PONE-D-22-21443SPARE-Tau: A Flortaucipir Machine-Learning Derived Early Predictor of Cognitive DeclinePLOS ONE

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Additional Editor Comments:

Please respond to reviewer's comments.

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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: Yes

Reviewer #2: Yes

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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: Yes

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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 is well conducted study.

The author showed, Flortaucipir indices showed the strongest clinical association among the studied biomarkers and were predictive in the preclinical disease stages.

Among the flortaucipir indices, the machine-learning derived SPARE-Tau index was the most sensitive clinical progression biomarker.

The combination of different biomarker modalities better predicted cognitive performance.

Reviewer #2: The authors developed the novel machine learning derived index, SPARE-Tau, to assess tau PET accumulation in the earliest disease stage of Alzheimer’s disease (AD) continuum. The authors demonstrated that SPARE-Tau was the most sensitive clinical progression biomarker among all tested tau PET indices. This paper proposed the novel unbiased index of tau PET with flortaucipir as predictive biomarker in the preclinical disease stages of AD continuum to facilitate clinical research and clinical trials. However, there remains several concerns that should be addressed before considering publication.

Major points.

1. Previous studies reported that CSF p-Tau best predicted longitudinal cognitive decline in cognitively unimpaired subjects [X], while the authors showed no significant association of baseline CSF p-tau with longitudinal cognitive decline. Although predictive performance of tau PET could become better using the novel machine learning-derived index (SPARE-Tau) instead of other conventional indices, the reason why the predictive performance of CSF p-Tau discrepancies between the present and previous studies should be discussed.

X. Mattsson-Carlgren N, et al., Neurology. 2020;94(21):e2233-e2244.

2. Recent study demonstrated that CSF p-tau generally becomes abnormal before tau PET and that cognitively unimpaired subjects with abnormal CSF p-Tau and positive PET scan have a risk of clinical progression, followed by cognitively unimpaired subjects with abnormal CSF p-Tau despite negative scan of Tau PET [Y]. In the present study, the median follow up was just 1.9 years, which is relatively short duration. Taken together, although we can conclude that elevated accumulation of Tau PET must predict future cognitive decline, we didn’t draw the robust conclusion whether the CSF p-Tau would predict future cognitive decline because of the short follow up period.

Y. Groot C, et al., Brain. 2022 Sep 9:awac329. doi: 10.1093/brain/awac329. Online ahead of print.

3. In the section of “MRI Acquisition and Processing” (page 7, lines 128-131), the authors explained that “The training data included only healthy controls with known-negative Aβ status and only dementia participants with known-positive Aβ status.”; however, then it is also written that “The model was trained with CU data only and applied to all participants included in this study”. What does it mean? Was SPARE-AD model retrained without data of dementia participants with known-positive Aβ status?

Minor points

4. Page 6, lines 115-117

Regarding the PET reading method, whether visual judgment was used or semi-quantitative values such as centiloid scale or SUVR were used, and if so, how the cutoff values were set should be briefly indicated.

5. Page 8, lines 149-151

The “)” may be missing in the sentence.

6. Page 8, line 155

Only the title of the chapter (SPARE-AD Training) is given, the contents is not written.

7. Page 10, line 201

“CSF p=tau” shold be corrected to “CSF p-tau“.

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Reviewer #1: No

Reviewer #2: Yes: Hitoshi SHIMADA

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SPARE-Tau: A Flortaucipir Machine-Learning Derived Early Predictor of Cognitive Decline

PONE-D-22-21443R1

Dear Dr. Habes,

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.

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Kind regards,

Kensaku Kasuga

Academic Editor

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Additional Editor Comments (optional):

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 #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 #2: Yes

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

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 #2: 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 #2: 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 #2: The authors addressed all of my questions and concerns.

I believe the manuscript vastly improved.

I would have no further criticism.

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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 #2: Yes: Hitoshi SHIMADA

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