Dear Dr. Casile,

Thank you very much for submitting your manuscript "Robust point-process Granger causality analysis in presence of exogenous temporal modulations and trial-by-trial variability in spike trains." for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

The manuscript is of considerable technical complexity. In order to make this comprehensible for the general readership of PLOS CB, the arguments in the manuscript need to be clarified. The reviewers have numerous constructive suggestions for this, and also for additional simulations that will help you to better support your conclusions.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Abigail Morrison

Associate Editor

PLOS Computational Biology

Kim Blackwell

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Review enclosed in pdf format.

Reviewer #2: Overview: The authors extend the existing Granger causality technique of Kim et. al. for continuous time signals to that for discrete spike trains that is robust to within-trial temporal variations in spike and between trial variability in spike magnitudes. The proposed model includes the aforesaid modulations as new parameters into a generalized linear model of the neuronal conditional intensity function. The hyperparameters of this model are tuned by minimizing Akaike’s information criterion. The authors claim that the model shows improvement over the existing Granger causality technique by recovering connectivity matrices with reasonable measures of accuracy and the technique allows decomposition of temporal variations of firing patterns into those effected by endogenous and exogenous components.

Premise: The existing Granger causality technique assumes that all sources of temporal modulations are endogenous to the set of processes considered. The authors herein attempt to highlight that temporal variations may be brought about by both exogenous and endogenous factors via modeling techniques, namely G-ETM. Furthermore, they claim that the computationally involved G-ETMV technique reveals patterns of functional connectivity which is different from synaptic connection motifs but important from a perspective of interpretation.

Main:

• The writeup for the results highlight that considering the effect of exogenous events in each non-overlapping window during each trial predicts the functional connectivity between neural units. However, none of the equations referred to (not even the ones proposed) appear in the main text, rather they all are provided under Methods. As a reader this leads to confusion: how to interpret the proposed model from the text without visualizing the actual mathematical change proposed to the existing technique.

• Building on the above: it would be *very* useful to have some sort of schematic showing a clear comparison between the proposed and prior modeling frameworks. Seeing the core equations side by side, e.g., in a Table, would allow the reader to clearly delineate the mathematical changes and understand the conceptual contribution of the proposed method.

• Figure 2 includes caption for subfigure C, i.e., estimates of the exogenous components of firing patterns, the subfigure itself is not shown in the current version.

• The authors claim that the simulations leading to Figure 3 depict the scenario when simultaneous recording from two different areas is done during an experiment. The idea is to investigate the nature of functional connectivity between subpopulations during such experimental setup. The ground truth model however did not have any interpopulation interactions and what the simulation suggests is that the introduced method could identify that there were no connections between subpopulations. However, it fails to capture how this method(G-ETM) would perform if there were weak/strong interactions between the two subpopulations. Intuitively this should be explored as well. Alternatively, if there are any reasons for omitting such connectivity in model simulations that should be stated.

• The point about self and mutual interactions being inhibitory/excitatory over different timescales needs further clarification/explanation.

• The discussion section does not include any discussion about the limitations/ potential sources of error while using these models that is if there are certain situations when the functional connectivity between neural units are not faithfully recovered using this method. In particular, the proposed method chooses a specific functional form for the new exogenous covariates. Are there situations where these parametric forms are expected to fail? Simulating spike trains from a mismatched model, perhaps even a spiking neuronal network model where a variety of modulating factors could be investigated, could be quite useful in this regard.

Other issues:

• In the Abtract/Intro there is some confusion regarding what is meant by ‘exogenous’. Spike rate modulation may not actually be exogenous to the population; rather it is exogenous to the prior granger formulation. Would it be better to simply refer to these as unmodeled factors?

• The Introduction could use a little more in terms of background on prior Granger methods. The authors introduce the abbreviations for their proposed techniques without actually defining them; it would be useful to get some initial context on what these methods are going to do (e.g., by adding new covariates to the point process framework).

• The notation is Equation (4) is somewhat nonstandard. I think this would be easier to follow if c was defined as an integer and used in the equation, with the (t/T)N_i definition being introduced later. Why are some parameters notated with uppercase while others lowercase?

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: No:

Reviewer #2: No: Primary data are not provided.

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Reviewer #1: Yes: P.G.L. Porta Mana

Reviewer #2: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions, please see http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methods

Attachments

Attachment

Submitted filename: ploscb_granger.pdf

Dear Dr. Casile,

Thank you very much for submitting your manuscript "Robust point-process Granger causality analysis in presence of exogenous temporal modulations and trial-by-trial variability in spike trains." for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. 

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Abigail Morrison

Associate Editor

PLOS Computational Biology

Kim Blackwell

Deputy Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Please see enclosed PDF file.

Reviewer #2: The authors have carefully addressed my prior comments. I have no further concerns.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: No: The authors state they will provide some of the scripts upon acceptance

Reviewer #2: Yes

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PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: PGL Porta Mana

Reviewer #2: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methods

Attachments

Attachment

Submitted filename: ploscb-reviewer1_round2.pdf

Dear Dr. Casile,

We are pleased to inform you that your manuscript 'Robust point-process Granger causality analysis in presence of exogenous temporal modulations and trial-by-trial variability in spike trains.' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Abigail Morrison

Associate Editor

PLOS Computational Biology

Kim Blackwell

Deputy Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I thank the authors for addressing all my concerns.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: No: Authors say data will be provided upon acceptance

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: PGL Porta Mana