Dear Dr Cabrera-Álvarez,

Thank you very much for submitting your manuscript "Modeling the role of the thalamus in resting-state functional connectivity: nature or structure" 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.

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,

Hayriye Cagnan

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section 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: The authors construct whole-brain network models based on the JR neuronal mass, in order to study the impact of the thalamus. Even though results do not support the initial hypothesis about the importance of the thalamus, the authors still perform a thorough mechanistic exploration of the model from dynamical point of view. As a such the manuscript could be of interest to the field of computational neuroscience that studies the brain as a systems, but a more critical analysis of the reasons for the observed shortcomings is necessary.

By the construction of the brain network model, all the nodes are equal. Hence the statement that any node could play the role of the thalamus is misleading, unless it is stated that this is the case only for the way the brain model has been constructed. If the thalamus was generated from a different neuronal mass to reflect its distinctive properties, this wouldn’t have been the case. Moreover, if the paths from the thalamus were directional as it is the case in the reality, this also wouldn’t have been the case. The authors should hence refer to the attempts to model specific brain regions as separate entities in the brain network model, through multi-scale or co-simulation models, such as for example the works using TVB by Meier et al, or the works on the whole brain scaffold modeling by the lab of E. D’Angelo.

Similarly in the line 138 authors say that the thalamus did not show an impact, when it fact it did and it made the fit of the model worse. The authors should discuss why this could be the case, along the above lines, not to avoid the issue.

A second issue is the importance of time-delays for spectral activation patterns and synchronization as shown by several papers from the lab of V. Jirsa (e.g. the normalization of the connectome). Is JR model sensitive on delays? It should be at least discussed that for oscillatory activity time-delays can be of equal importance as the weights.

Within the same line, the importance of the of the graph theory metrics (and non-identifiability of the regions) appears only because of the simplicity of the model that doesn’t distinguish different brain regions, nor the directed paths from the subcortical regions, and moreover, because the model is either not sensitive to time-delays, or their impact is negligible due to the too high conduction velocity of 15m/s. Lemarechal et al Brain 2022 used the largest ever empirical cohort (close to 1000 patients) to find mean values of ~3.3 m/s, which is also the value in Petkoski & Jirsa 2022 that yields the most recognizable spectral activation patterns. So the choice of the conduction velocity needs further justification.

Minor comments:

- fig. 1 could be made for anatomically adequate.

- while rsfMRI FC is discussed as being predictive for aging, lately metrics based on dFC have been shown to have higher predictive value, see e.g. Battaglia et al Neuroimage 2020 and Petkoski et al Cereb Cortex 2023. Also adding some context in why dFC is expected to contain more information would be useful.

- discarding the tracts longer than 180mm seems way too restrictive, unless the authors refer to the Euclidian distance. But I see no reason why to use Euclidian distance instead the actual tract lengths, which could go above 250 mm.

- not clear which for frequency band are the shown PLV values.

- why the choice of 5s for the windows of dFC? is this robust? and why even a fixed length window for every band? The authors should at least try to justify their choices.

Reviewer #2: In their article, Cabrera-Alvarez et al. simulate a set of coupled Jansen-Rit models as a whole-brain network model in order to compare the model functional connectivity with empirical functional connectivity obtained with MEG data. They use structural connectivity obtained using diffusion MRI to couple the local regional models and scaling parameter g which is a classical approach in these types of models. Then they compare the model-empirical FC fit between three different versions of the local connectivity in the thalamus - namely without, with a single node and with a parcellated node thalamus - for values of g that correspond to pre- (when the models behave as damped oscillators) and post - (in limit cycle oscillators regime) bifurcation space. They also compare model-empirical FC for each case with low and high values of noise input to the thalamic node(s) that is supposed to represent afferences from the reticular system and argue that best fits are obtained in the pre-bifurcation region with high noise input to parcellated thalamus. They then use this result to claim that thalamus could be playing a driving role in the emergence of overall RS-FC.

I find that the simulations in the paper are performed with adequate detail and correctly. However, I found the rationale/motivation for the type of modelling/analysis performed lacking. I also found the interpretation of the results problematic especially with regards to the final conclusion and the title of the paper.

My first major concern is with the emphasis the authors put on the pre-bifurcation region. Looking at their results in this region, the model-empirical FC fit for parcellated thalamus case with high noise is significantly higher than without thalamus & with single node thalamus models. However, the rPLV values in the post-bifurcation region are in the same range as these highest pre-bifurcation values (pTh + high noise). Therefore it’s not clear why the authors focussed on the pre-bifurcation region specifically later on, i.e. what additional biological/empirical evidence (power spectrum for instance?) implies that the pre- and not post-bifurcation region is the optimal one is not clear.

Instead of using global FC metrics, authors could consider specific FCs and see how they match between the model & empirical data. For instance which model - parcellated or single-node thalamus - gives a better matching empirical thalamocortical FC could be explored to fine-tune both the parameter-space (g) as well as the choice of pre-vs-post bifurcation regime.

Authors show that instead of the thalamus, using a parcellated cerebellum gives the same levels of model-empirical FC fits as with the thalamus. They use this result to say that the specific structure in the thalamus does not matter and it’s the noise. But the flip side of this observation is also that within the limited confines of the metrics (FC and dFC similarity) used by the authors and the model, thalamus is not unique. So how could one make a claim, based purely on these observations, that the thalamus is performing a driving role in emergence of FC? Perhaps it’s the cerebellum? Either the authors should moderate their claims about the role of the thalamus in the article or they should use additional metrics (perhaps local FC instead of global, brain-wide FC ?) to make a distinction between the thalamus vs cerebellum comparisons. Also, I was curious to see what happens if both the cerebellum and thalamus are parcellated (that would be closer to biological reality anyway) ?

It is not clear how the authors chose the noise levels. There should be an explanation for the values chosen by the authors.

The readability of the manuscript can be improved is significantly overall. Sub-figures can be labelled A, B etc. Figure captions at the moment are extremely inadequate and they can’t be understood without going through the results section. That should not be the case.

I take it that the horizontal blue lines in the FFT plots in figures 5 and 6 represent bifurcation thresholds only? I would suggest to use a different color that’s not included in the colormaps of these figures for the lines in that case.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: None

Reviewer #2: Yes

**********

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: No

Reviewer #2: Yes: MOHIT H ADHIKARI

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, we recommend that you deposit your 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. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear authors,

We are pleased to inform you that your manuscript 'Modeling the role of the thalamus in resting-state functional connectivity: nature or structure' 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. 

Please ensure to clarify data availability at the proof stage.

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,

Hayriye Cagnan

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section 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: The authors have addressed all of my comments

Reviewer #2: The authors have sufficiently addressed my comments.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: None

Reviewer #2: None

**********

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: No

Reviewer #2: Yes: Mohit Adhikari