Dear Dr Park,

Thank you very much for submitting your manuscript "Integration of Partially Observed Multimodal and Multiscale Neural Signals for Estimating a Neural Circuit Using Dynamic Causal Modeling" 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,

Daniele Marinazzo

Section 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: This study introduces a dynamic causal modeling (DCM) method to address the challenge of integrating unevenly observed, multiscale signals within a larger-scale neural circuit. This is achieved by employing a shared neural state model and observation models for different imaging modalities. The authors propose a method that reciprocally integrates local and global information from different modalities to infer the target circuit.

The proposed reciprocal and iterative technique substantially improves the estimation of local and global connectivity compared to traditional one-step estimation approaches, which typically rely on less informative priors for unobserved data.

This study raises important questions, particularly relevant to the DCM community, where causal inference is widely applied across different domains. The results are interesting, but the paper needs more clarity regarding what has been accomplished. Specifically, the introduction and abstract are written in a very generic manner, which raises issues related to what exactly has been done. From the introduction, I was left with the impression that one could infer calcium imaging (CaI) from fMRI data, which seems misleading!!

If local and global information informs each other, should there not be a sequential relationship between them, or can they be decoupled? Why are they not inferred simultaneously from the data?

In the introduction, the phrase "combining unevenly observed multimodal signals" is unclear—how does this apply? In terms of dimension? It wasn't clear from the introduction what is meant by multimodal multiscale inference. As a reader, should I expect to see the inference of CaI from VSDI or fMRI? Does this involve all the activity of CaI or only certain signals? Combining them could indeed improve model evidence, but the introduction gives the impression that one can infer CaI from EEG/fMRI or, more generally, infer fMRI from EEG/MEG, which seems overly ambitious. If this works, does it mean one can generate recordings that we currently don’t know how to measure?

My main concern is: if EEG and fMRI data are combined into a single system (shared neural state model), then the system must be dynamically rich and multiscale (i.e., involve slow and fast timescales, up/down states, and limit cycles). How is this feasible? Wouldn't you need different mappings (gain matrices) from source to sensors for each modality? Are these mappings known? How are the modalities coupled in the state space? For example, the Epileptor model offers a taxonomy of seizures (Jirsa et al., Brain 2014, 10.1093/brain/awu133), but this requires strong a priori knowledge about the combined modalities. It would be very helpful to formulate the problem in a state-space framework and clearly define what is known, what is observed, and what is inferred (with dimensions included).

For Figure 2, it would be helpful to show true versus estimated effective connectivity.

For Table 3, it would be beneficial to describe the estimations—if they are the same, perhaps in the caption, but if they differ, include a column for each experiment.

How strong is the prior, and what is the gain of information from prior to posterior? For instance, Hashemi et al. introduced a fully Bayesian method to examine the influence of priors on estimation (https://doi.org/10.1371/journal.pcbi.1009129).

What are the limitations of the study, and under what conditions does the algorithm fail (in-silico)? For example, in terms of parameter/variable dimensions? SEEG signals are sparse, and inference from them is a formidable challenge. The approach used in this paper could be very helpful in informing non-identifiable parameters.

Lastly, is there any investigation or comment on the degeneracy (or non-identifiability) of effective connectivity?

Reviewer #2: This study addressed an ambitious modelling challenge: how to infer the common neural dynamics that give rise to neuroimaging measurements at different spatial and temporal scales, specifically calcium imaging, voltage-sensitive dye imaging and BOLD fMRI. The novelty of the authors’ solution is 1) the pairing of a single neural model with modality-specific observation models, and 2) implementation of Bayesian model inversion methods, specifically the initialization of parameters using a scheme called Bayesian Optimization, and the design of an efficient scheme for iteratively estimating local and global parameters. This is an excellent piece of work and a strong contribution to the field. I just have a single query / suggestion for the authors, that relates to their novel scheme for model parameter estimation (3.1.2).

The authors began model estimation using Bayesian Optimization (BAYESopt) to select suitable priors for subsequent estimation using the typical Variational Laplace (VL) scheme in DCM. It took me a bit of time to understand this. I suggest adding a paragraph to the methods section introducing Bayesian Optimization and clarifying the distinction between this and the standard VL scheme (which of course is also a scheme for Bayesian optimization). In particular, could the authors explain whether BAYESopt offer some advantage over simply relaxing the prior variances to make them uninformative and using the VL scheme?

**********

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: Yes

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: 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, 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 Dr Park,

We are pleased to inform you that your manuscript 'Integration of Partially Observed Multimodal and Multiscale Neural Signals for Estimating a Neural Circuit Using Dynamic Causal Modeling' 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,

Daniele Marinazzo

Section Editor

PLOS Computational Biology

Daniele Marinazzo

Section Editor

PLOS Computational Biology

Feilim Mac Gabhann

Editor-in-Chief

PLOS Computational Biology

Jason Papin

Editor-in-Chief

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: Thanks to author for the detailed responses. The revision deals with satisfactory results.

**********

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: 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: Yes: MH