PONE-D-23-16414Harmonizing Healthy Cohorts to Support Multicenter Studies on Migraine Classification using Brain MRI DataPLOS ONE

Dear Dr. Wu,

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.

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Lorenzo Faggioni, M.D., Ph.D.

Academic Editor

PLOS ONE

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This work was supported by the United States Department of Defense W81XWH-15-1-0286, Schwedt, Todd,  (https://cdmrp.health.mil/search.aspx?LOG_NO=PR140037)

 National Institutes of Health K23NS070891, Schwedt, Todd,  (https://report.nih.gov/reportweb/web/displayreport?rid=568&ver=5)

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Reviewers' comments:

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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: Yes

Reviewer #2: Partly

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

Reviewer #1: Yes

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: Yes

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: This research study suggests a strategy designed to enhance the accuracy and generalizability of machine learning models applied in multicenter imaging studies. The ultimate objective is to classify individual migraine patients and healthy controls using brain MRI data. Authors employed the Maximum Mean Discrepancy (MMD) in Geodesic Flow Kernel (GFK) space, to harmonize data from two sources. Their approach consisted of identifying a group of homogeneously healthy controls, called the "healthy core". The authors suggest this methodology effectively mitigated typical confounding factors inherent in multicenter studies such as participant characteristics variations, MRI scanner disparities, and divergent imaging acquisition protocols. The study utilized two datasets: one with 120 individuals (comprising 66 migraine patients and 54 controls) and another with 76 (consisting of 34 migraine patients and 42 controls). The implementation of the "healthy core" concept reportedly led to a 25% enhancement in the accuracy of the classification models for both episodic and chronic migraine patients. The authors have proficiently conveyed their findings and offered comprehensive information about their experiments.

The primary aim of my comments (both major and minor) is to help the authors advance their study to the next level. This is achievable by addressing currently ambiguous aspects, thus enhancing the study's scientific merit and improving its readability and flow.

Please check the attachment for details

Reviewer #2: Dear authors,

Congratulations on your interesting work, which aims to tackle the challenge of having generalizable neuroimaging classification models and end up proposition a "healthy core" in the lines of "normative models", being taken by other researchers.

I believe that in the current form, the manuscript is not delivering its suitable potential, not only in clinical or physiological interpretability (the most important) and also regarding reproducibility of your work or methods by others. The materials and methods section feels incomplete or unclear, and some snipets of text seem to be misplaced in the results section. Also regarding results, be aware of the interpretation of machine-learning results, and notably the influence of having imbalanced datasets and the interpretation you derive from ROC AUC values. Maybe consider having a stronger support of someone more seasoned on those topics.

Also, I'm sending you in the attachment the pdf manuscript with suggestions of edits, comments and questions.

Please address these issues and provide a more clinical interpretation of the features chosen by the algorithms and models derived from such, to elevate your paper.

Consequently, I feel that your paper needs a major revision before being ready for publication and I'll be available to help with the review of the improved version.

Kind regards

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Reviewer #1: No

Reviewer #2: Yes: Hugo Alexandre Ferreira

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Attachments

Attachment

Submitted filename: PONE-D-23-16414_rev1_20230716.docx

Attachment

Submitted filename: PONE-D-23-16414_rev.pdf

PONE-D-23-16414R1HealthyCore: Harmonizing Brain MRI for Supporting Multicenter Migraine Classification StudiesPLOS ONE

Dear Dr. Wu,

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 submit your revised manuscript by Apr 01 2024 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'.
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• 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: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Lorenzo Faggioni, M.D., Ph.D.

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 #3: (No Response)

Reviewer #4: (No Response)

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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 #3: No

Reviewer #4: No

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

Reviewer #3: No

Reviewer #4: No

**********

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 #3: No

Reviewer #4: Yes

**********

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 #3: Yes

Reviewer #4: 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 #3: ## Summary

- In this study, the authors focused on enhancing generalizability in classifying individual migraine patients and healthy controls using brain MRI data through a data harmonization strategy by using the Maximum Mean Discrepancy (MMD) in Geodesic Flow Kernel (GFK) space. Their results showed a homogeneous dataset derived from a cohort of healthy controls yielded a significant 25% accuracy improvement for both episodic and chronic migraineurs.

- The differences in participants characteristics between multi-institutional datasets are a significant issue and have already been discussed somewhere [1,2]. Since their methodology appears to be novel, and if the utility of this method is demonstrated, the contribution of this paper could be substantial. However, at present, the descriptions of how the methods and results were obtained are unclear, and the reliability of the results is low, which would make it difficult to justify publication of this paper. I have provided detailed points below.

## **Major Comments**

- **Unclear Description of Proposed Method**

- The description of the authors' proposed method is unclear, especially in Figure 1 where it is not evident what processes are being carried out. For instance, it is completely unclear how the 204 features are being utilized. It is also not clear whether MMD is calculated for each feature or feature vector. What does 'Multiple view' mean in the Figure 1?

- Looking at the algorithm in the Appendix, it seems like matching, but is it a method of extracting a similar group of subjects from both Dataset 1 and Dataset 2 in equal numbers?

- It is written, “To address this challenge, we propose a novel concept, the 'healthy core,' which involves transforming and aligning imaging data from HCs using their brain MRI data.” However, it is not clear if modulation applies to the features in the proposed method. If it does, how does it apply? There is no explanation for this.

- Moreover, with this algorithm, if there are identical subjects in both Dataset 1 and Dataset 2, it seems like it would end at i=1. How should the number of subjects be determined?

- **Unnatural and Unclear Aspects in Results**

- The results in Table 10 are labeled as accuracy, which I assume means, for example, in 'Predicting CM from DS2', how many out of 26 were correctly predicted. However, the numbers in the table do not correspond to any possible outcomes. For instance, for 'EM from DS2' with n=8, the accuracy should be one of 0.125, 0.25, 0.375, 0.50, 0.625, 0.75, 0.875, 1.0, but the numbers in Table 10 do not match any of these.

- Figures 3 and 4 appear unnatural to me. For example, even though the relative positions of the red dots for EM are exactly the same between Figures 4A and 4B, the scales of the x and y axes are different. This could be due to a mistake in the program and should be checked.

Also, in Figures 3 and 4CD, the scales differ too much between the left and right figures, and it seems like only the data for EM and CM are changing. This isn't just plotting EM and CM separately using t-SNE, right? It's applied to the integrated data, right? There is no explanation about the methodology in this regard, making it impossible to assess. Furthermore, since t-SNE depends on initial values and other factors, it might be necessary to also check with PCA or MDS.

- While some harmonization methods like Linear regression for noise removal and ComBat have been proposed [3], the authors' method is explained as being different from these methods. However, to demonstrate its utility, a direct comparison of the performance of the discriminators is necessary.

- The authors compare AUC=0.65 from 20 test data samples with AUC=0.62 from 70 test data samples between Experiment 1 and Experiment 2, but the difference in the number of samples in the Test is too large. Therefore, if the authors want to conclude from those results, some statistical test should be necessary.

## **Minor Comments**

- On page 12, the authors said that “The test results indicate that DS1 and DS2 are not compatible after mean and variance adjustments,” from which results can this be said?

- In the results of Table 4, the differences due to measurement bias and sampling bias are not distinguished (see [1]). If there is a significant difference between DS1 and DS2, could it be due to a large measurement bias?

- There is no description of how the hyperparameters for the machine learning methods were determined. What criteria were used on the validation dataset? AUC, accuracy, sensitivity, or specificity?

- Is the regularized logistic regression L1, L2, or elastic net? Given the clear overfitting of the results, L1 might be better.

- Tables 5 and 7 appear to be the same.

- Include validation results in Tables 6A and 6B as well.

- Not only AUC but also accuracy, specificity, and sensitivity should be displayed for discrimination performance. While I acknowledge the usefulness of AUC for unbalanced data, relying on a specific metric for evaluating the performance of a discriminator is risky, and it is important to consider these metrics holistically.

- On page 14, it states, “This deterioration in accuracy is expected.” Why is this anticipated? If the model is designed to avoid overfitting, deterioration should not occur. Is this statement necessary?

- "In Experiment 3, when the authors mention 'without healthy core', does this mean the authors examined the sensitivity as in Experiment 2? In other words, training and validation were following Table 7, and then using only CM and EM for the test?”

[1] https://sci-hub.se/downloads/2021-08-31/6f/maikusa2021.pdf

[2] https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000042

[3] Johnson W E, et al. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–127.

Reviewer #4: In this study, the authors introduce a data harmonization approach, utilizing Maximum Mean Discrepancy, to identify a homogeneous healthy core from multicenter studies. They apply this method to two datasets with both healthy controls and migraine patients.

The work carried out by the group could be interesting. However, the method description is challenging to read and many details are reported in the results. Statistical analyses heavily focus on AUC but do not assess the accuracy of the analyses (balanced accuracy, considering groups of different sizes, sensitivity, specificity, confidence interval). The validation employs groups of 3 subjects, making result generalization very difficult. To highlight the method's strengths, it would have been more useful to analyze controls versus the entire migraine sample without dividing between chronic and episodic cases. These issues unfortunately present major challenges to drawing meaningful conclusions from the data.

Major:

- In the introduction, it should be specified that the focus is on harmonizing already collected data (on extracted features) and not during the acquisition phase (sequence parameters). Some references are missing.

- In FreeSurfer, despite being defined as Talairach transformation, it is applied between the original volume and the MNI305 atlas. Correct? The atlas used for the parcellation of the 68 regions is missing. Why are subcortical regions not considered?

- Many method sections are reported in the results.

- Tests (e.g., BDI, STAI) conducted on patients are mentioned but not used in the analyses.

- Tables lack details in both demographics and statistics (accuracy, sensitivity, specificity, confidence interval) and should be grouped. Statistical demographic differences between controls of the two groups are not reported.

- Resampling methods (e.g., bootstrap), a statistical procedure that resamples a single dataset to create many simulated samples, were not used to enhance result generalization.

- It's unclear which variables (thickness, area, surface) the results of various models are associated with.

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Reviewer #3: Yes: Ayumu Yamashita

Reviewer #4: No

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PONE-D-23-16414R2HealthyCore: Harmonizing Brain MRI for Supporting Multicenter Migraine Classification StudiesPLOS ONE

Dear Dr. Wu,

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 submit your revised manuscript by Jun 30 2024 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: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Lorenzo Faggioni, M.D., Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments:

I commend the authors on considerably improving their manuscript over the original submission. However, there are still some issues that should be addressed as per the reviewer's comments, with the main goal to put the study findings into perspective and strengthen the discussion section.

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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 #4: (No Response)

**********

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 #4: Partly

**********

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

Reviewer #4: 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 #4: Yes

**********

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 #4: 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 #4: The use of a ‘healthy core’ can be helpful in multicentre studies. I appreciate the changes made to the text, however, there are still points that require further review.

1. The discussion lacks a comparison with previous works and references. The only sentence where works are mentioned is 'Many prior publications (including some of our own) reported the accuracy of classification models for migraine train and test using the same dataset, with strategies such as the "leave-one-out" methodology [Schwedt, T.J. et al. 2015, Chong, C.D. et al., 2017, Lee M.J. et al., 2019, Zhang, Q. et al., 2018, Yang, H. et al., 2018, Chong, C.D., 2021].' It is not possible to evaluate the goodness of the method if not compared to other models, even those not on migraines.

2. There are too many tables and they are not well structured:

Combine Table 1 and Table 2 together. Follow the example of Schwedt 2015. Insert variables age and gender in separate rows.

Combine Table 6, 8, and 10 indicating with a row the experiment (1, 2, 3) they are linked to.

The tables lack explanatory captions. For example, what the symbol > or arrow (e.g., CM1>CM2) means.

Combine Table 7A and 7B together. Same for Table 9A and 9B.

Why was a different terminology used in Table 11 than in Table 10? Predicting CM from DS2 instead of DS1 arrow DS2? Choose the same terminology in all tables for consistency.

3. Reformulate the sentence related to Freesurfer's recon-all. The message is incorrect. The recon-all allows obtaining cortical surfaces. By applying the Desikan atlas, it is possible to obtain volume, area, and thickness measurements for cortical regions.

4. Check the acronyms. For example, at the beginning of the results, acronyms already used that had not been defined such as EM and CM are reported.

5. There are several sentences in the results that should go in the discussion. E.g., page 18: ‘These significant enhancements … improvements across all evaluated performance metrics’.

6. Modify the title of the paragraph '3.4. Discussions on Healthy Core'. It cannot be defined as a 'discussion'. It seems more like an in-depth analysis. I emphasize that this part of the result is not reported in the method section.

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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 #4: 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.

HealthyCore: Harmonizing Brain MRI for Supporting Multicenter Migraine Classification Studies

PONE-D-23-16414R3

Dear Dr. Wu,

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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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Lorenzo Faggioni, M.D., Ph.D.

Academic Editor

PLOS ONE

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 #4: 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 #4: Yes

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

Reviewer #4: Yes

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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 #4: Yes

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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 #4: Yes

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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 #4: I have no further comments to add. the authors have attempted to respond to my comments. they improved the reading of the text

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Reviewer #4: No

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