Dear Ms Malamud,

Thank you very much for submitting your manuscript "Characterizing the dynamics, reactivity and controllability of moods in depression with a Kalman filter" 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.

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[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).

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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,

Christoph Mathys

Academic Editor

PLOS Computational Biology

Andrea E. Martin

Section 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: This paper describes the application of a Kalman filter to model individual differences in self-reported mood dynamics. This is an interesting and clearly written paper showing that a new analytic approach can provide novel insights and incorporate additional information into dynamical systems analyses of mood.

Comments/concerns:

1) The authors briefly cover the use of summary statistics and VAR models to analyze mood dynamics, but does not review any previous work on state space models applied to this sort of data. Have previous studies used Kalman filter or similar state space approaches to study mood dynamics?

2) One motivation the authors provide in the introduction for using model-based analyses is that summary statistics approaches “do not explain the underlying data generation process”. Do the authors claim that their model represents the true underlying process generating emotion dynamics?

3) Why did the authors choose minutes as the time steps (versus hours, 15 minute intervals, etc.), given that this leads to very sparse observations? What effects does this choice have on the data, and is there information about the time course of emotion dynamics that can inform the optimal time step?

4) The parameter recovery analyses are useful to understand the limitations of the analyses. With more observations, does recovery improve (similar to the first column in figure 2)? Do these analyses provide any recommendations when designing studies to ensure good recovery?

5) The controllability analyses are interesting but I had a hard time following the interpretation. How can there be negative controllability (in figure 6B)? I am also having trouble connecting the data in figure 6B to the description in the paragraph below (“how controllable the most controllable direction was appeared to decrease over the three phases (baseline: 4.2, during discontinuation: 1.48, after transition: 1.07))”.

Reviewer #2: Thank you for the opportunity to review this manuscript by Malamud and colleagues, which applies a venerable state-space modelling framework (the Kalman filter) to the question of affect dynamics in depression. Three extant datasets were re-analysed using this method, and the results serve as a very compelling proof-of-concept for the application of this analytic framework to this type of data. I was very impressed by the manuscript in general, and have only several relatively minor comments below requesting further elaboration of several aspects of the results.

1. I agree with the manuscript that an important potential bonus of analysing affect dynamics within the state-space modelling framework is the ability of these models to account for the effects of external inputs on subjective affect. Given that potential advantage, therefore, I found it striking that the manuscript found that adding the estimation of input weights to the KF negatively affected inference overall. This is an important point, and should be discussed in more detail in the results (and potentially in the Discussion). In particular, I think more space should be given to discussing exactly which results point to this conclusion (and how), as well as a discussion of the circumstances under which this conclusion would be expected to hold. For instance, is it invariably the case that estimating input weights will negatively affect inference? What about if the subject is placed in an experimental environment where the inputs are exactly controlled and known (e.g., the in-lab modelling paradigm popularised by Robb Rutledge over the last decade or so). No need to do any analyses or simulations to answer these questions, but on reading through I felt it would be important for others to know whether the adverse effects of modelling input are a general feature of the Kalman filter or something more specific to the datasets that were analysed.

2. Very little of the manuscript's discussion is concerned with interpreting the clinical significance of results for theories of depression. I appreciate that this is not the primary focus of the manuscript, but even so I feel that the authors are well placed here to draw some links between their findings and the extant literature on affect dynamics in depression (e.g., work on inertia of affect in depression). I think it would be helpful for readers to have access to an expanded discussion of these effects in the context of psychiatric theory.

Minor points:

- Typo on page 7, line 171: the manuscript uses the word "punctuate" rather than "punctate", which I think is what is intended

- The labelling of subplots in Figure 1 is rather confusing; for instance, subplot B itself includes two subplots themselves labelled a and b. This has the potential to confuse readers and a more unambiguous labelling system should be chosen.

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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: No: datasets not publicly available

Reviewer #2: No: The URL for a GitHub repository containing data and analysis code links to a repository that does not seem to exist.

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Reviewer #1: No

Reviewer #2: No

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Dear Ms Malamud,

We are pleased to inform you that your manuscript 'Characterizing the dynamics, reactivity and controllability of moods in depression with a 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,

Christoph Mathys

Academic Editor

PLOS Computational Biology

Andrea E. Martin

Section 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: I thank the authors for their responses. I have no further comments.

Reviewer #2: The authors have addressed all my concerns in this revision.

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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

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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: No

Reviewer #2: No