CTD is to sequester Scc1’s Nterminal domain after it has dissociated from Smc3. For right or for wrong, we decided that this story could not be presented on its own but also required 1) an explanation for how Scc1 is induced to dissociate from Smc3 in the first place and 2) how to explain that the quarternary complex predicted for animal cells was not initially predicted for fungi such as yeast. The yeast situation was an exception that clearly needed explaining if the theory was to have any generality and it turned out that delving into the intricate details of the genetics of releasing activity in yeast was eventually required and yielded valuable new insights. We also believe that our work on the recruitment of Eco/Esco acetyl transferases to cohesin and the finding that sororin binds to the Smc3/Scc1 interface also provided important insight into how releasing activity is regulated. We acknowledge that the paper is indeed long but do not think that it is badly written. It is above all a long and complex story that in our view reveals numerous novel insights into how cohesin’s association with chromosomes is regulated and have endeavoured to eliminate any excessive speculation. We feel it is not our fault that cohesin uses complex mechanisms.

Notwithstanding these considerations, we have in fact simplified a few sections and removed one or two others but acknowledge that we have not made substantial cuts.

It was pointed out that a key feature of our modelling, namely the predicted association of Wapl’s C-terminal domain with SA/Scc3’s CES is inconsistent with published biochemical data. The AF predictions for this interface are universally robust in all eukaryotic lineages and crucially fully consistent with published and unimpeachable genetic data. We note that any model that explains all findings is bound to be wrong for the very simple reason that some of these findings will prove to be incorrect. There is therefore an art in Science of judging which data must be explained and accommodated and which should be ignored. In this particular case, we chose to ignore the biochemistry. Time will tell whether our judgement proves correct.

Last but not least, it was suggested that we might provide some experimental support for our proposed SA/Scc3-Pds5-Scc1-WaplC quaternary complex. We are in fact working on this by introducing cysteine pairs (that can be crosslinked in cells) into the proposed interfaces but decided that such studies should be the topic of a subsequent publication. It would be impossible with the resources available to our labs to follow up all of the potential interactions and we therefore decided to exclude all such experiments.

We are grateful for the detailed comments provided by both reviewers, many of which were very helpful, and in many but not all cases have amended the manuscript accordingly.

With regard to the more specific comments:

Reviewer #1 (Recommendations For The Authors):

1) One concern is that observed interfaces/complexes arise because AF-multimer will aim to pack exposed, conserved and hydrophobic surfaces or regions that contain charge complementarity. The risk is that pairwise interaction screens can result in false positive & non-physiological interactions. It is therefore important to report the level of model confidence obtained for such AF calculations:

A) The authors should color the key models according to pLDDT scores obtained as reported by AF. This would allow the reader to judge the estimated accuracy of the backbone and side chain rotamers obtained. At least for the key models and interactions it would be important to know if the pLDDT score is >90 (Correct backbone and most rotamers) or >70 (only backbone is correct).

B) It would also be important to report the PAE plots to allow estimation of the expected position error for most of the important interactions. pLDDT coloring and PEA plots can be shown side-by-side as shown in other published data (e.g. https://pubmed.ncbi.nlm.nih.gov/35679397/ (Supplementary data)

C) The authors should include a Table showing the confidence of template modeling scores for the predicted protein interfaces as ipTM, ipTM+pTM as reported by AlphaFold-multimer. Ideally, they would also include DockQ scores but this may not be essential. Addition of such scores would help classification into Incorrect, Acceptable or of high quality. For example, line 1073 et seq the authors show a model of a SCC1SA and ESCO1 complex (Fig. 37). Are the modeling scores for these interfaces high? It does not help that the authors show cartoons without side chains? Can the authors provide a close-up view of the two interfaces? Are the amino acids are indeed packed in a manner expected for a protein interface? Can we exclude the possibility that the prediction is obtained merely because the sequence segments (e.g. in ESCO1 & ESCO2) are hydrophobic and conserved?

We do not agree that including this level of detail to the text/figures of the manuscript would be suitable. All the relevant data for those who may be sceptical about the models are readily available at https://doi.org/10.6084/m9.figshare.22567318.v2. In our view, the cartoon versions of the models are easier for a reader to navigate. Anyone interested in the molecular details can look at the models directly.

Importantly, no amount of statistical analysis can completely validate these models. What is required are further experiments, which will be the topic of further work from our and I dare from other laboratories.

D) When they predict an interaction between the SA2:SCC1 complex and Sororin's FGF motif, they find that only 1/5 models show an interaction and that the interaction is dissimilar to that seen of CTCF. Again, it would be helpful to know about modeling scores. Can they show a close-up view of the SORORIN FGF binding interface to see if a realistic binding mode is obtained? Can they indicate the relevant region on the PAE plot?

Given that AF greatly favours other interactions of Sororin’s FGF motif over its interaction with SA2-Scc1, we do not agree that dwelling on the latter would serve any purpose.

2) Line 996: AF predicts with high confidence an interaction between Eco1 & SMC3hd. What are the ipTM (& DockQ if available) scores. Would the interface score High, Medium or Acceptable?

As mentioned, see https://doi.org/10.6084/m9.figshare.22567318.v2.

3) Line 1034 et seq: Eco1/ESCO1/ESCO2 interaction with PDS5. Interface scores need to be shown to determine that the models shown are indeed likely to occur. If these interactions have low model confidence, Fig. 36 and discussion around potential relevance to PDS5-Eco1 orientation relative to the SMC3 head remains highly speculative and could be expunged.

See https://doi.org/10.6084/m9.figshare.22567318.v2. It should be clear that the predictions are very similar in fungi and animals. Crucially, we know that Pds5 is essential for acetylation in vivo, so the models appear plausible from a biological point of view.

4) Considering the relatively large interface between ECO1 and SMC3, would the author consider the possibility that in addition to acetylating SMC3's ATPase domain, ECO1 remains bound to cohesin-DNA complex, as proposed for ESCO1 by Rahman et al (10.1073/pnas.1505323112)?

This is certainly possible but we would not want to indulge in such speculation.

5) E.g. Line 875 but also throughout the text: As there is no labeling of the N- and C-termini in the Figures, is frequently unclear what the authors are referring to when they mention that AF models orient chains in a certain manner.

Good point. This has been amended. However, the positions of N- and C- is all available at https://doi.org/10.6084/m9.figshare.22567318.v2.

6) Fig19B: PAE plots: authors should indicate which chains correspond to A, B, C. Which segment corresponds to the TYxxxR[T/S]L motif? Can they highlight this section on the PAE plot?

Good point and amended in the revised manuscript.

Minor comments:

1) Line 440: the WAPL YSR motif is not shown in Fig. 14A

2) Line 691: Scc3 spelling error.

3) Line 931: Sentence ending '... SCC3 (SCC3N).' requires citation.

4) Line 1008: Figure reference seems wrong. It should read: Fig. 34A left and right. Fig. 34B does not contain SCC1.

Many thanks for spotting these. Hopefully, all corrected.

5) Fig. 41 can be removed as it shows the absence of the interaction of Sororin with SMC1:SCC1. Sufficient to mention in the text that Sororin does not appear to interact with SMC1:SCC1.

This is possible but we decided to leave this as is.

Reviewer #2 (Recommendations For The Authors):

Minor points

(1) Are there any predicted models in which one of the two dimer interfaces of the hinge is open when the coiled coils are folded back, as seen in the cryo-EM structure of human cohesin-NIPBL complex in the clamped state?

No AF runs ever predicted half opened hinges. It is possible that the introduction of mutations in one of the two interfaces might reveal a half-opened state and we ought to try this. However, it would not be appropriate for this manuscript, we believe.

(2) Structures of the SA-Scc1 CES bound to [Y/F]xF motifs from Sgo1 and CTCF have been reported, suggesting that a similar motif could interact with SA/Scc3. Surprisingly, AF did not predict an interaction between Scc3/SA and Wapl FGF motifs, which only bind to the Pds5 WEST region. On the other hand, AF predicted interactions of the Sororin FGF motif with both Pds5 WEST and SA CES. Can the authors comment on this Wapl FGF binding specificity? What will happen if a Wapl fragment lacking the CTD is used in the prediction?

This seems to be an academic point as the CTD is always present.

Reviewer #1 (Public Review):

There are a number of outstanding questions concerning how cohesin turnover on DNA is controlled by various accessory factors and how such turnover is controlled by post-translational modification. In this paper, Nasmyth et al. perform a series of AlphaFold structure predictions that aim to address several of these outstanding questions. Their structure predictions suggest that the release factor WAPL forms a ternary complex with PDS5 and SA/SCC3. This ternary complex appears to be able to bind the N-terminal end of SCC1, suggesting how formation of such a complex could stabilize an open state of the cohesin ring. Additional calculations suggest how the Eco/ESCO acetyltransferases and Sororin engage the SMC3 head domain presumably to protect against WAPL-mediated release.

This work thus demonstrates the power of AF prediction methods and how they can lead to a number of interesting and testable hypotheses that can transform our understanding of cohesin regulation. These findings require orthogonal experimental validation, but authors argue convincingly that such validation should not be a pre-requisite to publication.

In their revised version, the authors did not systematically include model confidence scores, and it therefore remains difficult for the reader to evaluate the reliability of the models obtained. The authors correctly point out that such metrics are available on figshare. It is therefore possible to obtain such information. The caveat is that it remains to the user to identify and extract the relevant information. While they claim that they have labeled N- and C-termini in their figures, no such labeling can be seen in the revised version. Addition of such labels, at least for some of the figures, would help the user to navigate the models.

The authors have now updated figure legends to indicate which protein is referred to by the chain labels shown in PAE plots.

It is exciting to see AF-multimer predictions being applied to cohesin. As some of the reported interactions are not universally conserved and some involve relatively small interfaces the possibility arises that these interfaces show poor or borderline confidence scores. As some of these interfaces map to mutants that have previously been obtained by hypothesis-free genetic screens and mutational analyses, they appear nevertheless valid. Thus, an important point to make is that even interfaces that show modest confidence scores may turn out to be valid while others may be not.

Reviewer #1 (Public Review):

Overall, the experiments are well-designed and the results of the study are exciting. We have one major concern, as well as a few minor comments that are detailed in the following.

Major:<br /> 1. The authors suggest that "Visuomotor experience induces functional and structural plasticity of chandelier cells". One puzzling thing here, however, is that mice constantly experience visuomotor coupling throughout life which is not different from experience in the virtual tunnel. Why do the authors think that the coupled experience in the VR induces stronger experience-dependent changes than the coupled experience in the home cage? Could this be a time-dependent effect (e.g. arousal levels could systematically decrease with the number of head-fixed VR sessions)? The control experiment here would be to have a group of mice that experience similar visual flow without coupling between movement and visual flow feedback. Either change would be experience-dependent of course, but having the "visuomotor experience dependent" in the title might be a bit strong given the lack of control for that. We would suggest changing the pitch of the manuscript to one of the conclusions the authors can make cleanly (e.g. Figure 4).

Minor:<br /> 2. "ChCs shape the communication hierarchy of cortical networks providing visual and contextual information." We are not sure what this means.

3. "respond to locomotion and visuomotor mismatch, indicating arousal-related activity" This is not clear. We think we understand what the authors mean but would suggest rephrasing.

4. 'based on morphological properties revealed that 87% (287/329) of labeled neurons were ChCs" Please specify the morphological properties used for the classification somewhere in the methods.

5. We may have missed this - in the patch clamp experiment (Fig.1 H-K), please add information about how many mice/slices these experiments were performed in.

6. "These findings suggest that the rabies-labeled L1-4 neurons providing monosynaptic input to ChCs are predominantly inhibitory neurons". We are not sure this conclusion is warranted given the sparse set of neurons labelled and the low number of cells recorded in the paired patch experiment. We would suggest properly testing (e.g. stain for GABA on the rabies data) or rephrasing.

7. Figure 2E. A direct comparison of dF/F across different cell types can be subject to a problematic interpretation. The transfer function from spikes to calcium can be different from cell type to cell type. Additionally, the two cell populations have been marked with different constructs (despite the fact that it's the same GECI) further reducing the reliability of dF/F comparisons. We would recommend using a different representation here that does not rely on a direct comparison of dF/F responses (e.g. like the "response strength" used in Figure 3B). Assuming calcium dynamics are different in ChCs and PyCs - this similarity in calcium response is likely a coincidence.

8. If ChCs are more strongly driven by locomotion and arousal, then it's a bit counterintuitive that at the beginning of the visual corridor when locomotion speed consistently increases, the activity of ChCs consistently decreases. This does not appear to be driven by suppression by visual stimuli as it is present also in the first and last 20cm of the tunnel where there are no visual stimuli. How do the authors explain this?

9. The authors mention that "ChC responses underwent sensory-evoked plasticity during the repeated visual exposure, even though the visual stimuli were different from those encountered during training in the virtual tunnel". How would this work? And would this mean all visual responses are reduced? What is special about the visual experience in the virtual tunnel? It does not inherently differ from visual experience in the home cage, given that the test stimuli (full field gratings) are different from both.

10. Just as a point to consider for future experiments: For the open-loop control experiments, the visual flow is constant (20cm/s) - ideally, this would be a replay of the running speed the mouse previously generated to match statistics.

11. We would recommend specifying the parameters used for neuropil correction in the methods section.

12. If we understand correctly, the F0 used for the dF/F calculation is different from that used for division. Why is this?

13. Authors compare neuronal responses using "baseline-corrected average". Please specify the parameters of the baseline correction (i.e. what is used as baseline here).

The grandfather wants the writer to immortalize the Howlands because he never achieved greatness as a writer himself and lost most of his family's history to the "carpetbaggers."

ბლოგი,,მთის მიგრაცია შვეიცარიასა და საქართველოში"მომზადებულია ციურიხის უნივერსიტეტისა და თბილისის სახელმწიფო უნივერსიტეტის სტუდენტების:ხატია გორგიშელის,თიმონ დუფნერისა და ანნიკა სპაარის კოლაბორაციით. აუცილებელია აღვნიშნით,რომ განსახილველი სტატია გამოქვეყნდა 2022 წელს და მასში მკვეთრად იკვეთება პრობლემა,კერძოდ, მიგრაცია ისეთ ქვეყნებში,როგორიცაა საქართველო და შვეიცარია. ბლოგის გაცნობის შედეგად,თამამად შეიძლება ითქვას,რომ მთიანი რეგიონებიდან მიგრაცია-არის ჩვეულებრივი და მეტად ბუნებრივი მოვლენა. მოგეხსენებათ,რომ შვეიცარია და საქართველო სრულიად განსხვავდება როგორც პოლიტიკური,ასევე კულტურულ ასპექტში. როგორც ბლოგიდან ირკვევა,ქვეყნებში მიგრაცია მე-17 საუკუნიდან იწყება,გამომწვევი მიზეზი ძალიან მასშტაბურია. მთიან რეგიონში მყოფი საზოგადოება საცხოვრებლად დაბლობს და ეკონომიკურად სტაბილურ ადგილს ირჩევს.ფაქტია,რომ ორივე ქყვეყანა მთიანი რეგიონია,მთაში მრავალი პრობლემა იკვეთება და შესაბამისად,ადამიანის მოთხოვნასთან ერთად აუცილებელია დაკმაყოფილებული იქნას ისეთი საჭიროებები,როგორიცაა:განათლების მიღება,სამუშაო სივრცეების არსებობა და ა.შ. ფაქტია,რომ აღნიშნული პრობლემის აღმოსაფხვრელად ახალგაზრდები მიგრაციას ამჯობინებენ. ერთ-ერთი მთავარი პუნქტი როგორც საქართველოში,ასევე შვეიცარიაში ახალგაზრდების რესურსის დაკარგვა/აუთვისებლობაა. ისინი ტოვებენ მუდმივ საცხოვრებელს და გადადიან იქ,სადაც მეტი შესაძლებლობა ექნებათ ფორმალური თუ არაფორმალური განათლების მისაღებად.მთიანი რეგიონი კი კარგავს ადამიანებს,რომლებიც მომავლისთვის,განვითარებისა და არსებობისთვის ყველაზე მეტად სჭირდება. აუცილებელია აღვნიშნოთ,რომ ახალგაზრდების მიგრაცია პრობლემას წარმოშობს როგორც სოფლებსა და რაიონებში,ასევე ქალაქში.შესაბამისად,პრობლემის აღმოსაფხვრელად აუცილებელია გადაწყვეტილებების მიღება მთიან რეგიონებში,რაც მოიცავს როგორც სამუშაო გარემოს შექმნას ,ასევე განათლებისთვის საჭირო რესურსების მოძიებასა და გამოყენებას ახალგაზრდების სასარგებლოდ.

It's hard to not take her literally. It feels like she is burdened. However with the talk of 'sunlight', there seems to be hope. I want to believe so.

I enjoy how the author's voice is evident throughout the article. It's a sort of "dark humor" situation that makes the article more entertaining. Using the word "shitty" in the title also gives insight to their writing style or voice.

Going through the process of writing multiple drafts each round helps you clean up your piece, but the first draft is crucial to get all the ideas and thoughts that are rambling in your head out so you have somewhere to start.

Beat 1: Beaudricourt - to resolve the issue of Joan annoying his guards in order to restore status quo. He not only wants to do away with Joan, he also is very curious of why she has gone to all the trouble of bothering his guards for three days.

Joan - to gain an audience with Beaudricourt in order to obtain a horse, men's armor, and an escort to Chinon. Joan believes the sire is the only one who will grant her requests and give her the mount, supplies, and assistance she needs, because the Archangel told her he would.

CC: Trigger: Beaudricourt explains to Joan how most young girls get what they want from him.

Heap: Joan, in her innocence, does not realize what Beaudricourt is saying, and tells him she was sent by an angel.

Information about the people who posted the viral video that is used as the example for this argument, they are a popular group that posts many different clips that are likely to be popular but are also staged for entertainment.

Author Response

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

We appreciate very much the comments and suggestions on our manuscript "Cylicins are a structural component of the sperm calyx being indispensable for male fertility in mice and human". According to the comments, we performed a series of further experiments, re-worded and re-wrote several paragraphs and re-structured the manuscript according to the reviewers’ comment. We think that the manuscript is now improved and are looking forward to the further evaluations. We provide a point by point response to all comments and have prepared a version.

Recommendations for the authors:

Editor’s comment:

1) As pointed out by all three reviewers, it is critical to show the specificity of the antibodies used. The authors should clarify how the specificity of antibodies is tested. Western blot analysis to show the absence of the protein in the knockout is essential.

As suggested by all reviewers, we additionally performed Western Blot analysis on cytoskeletal protein fractions to further verify the specificity of generated antibodies and the generation of functional knockout alleles. Results nicely confirm the results of the IF staining, however, both anti-bodies detected the bands lower than the predicted molecular weight. In addition, Mass Spectrometry was performed to search for the presence of peptides in the cytoskeletal protein fractions. The paragraph reads now as follows:

Line 127-134: Additionally, Western Blot analyses confirmed the absence of CYLC1 and CYLC2 in cytoskeletal protein fractions of the respective knockout (Fig. 1 G), thereby demonstrating i) specificity of the antibodies and ii) validating the gene knockout. Of note, the CYLC1 antibody detects a double band at 40-45 KDa. This is smaller than the predicted size of 74 KDa as, but both bands were absent in Cylc1-/y. Similarly, the CYLC2 Antibody detected a double band at 38-40 KDa instead of 66 KDa. Again, both bands were absent in Cylc2-/-. Next, Mass spectrometry analysis of cytoskeletal protein fraction of mature spermatozoa was performed detecting both proteins in WT but not in the respective knockout samples (Figure 1 – supplement 5; Figure 1 – supplement 6).

Specificity of antibodies was additionally proven by immunohistochemical staining, showing a specific staining only in testis sections but not in any other organ tested. The section reads now as follows:

Line 115-117: Specificity of antibodies was proven by immunohistochemical stainings (IHC), showing a specific signal in testis sections only, but not in any other organ tested (Figure 1 – supplement 2)

2) Re-structuring/streamlining of the figures is recommended. Please consider the flow suggested by reviewer #2 and shorten the evolutionary analysis which takes up more space than it adds to the value of the story.

We thank the reviewers and editor for the valuable suggestion. We re-structured the figures as suggested and rewrote the results section accordingly. The evolutionary analysis was significantly shortened.

3) Provide statistics for the imaging analysis such as TEM as only a single representative image is shown.

We agree that the observed morphological defects require a detailed statistical evaluation. TEM analysis was performed to confirm the results from optical microscopy and representative images with high magnification are shown for a detailed visualization of the defects. For additional quantification, we included statistics for IF stainings against calyx proteins CCIN and CapZa (Fig. 2 I-J). For TEM, we added additional images to the supplement (Figure 3 – supplement 1). Furthermore, we quantified the manchette length of step 10-13 spermatids to prove the increased elongation of the manchette in Cylc2-/- and Cylc1-/y Cylc2-/- spermatids (Fig. 5 A-B).

4) Please consider other points raised by the reviewers below to improve the manuscript and provide responses on how the authors have addressed them.

We thank all reviewers for the detailed review of our manuscript and their valuable suggestions, which helped a lot to improve the manuscript. We considered all points raised by the reviewers to the best of our knowledge and hope that the reviewers will approve the manuscript ready for publication. We added a point-by-point discussion of all comments/suggestions hereafter.

Reviewer #1 (Recommendations For The Authors):

Major comments:

(1) Antibody specificity: Fig 1E - there are some unspecific binding in Cylc2-/- for CYLC2 and in Cylc1/y Cylc2+/- for CYLC1 in the testis (and elongating spermatids in Figure 1 – Supplement 4). Could authors elaborate/comment on this? Western blot analysis would be also helpful to further support the antibody specificity.

The very weak unspecific staining in the testis for CYLC2 (in Cylc2-/-) and CYLC1 (in Cylc1-/y Cylc2+/-) is only present in the lumen of the seminiferous tubules and/or the residual bodies of the testicular sperm cells and can be referred to as background signal. Importantly, the signal is entirely lost in the PT region, proving specificity of the generated antibodies. We added the following paragraph to the results section:

Line 124-127: The generated antibodies did not stain testicular tissue and mature sperm of Cylc1- and Cylc2-deficient males, except for a very weak unspecific background staining in the lumen of seminiferous tubules and the residual bodies of testicular sperm (Fig. 1 F).

Specificity of antibodies was additionally proven by immunohistochemical staining, showing a specific staining only in testis sections but not in any other organ tested.

Line 115-117: Specificity of antibodies was proven by immunohistochemical stainings, showing a specific staining in testis sections only, but not in any other organ tested (Figure 1 – supplement 2)

To further verify the specificity of generated antibodies and the generation of functional knockout alleles, we additionally performed Western Blot analysis on cytoskeletal protein fractions, confirming the results of the IF staining. No unspecific bands were detected in the Western Blot, further supporting the notion that the weak unspecific signals in IF resemble staining artifacts.

The paragraph reads now as follows:

Line 127-132: Additionally, Western Blot analyses confirmed the absence of CYLC1 and CYLC2 in cytoskeletal protein fractions of the respective knockout (Fig. 1 G), thereby demonstrating i) specificity of the antibodies and ii) validating the gene knockout. Of note, the CYLC1 antibody detects a double band at 40-45 KDa. This is smaller than the predicted size of 74 KDa as, but both bands were absent in Cylc1-/y. Similarly, the CYLC2 Antibody detected a double band at 38-40 KDa instead of 66 KDa. Again, both bands were absent in Cylc2-/-.

(2) Please provide more interpretation of the gene dosage effect of Cylicin 2. It is not common to see a gene dosage effect in the sperm phenotype as transcripts and proteins can be shared between haploids due to syncytium formation during spermatogenesis.

We agree and we apologize for the misinterpretation. In Cylc2+/- mice expression of Cylc2 was reduced by half but there was no altered phenotype observed. The sentence now reads as follows:

Line 112: In Cylc2+/- animals expression of Cylc2 was reduced by 50 %.

(3) Line 194-196 - the authors say that the sperm are smaller, with shorter hooks and increased circularity of the nuclei, and reduced elongation. Are these statistically significant? There seems to be a high variation in the graph in S2D and the statistical analysis is not given.

We agree, performed statistical analyses, and highlighted significantly altered values for sperm head elongation and circularity in Figure 2 – Supplement 3.

(4) Line 153-164 It is interesting that the absence of Cylc2 affected many parts of sperm structure. I think some ratios of sperm always have a morphological defect in diverse ways, so it is hard to confirm the finding only with a single sperm image. I think that it will be important to do some statistical analysis or at the minimum show more TEM images from TEM.

We agree that the observed morphological defects require a detailed statistical evaluation. TEM analysis was performed to confirm the results from optical microscopy and representative images with high magnification are shown for a detailed visualization of the defects. For additional quantification, we included statistics for IF stainings against calyx proteins CCIN and CapZa (Fig. 2 I-J). For TEM, we added additional images to the supplement (Figure 3 – Supplement 1).

(5) Line 236-242 - I believe that the phenotype described applies to the sperm from Cylc2-/- and Cylc1/y Cylc2-/- animals; however, I think that the Cylc1-/y Cylc2+/- has a more subtle, intermediate phenotype between the WT and the genotypes missing both Cylc-/- alleles.

We agree and we added a quantification of manchette length at step 10-13 to visualize the differences between the genotypes. The section reads now as follows: Line 268-272: Manchette length was measured starting from step 10 until step 13 spermatids and the mean was obtained, showing that the average manchette length was 76-80 nm in wildtype, Cylc1-/Y and Cylc2+/- while for Cylc2-/- and Cylc1-/Y Cylc2-/- spermatids mean manchette length reached 100 nm (Fig. 5 B). Cylc1-/Y Cylc2+/- spermatids displayed an intermediate phenotype with a mean manchette length of 86 nm.

(6) Since CYLC1 staining is absent in Fig 5B, does that mean that the mutation resulted in protein degradation/instability? Is RNA present? Additional biochemical studies of Cyclins demonstrating the deleterious nature of the mutations would strengthen the molecular pathogenesis of the human mutations.

Thank you for raising these important questions. The CYLC1 variant c.1720G>C is predicted to cause the amino acid substitution p.(Glu574Gln). It is, thus, conceivable that the RNA is present but either the protein is degraded or misfolded and, therefore, not detectable by IF. Unfortunately, for personal reasons of the patient, it is currently not possible to receive additional semen samples, preventing additional analyses of the semen, e.g. analysis of Cylicin transcript level.

(7) Strongly suggest shortening the evolutionary analysis - all the corresponding materials are in supplemental while texts are extensive- or even consider entirely omitting. It does not add a lot to the current study.

We agree that the evolutionary analysis was very detailed. However, we think that the results are important to understand the role of Cylicins for male reproduction in general. The results obtained from the mouse model might be transferable to other species, including humans. Further, the results present a possible explanation for the subfertility of Cylc1-deficient mice, in contrast to infertility of Cylc2-deficient males. We shortened the section, the paragraph reads as follows:

Line 287-302: To address why Cylc2 deficiency causes more severe phenotypic alterations than Cylc1deficiency in mice, we performed evolutionary analysis of both genes. Analysis of the selective constrains on Cylc1 and Cylc2 across rodents and primates revealed that both genes’ coding sequences are conserved in general, although conservation is weaker in Cylc1 trending towards a more relaxed constraint (Fig. 6). A model allowing for separate calculation of the evolutionary rate for primates and rodents, did not detect a significant difference between the clades, neither for Cylc1 nor for Cylc2, indicating that the sequences are equally conserved in both clades.

To analyze the selective pressure across the coding sequence in more detail, we calculated the evolutionary rates for each codon site across the whole tree. According to the analysis, 34% of codon sites were conserved, 51% under relaxed selective constraint, and 15% positively selected. For Cylc2, 47% of codon sites conserved, 44% under neutral/relaxed constraint, and 9% positively selected. Interestingly, codon sites encoding lysine residues, which are proposed to be of functional importance for Cylicins, are mostly conserved. For Cylc1, 17% of lysine residues are significantly conserved and 35% of significantly conserved codons encode for lysine. For Cylc2, this pattern is even more pronounced with 27.9% of lysine codons being significantly conserved and 24.3% of all conserved sites encoding for lysine (Fig. 6).

Minor comments:

(1) Line 114, 115, 118 à Figure 1D is already well-described in the previous paragraph and thus redundant. Ref Fig 1D, E; but only figure E shows IF. Maybe supposed to be E and F or just 1E?

We apologize for the mix-up with the subfigures. The mentioned paragraph refers to Fig. 1 E-F, which was corrected accordingly.

Line 117-123: Immunofluorescence staining of wildtype testicular tissue showed presence of both, CYLC1 and CYLC2 from the round spermatid stage onward (Fig. 1 E). The signal was first detectable in the subacrosomal region as a cap-like structure, lining the developing acrosome (Fig. 1 E-F, Figure 1 – supplement 3). As the spermatids elongate, CYLC1 and CYLC2 move across the PT towards the caudal part of the cell (Figure 1 – supplement 4). At later steps of spermiogenesis, the localization in the subacrosomal part of the PT faded, while it intensified in the postacrosomal calyx region (Fig. 1 E-F).

(2) Figure 1F - Arguably, IF images show expression of both CYLC1 and CYLC2 to reach/include the acrosome/hook portion of the sperm head, but the diagram does not reflect that. Why is that?

We agree and apologize for the inconsistency. The illustration was adjusted according to the experimental data showing localization of Cylicins in the whole ventral part of the sperm.

(3) Line 124 - PAS staining mentioned on line 124, is not explained (Periodic acid Schiff staining) until line 605

We agree and introduced the abbreviation accordingly. The PAS staining was moved to Fig. 4. The paragraph reads now as follows:

Line 220-222: To study the origin of observed structural sperm defects, spermiogenesis of Cylicin deficient males was analyzed in detail. PNA lectin staining and Periodic Acid Schiff (PAS) staining of testicular tissue sections were performed to investigate acrosome biogenesis.

(4) Some figures are hard to read due to being very small (S1B, 3F).

We agree and we increased the figure size. For former Figure 3F (now figure 4A), insets with higher magnification of representative sperm were added. Insets are additionally shown in Figure 4 – Supplement 1 in higher resolution.

(5) Line 139 Please specify whether the sperm was capacitated or not.

Analysis of the flagellar beat was performed with non-capacitated sperm. We clarified this in the main text:

Line 203: The SpermQ software was used to analyze the flagellar beat of non-capacitated Cylc2-/- sperm in detail 22.

As described in the Material and Methods section, sperm were only activated in TYH medium, prior to analysis:

Line 732-733: Sperm samples were diluted in TYH buffer shortly before insertion of the suspension into the observation chamber.

(6) Line 142-145; The sentence is interrupted strangely, perhaps the authors meant to write: "Interestingly, we observed that the flagellar beat of Cylc2-/- sperm cells was similar to wildtype cells, however, with interruptions during which midpiece and initial principal piece appeared stiff whereas high-frequency beating occurs at the flagellar tip"

We corrected the sentence accordingly.

Line 206-208: Interestingly, we observed that the flagellar beat of Cylc2-/- sperm cells was similar to wildtype cells, however, with interruptions during which midpiece and initial principal piece appeared stiff whereas high frequency beating occurs at the flagellar tip (Fig. 3 C, Video 1, Video 2).

(7) Line 142 -Wrong Figure number. Figure S4A is a phylogenic analysis.

We regret the mix up and corrected the Figure reference accordingly. Line 204-205: Cylc2-/- sperm showed stiffness in the neck and a reduced amplitude of the initial flagellar beat, as well as reduced average curvature of the flagellum during a single beat (Figure 3 – supplement 2).

(8) L146-147 Better placed in Discussion.

We agree, and we omitted this sentence from the results part.

(9) Line 154-156 - The white arrowheads are present in both WT and KO sperm, thus it appears they denote the basal plate, not necessarily the dislocation/parallel position as the current text seems to suggest. Furthermore, the position of the WT and KO sperm is somewhat different with the tail coiling differently, so it is hard to see whether the two are comparable.

We agree and we removed the white arrowhead in the WT sperm picture, and it now depicts only the dislocation of the basal plate in the Cylc2-/- sperm. Due to the morphological anomalies of Cylc2-/- sperm cells, it’s difficult to determine the exact angle of the depicted cell. However, we added more TEM pictures of the sperm cells (3 for WT and 6 for Cylc2-/-) in Figure 3 – Supplement 1.

(10) Line 164 Please describe in detail what mitochondrial damage the readers expect to see from the TEM image.

We evaluated the observed mitochondrial damage in more detail. Unfortunately, the defects described initially seem to be an artifact of apoptotic sperm cells and could not be identified in vital sperm cells in either of the knockout mouse models. We apologize for this misinterpretation, and we deleted this section in the manuscript.

(12) Figure S2A - no WT comparison, difficult to compare without it (mitochondria in Cylc2-/-)

See (10). We evaluated the observed mitochondrial damage in more detail and in comparison to WT. Unfortunately, the defects described initially seem to be an artifact of apoptotic sperm cells and could not be identified in vital sperm cells in either of the knockout mouse models. We apologize for this misinterpretation and we deleted this section in the manuscript.

(13) Line 172-173 - Fig 3C denotes quantification of abnormal acrosome only, however, the text mentions sperm coiled tail being quantified within this graph - which is it? Is it both of them? Or only one of them?

Figure 3 C (now Figure 2G) showed the percentage of abnormal sperm in general comprising acrosomal as well as flagellar defects. We modified the figure and evaluated acrosomal defects and tail defects separately. The results section was changed accordingly and reads now as follows:

Line 152-159: Loss of Cylc1 alone caused malformations of the acrosome in around 38% of mature sperm, while their flagellum appeared unaltered and properly connected to the head. Cylc2+/- males showed normal sperm tail morphology with around 30% of acrosome malformations. Cylc2-/- mature sperm cells displayed morphological alterations of head and mid-piece (Fig. 2 F-G). 76% of Cylc2-/- sperm cells showed acrosome malformations, bending of the neck region, and/or coiling of the flagellum, occasionally resulting in its wrapping around the sperm head in 80% of sperm (Fig. 2 F). While 70% of Cylc1-/Y Cylc2+/- sperm showed these morphological alterations, around 92% of Cylc1-/YCylc2-/- sperm presented with coiled tail and abnormal acrosome (Fig. 2 F-G).

(14) Fig 3D - CCIN in the text, cylicin in the figure - this should be consistent. Furthermore, since only the head is being shown, is CCIN ever detected in the WT sperm tail?

We apologize for the inconsistency, and we added the abbreviation “CCIN” to the figure. CCIN is solely detectable in the sperm head of wildtype sperm as published previously. Irregular staining patterns showing signals in the tail region are only observed upon Cylicin deficiency.

(15) Line 199-200 - To say that head of Cylc2-deficient sperm appears less concave seems redundant, likely the observed increased circularity is contributed to by sperm head being less concave in this region; unless there is an extra point that the authors are trying to make and if so, this needs to be elaborated on

We agree and we deleted the sentence from the manuscript.

(16) Figure legend of Fig S3 is wrong. Only S3A and S3B are present, and in the figure legend S3C corresponds to figure S3B.

We agree and corrected the Figure legends accordingly. Due to the re-structuring of the manuscript, Figures and Supplementary figures were re-ordered as well.

(17) Figure 4B - figure legend and/or text should specify that lectin is green and HOOK1 is in red

We specified the figure legend as well as the main text accordingly: Line: 279-281: Co-staining of the spermatids with antibodies against PNA lectin (green) and HOOK1 (red) revealed that abnormal manchette elongation and acrosome anomalies simultaneously occurred in elongating spermatids of Cylc2-/- male mice (Fig. 5 C).

Line: 560-562: Co-staining of the manchette with HOOK1 (red) and acrosome with PNA-lectin (green) is shown in round, elongating and elongated spermatids of WT (upper panel) and Cylc2-/- mice (lower panel).

(18) Line 261-263 - It is difficult to see what is going on with microtubules in these images, as the resolution is low

We increased the pictures and improved their quality. Microtubules are also depicted with letter ‘m’

(19) Line 265-266 - It seems that there is a persistence of manchette, rather than elongation. From these images, I cannot see gaps, and I am not sure where to look for them. This needs to be labelled further and higher-resolution images could be included for clarity.

We agree, although we observed both excessive elongation and persistence of the manchette. The average length of the manchette is now shown in figure 5B.

The paragraph now reads as follows:

Line 235-239: Microtubules appeared longer on one side of the nucleus than on the other, displacing the acrosome to the side and creating a gap in the PT (Fig. 4 C). Whereas elongated spermatids at step 14-15 in wildtype sperm already disassembled their manchette and the PT appeared as a unique structure that compactly surrounds nucleus, in Cylc2-/- spermatids, remaining microtubules failed to disassemble.

The gaps in the perinuclear theca are better visible in TEM micrographs and the description is now in the paragraph describing TEM.

(20) Line 269 Please include the information of "White arrowhead".

We added the information accordingly.

Line 240-242: In addition, at step 16, the calyx was absent, and an excess of cytoplasm surrounded the nucleus and flagellum (Fig. 4 C, white arrowhead).

(21) Line 276-280 This should be placed in the Discussion.

We agree, and we deleted this concluding remark from the results section.

(22) Is Cylc1 and/or Cylc2 conserved/expressed amongst species other than rodents and primates?

Yes, Cylc1 and Cylc2 homologs were identified in C. elegans for example. We added a schematic to the introduction showing the protein structure of human, mouse and C. elegans CYLC1 and CYLC2 (Figure 1 – supplement 1).

The section reads now as follows:

Line 73-78: In most species, two Cylicin genes, Cylc1 and Cylc2, have been identified (Figure 1- supplement 1). They are characterized by repetitive lysine-lysine-aspartic acid (KKD) and lysine-lysine-glutamic acid (KKE) peptide motifs, resulting in an isoelectric point (IEP) > pH 10 14, 15. Repeating units of up to 41 amino acids in the central part of the molecules stand out by a predicted tendency to form individual short α-helices 14. Mammalian Cylicins exhibit similar protein and domain characteristics, but CYLC2 has a much shorter amino-terminal portion than CYLC1 (Figure 1-supplement 1).

(23) The whole chapter "Cylc2 coding sequence is slightly more conserved among species than Cylc1" references only supplemental figures/tables. I find this unusual.

We agree, and in order to show the results of the evolutionary analysis more clearly, we moved the panel to main Figure 6.

Line 286-302: To address why Cylc2 deficiency causes more severe phenotypic alterations than Cylc1deficiency in mice, we performed evolutionary analysis of both genes. Analysis of the selective constrains on Cylc1 and Cylc2 across rodents and primates revealed that both genes’ coding sequences are conserved in general, although conservation is weaker in Cylc1 trending towards a more relaxed constraint (Fig. 6 A). A model allowing for separate calculation of the evolutionary rate for primates and rodents, did not detect a significant difference between the clades, neither for Cylc1 nor for Cylc2, indicating that the sequences are equally conserved in both clades.

To analyze the selective pressure across the coding sequence in more detail, we calculated the evolutionary rates for each codon site across the whole tree. According to the analysis, 34% of codon sites were conserved, 51% under relaxed selective constraint, and 15% positively selected. For Cylc2, 47% of codon sites conserved, 44% under neutral/relaxed constraint, and 9% positively selected. Interestingly, codon sites encoding lysine residues, which are proposed to be of functional importance for Cylicins, are mostly conserved. For Cylc1, 17% of lysine residues are significantly conserved and 35% of significantly conserved codons encode for lysine. For Cylc2, this pattern is even more pronounced with 27.9% of lysine codons being significantly conserved and 24.3% of all conserved sites encoding for lysine (Fig. 6 B).

(24) Line 332 - CYCL2 should be CYLC2

We corrected the typo accordingly.

(25) Line 340 The ratio of head defects is different from Table 1 (98% here and 99 % in the table). Please check this information.

We apologize for the inconsistency. We checked the raw data and corrected the table accordingly.

(26) Line 344-345 From figure 5C it is difficult to determine whether the sperm are "headless" or whether the heads are attached to the highly coiled tails next to them

We agree and we quantified the percentage of sperm showing abnormal flagella and a headless phenotype. Furthermore, we added an arrowhead to figure 6C to highlight headless sperm. The paragraph reads now as follows:

Line 335-339: Bright field microscopy demonstrated that M2270’s sperm flagella coiled in a similar manner compared to flagella of sperm from Cylicin deficient mice. Quantification revealed 57% of M2270 sperm displaying abnormal flagella compared to 6% in the healthy donor (Fig. 7 D). Interestingly, DAPI staining revealed that 27% of M2270 flagella carry cytoplasmatic bodies without nuclei and could be defined as headless spermatozoa (Fig. 7 C, white arrowhead; Fig. 7 E).

(27) L367-368 I agree with the authors' logic of this sentence. Although, it is better to show the co-localization of proteins using multi-channel immunocytochemistry. As you mentioned on L369 this will make your finding more obvious. If it is available, please include the colocalization images of the proteins.

We performed the multi-channel staining against Cylicin1 and Calicin, as well as Cylicin2 and Calicin on mouse epipidymal sperm and it’s shown in Figure 2 – supplement 4. Unfortunately, we did not manage to obtain stainings of tissue sections since antibodies against Cylicins and Calicin require different sample processing.

The sentence was added in the section describing calyx integrity:

Line 168-169: In epididymal sperm, CCIN co-localizes with both CYLC1 and CYLC2 in the calyx (Figure 2 – supplement 4).

(28) Line 376 Please keep the abbreviation. "Calicin" "CCIN".

We included the abbreviation accordingly.

Line 377-378: CCIN is shown to be necessary for the IAM-PT-NE complex by establishing bidirectional connections with other PT proteins.

(29) Line 377-378 "Based on ~". The authors did not prove the interaction between CCIN and Cylicins in this article. The mislocalization of CCIN might be resulted in the loss of Cylicins, without any "interaction". To reach this conclusion, a more direct result should be provided.

We agree that we overinterpreted this as we and others did not prove the interaction between CCIN and Cylicins so far. We therefore weakened this statement and formulated it as a hypothesis.

Line 377-381: CCIN is shown to be necessary for the IAM-PT-NE complex by establishing bidirectional connections with other PT proteins. Zhang et al. found CYLC1 to be among proteins enriched in PT fraction 7. Based on their speculation that CCIN is the main organizer of the PT, we hypothesize that both CCIN and Cylicins might interact, either directly or in a complex with other proteins, in order to provide the ‘molecular glue’ necessary for the acrosome anchoring.

(30) Line 499 Please specify which is the target of the immunostaining on the Figure legend. (Tubulin à acetylated α-tubulin)

We specified that α-Tubulin was stained. The figure legend reads now as follow: Line 555-557: Immunofluorescence staining of α-Tubulin to visualize manchette structure in squash testis samples of WT, Cylc1-/y, Cylc2+/-, Cylc2-/-, Cylc1 -/y Cylc2+/- and Cylc1-/y Cylc2-/- mice.

(31) Line 502 Please specify which color indicates which target protein (not only cellular structure).

Line 560-562: Co-staining of the manchette with HOOK1 (red) and acrosome with PNA-lectin (green) is shown in round, elongating and elongated spermatids of WT (upper panel) and Cylc2-/- mice (lower panel).

(32) Line 509 Please include scale bar information in the figure legend like Figure 4 (The magnifications of Figure 5 B, C, and D seem different).

We included the scale bar information accordingly (now Figure 6).

Line 575-588: Figure 6: Cylicins are required for human male fertility

(A) Pedigree of patient M2270. His father (M2270_F) is carrier of the heterozygous CYLC2 variant c.551G>A and his mother (M2270_M) carries the X-linked CYLC1 variant c.1720G>C in a heterozygous state. Asterisks (*) indicate the location of the variants in CYLC1 and CYLC2 within the electropherograms.

(B) Immunofluorescence staining of CYLC1 in spermatozoa from healthy donor and patient M2270. In donor’s sperm cells CYLC1 localizes in the calyx, while patient’s sperm cells are completely missing the signal. Scale bar: 5 µm.

(C) Bright field images of the spermatozoa from healthy donor and M2270 show visible head and tail anomalies, coiling of the flagellum as well as headless spermatozoa who carry cytoplasmatic residues without nuclei. Heads were counterstained with DAPI. Scale bar: 5 µm.

(D-E) Quantification of flagellum integrity (D) and headless sperm (E) in the semen of patient M2270 and a helathy donor.

(F-G) Immunofluorescence staining of CCIN (F) and PLCz (G) in sperm cells of patient M2270 and a healthy donor. Nuclei were counterstained with DAPI. Scale bar: 3 µm.

(33) S2A is not clear. Please describe specifically what the left panel and right panel are about to show with a clear indication of what is PM, mitochondria, etc. On the right, in only one cross-section that shows both mitochondria and the 9+2 axoneme, they look both unaltered whereas on the left, there are unpacked, not aligned mitochondria but the tail boundary is not clear to grasp at first sight.

We apologize for the bad quality of the TEM pictures showing the axonemes and the missing labeling. We recorded and included new images showing an intact 9+2 microtubular structure in Cylc2-/-. Furthermore, we added an image for the wildtype control.

(34) S2D: colors of the last three plots of each graph are too close to tell apart

We agree and changed the color scheme for better visualization.

Reviewer #2 (Recommendations For The Authors):

However, I find the manuscript a bit messy, and I will propose to reorganize the figures: following figure 1, showing the reproductive phenotype, I would continue with a figure showing the morphology of sperm in optical microscopy and showing the morphological defect of the nucleus (Fig 3B and 3E), followed with one figure focusing on the flagellum, with images obtained with optical and electronic microscopies, allowing to present the abnormalities of the flagellum and finally the impact on sperm motility and flagellum beating (mix of figure 2FG/3A); next, one figure focusing on acrosome. After that, I would present all data concerning spermiogenesis, starting with figure 2C.

We thank the reviewer for the valuable suggestion, which helps a lot to improve the structure and comprehensibility of the manuscript. We re-organized the figures and the results section accordingly.

Major remarks

1) Line 111. The specificity of raised Ab is not clear. Please specify if antibodies are specific: what immune-decorates anti-CYLC1: only CYLC1 or CYLC1 and CYLC2. Same question for anti-CYLC2

Both antibodies were raised against specific peptides of the CYLC1 or CYLC2 protein, respectively. The antigen peptides used for immunization are depicted in the Material and Methods section (AESRKSKNDERRKTLKIKFRGK and KDAKKEGKKKGKRESRKKR peptides for CYLC1; KSVGTHKSLASEKTKKEVK and ESGGEKAGSKKEAKDDKKDA for CYLC2). The peptides used for immunization are specific as they do not resemble the highly conserved and repetitive KKD/KKE motives present in both, Cylc1 and Cylc2.

The specificity of raised antibodies was validated by IF staining of wildype and Cylicin-deficient testis sections. The results clearly show, that CYLC1 signal is absent in Cylc1-deficient spermatids and CYLC2 signal being absent in Cylc2 deficient spermatids.

Specificity of antibodies was additionally proven by immunohistochemical stainings, showing a specific staining only in testis sections but not in any other organ tested.

Line 115-117: Specificity of antibodies was proven by immunohistochemical stainings, showing a specific staining only in testis sections but not in any other organ tested (Figure 1 - supplement 2)

To further verify the specificity of generated antibodies and the generation of functional knockout alleles, we additionally performed Western Blot analysis on cytoskeletal protein fractions, confirming the results of the IF staining.

The paragraph reads now as follows:

Line 127-134: Additionally, Western Blot analyses confirmed the absence of CYLC1 and CYLC2 in cytoskeletal protein fractions of the respective knockout (Fig. 1 G), thereby demonstrating i) specificity of the antibodies and ii) validating the gene knockout. Of note, the CYLC1 antibody detects a double band at 40-45 KDa. This is smaller than the predicted size of 74 KDa as, but both bands were absent in Cylc1-/y. Similarly, the CYLC2 Antibody detected a double band at 38-40 KDa instead of 66 KDa. Again, both bands were absent in Cylc2-/-. Next, Mass spectrometry analysis of cytoskeletal protein fraction of mature spermatozoa was performed detecting both proteins in WT but not in the respective knockout samples (Figure 1 – supplement 5; Figure 1 – supplement 6).

2) Line 115 and figure 1D. From the images presented in figure 1D, it is not clear where CYLC1 and CYLC2 are localized in the round and in elongated spermatids. Please make double staining using a second Ab to identify the acrosome such as DPY19L2 (best option) or SP56 and the manchette such as acetylated alpha-tubulin.

We agree, and we added a double staining of CYLC1/CYLC2 and SP56 to the supplement (Figure 1 - supplement 3), showing co-localization of the developing acrosome and Cylicins. Manchette staining was not performed due to antibodies being available in same species as those against Cylicins (anti-rabbit).

Line 117-120: Immunofluorescence staining of wildtype testicular tissue showed presence of both, CYLC1 and CYLC2 from the round spermatid stage onward (Fig. 1 E, Figure 1 – supplement 3). The signal was first detectable in the subacrosomal region as a cap like structure, lining the developing acrosome (Fig. 1 E-F, Figure 1 – supplement 3).

3) Line 118 and figure 1. The drawing showing the localization of Cylicin in mature sperm does not fit with the experimental data. Cylicins are located on the whole ventral face of the sperm.

We agree and apologize for the inconsistency. The illustration was adjusted according to the experimental data showing localization of Cylicins in the whole ventral part of the sperm.

4) Figure 1: Change "expression of Cylicin" to "localization of cylicin" (green)

We changed the legend accordingly.

5) Line 124 and figure 2C. In the figure provided, the PAS staining seems defective. The acrosomes do not seem stained (in pink as expected for a PAS staining). It may be due to the low quality of the pdf file, nevertheless, it is important to provide in supplementary data, an enlargement of the spermatid region showing the staining of the acrosome.

We apologize for the bad quality of the PDF file and the low magnification. We restructured the subfigure showing PAS stained spermatids at different steps of spermiogenesis at higher magnification. According to the initial reviewer’s suggestion, the PAS staining was moved to figure 4. The PAS staining in figure 2 was replaced by HE-stained overview testis sections in Figure 3 – supplement 1 showing intact spermatogenesis in all genotypes.

6) Line 130. Please indicate a reference for the lower limit of 58%. If this lower limit corresponds to human sperm, it should be omitted.

Indeed, the given reference limit of 58% is only valid for human sperm samples. Therefore, we omitted the reference limit. The paragraph reads now as follows: Line 144-146: Eosin-Nigrosin staining revealed that the viability of epididymal sperm from all genotypes was not severely affected (Fig. 2 D, Figure 2 – supplement 2).

7) line 152 Sperm morphology. Before showing the ultrastructure of the sperm, it would be important to show sperm morphology observed by optical microscopy. Therefore, I recommend including figure S2 as a principal figure, with a mix of Figures 3B and 3E.

We thank the reviewer for the suggestion. The results section was re-structured accordingly, first showing results of optical microscopy (Fig. 2), followed by an in-depth ultrastructural investigation of morphological defects and their effects on sperm motility. Brightfield images of epididymal sperm were moved from former Figure S2 to main Figure 2.

8) Line 164. figure S2A, showing that the 9+2 pattern is normal in KO sperm, is not convincing. Enlargement is required to conclude that the axoneme structure is normal; from the pictures, it rather seems that some doublets are missing.

We apologize for the bad quality of the TEM pictures showing the axonemes. We recorded and included new images showing an intact 9+2 microtubular structure.

9) Line 196. I would suggest removing the term "mild globozoospermia". Globozoospermia is rather complete (100% of round sperm heads) or incomplete (<90 % of round sperm heads). The anomalies observed on sperm heads, sperm motility, and the decrease in sperm concentration are rather suggestive of an OAT.

We agree and we omitted the term “mild globozoospermia”. Instead, we added a concluding remark to the section, summarizing the described defects as OAT syndrome. The section reads now as follows:

Line 215-217: Taken together, observed anomalies of sperm heads, impaired sperm motility, and the decrease in epididymal sperm concentration show that Cylc deficiency results in a severe OAT phenotype (Oligo-Astheno-Teratozoospermia-syndrome) described in human.

10) Line 248. It is not clear from the data of figure 4B that "the developing acrosome lost its compact adherence to the nuclear envelope". From this figure, only defective morphologies of the acrosome are observed

We agree and we omitted the sentence. Furthermore, it does not add additional information to the manuscript, since defects in the attachment of the acrosome to the nuclear envelope have been described in detail in Figure 4C.

11) line 260-264. Manchette defects appear at stages 9-10. At this stage, the HTCA is already attached to the nucleus of the spermatid. see for instance figure 2 from Shang Y, Zhu F, Wang L, Ouyang YC, Dong MZ, Liu C, Zhao H, Cui X, Ma D, Zhang Z, Yang X, Guo Y, Liu F, Yuan L, Gao F, Guo X, Sun QY, Cao Y, Li W. Essential role for SUN5 in anchoring sperm head to the tail. Elife. 2017 Sep 25;6:e28199. doi: 10.7554/eLife.28199 . Therefore, the hypothesis that "abnormal attachment of the developing flagellum to the basal plate and consequently flipping of the head and coiling of the tail in mature spermatozoa" is unlikely and I suggest modifying this paragraph. In the HOOK paper, the manchette defects occurred earlier.

We read the suggested literature and we agree to this reviewer’s comment. Manchette defects that we observe appear at later stages and are probably not responsible for the morphological anomalies of the mature sperm. We also re-analyzed all the manchette staining pictures and didn’t find any defects at earlier stages, so we decided to delete the sentence from the manuscript.

12) Line 344. Please indicate a percentage of headless spermatozoa. Many sperm is too vague.

We agree and we quantified the percentage of sperm showing abnormal flagella and a headless phenotype. The paragraph reads now as follows:

Line 335-339: Bright field microscopy demonstrated that M2270’s sperm flagella coiled in a similar manner compared to flagella of sperm from Cylicin deficient mice. Quantification revealed 57% of M2270 sperm displaying abnormal flagella compared to 6% in the healthy donor (Fig. 7 D). Interestingly, DAPI staining revealed that 27% of M2270 flagella carry cytoplasmatic bodies without nuclei and could be defined as headless spermatozoa (Fig. 7 C, white arrowhead; Fig. 7 E).

13) Any data concerning the success of ICSI for this patient?

Yes, the outcome of the ICSI were added to the main text. Line 309-311: The couple underwent one ICSI procedure which resulted in 17 fertilized oocytes out of 18 retrieved. Three cryo-single embryo transfers were performed in spontaneous cycles, but no pregnancy was achieved.

14) Finally, it would be interesting to study the localization of PLCzeta in this model, since its localization in the perinuclear theca has been clearly shown (Escoffier et al, 2015 doi:10.1093/molehr/gau098 )

We thank the reviewer for the valuable suggestion and performed PLCzeta staining on human sperm, clearly showing an irregular PT staining pattern in sperm of patient M2270 compared to healthy control sperm. Of note, staining was not possible in the mouse due to the antibody being reactive only for human samples.

The section reads as follows:

Line 343-349: Testis specific phospholipase C zeta 1 (PLCζ1) is localized in the postacrosomal region of PT in mammalian sperm (Yoon and Fissore, 2007) and has a role in generating calcium (Ca²⁺) oscillations that are necessary for oocyte activation (Yoon, 2008). Staining of healthy donor’s spermatozoa showed a previously described localization of PLCζ1 in the calyx, while sperm from M2270 patient presents signal irregularly through the PT surrounding sperm heads (Fig. 7 G). These results suggest that Cylicin deficiency can cause severe disruption of PT in human sperm as well, causing male infertility.

Reviewer #3 (Recommendations For The Authors):

1) Why the Cylc1-/y Cylc2+/- males were infertile? It would be helpful to show the homologue of the two proteins;

To elaborate more on the homology of CYLC1 and CYLC2, we added a more detailed section about the protein and domain structure to the introduction.

Line 73-78: In most species, two Cylicin genes, Cylc1 and Cylc2, have been identified (Figure 1supplement 1). They are characterized by repetitive lysine-lysine-aspartic acid (KKD) and lysine-lysineglutamic acid (KKE) peptide motifs, resulting in an isoelectric point (IEP) > pH 10 14, 15. Repeating units of up to 41 amino acids in the central part of the molecules stand out by a predicted tendency to form individual short α-helices (Hess et al., 1993). Mammalian Cylicins exhibit similar protein and domain characteristics, but CYLC2 has a much shorter amino-terminal portion than CYLC1 (Figure 1supplement 1).

Speculations about the infertility of Cylc1-/y Cylc2+/- males was added to the discussion:

Line 410-413: Interestingly, Cylc1-/Y Cylc2+/- males displayed an “intermediate” phenotype, showing slightly less damaged sperm than Cylc2-/- and Cylc1-/Y Cylc2-/- animals. This further supports our notion, that loss of the less conserved Cylc1 gene might be at least partially compensated by the remaining Cylc2 allele.

2) Western blot is important to show the absence of the two proteins in the mouse models;

To further verify the specificity of generated antibodies and the generation of functional knockout alleles, we additionally performed Western Blot analysis on cytoskeletal protein fractions, confirming the results of the IF staining.

A paragraph was added to the manuscript and reads as follows:

Line 127-134: Additionally, Western Blot analyses confirmed the absence of CYLC1 and CYLC2 in cytoskeletal protein fractions of the respective knockout (Fig. 1 G), thereby demonstrating i) specificity of the antibodies and ii) validating the gene knockout. Of note, the CYLC1 antibody detects a double band at 40-45 KDa. This is smaller than the predicted size of 74 KDa as, but both bands were absent in Cylc1-/y. Similarly, the CYLC2 Antibody detected a double band at 38-40 KDa instead of 66 KDa. Again, both bands were absent in Cylc2-/-. Next, Mass spectrometry analysis of cytoskeletal protein fraction of mature spermatozoa was performed detecting both proteins in WT but not in the respective knockout samples (Figure 1 – supplement 5; Figure 1 – supplement 6).

3) On Page 7, line 227 and line 243, was the acetylated α-tubulin or α-tubulin antibody used?

For all stainings α-tubulin antibody was used. We corrected this accordingly. Line 257-259: We used immunofluorescence staining of α-tubulin on squash testis samples containing spermatids at different stages of spermiogenesis to investigate whether the altered head shape, calyx structure, and tail-head connection anomalies originate from possible defects of the manchette structure.

4) Fig. 2S: A cartoon showing the elongation and circularity of nuclei for evaluation is helpful; The TEM images from the control and Cylc1 KO mice are needed;

Cylc1-/Y TEM picture was added in Figure 3A.

5) The discussion should be rewritten. The current version is to repeat the experiments/findings. The authors should discuss more about the potential mechanisms.

We discussed the observed defects of Cylc-deficient animals and discussed this in relation to other published mouse models deficient in Calyx components. Furthermore, we speculated about potential interaction partners of Cylicins and the importance of these protein complexes for male fertility. However, to this point, we think that it is too farfetched to speculate about potential mechanisms without any evidence for Cylc interaction partner or their exact molecular function. This requires further research.

eLife assessment

This study provides valuable insights into the role of two under-researched sperm-specific proteins (Cylicin 1 and Cylicin 2). The authors provide convincing evidence that they have an essential role in sperm head structure during spermatogenesis, and that their loss leads to subfertility or infertility, with a dose-dependent phenotype. Importantly, the authors identify infertile males with mutations in both Cylicin1 and Cylicin2. Thus, the findings from the mouse models might be applicable to understanding human male infertility with similar structural defects.

Reviewer #1 (Public Review):

Mice and humans have two Cylicin genes (X-linked Cylicin 1 and the autosomal Cylicin 2) that encode cytoskeletal proteins. Cylicins are localized in the acrosomal region of round spermatids, yet they resemble a calyx component within the perinuclear theca of mature sperm nuclei. The function of Cylicins during this developmental stage of spermiogenesis (tail formation and head elongation/shaping) was not known. In this study, using CRISPR/Cas genome editing, the authors generated Cylc1-and Cylc2-knockout mouse lines to study the loss-of-function of each Cylicin or all together.

The major strengths of the study are the rigorous and comparative phenotypic analyses of all the combinatorial genotypes from the cross between the two mouse lines (Cylc1-/y, Cylc2-/-, Cylc1-/y Cylc2+/- and Cylc1-/y Cylc2-/-) at the levels of male fertility, cellular, and subcellular levels to support the conclusion of the study. While spermatogenesis appeared undisturbed, with germ cells of all types detected in the testis, low sperm counts in epididymis were observed. Mice were subfertile or infertile in a dose-dependent manner where fewer functional alleles had more severe phenotypes; the loss of Cylc2 was less tolerated than the loss of Cylc1. Thus, loss of Cylc1, and to an even greater extent, loss of Cylc2, leads to sperm structure anomalies and decreased sperm motility. Particularly, the sperm head and sperm head-neck region are affected, with calyx not forming in the absence of Cylicins, the acrosomal region being attached more loosely, and the sperm head itself appearing structurally rounder and shorter. Furthermore, manchette, which disassembles during spermiogenesis, persists in mature sperm of mice missing Cylc2. It is interesting that the study identifies a human male that has mutations in both CYLC1 and CYLC2 genes and suffers from infertility, with similar motility and sperm structure defects compared to the mouse models. CYLC1 in the sperm from the infertile patient sperm is absent, providing evidence that in both rodents and primates, Cylicins are essential for male fertility. Evolutionary analysis of two genes adds an interesting point. The authors show that the reason for the loss of Cylc2 being more severe is due to the higher conservation of Cylc2 compared to Cylc1 in rodents and primates.

Overall, the work highlights the relevance and importance of Cylicins in male infertility and advances our understanding of perinuclear theca formation during spermiogenesis.

I agree that I do indeed do not like to be duped. I am a person that likes to have control over situations even when it is most definitely out of my own personal control. Being lied to and having to accept the fact that it was false reality is always something that takes time for me to wrap my head around. On top of this, it would give me even more mental trouble to have found out that it was to fuel someone else's amusement.

I am not sure a parasocial relationship can be truly authentic from the celebrity to the viewer, since the celebrity obviously does not know the follower; however, I think some personas which the celebrity presents to their following may seem authentic because it is or because they play the role well. I also think that in some situations, personas can be both authentic and inauthentic since the definition is based off of matching reality. There is a popular saying to "never meet your heroes" because they almost never match the mental model in your head, so before there may have been authentic connection, but after meeting, the connection may be seen as inauthentic.

Author Response

We thank the reviewers for their suggestions. We are confident in the model that predicts odor vs odor (OCT-MCH) preference using calcium activity, but we acknowledge the relative weakness of the model that predicts odor (OCT) vs air preference. We are preparing an updated manuscript that will prioritize our interpretation of the OCT-MCH results and more fully document uncertainties around our estimates of prediction capacity.

Reviewer #1 (Public Review):

Summary: The authors seek to establish what aspects of nervous system structure and function may explain behavioral differences across individual fruit flies. The behavior in question is a preference for one odor or another in a choice assay. The variables related to neural function are odor responses in olfactory receptor neurons or in the second-order projection neurons, measured via calcium imaging. A different variable related to neural structure is the density of a presynaptic protein BRP. The authors measure these variables in the same fly along with the behavioral bias in the odor assays. Then they look for correlations across flies between the structure-function data and the behavior.

Strengths: Where behavioral biases originate is a question of fundamental interest in the field. In an earlier paper (Honegger 2019) this group showed that flies do vary with regard to odor preference, and that there exists neural variation in olfactory circuits, but did not connect the two in the same animal. Here they do, which is a categorical advance, and opens the door to establishing a correlation. The authors inspect many such possible correlations. The underlying experiments reflect a great deal of work, and appear to be done carefully. The reporting is clear and transparent: All the data underlying the conclusions are shown, and associated code is available online.

We are glad to hear the reviewer is supportive of the general question and approach.

Weaknesses: The results are overstated. The correlations reported here are uniformly small, and don't inspire confidence that there is any causal connection. The main problems are

We are working on a revision that overhauls the interpretations of the results. We recognize that the current version inadequately distinguishes the results that we have high confidence in (specifically, PC2 of our Ca++ data as a predictor of OCT-MCH preference) versus results that are suggestive but not definitive (such as the PC1 of Ca++ data as a predictor of Air-OCT preference).

It’s true that the correlations are small, with r2 values typically in the 0.1-0.2 range. That said, we would call it a victory if we could explain 10 to 20% of the variance of a behavior measure, captured in a 3 minute experiment, with a circuit correlate. This is particularly true because, as the reviewer notes, the behavioral measurement is noisy.

1) The target effect to be explained is itself very weak. Odor preference of a given fly varies considerably across time. The systematic bias distinguishing one fly from another is small compared to the variability. Because the neural measurements are by necessity separated in time from the behavior, this noise places serious limits on any correlation between the two.

This is broadly correct, though to quibble, it’s our measurement of odor preference which varies considerably over time. We are reasonably confident that the more variance in our measurements can be attributed to sampling error than changes to true preference over time. As evidence, the correlation in sequential measures of individual odor preference, with delays of 3 hours or 24 hours, are not obviously different. We are separately working on methodological improvements to get more precise estimates of persistent individual odor preference, using averages of multiple, spaced measurements. This is promising, but beyond the scope of this study.

2) The correlations reported here are uniformly weak and not robust. In several of the key figures, the elimination of one or two outlier flies completely abolishes the relationship. The confidence bounds on the claimed correlations are very broad. These uncertainties propagate to undermine the eventual claims for a correspondence between neural and behavioral measures.

We are broadly receptive to this criticism. The lack of robustness of some results comes from the fundamental challenge of this work: measuring behavior is noisy at the individual level. Measuring Ca++ is also somewhat noisy. Correlating the two will be underpowered unless the sample size is huge (which is impractical, as each data point requires a dissection and live imaging session) or the effect size is large (which is generally not the case in biology). In the current version we tried to in some sense to avoid discussing these challenges head-on, instead trying to focus on what we thought were the conclusions justified by our experiments with sample sizes ranging from 20 to 60. We are working on a revision that is more candid about these challenges.

That said, we believe the result we view as the most exciting — that PC2 of Ca++ responses predicts OCT-MCH preference — is robust. 1) It is based on a training set with 47 individuals and a test set composed of 22 individuals. The p-value is sufficiently low in each of these sets (0.0063 and 0.0069, respectively) to pass an overly stringent Bonferonni correction for the 5 tests (each PC) in this analysis. 2) The BRP immunohistochemistry provides independent evidence that is consistent with this result — PC2 that predicts behavior (p = 0.03 from only one test) and has loadings that contrast DC2 and DM2. Taken together, these results are well above the field-standard bar of statistical robustness.

In the revision we are working on, we are explicit that this is the (one) result we have high confidence in. We believe this result convincingly links Ca++ and behavior, and warrants spotlighting. We have less confidence in other results, and say so, and we hope this addresses concerns about overstating our results.

3) Some aspects of the statistical treatment are unusual. Typically a model is proposed for the relationship between neuronal signals and behavior, and the model predictions are correlated with the actual behavioral data. The normal practice is to train the model on part of the data and test it on another part. But here the training set at times includes the testing set, which tends to give high correlations from overfitting. Other times the testing set gives much higher correlations than the training set, and then the results from the testing set are reported. Where the authors explored many possible relationships, it is unclear whether the significance tests account for the many tested hypotheses. The main text quotes the key results without confidence limits.

Our primary analyses are exactly what the reviewer describes, scatter plots and correlations of actual behavioral measures against predicted measures. We produced test data in separate experiments, conducted weeks to months after models were fit on training data. This is more rigorous than splitting into training and test sets data collected in a single session, as batch/environmental effects reduce the independence of data collected within a single session.

We only collected a test set when our training set produced a promising correlation between predicted and actual behavioral measures. We never used data from test sets to train models. In our main figures, we showed scatter plots that combined test and training data, as the training and test partitions had similar correlations.

We are unsure what the reviewer means by instances where we explored many possible relationships. The greatest number of comparisons that could lead to the rejection of a null hypothesis was 5 (corresponding to the top 5 PCs of Ca++ response variation or Brp signal). We were explicit that the p-values reported were nominal. As mentioned above, applying a Bonferroni correction for n=5 comparisons to either the training or test correlations from the Ca++ to OCT-MCH preference model remains significant at alpha=0.05.

Our revision will include confidence limits.

Reviewer #2 (Public Review):

Summary:

The authors aimed to identify the neural sources of behavioral variation in a decision between odor and air, or between two odors.

Strengths:

-The question is of fundamental importance.

-The behavioral studies are automated, and high-throughput.

-The data analyses are sophisticated and appropriate.

-The paper is clear and well-written aside from some strong wording.

-The figures beautifully illustrate their results.

-The modeling efforts mechanistically ground observed data correlations.

We are glad to read that the reviewer sees these strengths in the study. We hope the forthcoming revision will address the strong wording.

Weaknesses:

-The correlations between behavioral variations and neural activity/synapse morphology are (i) relatively weak, (ii) framed using the inappropriate words "predict", "link", and "explain", and (iii) sometimes non-intuitive (e.g., PC 1 of neural activity).

Taking each of these points in turn: i) It would indeed be nicer if our empirical correlations are higher. One quibble: we primarily report relatively weak correlations between measurements of behavior and Ca++/Brp. This could be the case even when the correlation between true behavior and Ca++/Brp is higher. Our analysis of the potential correlation between latent behavioral and Ca++ signals was an attempt to tease these relationships apart. The analysis suggests that there could, in fact, be a high underlying correlation between behavior and these circuit features (though the error bars on these inferences are wide).

ii) We are working to guarantee that all such words are used appropriately. “Predict” can often be appropriate in this context, as a model predicts true data values. Explain can also be appropriate, as X “explaining” a portion of the variance of Y is synonymous with X and Y being correlated. We cannot think of formal uses of “link,” and are revising the manuscript to resolve any inappropriate word choice.

iii) If the underlying biology is rooted in non-intuitive relationships, there’s unfortunately not much we can do about it. We chose to use PCs of our Ca++/Brp data as predictors to deal with the challenge of having many potential predictors (odor-glomerular responses) and relatively few output variables (behavioral bias). Thus, using PCs is a conservative approach to deal with multiple comparisons. Because PCs are just linear transformations of the original data, interpreting them is relatively easy, and in interpreting PC1 and PC2, we were able to identify simple interpretations (total activity and the difference between DC2 and DM2 activation, respectively). All in all, we remain satisfied with this approach as a means to both 1) limit multiple comparisons and 2) interpret simple meanings from predictive PCs.

-No attempts were made to perturb the relevant circuits to establish a causal relationship between behavioral variations and functional/morphological variations.

We did conduct such experiments, but we did not report them because they had negative results that we could not definitively interpret. We used constitutive and inducible effectors to alter the physiology of ORNs projecting to DC2 and DM2. We also used UAS-LRP4 and UAS-LRP4-RNAi to attempt to increase and decrease the extent of Brp puncta in ORNs projecting to DC2 and DM2. None of these manipulations had a significant effect on mean odor preference in the OCT-MCH choice, which was the behavioral focus of these experiments. We were unable to determine if the effectors had the intended effects in the targeted Gal4 lines, particularly in the LRP experiments, so we could not rule out that our negative finding reflected a technical failure. We are reviewing these results to determine if they warrant including as a negative finding in the revision.

We believe that even if these negative results are not technical failures, they are not necessarily inconsistent with the analyses correlating features of DC2 and DM2 to behavior. Specifically, we suspect that there are correlated fluctuations in glomerular Ca++ responses and Brp across individuals, due to fluctuations in the developmental spatial patterning of the antennal lobe. Thus, the DC2-DM2 predictor may represent a slice/subset of predictors distributed across the antennal lobe. This would also explain how we “got lucky” to find two glomeruli as predictors of behavior, when were only able to image a small portion of the glomeruli. In analyses we did not report, we explored this possibility using the AL computational model. We are likely to include this interpretation in the revised discussion.

Reviewer #3 (Public Review):

Churgin et. al. seeks to understand the neural substrates of individual odor preference in the Drosophila antennal lobe, using paired behavioral testing and calcium imaging from ORNs and PNs in the same flies, and testing whether ORN and PN odor responses can predict behavioral preference. The manuscript's main claims are that ORN activity in response to a panel of odors is predictive of the individual's preference for 3-octanol (3-OCT) relative to clean air, and that activity in the projection neurons is predictive of both 3-OCT vs. air preference and 3-OCT vs. 4-methylcyclohexanol (MCH). They find that the difference in density of fluorescently-tagged brp (a presynaptic marker) in two glomeruli (DC2 and DM2) trends towards predicting behavioral preference between 3-oct vs. MCH. Implementing a model of the antennal lobe based on the available connectome data, they find that glomerulus-level variation in response reminiscent of the variation that they observe can be generated by resampling variables associated with the glomeruli, such as ORN identity and glomerular synapse density.

Strengths:

The authors investigate a highly significant and impactful problem of interest to all experimental biologists, nearly all of whom must often conduct their measurements in many different individuals and so have a vested interest in understanding this problem. The manuscript represents a lot of work, with challenging paired behavioral and neural measurements.

Weaknesses:

The overall impression is that the authors are attempting to explain complex, highly variable behavioral output with a comparatively limited set of neural measurements…

We would say that we are attempting to explain a simple, highly variable behavioral measure with a comparatively limited set of neural measurements. I.e. we make no claims to explain the complex behavioral components of odor choice, like locomotion, reversals at the odor boundary, etc.

Given the degree of behavioral variability they observe within an individual (Figure 1- supp 1) which implies temporal/state/measurement variation in behavior, it's unclear that their degree of sampling can resolve true individual variability (what they call "idiosyncrasy") in neural responses, given the additional temporal/state/measurement variation in neural responses.

We are confident that different Ca++ recordings are statistically different. This is borne out in the analysis of repeated Ca++ recordings in this study, which finds that the significant PCs of Ca++ variation contain 77% of the variation in that data. That this variation is persistent over time and across hemispheres was assessed in Honegger & Smith, et al., 2019. We are thus confident that there is true individuality in neural responses (Note, we prefer not to call it “individual variability” as this could refer to variability within individuals, not variability across individuals.) It is a separate question of whether individual differences in neural responses bear some relation to individual differences in behavioral biases. That was the focus of this study, and our finding of a robust correlation between PC2 of Ca++ responses and OCT-MCH preference indicates a relation. Because behavior and Ca++ were collected with an hours-to-day long gap, this implies that there are latent versions of both behavioral bias and Ca++ response that are stable on timescales at least that long.

The statistical analyses in the manuscript are underdeveloped, and it's unclear the degree to which the correlations reported have explanatory (causative) power in accounting for organismal behavior.

With respect, we do not think our statistical analyses are underdeveloped, though we acknowledge that the detailed reviewer suggestions included the helpful suggestion to include uncertainty in the estimation of confidence intervals around the point estimate of the strength of correlation between latent behavioral and Ca++ response states. We are considering those suggestions and anticipate responding to them in the revision.

It is indeed a separate question whether the correlations we observed represent causal links from Ca++ to behavior (though our yoked experiment suggests there is not a behavior-to-Ca++ causal relationship — at least one where odor experience through behavior is an upstream cause). We attempted to be precise in indicating that our observations are correlations. That is why we used that word in the title, as an example. In the revision, we are working to make sure this is appropriately reflected in all word choice across the paper.

ID: Action: social anxiety

I loved how descriptive Cisneros was in this part of the story because it helped me paint the perfect picture in my head of what was happening, and it made me think about how my sister and her friend used to do similar things like the narrator and Lucy did when they were young. Furthermore, mentioning how the narrator and Lucy have the same exact flip flops that they bought with each other shows how they have a perfect friendship with one another, that most of us were blessed enough to have with someone else growing up as well. I wonder if the narrator likes Lucy so much because of her big family that she doesn't have, and I also wonder if the narrator envy's Lucy so intensely that she wants to literally be Lucy in general?

Dick/interesting: Funny how he goes back to the hairs on the head

plot (before this they're both trying to escape

Plot/Character: Dick turns Perry in

I really agree with this quote because the way the world is head is so technologically advanced that it is for everyone. we are going to have to use computational thinking. -Samantha Vinson

A pillory is a wooden framework with holes for the head and hands, in which an offender was imprisoned and exposed to public abuse.

Reviewer #1 (Public Review):

Summary:<br /> In this paper, the effects of two sensory stimuli (visual and somatosensory) on fMRI responsiveness during absence seizures were investigated in GEARS rats with concurrent EEG recordings. SPM analysis of fMRI showed a significant reduction in whole-brain responsiveness during the ictal period compared to the interictal period under both stimuli, and this phenomenon was replicated in a structurally constrained whole-brain computational model of rat brains.

The conclusion of this paper is that whole-brain responsiveness to both sensory stimuli is inhibited and spatially impeded during seizures.

I also suggest the manuscript should be written in a way that is more accessible to readers who are less familiar with animal experiments. In addition, the implementation and interpretation of brain simulations need to be more careful and clear.

Strengths:<br /> 1. ZTE imaging sequence was selected over traditional EPI sequence as the optimal way to perform fMRI experiments during absence seizures.

2. A detailed classification of stimulation periods is achieved based on the relative position in time of the stimulation period with respect to the brain state.

3. A whole-brain model embedded with a realistic rat connectome is simulated on the TVB platform to replicate fMRI observations.

Weaknesses:<br /> 1. The analysis in this paper does not directly answer the scientific question posed by the authors, which is to explore the mechanisms of the reduced brain responsiveness to external stimuli during absence seizures (in terms of altered information processing), but merely characterizes the spatial involvement of such reduced responsiveness. The same holds for the use of mean-field modeling, which merely reproduces experimental results without explaining them mechanistically as what the authors have claimed at the head of the paper.

2. The implementations of brain simulations need to be more specific.

Contribution:<br /> The contribution of this paper is performing fMRI experiments under a rare condition that could provide fresh knowledge in the imaging field regarding the brain's responsiveness to environmental stimuli during absence seizures.

behavior

Hello everyone, my name is Glendy Sanchez and it is very nice to get to know some of you guys. I know I am extremely late, but I was just now able to figure out CUNY commons and recently sign in. My major is criminology which was criminal justice at first, but once I transferred from Limestone College in SC, to John Jay College of Criminal Justice I changed my major. Criminal Justice is based more on the legal system and I am more interested in studying behavior which is what criminology is more about. Criminology is what I initially wanted to study to begin with but Limestone College only offered criminal justice. I have about 96 credits and a 3.78 cumm. GPA since I have to be thorough due to my anxiety and ADHD. Getting into John Jay was one of the best decisions I have made. I love this school compared to my school in SC, and no I am not lying, because I have actually learned so much already than I ever would have at Limestone, the school system works better for me, and they focus more on understanding and grasping the concept rather than overload you with work you have no idea about because the class structure is horrible at Limestone College. I am also a Twin sister which I am very proud of. Her name is Glenda so yes it's Glendy and Glenda or Glenda and Glendy. Confused? lol,it's ok, I get it. My goal is to become a homicide detective but I am also interested in becoming a criminal profiler for the FBI which is why I am getting my master's in Clinical psychology. The way I function, due to my personality, is having and creating more than one option for me, therefore; I want to be a homicide detective because I get to do both go to the crime scene and investigate, etc meaning not just one job or a criminal profiler which is like solving a puzzle in my head, to me and this way I do not feel restricted to work in just one field. I like to learn, having discussions, reading, (although, I read slow), and just being around my family, etc. My favorite subject in school was always science. I love biology and anatomy and physiology but I mean, my mom is a Science (Chemistry & Biology) teacher so I guess I get it from her. I am also told I am funny without trying to be and I enjoy helping people, usually to defend their rights. I am an activist of social and economic reform. I believe in gender, class, and race equality. In addition, I went to the congress building as an activist with other activists to speak on behalf of the Justice Clemency Act and spoke and gave statistical facts to congressmen as well as preparing a campaign to help immigrants get their citizenship through an internship I took. I was born and raised in The Bronx but I am Dominican and Dominican cultured. I am 26 years old with no children yet and I love to travel and learn new cultures. It amazes me. I can keep going and it is nice talking to you but I think I am talking too much now, so I will stop here, but if you have any questions you would like to know about me feel free to ask. I am open to share and no I don't bite. I consider myself very nice, having good heart intentions, and pretty cool person. May you all have a great rest of your day.

This is our first look into how Jonah actually died and it opens up a world of possibilities. Before it was fair to assume that he died in combat, but this twists that thought on its head and opens the reader's eyes to other potential causes to Jonah's death.

When looking at UI and ethics the first thing that popped up into my head was the infinite scroll, as now more than ever the format has caught on and almost all social media sites have caught on to this along with the short form content format. The ethical implications of these two things are quite unclear to me. On the one hand, companies are profiting from having people glued to their phones and amplifying this with whatever algorithm they're using, on the other hand, how would something like this be regulated?

CRL in the first trimester From the crown (Highest point) to the Rump (Lowest Point)

BPD in the second trimester (Biparietal Diameter: Between the two parietal bones) The aforementioned are in the ??? view

Head circumference (HC) Abdominal Circumference (AC): Requires 2 ribs in the view and other things Femur Length (FL): View from diaphysis to diaphysis

????

James II married a catholic princess and had a son who would be the heir of the throne => a catholic head of state of a protestant state.

Not the same countries but they had the same Head of State.

I think this is just one of those times where when anyone given too much power or freedom it gets to one's head. It's sad that exciting and hopeful ideas such as 8Chan (now 8Kun) are ruined by ill intentioned individuals. Although no one enjoys having them -- sometimes rules and regulations are crucial in keeping good things good and not allowing any bad things to have a space to breed. People who have the goal and aim to spread ideas of hate and ignorance will find a way that's why media sites have really got to lock down (ex: Instagram being more restrictive on chats and content).

not sure why in my head I thought that the newspaper was going to be really small. What other newspapers are similar to this in Israel, or is this the biggest one?

ID: Action: He is consciously trying to appear normal because he is self conscious of how he is being perceived

I'm very interested in the use of color here. Why is it that in a span of one stanza—in fact, a mere three lines—two colors are referenced: white and brown? I was interested in finding more about this, so the first place I looked to was the etymologies of these colors. From the Online Etymology Dictionary:

Brown: Old English brun "dark, dusky," developing a definite color sense... from PIE root *bher- (2) "bright; brown."

And:

White: ...in late Old English "a highly luminous color devoid of chroma."

Also, interestingly, with white:

...Meaning "morally pure" was in Old English. Association with royalist causes is late 18c. Slang sense of "honorable, fair" is 1877, American English; in Middle English it meant "gracious, friendly, favorable."

Brown and white have stark differences—of course, with the most obvious being the fact that brown is "developing a definite color sense" while white is "devoid of chroma." This is consistent with the basic science behind color: white reflects all wavelengths of light—the absence of color—whereas brown/black absorbs them—a complete presence of color. As the etymology indicates, evidently, one can analogize the absence/presence of color as the absence/presence of sin: white, being absent of "chroma," is as a consequence regarded as "favorable," "honorable," and "morally pure." Of course, one can delve even more into the repercussions this had on white supremacy—but, within the context of the poem, a "white road" may be seen as a pure one: a road, or a path, free of sin (chroma).

Interestingly, however, this idea is turned on its head in the Visuddhi-Magga reading. The "white road" from line 362 can be related to the path from Mt. Cetiya to Anuradhapura; this is hinted by line 365, which mentions the ambiguity of the gender of a figure just as the Visuddhi-Magga reading did on pg. 298. On the same page, however, elements that are white aren't indications of purity—but impurity:

The elder looked up inquiringly, and observing her teeth, realized the impurity of her body...

Also:

But this I know, a set of bones / Is traveling on upon this road.

Both bones and teeth are white—yet they are the utmost signs of the impurities, the least honorable and favorable and gracious elements of the human self.

Finally, with regards to brown—though brown is a "dark" and "dusky" color, it has one commonality with white: the element of brightness, of luminosity, in their etymologies. I am immediately reminded of Dracula when I read this: Dracula comes out only in the absence of brightness, as he is a "nocturnal existence." Yet the mantle—the cloak—worn by this figure, much like one that Dracula is described as wearing in the novel, is brown. From conventional associations of color, if the mantle was a symbol of darkness and evil, one would not expect it to be brown—which even has the etymology of brightness. Instead, this figure may be the antithesis of Dracula itself. This would make sense because, as Angela hinted at, the idea of a "third figure" may symbolize a character of divinity, of providence—or of brightness.

Eliot has a playful approach with this line, just as he demonstrates through "Weialala... la la", "Twit twit twit / Jug jug jug jug jug jug", and many other instances. I'll refer back to this line at the end of this annotation, but for now, I am most intrigued by the connection between the hermit-thrush and water. First, Eliot's footnote deems that this bird's "water-dripping song is justly celebrated". The fact that the hermit-thrush's melody is described as "water-dripping" evidently alludes to the connection between the two. Yet, important to note is that this "water-dripping" trait comes from the bird's "purity and sweetness", which comes from Chapman distinguishing that this kind is "the sweetest singer of all American birds". Water is pure, probably the purest of all liquid substance. But is it sweet? In general, no. But maybe it is in a case of drought and a situation with "no water but only rock". If one is deprived of water for long, where one's "flesh longeth for [God] in a dry and thirsty land", I'd say one would be soured with overwhelming bitterness and dissatisfaction (Psalm 63:1). Even when the hermit-thrush appears, bringing its "water-dripping song", one's thirst is still not quenched by the intangible. The hermit-thrush, with "hermit" alluding to "solitude" (343), is not an actual source of aid, thus "There is not even solitude in the mountains / But red sullen faces" – their countenance red from dehydration or head perhaps. Moreover, the hermit-thrush "attracts little notice" and "often finds seclusion" as Chapman notes. Is the bird keeping its "water-dripping song" for itself, finding solitude that the poem, narrator (Tiresias?) is unable to locate? Mother Nature has a world of its own, maybe we just can't step beyond our line of humanity and into this nature's secrecy. Maybe the natural world is punishing us through this drought, stripping us from its natural resources. Thus, we are left with mere sound tempting us, just as springtime teases us with breeding lilacs out of the dead land, making us love again only to face loss in the wintertime. The hermit-thrush's song begins with two drips and drops but finalizes with the repetition of "drop". Does this allude to humanity dropping underground post-death? Are we going to be next to be planted in the dead land, just for the next generation to rise again? "But there is no water"; perhaps there won't even be a next to breed out of the seeds of our corpses. Maybe we'll just "drop drop drop drop" into the deep realms of Hell and never see the light of day nor hear the sound of water.

Though this section appears much more concise than the rest, I believe its brevity alludes to something greater, specifically the grand, infinite cycle of change through time. Death, evidently, is a natural stage in life. Though usually interpreted as the end of something, occasionally, and generally in TWL, death represents the beginning of something new. Thus, as Phlebas' death is portrayed in this scene, some part of him still lives on, as this corpse "passe[s] the stages of his age and youth / Entering the whirlpool", retracing moments of his life in a supposed afterlife. What's interesting is that Phlebas is still the subject of this action despite proclaimed dead. Though, "A current under sea" technically is the actual subject in charge of his corpse's motion, Eliot decides on sticking with "He" as the subjective noun, perhaps emphasizing the significance of humanity and its course. However, I believe nature still prevails over mankind. Pheblas "rose and fell" only because of the "current under sea"; without the sea's motion, this corpse would remain static. This theme is corroborated in multiple sources – Dante's "a whirlwind that struck the ship head-on" shows natural force over a mere manmade construct; De Quincy's depiction of "the treacherous sands gathering above [a woman's] head, so that "no memorial of the fair young girl remained on earth".

Phlebas' rising and falling in the sea also reminds me of the references to motion and time in the sources. De Quincey's "from the rising to the setting sun" and the plot's progression over a storm at sea till dawn evokes the image of nature's cyclic continuity. Tennyson's "every hour is saved / From that eternal silence, something more, / A bringer of new things" further alludes to vitality (new things) that comes with time (every hour). Nature and mankind seem intertwined; their motions reflect each other. Or perhaps, nature actually determines human motion and status. As the sea is the one acting in this section, it wouldn't be a stretch to say that this body of water has committed acts, including death. I'm starting to think that the "by" in "Death by Water" is not a preposition that alludes to a location, but rather identifies the agent performing an action.

he got hit in the head with a brick metaphorically and he got right back up again

writer seems almost focused on having an enjoyable experience no matter any negativity that occurs.

Some handy tips someone has on how they increased their head voice range as a baritone.

Reviewer #1 (Public Review):

In this manuscript, Kim et al. investigate the molecular basis for hindbrain segmentation by performing combined single cell nucleus RNAseq and ATACseq (scMultiome) on zebrafish embryonic hindbrain tissue. Hindbrain segmentation is fundamental to head development in vertebrate species. Decades of research have provided many insights into the gene regulatory cascades that control the progressive subdivision of the hindbrain territory into segments (rhombomeres). These studies have enabled the formulation of gene regulatory network (GRN) models that depict these regulatory interactions. However, many aspects of the GRN need further clarification, including the early steps of pre-rhombomeric patterning, and the factors that respond to axial signaling pathways such as RA and FGF. The dataset in this study provides a comprehensive view of gene expression and chromatin states during hindbrain segmentation, thus it is a valuable resource for characterizing the underlying GRN. The authors demonstrate the utility of this data by comparing the molecular profiles between different rhombomeres and tracing when and how these profiles arise during development.

Four main findings are presented:

1. Each rhombomere has a unique molecular profile.<br /> 2. There is no clear molecular signature for odd versus even rhombomeres, nor any overt repeating two-segment molecular identities.<br /> 3. The mature rhombomeres emerge through the subdivision of three mixed-identity 'primary hindbrain progenitor domains' (PHPDs) that correspond to r2/r3, r4, and r5/r6, respectively.<br /> 4. RA and FGF signaling control formation of the primary hindbrain progenitor domains.

These findings are well supported by the data but in my opinion they mainly confirm what was already known and do not significantly advance our mechanistic understanding of rhombomere formation, which is the aim of the paper.

Strengths:<br /> This comprehensive dataset will be very valuable to researchers in the field. The authors successfully demonstrate its utility by resolving unique molecular profiles for each rhombomere and identifying some novel markers.

The authors make excellent use of HCR to validate their findings, such as the co-expression of vgll3 and egr2b in r2/r3 cells at 10hpf, which implies mixed identities of PHPD cells.

The performance of scMultiome analysis on tissue from DEAB-treated embryos (depleted RA signaling) is exciting and holds much promise for identifying RA-dependent gene regulatory cascades that govern caudal hindbrain patterning. Assessing the contribution of control versus DEAB-treated cells to the various UMAP clusters is a very nice way to identify the altered cell states in the RA-depleted hindbrain. This confirms a complete absence of r5 and r6 in the DEAB-treated embryos at this developmental stage, as was inferred from in-situ approaches in earlier studies.

Weaknesses:<br /> The major weakness of this work is that it only provides an incremental mechanistic advance to our current understanding of the molecular basis for rhombomere formation. The descriptions of gene expression are useful but for the most part they are rather shallow lines of enquiry that confirm what was already known from previous, less comprehensive studies of gene expression. For example, regarding the identification of PHPDs, it has long been known that r5/r6 share a progenitor domain that is demarcated by mafba expression. Similarly, RA and Fgf signaling have already been shown to be required for anterior-posterior patterning in the pre-rhombomeric hindbrain. The identification of mixed-identity progenitors in PHPDs, and the characterisation of the changes in transcription and chromatin state in response to RA signaling perturbation are really exciting starting points for deeper analysis of the underlying GRN. However, it is a shame that no effort is made to glean mechanistic insights from this dataset by computational GRN inference.

Nice imagery.

In Sutherland's thesis, he discusses the concept of ring structures, which can be thought of as a type of data structure where elements are connected in a circular manner. This can be compared to a Python list where the last element points back to the first one, forming a "ring".

In the context of his thesis, Sutherland uses the terms "hen" and "chicken" to represent elements in this ring structure. A "hen" can be thought of as the starting point or the head of the list, while a "chicken" represents other elements in the list.

The operations he describes - inserting a new chicken into a ring at a specific location, removing a chicken from a ring, putting all the chickens of one ring into another at a specific location, and performing some auxiliary operation on each member of a ring in either forward or reverse order - can be compared to operations on a Python list.

For example, inserting a new chicken into a ring can be compared to inserting an element into a Python list at a specific index:

```python

Python list

ring = ['hen', 'chicken1', 'chicken2']

Insert new chicken at index 1

ring.insert(1, 'new_chicken') ```

Removing a chicken from a ring can be compared to removing an element from a Python list:

```python

Remove 'chicken1' from the list

ring.remove('chicken1') ```

Putting all the chickens of one ring into another at a specific location can be compared to extending a Python list with another list:

```python

Another ring

another_ring = ['hen2', 'chicken3', 'chicken4']

Extend ring with another_ring

ring.extend(another_ring) ```

Performing some auxiliary operation on each member of a ring in either forward or reverse order can be compared to iterating over a Python list in forward or reverse order:

```python

Forward order

for chicken in ring: print(chicken)

Reverse order

for chicken in reversed(ring): print(chicken) ```

The concept of "MACRO instructions" he mentions can be compared to Python functions that encapsulate these operations for easy use.

Author Response

The following is the authors’ response to the previous reviews

Reviewer #2 (Public Review):

DeKraker et al. propose a new method for hippocampal registration using a novel surface-based approach that preserves the topology of the curvature of the hippocampus and boundaries of hippocampal subfields. The surface-based registration method proved to be more precise and resulted in better alignment compared to traditional volumetric-based registration. Moreover, the authors demonstrated that this method can be performed across image modalities by testing the method with seven different histological samples. This work has the potential to be a powerful new registration technique that can enable precise hippocampal registration and alignment across subjects, datasets, and image modalities.

We thank the Reviewer, and feel this is an accurate summary of our work.

Reviewer #3 (Public Review):

Summary:

In the current manuscript, Dekraker and colleagues have demonstrated the ability to align hippocampal subfield parcellations across disparate 3D histology samples that differ in contrast, resolution, and processing/staining methods. In doing so, they validated the previously generated Big-Brain atlas by comparing across seven different ground-truth subfield definitions. This is an impressive effort that provides important groundwork for future in vivo multi-atlas methods.

Strengths:

DeKraker and colleagues have provided novel evidence for the tremendously complicated curvature/gyrification of the hippocampus. This work underscores the challenge that this complicated anatomy presents in our ability to co-register other types of hippocampal data (e.g. MRI data) to appropriately align and study a structure in which the curvature varies considerably across individuals.

This paper is also important in that it highlights the utility of using post-mortem histological datasets, where ground truth histology is available, to inform our rigorous study of the in vivo brain.

This work may encourage readers to consider the limitations of the current methods that they currently use to co-register and normalize their MRI data and to question whether these methods are adequate for the examination of subfield activity, microstructure, or perfusion in the hippocampal head, for example. Thus the implications of this work could have a broad impact on the study of hippocampal subfield function in humans.

Weaknesses:

As the authors are well aware, hippocampal subfield definitions vary considerably across laboratories. For example, some neuroanatomists (Ding, Palomero-Gallagher, Augustinack) recognize that the prosubiculum is a distinct region from subiculum and CA1 but others (e.g. Insausti, Duvernoy) do not include this as a distinct subregion. Readers should be aware that there is no universal consensus about the definition of certain subfields and that there is still disagreement about some of the boundaries even among the agreed upon regions.

We thank the Reviewer, and feel this is an accurate summary of our work that also provides useful scientific context.

Reviewer #2 (Recommendations For The Authors):

The authors have done a great job with the revisions and have addressed all my concerns. They have clarified aspects of the method and procedure and have included a helpful walk-through explanation of an example subject. The authors have also expanded the discussion and addressed the motivation and justification for certain steps of the procedure.

We thank the Reviewer.

Reviewer #3 (Recommendations For The Authors):

The authors have addressed my previous comments and I believe the impact and take home message of the paper is more clear.

We thank the Reviewer.

In Figure 1, is the proximal-distal label reversed for panel B? I think P (proximal) should be closer to CA4/DG and D (distal) should be closer to subiculum. Am I misreading the graph?

We thank the Reviewer for this consideration, but the label is as intended. The terms proximal/distal in the hippocampal literature are sometimes relative to the dentate gyrus and sometimes relative to the rest of the cortex. In our case, we use the terms relative to the neocortex, following Ding and Van Hoesen (2015). We have now added the following to clarify this point at the first use of these terms (p.5):

“The current work, however, defined this tessellation as a regular mesh grid in unfolded space consisting of 256×128 points across the anterior-posterior (A-P) and proximal-distal (P-D) (relative to the neocortex) axes of the unfolded hippocampus, respectively.”

Reviewer #2 (Public Review):

Summary:

In the current manuscript, Dekraker and colleagues have demonstrated the ability to align hippocampal subfield parcellations across disparate 3D histology samples that differ in contrast, resolution, and processing/staining methods. In doing so, they validated the previously generated Big-Brain atlas by comparing across seven different ground-truth subfield definitions. This is an impressive effort that provides important groundwork for future in vivo multi-atlas methods.

Strengths:

DeKraker and colleagues have provided novel evidence for the tremendously complicated curvature/gyrification of the hippocampus. This work underscores the challenge that this complicated anatomy presents in our ability to co-register other types of hippocampal data (e.g. MRI data) to appropriately align and study a structure in which the curvature varies considerably across individuals.

This paper is also important in that it highlights the utility of using post-mortem histological datasets, where ground truth histology is available, to inform our rigorous study of the in vivo brain.

This work may encourage readers to consider the limitations of the current methods that they currently use to co-register and normalize their MRI data and to question whether these methods are adequate for the examination of subfield activity, microstructure, or perfusion in the hippocampal head, for example. Thus the implications of this work could have a broad impact on the study of hippocampal subfield function in humans.

Weaknesses:

As the authors are well aware, hippocampal subfield definitions vary considerably across laboratories. For example, some neuroanatomists (Ding, Palomero-Gallagher, Augustinack) recognize that the prosubiculum is a distinct region from subiculum and CA1 but others (e.g. Insausti, Duvernoy) do not include this as a distinct subregion. Readers should be aware that there is no universal consensus about the definition of certain subfields and that there is still disagreement about some of the boundaries even among the agreed upon regions.

Author Response

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

We thank the reviewers for their thoughtful assessment of our work and their valuable critiques which we will address in the “Recommendations for the authors” section below. In particular, we appreciate Reviewer #3 noting the value of the C. elegans model system and our efforts to bridge models with our study. We agree with the reviewer that there is a need to clarify the rationale, presentation and interpretation of our results. We have substantially revised the text in our manuscript and Figure legend to address this issue, and provided extensive new commentary and citations to lay out the logic behind our experiments. Indeed, it was our oversight not being more thorough about this initially. We have further adjusted our conclusions to be less unequivocal. Finally, we added an RPM-1 signaling diagram (Fig. 8A) to more clearly annotate the players in the RPM-1/MYCBP2 signaling network that were evaluated genetically in Fig. 8. Importantly, we provide clearer commentary on how genetic enhancer effects with known RPM-1 binding proteins and the absence of genetic suppression in vab-1/Eph receptor double mutants with components of the RPM-1/FSN-1 ubiquitin ligase complex are consistent with the biochemical finding that MYCBP2 stabilizes but does not degrade EphB2. Text edits reflecting these points are in the abstract, the C. elegans results section starting on line 411, and the discussion on lines 499, 502-504 and 541.

Following extensive discussions between the three reviewers, all three agree that the C. elegans data, as presented, does not add to, and in fact might harm, your bottom line. Our combined suggestion is to take this data out unless you plan to improve it substantially. All reviewers are perplexed by Figure 2F and the presumed interactions of cytosolic proteins with the extracellular domain of EPHB2. At the very least, please provide some suggestions/model/interpretation.

We have adjusted our manuscript substantially to address this. Please see detailed comments in the individual Reviewer sections below.

We would like to thank the reviewers for their thorough examination of our manuscript, constructive criticisms, and helpful suggestions.

Reviewer #1 (Recommendations For The Authors):

The work is extensive in my view, and mostly of high quality. See minor comments on some of the figures below.

Thank you very much.

Two more major comments :

• I don't think the C. elegans work adds to - in fact I think it hurts - the statement that this regulatory mechanism is specific to EphB2. I would advise the authors to take it out.

We agree that C. elegans has a sole Eph receptor called VAB-1 and is therefore not a specific model for EPH2B. However, testing MYCBP2 specificity for EPHB2 was not the goal or our perceived value for the C. elegans experiments. We now clarify this in the text of the Results section.

Rather, we are providing evidence that the C. elegans ephrin receptor interacts genetically with known MYCBP2/RPM-1 binding proteins. Moreover, we now provide an extensive array of citations to note that genetic enhancer interactions between different RPM-1/MYCBP2 binding proteins is well established. The reviewer has nicely highlighted for us that we handled the C. elegans genetics in too cursory a fashion in our original manuscript. We appreciate this being noted and have now aimed to make this substantially clearer. We hope the reviewer agrees that our revised C. elegans section accomplishes this goal.

Furthermore, we extensively revised the text of the Results to emphasize a key point: our observation that axon termination defects are not suppressed in vab-1; fsn-1 and vab-1; rpm-1 double mutants excludes the possibility that the VAB-1 Eph receptor is a substrate that is inhibited or degraded by the RPM-1/FSN-1 ubiquitin ligase complex. If the VAB-1 Eph receptor were ubiquitinated and degraded by the RPM-1/FSN-1 complex, we would have observed a suppression of phenotype in vab-1; rpm-1 double mutants. The precedent for this genetic relationship between the RPM-1 ubiquitin ligase and its substrates that are degraded has been established by several prior studies (PMID: 15707898; PMID: 31676756; PMID: 35421092). We now more clearly note that the absence of genetic suppression in vab-1; rpm-1 double mutants and vab-1; fsn-1 double mutants is consistent with the non-canonical stabilizing role of MYCBP2 on EPHB2 that was observed in our biochemical experiments with mammalian cells.

We also adjusted the text of the manuscript to stress that we are testing genetic interactions between the VAB-1 Eph receptor and known RPM-1 binding proteins. This is a key point, as genetic enhancer interactions are consistent with the Eph receptor functioning in the RPM-1 signaling network. This concept has been well established for RPM-1 binding proteins as now noted in our revised text with an extensive number of additional citations to published work.

Based on the above arguments, we respectfully disagree with the reviewer that our C. elegans data should be removed from the paper. To re-iterate, we are not trying to evaluate specificity for MYCBP2 and EPHB2 in C. elegans. Rather, our goals are twofold: 1) To ask whether there is an evolutionarily conserved functional genetic link between Eph receptors and known RPM-1 binding proteins. 2) To provide further in vivo genetic evidence invalidating the hypothesis that Ephrin receptors could be ubiquitination substrates that are inhibited/degraded by MYCBP2.

Text edits reflecting these points are in the abstract, the C. elegans results section starting on line 411, and the discussion on lines 499, 502-504 and 541.

• The cellular responses are not robust and the effects of MYCBP2 KO - although significant - are minor in most cases. But I don't think more experiments will help here.

We interpret the comment about the robustness to mean that the extent to which a given cellular response is affected by the loss of MYCBP2 is minor. First, the cellular responses themselves are typical of previous studies and depend on the cellular biology underlying them. For example, a growth collapse of ~50-60% over a background of 10% (Fig. 7) is typical for these sorts of assays (PMID: 37369692; PMID: 33972524; PMID: 17785182). A decrease of cell area by ~25% (Fig. 3) is quite substantial if one considers how much of a cell’s volume is taken up by the nucleus and organelles. Second, the phenotypes elicited by the loss of MYCBP2 are likely brought on by a decrease in EphB2 protein levels, but not its complete absence, as suggested by our biochemical experiment. Given that EphB2 complete loss only affects the cellular responses to a limited extent, the minor effects are not a surprise (e.g. for GC collapse: PMID: 23143520). Nevertheless, the subtle changes in cellular phenotypes, elicited by EPHB2 signaling are often sufficient to achieve proper cell positioning and cell response to guidance cues. For instance, regulation of the growth cone collapse of the outgrowing axons requires delicate changes that are dynamic and temporal.

Minor:

Fig 1C - EPHA3 and EPHB2 seem to run in different sizes, is this the case? In 2A they run at the same size.

We believe this size discrepancy is due to different percentages of SDS-PAGE gels used to resolve proteins. In Fig. 1C, we used a 6% gel for a Western blot analysis of both EPHA3/-B2-FLAG (~130 kDa) and MYCBP2 (~510 kDa). In Fig. 2A however, we performed Western blot analysis using 10% resolving gel to separate and detect EPHA3/-B2-FLAG along with MYC-FBXO45 (~30 kDa). We have reviewed the results obtained from additional biological replicates of this experiment, and observed a similar pattern in gel migration of EPHA3/-B2-FLAG across all replicates.

Fig1F - I can't trust the MYCBP2 blot.

Indeed, the MYCBP2-EPHB2 co-IP with endogenous proteins was not convincing. We now repeated this experiment using rat cortical neurons, and the results replace the previous Fig. 1F panel as mentioned on line 158.

In Fig2b the authors claim that there is enhancement in the binding of MYCBP2 and EPHB2 upon FBXO45 expression. For this type of statement quantification is required.

The quantification is now included in Fig. 2C and its significance is mentioned on line 180. Our conclusion about the enhancement stands.

Fig2G - it remained unclear to me where the binding site to MYCBP2 is, how long is the cytoplasmic tail in the DeltaICD protein?

Based on our experimental observations from Fig. 2E-H, we concluded that the fragment encompassing the extracellular domain(s) and/or transmembrane (TM) domain of EPHB2 is necessary for the protein complex formation with MYCBP2. We would like to accentuate that the EPHB2-MYCBP2 interaction might not be direct, and might involve other transmembrane protein(s) acting as a scaffold for EPHB2 and MYCBP2 binding. We did not pursue experiments to determine the exact region of the extracellular-TM portion of EPHB2 that is required for the interaction with MYCBP2.

The cytoplasmic tail in ΔICD protein consists of 25 aa of the N-terminal fragment of EPHB2 juxtamembrane (JM) region, which is adjacent to the TM helix, and followed by the 8 aa FLAG tag (EPHB2 ΔICD domain composition: extracellular domains – TM domain – 25 aa fragment of JM region – FLAG). We have determined the TM and JM sequences based on Hedger et al. (PMID: 25779975) and included the N-terminal portion of the JM region to facilitate proper ΔICD protein localization within the plasma membrane (PMID: 35793621). We modified the schematic in Fig. 2G to better visualise the EPHB2 truncations and now provide information on their size in the figure legend.

Always good to have a model of how all these proteins work together.

While we acknowledge that this would be helpful, we do not have a clear answer on how the EPHB2-MYCBP2 complex formation occurs. This requires further elucidation of the putative proteins involved in this ternary complex or testing the possibility that a MYCBP2 fragment is extruded extracellularly. Without these experiments there are too many possibilities to summarise into a clear model figure. We thus did not make any edits regarding these possibilities in the section starting on line 195.

Reviewer #2 (Recommendations For The Authors):

Overall, the experiments are classical experiments of co-immunoprecipitations, swapping experiments, collapse assays, and stripe assays which all are well carried out and are convincing.

Thank you for your encouraging comments.

Controls for the stripe assay may include Fc / Fc stripe assays.

We have performed these control experiments and now include their quantifications in the results sectioning concerning Fig. 3, starting on line 249, and those concerning Fig. 6 on line 381.

It is not clear to me why SD and not SEM has been used here for presentations.

Standard deviation (SD) measures the dispersion of a dataset relative to its mean. The standard error of the mean (SEM) measures how much discrepancy is likely in a sample’s mean compared with the population mean. Thus, SEM includes a statistical inference about the sampling distribution while SD is a less “processed” measurement that by definition is larger than SEM. SEM might make the data look less dispersed and many journals encourage the use of SD in bar graphs (PMID: 16223828).

Fig 7A: it is rather difficult to see 'branches' in Fig. 7A, better pictures and close-ups should be provided. How are branches defined? This piece of work needs more attention.

To remedy this shortcoming, we now provide inverted images with GFP signal in dark pixels overlaid on Fc (white) / eB2 (pink) stripes next to the original images.

Reviewer #3 (Recommendations For The Authors):

1) My most important suggestion to the authors would be to more carefully describe the results and their interpretation of the results. Sometimes, the distinction is not clear.

We modified the text throughout the manuscript to address this.

2) There are several cases, when the authors report on trends that are not statistically significant (1D, for example), or report no change, when it is clear that the addition of one more sample could have dramatically made a difference (4M - see point 12).

We agree that some of the nonsignificant differences could become significant if we added more Ns. But we prefer not to move our experimental design towards N-chasing and p-hacking (PMID: 25768323). The number of biological replicates is normally pre-determined before the onset of the experiment. Of course, some replicates can be discarded if there is a valid reason, such as a technical issue with the experiment or a positive control not working but this is not relevant for the dataset we have provided.

3) Data in 1F is very difficult to interpret.

As in response to Reviewer #1: Indeed, the MYCBP2-EPHB2 co-IP with endogenous proteins was not convincing. We now repeated this experiment using rat cortical neurons, and the improved results are in revised Fig. 1F.

4) Figure 2 puts Figure 1 in a strange perspective. If I understand correctly, fig 2 claims that EPHB2 interaction with MYCBP2 depends on FBXO45 - if that is the case then how does the binding in Figure 1 occur?

Indeed, we propose that the EPHB2-MYCBP2 interaction depends on FBXO45. In Fig. 2, we reveal that FBXO45 enhances the formation of the EPHB2-MYCBP2 complex. Thus, we suspect that the endogenous FBXO45 present in HeLa cells and neurons would mediate the interaction between EPHB2 and MYCBP2 in Fig. 1 experiments. We were unable to show this by Western blotting due to lack of reliable commercial antibodies against FBXO45, the complex containing endogenous FBXO45 and EPHB2 is also implied by our AP-MS data (Fig. 1B) and published databases.

5) I am still trying to wrap my mind around the results in 2G-H. So do MYCBP2 and FBXO45 bind the extracellular domain of EPHBP2? What does that mean?

(see also our response to Reviewer #1, end of their section) Based on our experimental observations from Fig. 2G-H, we conclude that the fragment encompassing the extracellular domain(s) and/or transmembrane domain of EPHB2 is necessary for the protein complex formation with MYCBP2 and FBXO45. Although there is a possibility that MYCBP2 directly binds the extracellular portion of EPHB2, we have not formally tested this hypothesis. MYCBP2 has been previously shown to interact with the extracellular portion of transmembrane N-cadherin (CDH2) via BioID proximity labeling and AP-MS proteomics approaches (PMID: 32341084).

Considering the results in Fig. 2A-B, we suspect that EPHB2-MYCBP2 interaction is indirect, as FBXO45 enhances this association. Secretion of FBXO45 and direct binding of FBXO45 to the extracellular cadherin (EC1-2) domains of N-cadherin has been documented (PMID: 25143387; PMID: 32341084). Although, not tested, this is also a possibility for EPHB2-FBXO45 mode of interaction. Nevertheless, we also cannot rule out the possibility that an unknown transmembrane protein binds EPHB2 extracellularly and the same unknown protein binds MYCBP2/FBXO45 intracellularly. Resolving this model is beyond the scope of this study and will require us to pursue extensive new lines of investigation.

6) I don't understand the stable Hela cell line CRISPR - is this a stable MYCBP2 deletion? In which case why is there only a reduction, not complete elimination of the protein? Or, is this a stable integration of a plasmid generating gRNA against MYCBP2? In which case, I would expect a homozygous null to emerge at some point. In any case, this is not well explained.

These lines are not derived from single cells infected with the CRISPR sgRNA-carrying viruses, therefore they are not clonal and probably contain some cells that express normal levels of MYCBP2, hence its detection on a Western. This is now clarified starting on line 221 and on line 608.

7) In 3C - is this the right statistical analysis?? I would say you want to claim the different effect of the control +/- eB2 compared to the effect in the mutant +/- eB2. Still should be significant but I think a more correct analysis.

We now include this comparison in Fig. 3C as well in the results section starting on line 234.

8) The robustness of the assay in Figure 3D is underwhelming – how was the area measured?

This is a live imaging experiment. Fig. 3D plots cell area at 60 minutes after ephrin-B2 addition as a fraction of the same cell’s area at 0 minutes (ephrin-B2 addition). For control cells that is a decrease of ~25%. If one considers that a cell’s nucleus and organelles like the Golgi Apparatus take up most of its volume, the magnitude is not that surprising.

9) Figure 3F – did you try to plot the relative area of overlap divided by the total cellular area? You might get a more striking phenotype. Also – claiming that this confirms that MYCBP2 is REQUIRED for EPHB2 function is a bit overstated, especially given that we don’t know (do you?) the EPHB2 mutant phenotype in this assay.

We preferred to stay with the original method of image quantification which we use for other assays. With respect to the requirement of MYCBP2 for EPHB2 function in the stripe assay, our logic is rooted in the observation that native HeLa cells do not respond to ephrin-B2 stripes (45.46 ± 7.62% of cells on eB2 stripes v. Fc; data not shown). When they are transfected with EPHB2 expression plasmids they do, therefore we assume that EPHB2 expression endows them with a sensitivity to eB2 stripes. A loss of MYCBP2 attenuates this sensitivity. We clarified this starting on line 246 and on line 251.

10) I didn't quite get the difference between 4A and 4B.

We apologize for the confusion. In Fig 4A, we used a stable HeLa cell line that has tetracycline-inducible expression of EPHB2-FLAG. Using these cells, we subsequently generated CTRLCRISPR or MYCBP2CRISPR cells. In these cells we then induced EPHB2 expression with tetracycline and observed that deletion of MYCBP2 resulted in the reduction of EPHB2 protein levels. To confirm this observation and to rule out the possibility that EPHB2 protein reduction is an effect of the CRISPR lines generation, we tested whereas MYCBP2 deletion reduces EPHB2, which has been transiently overexpressed (Fig. 4B). We hence conclude that loss of MYCBP2 decreases EPHB2 that was either expressed from a stable locus (Fig. 4A) or from transient transfection (Fig. 4B). We modified the Results section starting on line 262 to make this point clear.

11) The entire link to lysosomal degradation should be strengthened. Perhaps I am confused, but if the reduced EPHB2 levels in MYCBP2 mutant cells result from impaired lysosomal degradation then inhibiting the lys-deg should bring the protein levels back to normal (i.e. CRISPR control) - no? As currently presented, I do not understand nor do I think the claim is strongly supported by the data.

Before treatment with inhibitors, EPHB2 levels in MYCBP2CRISPR cells are already 40% lower than they are in CTRLCRISPR cells and in all our attempts, inhibitors can only rescue/restore EPHB2 in MYCBP2CRISPR cells to a level that is lower than in CTRLCRISPR cells. But this restoration is greater in MYCBP2CRISPR than in MYCBP2CTRL cells (BafA1: 19% increase in CTRL cells and 40% in MYCBP2CRISPR cells; CoQ: 10% comparing to 35%). This indicates that EPHB2 degradation through the lysosomal pathway in MYCBP2CRISPR cells is stronger, explaining why EPHB2 degradation is promoted in MYCBP2CRISPR cells, compatible with reduced EPHB2 levels and enhanced EPHB2 ubiquitination.

12) 4M, O - reporting ns based on these data seems a bit strange to me... Add one point and it will be strongly significant.

See our response to point (2), above. We prefer not to invoke potential p-hacking.

13) 7d - so what are you claiming? That the cellular response to eB1 but not eB2 is affected by the addition of FBD1? this is almost the opposite of what you wrote in the text...

We treated the cells with two different ephrin-B ligands to make a stronger conclusion. When using ephrin-B1, growth cone collapse in FBD1 WT is not significant comparing to Fc treatment. When using ephrin-B2, growth cone collapse in FBD1 WT is not as significant as it is in FBD1 mut group (* versus ). We interpret this as meaning that the EPHB2-mediated growth cone collapse to both ligands is dampened, when we disrupt the EPHB2-MYCBP2 association. The difference between these two ligands might be due to their different affinities for the receptor or signalling kinetics.

14) By far the weakest link in this paper is the worm part. I think it's a pity because strengthening this would affect the significance of the finding. First, the authors mention new genes without introducing their relationship to the signaling pathway tested. Second, the textual logics should be strengthened. Finally and most importantly, when the difference between the phenotypic severity is so strong (vab-1 and rpm-1) then I think it's impossible to say anything from the double mutant.

We appreciate the reviewer noting that they appreciate the value and importance of the C. elegans model. The goals of our C. elegans experiments were twofold:

1) To evaluate genetic interactions between the VAB-1 Eph receptor and known RPM-1 binding proteins. This was not clearly explained in the original manuscript nor was the published precedent for these types of genetic enhancer experiments provided. We have now rectified this by substantially revising the text of the Results C. elegans section starting on line 431 and by adding several citations.

2) Our C. elegans genetics confirmed that the VAB-1 Eph receptor is not inhibited/degraded by the RPM-1/MYCBP2 ubiquitin ligase complex. We have now revised the text to draw this point out more clearly.

To further address the reviewer’s concerns, we have added a new schematic (Fig. 8A) to show the relationship between the RPM-1 and the RPM-1 binding proteins (FSN-1/FBXO45 and GLO-4/SERGEF) we are testing. We chose FSN-1 because it is part of the RPM-1 ubiquitin ligase complex and we chose GLO-4 because it functions outside the context of RPM-1 ubiquitin ligase signaling via the GLO-1 Rab GTPase to influence late endosomal/lysosomal biogenesis.

Regarding the reviewer’s concern that different penetrance/frequency of defects between rpm-1 mutants and vab-1 mutants means outcomes with vab-1; rpm-1 double mutants cannot be interpreted. We respectfully disagree. An extensive number of published studies have demonstrated that RPM-1 binding proteins have milder phenotypes than rpm-1 mutants and display genetic enhancer effects as double mutants with one another (PMID:17698012, PMID: 22357847, PMID: 25010424, PMID: 24810406). We now make this point much more clearly. While the frequency of axon termination defects in rpm-1 mutants is high it is not completely saturated as the defect is not 100%. Moreover, a major point of the vab-1; rpm-1 double mutants is that they do not have a significant reduction in phenotypic penetrance/frequency. Thus, our system is fully capable of resolving genetic suppression, which did not occur. We now make this point much more carefully and clearly.

To further address the reviewer’s concern, we have softened language about the VAB-1/Eph receptor functioning in the same pathway as RPM-1 throughout the manuscript. While we think this is still the case, because the frequency of axon termination defects is not fully saturated in rpm-1 mutants and defects could potentially become more severe (i.e. the hook might occur closer to the head of the animal rather than in the midbody). Nonetheless, this is not a critical point and we think it is more important to be clear about the two major goals and objectives of our C. elegans experiments. We hope the reviewer agrees that our rationale, logic and conclusions are more clearly and accurately drawn in the revised paper.

Joint Public Review:

Summary<br /> This is a very meticulous and precise anatomical description of the external sensory organs (sensillia) in Drosophila larvae. Extending on their previous study (Rist and Thum 2017) that analyzed the anatomy of the terminal organ, a major external taste organ of fruit fly larva, the authors examined the anatomy of the remaining head sensory organs - the dorsal organ, the ventral organ, and the labial organ-also described the sensory organs of the thoracic and abdominal segments. Improved serial electron microscopy and digital modeling are used to the fullest to provide a definitive and clear picture of the sensory organs, the sensillia, and adjacent ganglia, providing an integral and accurate map, which is dearly needed in the field. The authors revise all the data for the abdominal and thoracic segments and describe in detail, for the first time, the head and tail segments and construct a complete structural and neuronal map of the external larval sensilla.

Strengths<br /> It is a very thorough anatomical description of the external sensory organs of the genetically amenable fruitfly. This study represents a very useful tool for the research community that will definitely use it as a reference paper. In addition to the classification and nomenclature of the different types of sensilla throughout the larval body, the wealth of data presented here will be valuable to the scientific community. It will allow for investigating sensory processing in depth. Serial electron microscopy and digital modeling are used to the fullest to provide a comprehensive, definitive, and clear picture of the sensory organs. The discussion places the anatomical data into a functional and developmental frame. The study offers fundamental anatomical insights, which will be helpful for future functional studies and to understand the sensory strategies of Drosophila larvae in response to the external environment. By analyzing different larval stages (L1 and L3), this work offers some insights into the developmental aspects of the larval sense organs and their corresponding sensory cells.

Weaknesses<br /> There are no apparent weaknesses, although it is not a complete novel anatomical study. It revisits many data that already existed, adding new information. However, the repetitiveness of some data and prior studies may be avoided for easy readability.

eLife assessment

Lee and colleagues examined how neural representations are transformed between the olfactory tubercle (OT) and the ventral pallidum (VP) using single neuron calcium imaging in head-fixed animals trained in classical conditioning. They show that the dimensionality of neural responses is lower in the VP than in the OT. The study provides important results, and the data are overall solid although the reviewers thought some of the conclusions are not fully supported by the data and overstated, including the main conclusion that the OT neurons primarily encode odor identity but not value.

Reviewer #1 (Public Review):

Summary:<br /> This paper presents an innovative decoding approach for brain-computer interfaces (BCIs), introducing a new method named MINT. The authors develop a trajectory-centric approach to decode behaviors across several different datasets, including eight empirical datasets from the Neural Latents Benchmark. Overall, the paper is well written and their method shows impressive performance compared to more traditional decoding approaches that use a simpler approach. While there are some concerns (see below), the paper's strengths, particularly its emphasis on a trajectory-centric approach and the simplicity of MINT, provide a compelling contribution to the field.

Strengths:<br /> The adoption of a trajectory-centric approach that utilizes statistical constraints presents a substantial shift in methodology, potentially revolutionizing the way BCIs interpret and predict neural behaviour. This is one of the strongest aspects of the paper.

The thorough evaluation of the method across various datasets serves as an assurance that the superior performance of MINT is not a result of overfitting. The comparative simplicity of the method in contrast to many neural network approaches is refreshing and should facilitate broader applicability.

Weaknesses:<br /> Scope: Despite the impressive performance of MINT across multiple datasets, it seems predominantly applicable to M1/S1 data. Only one of the eight empirical datasets comes from an area outside the motor/somatosensory cortex. It would be beneficial if the authors could expand further on how the method might perform with other brain regions that do not exhibit low tangling or do not have a clear trial structure (e.g. decoding of position or head direction from hippocampus)

When comparing methods, the neural trajectories of MINT are based on averaged trials, while the comparison methods are trained on single trials. An additional analysis might help in disentangling the effect of the trial averaging. For this, the authors could average the input across trials for all decoders, establishing a baseline for averaged trials. Note that inference should still be done on single trials. Performance can then be visualized across different values of N, which denotes the number of averaged trials used for training.

The poem seems like it is a list of the ways wives perform perfection. This line reminds me of the essence of the phrase coined by RuPaul "you're born naked and the rest is drag" as if wearing a head as a mask.

mask

Accountability : Accountability means making clear commitments and sticking with them. Individuals and organizations must show they keep their promises and own up to any that they're broken. Everyone involved should be able to verify this and outside experts should be able to do so as well, not passing the buck and no playing in the blame game

1. I think that this is interesting, i never really thought that a drawing in a cave could be someone just trying to make their artwork perfect. It would make perfect sense if i saw a drawing on paper of someone now but i never really looked at cave drawings or statutes could have been "erased" and redone.

This harsh cut between scenes does an amazing job at demonstrating that he's still thinking about the events of that night and playing the moment through his head while he's somewhere else entirely. It feels like a scene shift out of a movie and it's astounding.

Author Response

We outline reviewer/editor queries, our responses are indicated below we thank the reviewers for their suggestions that we address below and with minor edits (that do not appreciably change the content such as figure lettering and methods information).

Reviewer #1 (Public Review):

The paper by Dongsheng Xiao, Yuhao Yan and Timothy H Murphy presents a timely approach to record neuronal activity at multiple temporal and spatial scales. Such approaches are at the forefront of system neuroscience and a few examples include, among others, fMRI alongside electrophysiology (Logothetis et al, 2021. Nature) or widefield calcium imaging (Lake et al, 2020. Nat Meth) , or functional ultrasound imaging and multi unit recording (Claron et al, 2023 Cell Reports), The method presented here combines "low resolution" (i.e. cortical regions) widefield calcium imaging across most of the dorsal portions of the murine cortex combined with electrical recording of single neurons in specific cortical and subcortical locations (as a matter of fact, this later components can be used everywhere in the murine brain).

The method presented here is straightforward to implement and very well documented. Examples of novel insights that this approach can generate are well presented and demonstrate the strength of the presented approach, some aspects of the analysis require clarification.

For example, the author reveal Spike-Triggered average cortical activation Maps (STMs) linked to the activity of single neurons (Figs 4 and 5) This allows to directly asses the functional connectivity between cortical and sub-cortical areas. It nevertheless unclear what is the stability of the established relationships. The nature of the "recordings" in Fig 4. is unclear. It looks like these are imaging sessions on the same day, the length of these recordings as well as the interval between them is not stated. It will be fundamental to build a metric to compare STMs variability across sessions/recordings/days; a root-mean-square from an average map across all recordings could provide a starting point.

Our goal was to present a well-documented protocol for implanting electrodes (tetrodes and peripheral nerve) that do not impede cortical mesoscale imaging and support chronic investigation of spike trains. We do provide examples of repeated spiking measurements across days from the same electrodes and animals. Unfortunately, due to the pandemic interrupting data collection and other factors, this dataset does not contain a thorough analysis of response longevity using these electrodes, but we do show examples in the figures. In Figure 1F, G, we showed that the single unit activity was relatively stable during one week, two weeks, and two months of recordings after implantation. In Figure 4B we showed spiking activity in the hippocampus was stable across day 8 and day 9. We also showed that the STM of the hippocampus neuron was consistently associated with the RSP, BCS, and M2 region for 10 recording sessions across days. In Figure 4D, We showed that the STMs of a midbrain neuron were relatively stable over 2 months. The spiking activity of the neuron on different days was consistently correlated with the lower limb, upper limb, and trunk sensorimotor areas on both hemispheres of the cortex.

Also with respect to the STMs analysis, the data-driven choice of 10 clusters might need a bit more explorations. While the silhouette clustering accuracy peaks at 10 (Fig 5A), this metrics comes without a confidence intervals making it difficult to know if a difference of less than 10% (i.e. 11 or 13 clusters) should be deemed different. Maybe a bootstrapping approach could be used here to build such confidence intervals. Another approach to reach the number of cluster to use could be based on "consensus" between different partitioning algorithms (e.g. Strehl, A. & Ghosh, J. itions. J. Mach. Learn. Res. 3, 583-617 (2001). A much stronger argument should be provided to use the 0.3 correlation cutoff value which seems to be arbitrarily low. The main point here is that the authors should show that their conclusions hold within a range of parameter values (number of clusters and correlation threshold).

Thank you for the interesting suggestions regarding cluster numbers. We agree that the number (10 clusters) could be taken as an arbitrary value. However, we have done previous work examining cortical connectivity maps in Mohajerani et al. 2013 Nature Neurosci. and found that cortical mesoscale activity has a degree of freedom (number of unique elements) in the range of 10-15. This number is also supported by major structural networks found by the Allen Brain Connectivity Atlas and within functional imaging data. In other work using unsupervised methods Xiao et al. 2021 Nature Comm a similar number of clusters were identified so these numbers are without some basis.

Reviewer #1 (Recommendations For The Authors):

I enjoyed very much reading the manuscript!

Minor comments (aesthetics and typos)

Please clarify how the hemodynamic correction was performed. The text refers to "substracted". This usually involves the computation of a general of per-pixel weight. Is this correction constant along the longitudinal imaging session (i.e. over weeks)?

The hemodynamic correction was calculated based on the results of each daily session. Typically these corrections have minimal impact on overall values and are not expected to appreciably change over time.

In Figure 3, authors might reconsider scaling down the size of panel A and enlarging the data presented in D. Also, with respect to panel D, what does the gray band represent, confidence intervals, standard dev? Please clarify.

The gray bands correspond to the standard deviation of random trigger average traces.

Lines in 4E could be made thicker.

In the caption of fig6, panel D is mentioned twice (should be E).

Thanks for catching this mistake we have changed the caption in the online version.

Reviewer #2 (Public Review):

The article presents 'Mesotrode,' a technique that integrates chronic widefield calcium imaging and electrophysiology recordings using tetrodes in head-fixed mice. This approach allows recording the activity of a few single neurons in multiple cortical/subcortical structures, in which the tetrodes are implanted, in combination with widefield imaging of dorsal cortex activity on the mesoscale level, albeit without cellular resolution. The authors claim that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods of time, up to 60 days post-implantation. The results demonstrate that the activity of neurons recorded from distinct cortical and subcortical structures are coupled to diverse but segregated cortical functional maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. The study also extends the capability of Mesotrode by conducting electrophysiological recordings from the facial motor nerve. It demonstrates that facial nerve spiking is functionally associated with several cortical areas( PTA, RSP, and M2), and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice.

Studying the relationship between widefield cortical activity patterns and the activity of individual neurons in cortical and subcortical areas is very important, and Murphy's lab has been a pioneer in the field. However, the choice of low-yield recording methods (tetrode) instead of more high-yield recording techniques, such as silicon probes, makes the approach presented in this study somewhat less appealing. Also, the authors claim that a tetrode-based approach can allow chronic recordings of single neural activity over days - a topic that is very controversial. In terms of results, I was under the impression that most of the conclusions presented in the bulk of the paper ( Figures 1-5) are very similar to what previous work from Murphy's lab and other labs has shown using acute preparation. In this respect, the paper can benefit from a more in-depth analysis of the heterogeneity of single-neuron functional coupling. The last part of the facial nerve recording is interesting (Figure 6), but I think it can be integrated better into the rest of the paper.

Reviewer #2 (Recommendations For The Authors):

Major Comments:

1) The methodology described in the paper is based on chronic tetrode recordings combined with widefield calcium imaging. The authors emphasize the advantages of using tetrodes in that they are 1) easy to implant 2) have a small footprint, and 3) allow to record the same neurons over days.

I agree regarding the first advantage, however, the ability to reliably record the activity of the same neurons over days using electrophysiological recordings is controversial. The authors claim that:

'We found that the single unit activity was relatively stable, during one week, two weeks, and two months of recordings after implantation (Figure 1F, G)',

The only 'proof' the authors show for recording stability are waveforms of one neuron on one channel (out of presumably four channels), which seem to differ in amplitude over days. Two-dimensional plots of the neuron waveform for all channel combinations could be a more convincing way to make this claim. But, as I already mentioned - the ability to record from the same neurons chronically with electrophysiological methods is rather controversial, especially with tetrodes that don't allow for laminar profiling of neuronal response to account for a potential drift over time.

We now make it more clear that examples of mesotrode stability are indicated in the figures. Furthermore, we acknowledge caveats that spike sorting experiments required to more conclusively identify single neurons would be improved with larger format silicon probes. Our work employs compact tetrode electrodes that permit simultaneous resolution of single units and mesoscale GCAMP activity. It is conceivable that improvements in spike sorting fidelity could be made by switching to more densely spaced silicon probes. While this is an obvious advantage, these probes do not have a compact footprint and would interfere with regional imaging.

2) The authors present little analysis justifying the advantage of conducting chronic electrophysiological recordings instead of acute recordings with their data. In fact, throughout the paper, the authors mention that the results were consistent with their previous work with acute recordings. The only longitudinal analysis in this paper is qualitative and suggests that cortical maps were stable over days. I believe this was also shown in the past already. More in depth analysis of across days dynamics or showcase of an experiment centered on across days dynamics will strengthen the appeal of this approach. Generally speaking, there is very little quantitative analysis of longitudinal maps/functional coupling of single neurons over days. The paper will benefit from at least some quantification of this part.

To our knowledge data showing the persistence of spike-associated maps longer than an acute experiment is novel. However, due to a low yield of recorded single neurons, we have not been able to follow these maps over a longer period in a population that would permit group statistics. We suggest that future experiments could be done using silicon probes with larger yields which would help to better align electrophysiological features with mesoscale GCAMP maps.

3) Recording with tetrodes gives very low yields compared to silicon probe recordings. While silicon probes have a larger footprint and may occlude the widefield imaging on the side of the silicon probe implant, it is unclear why not to use denser electrode arrays on one side of the brain and image from the other hemispheres, given that the maps are very correlated across hemispheres

Taking advantage of mirrored activity in the opposite hemisphere is a great idea. Future studies could include experiments that would take advantage of bilateral symmetry by placing high-resolution silicon probes in one hemisphere and then reading out mesoscale maps in the other.

4) The advantage of the electrophysiological recordings is in providing access to single-neuron activity at high temporal resolution. The authors could add more quantifications regarding individual neuron functional coupling diversity. For instance, in the per-area distributions in Figure 5D -- did all neurons from a given area participate in the same functional maps, or did different neurons show diversity in the functional coupling. Did simultaneous recordings of neurons from the same tetrode show more similar maps, than recordings of other neurons from the same area conducted on different days/in different animals? Did the map differ when the neurons were bursting/were at specific phases of the LFP, etc.

Unfortunately the yield of neurons was not enough to investigate some of the interesting state-dependent phenomena the reviewer describes. In previous work we have examined heterogeneity between single neuron responses in more detail Xiao et al. 2027 in acute work.

5) Facial nerve stimulation. This part feels detached from the rest of the paper and is not explained/discussed in sufficient detail. For example, there is no description of the surgical procedure or the electrode used for facial nerve recordings in the Methods (in the Results section, the authors mention 'micro-wires', but the Method section only contains information about tetrodes).

Thank you for bringing up the issue of surgical details for facial nerve experiments are now in the methods. This information is also available by contacting the authors and below.

For facial nerve recordings, peripheral nerve activity was measured by fine wire recording directly from the nerves subserving the whisker. During surgery, mice will be anesthetized and positioned on a warming pad connected to a rectal probe, and the temperature maintained at 37 °C. A skin incision was made, exposing a small part of the buccal branch of the left facial nerve. Magnification of the surgical field with a dissecting microscope allowed a careful dissection of a nerve branch with minimum disruption of the tissues and blood supply surrounding the nerve. The appropriate site of exposure was determined by using two projection lines: a vertical line running downward, posterior from the outer corner of the eye, and a horizontal line running in the caudal direction, starting at the whisker E-row. Then two insulated fine wires (about 25 µm tips) were hooked and placed around the nerve separated about 2 mm from one another. The insulation at the ends of the wires was removed and a knot was made on each wire to prevent it from slipping. The opposite ends of each wire were soldered to a mini connector attached by dental cement to the skull. Finally, 6-0 silk sutures were used to close the skin incisions.

The functional maps associated with facial nerve spiking show different patterns from the optogenetic stimulation maps that led to significant facial nerve responses. Specifically, the STM maps show responses in the posterior parts of the cortex, but the photostimulation map showed almost an opposite pattern, where the effects were observed in the anterior parts. The authors do not discuss this mismatch in sufficient detail. Further, the authors refer to area PTA but use partitions based on the Allen Institute, which does not indicate this area.

The posterior parietal area location is based on our previous work Mohajerani et al. 2013 and using the Allen Institute Brain Atlas for guidance.

Minor comments

6) The authors mention that "on average, we obtained 3-5 neurons per tetrode implanted, and this yield was consistent across regions (Figure 2C). " -- for how long, on average, could the authors record single-neuron activity from each tetrode?

The 3-5 neurons obtained per tetrode were recorded 1 week after tetrode implantation.

7) Figure 4B - it is unclear what the labels "recording 1, ...5, " correspond to. Are these different recording sessions within the same day "day 8"?

The labels "recording 1, ...5, " correspond to different recording sessions within the same day.

VAP is very common in ventilated patient, so the intervention that reduces incidence is crucial. It is true that combined interventions that includes, but not limited to frequent hand washing, elevated head to 30-45 degrees, sedation vacation, daily oral care with chlorhexidine, PUD prophylactically etc are scientifically proven that measures to reduce the incidence of VAP. DK

All of these interventions are important to know as a nurse when taking care of a patient on a ventilator. This allows us to take action and prevent our patient's from acquiring a serious illness.

It is important to understand interventions to help prevent VAP. This includes oral care with chlorhexidine, suctioning, and having the head of the bed elevated. If the patient does get VAP, it is important to know the signs and symptoms such as purulent tracheal discharge, fever, respiratory distress. A chest radiograph can also help diagnose a new or progressive infiltrate.

KP + AB

Interventions proven to reduce VAP include: - sedation and weaning protocols - semi-recumbent positioning - oral and hand hygiene - prophylaxis for peptic ulcer disease and DVT - subglottic suctioning - cuff pressure control EC

Just like hand hygiene, raising the HOB is one of the easiest interventions we can implement to prevent VAP. It also has other benefits, such as giving the patient an easier work of breathing. -NW & MB

• Head-of-Bed elevation (30° to 45°) was widely adopted in all studies except one.
• The second most common intervention was oral hygiene with chlorhexidine 0.12%.
• Some studies used alternatives like sodium bicarbonate and sponges/mouthwashes, but these didn't significantly reduce VAP.
• Six studies didn't include oral care in their VAP prevention measures.