PONE-D-20-17526

Within- and between-module functional connectivity in the somatomotor and the frontal-cingulate-parietal networks associates with motor inhibition

PLOS ONE

Dear Dr. Hsieh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Both Reviewers have a number of major concerns regarding the manuscript that should be carefully addressed in your revision. I advise you to pay particular attention to the methodological concerns that have been raised, especially regarding the use of some additional validation of the results, as both Reviewers shared this concern. Please also ensure that you clarify points of ambiguity that have been pointed out by the Reviewers.

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Niels Bergsland

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Overview

In this paper, Hsu and colleagues use resting-state fMRI in distributed brain networks to predict individual differences in task performance (in this case, the stop-signal task). In particular, for each of the seventeen brain networks they analyzed, they calculated two graph-theoretic statistics (Participation Coefficient, PC, which measures between network connectivity, and Within-module-degree, WMD, which measures within network connectivity) and trained a multiple linear regression model to predict SST performance across subjects. The paper’s motivations and scope are, in general, well-written and clear. However, there are serious concerns with the statistical methods that were employed. Therefore, I believe that the manuscript is not currently suited for publication in its current form. If the authors can address these all of these concerns, I would be happy to look at a revised version. The following contains my specific concerns with the current publication in order of how important I think they are.

1. Problem with training paradigm of regression model:

The authors train a linear regression model to predict stop-signal task performance across subjects using graph-theoretic statistics of the functional brain networks. It seems that the authors did not evaluate the linear regression model on a set of held-out subjects. If this is the case, then the authors will not be able to claim that these network statistics can predict individual differences in behavior. The current results can only suggest that there is a potential linear association between these network statistics and the individual differences in behavior. In order to show brain data can predict individual differences in behavior for a particular dataset, one must employ some kind of cross-validation paradigm where predictions from the regression model are evaluated on subjects that were not included in the training of the regression model. This is a standard procedure for studies that use functional brain data to explain individual differences in behavior. For example, Rosenberg et al. 2016 Nature Neuroscience use leave-one-out cross validation in their internal validation analyses to demonstrate their model accurately predict behavioral scores of unseen subjects. Additionally, they also show that their models have strong generalizability by showing their linear regression models trained on a dataset with a particular behavior measure and set of subjects generalize to a dataset with a completely different set of subjects and a different behavioral measure. Generalizing to different task performance measures might be out of the scope in the current study, but I think it is reasonable to ask the authors to employ a more principled approach in terms of having test sets of held-out subjects during training.

2. Reporting p-values

I was not able to find details on how the p-values were determined for reporting the Pearson correlation that determined model performance in the current study. I understand that they were corrected for using Bonferroni corrections, but how exactly were the original uncorrected p-values determined before they were subsequently corrected? Were they determined parametrically or non-parametrically? I recommend the p-values be determined non-parametrically, because a non-parametric approach would not make any assumptions on the data and thus one would avoid possibly violating certain assumptions that invalidate the p-value. If the authors decide to employ cross-validation to address my first point above, then this point will be even more relevant. Here is an example of calculating a p-value non-parametrically (the permutation test): shuffle the participants’ task performance, perform the whole analysis pipeline, calculate regression performance (in this study’s case, Pearson’s correlation), and repeat procedure many times and report the percentage of iterations that had a Pearson’s correlation as high as the one obtained from unshuffled data. This procedure should be rigorous enough to obtain reliable p-values in the current study regardless of the analysis pipeline.

3. Addressing head motion during analysis stage in addition to the preprocessing stage

The effect of head motion on functional connectivity analyses is well documented. It’s good that head motion parameters were regressed out in the preprocessing stage, but I think it is important that head motion is ensured to not be affecting the main analyses. Hsu et al. 2018 Social Cognitive and Affective Neuroscience is a similar study to the current study in that the authors are also using strength of functional brain network to predict individual differences in behavior and assess their predictions by correlating predicted behavior scores from brain activity to actual behavior scores (in their case, the behaviors were personality traits). This study addressed head motion by using partial Pearson correlations between observed scores and predicted scores to control for head motion. A similar kind of control for head motion in both the preprocessing and analysis stage (rather than only in the preprocessing stage) would greatly strengthen the current work by ensuring head motion is not confounding the main results.

4. Clarity on describing the brain network statistics

Although the authors point to previous work explaining how to calculate PC and WMD and include useful high-level descriptions in the current manuscript, I think it would be useful for them to include a specific section with precise mathematical descriptions of how these graph-theory statistics are calculated. Doing so will make it clearer for readers interested in reproducing the study’s results.

5. Clarity on results figure.

Figure 2a contains the effect sizes (Pearson correlations) from the statistical analysis of each of the brain networks (predicting SST performance from graph-theoretic statistics). It would be useful for the figure to mark which brain networks yielded statistically significant results (possibly with a star next to the network name). Additionally, in order to have a better sense of the variance of the correlation, it would be nice if the authors included error bars to each of the bars in Figure 2a. I recommend these error bars be 95% confidence intervals calculated non-parametrically using the bootstrap method (for the same reasons I addressed in point 2 when I suggested using non-parametric p-value calculations).

6. Performing a multiple regression across all brain networks

The authors did a regression for each individual brain network to predict subjects’ individual differences in SST performance. Why not do a multiple linear regression that uses all 17 brain networks to predict the behavior scores? This could give interesting information on how much each brain network is contributing to the prediction (using the magnitude of the regression weights that have been assigned to each of the brain networks).

7. Clarity on preprocessing section

Section 2.3.2 is a little unclear to me. Nuisance covariates were regressed out in the second step, and then certain parameters are set as nuisance covariates in the third step and a filter is applied on them in the fourth step. I’m a little confused on what role these third and fourth steps are playing.

8. Small error in describing previous work.

Line 124 indicates “there is no prior research employing graph-theoretic analysis methods on network properties of rs-fMRI data to associate with SSRT.” However, Kumar et al. 2019 Brain and Behavior, which is cited previously in the introduction as citation 24, use a graph theory quantity (maximum flow) to predict task performance in a dataset that employs the Stop Signal Task. The difference is that the current study is using different graph-theoretic quantities and focuses on motor inhibition (whereas Kumar and colleagues focus on attention) but this detail should be emphasized.

9. Minor points

In line 149, I think “form” should be “from.”

References

1. Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature neuroscience, 19(1), 165-171.

2. Hsu, W. T., Rosenberg, M. D., Scheinost, D., Constable, R. T., & Chun, M. M. (2018). Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Social cognitive and affective neuroscience, 13(2), 224-232.

3. Kumar, S., Yoo, K., Rosenberg, M. D., Scheinost, D., Constable, R. T., Zhang, S., ... & Chun, M. M. (2019). An information network flow approach for measuring functional connectivity and predicting behavior. Brain and behavior, 9(8), e01346.

Reviewer #2: In this manuscript, the authors used resting-state fMRI to investigate associations between motor inhibition and connectomics. The methods are sound, the manuscript clearly written and the conclusions supported by the data. The large sample size is a strength of this study. I have nevertheless some comments/suggestions which I strongly believe would improve the manuscript and clarify its impact

Major points:

Introduction: The rationale for a graph-theory analysis in this context needs further elaboration; while, the authors present the characteristics of this analysis and its potential to unravel topological features, it is not clear why/how this approach will bring new insights to answer this specific research question

Methods: Please add reference of ethical approval

Participants: How did the authors decide on sample size? The manuscript does not contain any evidence of power calculation

MRI: Can the authors expand on the specific instructions given to the participants for the resting-state scan? Did the authors check by any mean alertness during the scan? Were the scans acquired at the same time of the day? Further information on this extra sources of variability would be interesting to know

Preprocessing: Did the authors perform any QC on movement beyond censoring "bad volumes"? Did they exclude any subject because of excessive movement? What were the maximum number of censored volumes the authors thought to be acceptable for including a subject?

Connectomics: The description of the methods for calculating the PC and WMD is highly insufficient; in the absence of this information, the reader cannot follow or scrutinize the approach taken by the authors to derive these metrics

Multiple regression: Were the assumptions of this statistical model verified? If yes, how? Also, given the large sample size i feel it would strength the message if the authors would consider including a further cross-validation analysis (k-folds) to examine how well the model will generalize to new observations; Given their focus on the somatomotor network, are the authors confident that this association is not explained by inter-individual differences in head movement? Did authors control for head movement (i.e. mean framewise displacement) in their regression models?

Discussion/Conclusion: I am afraid the reader is left without a clear understanding of what this study brings that is new; please consider expanding on the contribution and implication of these findings

Minor points:

Line 398: "shows more explanations" - please rephrase

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Reviewer #1: No

Reviewer #2: No

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PONE-D-20-17526R1

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLOS ONE

Dear Dr. Hsieh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I strongly encourage you to address the issue of correcting for multiple comparisons. As pointed out by Reviewer 1, Bonferroni correction is likely to be overly conservative. You could consider correcting for the false discovery rate, as an alternative.  Also, as stated by Reviewer 2, please address the concerns about post-hoc power analysis.

Please submit your revised manuscript by Nov 23 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Niels Bergsland

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I believe the authors have provided a very comprehensive revision and have addressed the majority of my concerns.The paper is much stronger than before, but there are three main issues that I think need to be addressed before this paper is appropriate for publication. If these can be addressed, I think this paper will be well-suited for publication. The following are the aforementioned issues, in order of how crucial I believe they are:

1. Accounting for multiple comparisons in the determination of statistical significance

The authors have done a great job in using cross-validation to generate and evaluate predictions on held-out subjects as well as utilizing permutation testing and non-parametric p-values. However, since there are many of these statistical tests being done (seventeen brain networks and two graph-theory statistics for 34 statistical tests in total). It is absolutely necessary to adjust the statistical tests for the multiple comparisons issue. The previous iteration of the manuscript used the Bonferroni method, which is a great method to use for multiple comparisons, but it seems that this has been taken out in the current manuscript.

Providing a statistical test that incorporates both non-parametric permutation testing as well as takes into account for multiple comparisons will be the best way to ensure the study’s results are statistically solid enough for publication.

The authors could use Bonferroni in this situation, but I do recognize that Bonferroni is highly conservative and it may be that none of the authors’ results may end up being statistically significant if they use Bonferroni. I would be fine if the authors used a less conservative method of multiple comparisons, but I think it is absolutely necessary to at least incorporate some kind of statistical correction for multiple comparisons.

2. Error in displaying error bars in Figure 2a.

The authors have added error bars in Figure 2a to give an estimate of the effect size of the regression models’ performance, which is a great addition. However, I think there has been a mistake in adding these error bars. The error bars are at the beginning of each bar in the barplot (where it starts at 0) instead of the end of the bar (which is where the calculated R value lies). This should be fixed immediately as it makes the figure very confusing.

3. Clarity in displaying which brain networks/graph-theory statistics are significant

Table 3 displays r and p-values for each of the statistical tests. It is difficult to find which one is significant, so it would be helpful for the authors to bold-face the rows in which the p-value was significant. Related to this, it is similarly difficult to find which brain network/graph theory statistic was significant in Figure 2. A similar bold-facing on the network name would be really helpful here as well.

Reviewer #2: The authors have addressed most of my previous comments. However, I am afraid that the power analyses the authors now added need some further contextualization. The authors used the degrees of freedom and effect sizes from their own data to provide a picture of actual achieved power for each regression model. This does not inform the reader on how the sample size was decided, but can provide information for appraising each individual finding. This needs to be better explained, otherwise these analyses sound rather seldom. Also, if it was the case that a priori power analyses to decide on sample size were not conducted, please state this openly - I don't see a major problem here, but for clarity it is important that the reader is aware of such circumstance.

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7. 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

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment 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. Registration is free. 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 PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PONE-D-20-17526R2

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLOS ONE

Dear Dr. Hsieh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please address the comment from Review 1 regarding the error bars. If there is some reason that you wish to keep the Figure as-is, please provide a specific rationale.

Please submit your revised manuscript by Dec 25 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Niels Bergsland

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I believe the major comments on the statistics have been met, but I do request that the error bars in Figure 2 for SalVentAttnB in PC be corrected. It seems the bars aren't centered on the end of the bar. It is very important that these error bars are placed correctly, so I implore the authors to double check this figure.

Reviewer #2: As far as i can tell, the authors have addressed all the comments neatly. I commend the authors for the Bayesian analyses included in the revised version of the manuscript, which are important to interpret some of their null findings. I believe the current version meets the standards of Plos One; hence, i am delighted to recommend this manuscript for publication.

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7. 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

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment 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. Registration is free. 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 PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PONE-D-20-17526R3

Dear Dr. Hsieh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Niels Bergsland

Academic Editor

PLOS ONE

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