Dear Dr. Pascucci,

Thank you very much for submitting your manuscript "Modeling time-varying brain networks with a self-tuning optimized Kalman filter" for consideration at PLOS Computational Biology.

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Francesco P. Battaglia

Associate Editor

PLOS Computational Biology

Lyle Graham

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: In this paper, the authors present a modification of the Kalman filter with an adaptive tuning parameter, intended to adjust to the non-stationarities in the smoothness of the signal. The method is applied to both synthetic and EEG data, with convincing results.

In my opinion, this is a very solid methodological paper, although has no major neuroscience/biology contribution.

I only have relatively minor comments:

- I can imagine the computational complexity could be an issue. The experimental data seems to be of small to moderate size (I could not find the duration of the data, please specify if missing). How would the method escalate to bigger data, both in terms of number of channels and number of time points?

- "In its current form, the STOK filter is a multi-trial algorithm, leveraging regularities and correlations across trials under the assumption that multiple trials are coherent, temporally aligned realizations of the same process". Could the authors elaborate on whether the method could be applied, or adapted, to continuous data? Also, previous work has hinted to possible differences in durations of stimulus processing between trials [1,2]. Could the authors comment on how the method would behave to differences in such timing? I think it could be interesting to comment on this, even if briefly.

[1] Stokes MG, Spaak E. 2016. The importance of single-trial analy-ses in Cognitive Neuroscience. Trends Cogn Sci. 20:483–486

[2] Vidaurre D, Myers N. Stokes MG, Nobre AC, Woolrich MW 2019 Temporally Unconstrained Decoding Reveals Consistent but Time-Varying Stages of Stimulus Processing. Cerebral Cortex Volume 29: 863–874

- As a general comment, it feels to me that the Discussion is too lenghty and methods-oriented for a journal focused on Computational Biology. I would suggest to make it a bit less dense.

- Importantly, the two links that the authors provide for the code don't work. Given the type of contribution that this paper makes, and the complexity of the implementation, I believe it's fundamental that the authors provide code in good shape. I have responded No to the question of whether all data was made available for this reason.

More minor:

- Please label the Figures more clearly. For instance, Fig 2F is confusing, what are exactly the two panels, and what is represented in the X-axis (given that the selected p is indicated in the title and the ground truth is 6)? Panels 3C don't have labels in the X-axis, etc.

- Fig 3F, what happens with KF at 1000Hz? I wouldn't say these look like "similar dynamics". In 3D, KF at 1000Hz looks also strange. Isn't it at odds with the statement "Previous work has demonstrated that downsampling can have adverse effects on connectivity estimates"?

Reviewer #2: review has been uploaded as an attachment

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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: Code links don't work

Reviewer #2: Yes

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Reviewer #1: Yes: Diego Vidaurre

Reviewer #2: No

Figure Files:

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Attachments

Attachment

Submitted filename: review_v2.docx

Dear Dr. Pascucci,

We are pleased to inform you that your manuscript 'Modeling time-varying brain networks with a self-tuning optimized Kalman filter' 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,

Francesco P. Battaglia

Associate Editor

PLOS Computational Biology

Lyle Graham

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 #2: The authors have done a good job in responding to my previous critiques.

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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 #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 #2: No