Machine learning for predicting cognitive decline within five years in Parkinson’s disease: Comparing cognitive assessment scales with DAT SPECT and clinical biomarkers

Arman Gorji; Ali Fathi Jouzdani

doi:10.1371/journal.pone.0304355

Peer Review History

Original SubmissionOctober 12, 2023
7 Feb 2024 Decision Letter - Stephen D. Ginsberg, Editor PONE-D-23-28754Comparing Cognitive Assessment Scales for Predicting Cognitive Decline in Parkinson's Disease: A Hybrid Machine Learning Approach Using DAT SPECT and Clinical BiomarkersPLOS ONE Dear Dr. Fathi Jouzdani, 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. ============================== ACADEMIC EDITOR: Thank you for submitting your manuscript to PLOS ONE. After careful consideration by 2 Reviewers and an Academic Editor, all of the critiques of both Reviewers must be addressed in detail in a revision to determine publication status. 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Thank you for stating the following in the Acknowledgments Section of your manuscript: “Parkinson’s Progression Markers Initiative (a public-private partnership) is funded by the Michael J Fox Foundation for Parkinson’s Research and funding partners, including AbbVie, Allergan, Avid Radiopharmaceuticals, Biogen, BioLegend, Bristol-Myers Squibb, Celgene, Denali, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, Verily, Voyager Therapeutics, and Golub Capital. Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data).” We note that you have provided additional information within the Acknowledgements Section that is not currently declared in your Funding Statement. Please note that funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: “No- The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” Please include your amended statements within your cover letter; we will change the online submission form on your behalf. [Note: HTML markup is below. Please do not edit.] 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: Partly Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes ****** 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 ****** 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: No Reviewer #2: Yes ****** 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: In the manuscript entitled “Comparing Cognitive Assessment Scales for Predicting Cognitive Decline in Parkinson's Disease: A Hybrid Machine Learning Approach Using DAT SPECT and Clinical Biomarkers”, the authors incorporated a special technique to visualize the dopamine levels in the brain region, especially striatum along with the machine learning. However, the manuscript does not seem to be novel as there are lots of publications that are available on PUBMED related to the same approach. For instance, Mahdi Hosseinzadeh et al., 2023, Hannes Almgren et al., 2023, Hojoong M Kim et al., 2019, Mohammad Salmanpour et al., 2020, and many others. Additionally, the manuscript hold the space for improvement as it contains several flaws, which should be rectified before any decision. The writing and arrangement of the manuscript is also not up to the mark, and thus, the authors need to improve it. 1. The authors in the manuscript mentioned that they involved the PD patients in a 4-year interval. This is hard to understand why they use 4-year intervals and what is the rationale behind this selection. They should use other time intervals. It is noteworthy to mention that if they use different time intervals then it will be a good comparative study. 2. In the introduction section, more focus should be on HMLSs and its relationship with DAT SPECT. Here, the authors need to elaborate the previous studies that involved theses two techniques and how theses techniques contribute to PD diagnosis. 3. In the section clinical data, the authors mentioned that they involved only 5 clinical biomarkers. The most important question is why they neglect other important biomarkers, such as gait abnormalities, which is one of the most important characteristic features of PD. The authors should mention the inclusion and exclusion criteria in the entire manuscript in a different subsection to increase the readability of the manuscript. 4. Further, the authors incorporated APOE genetic variation. However, studies demonstrated that genetic variation in α-Synuclein is related to PD more extensively. Thus, the authors should use genetic variation in α-Synuclein against the genetic variation in APOE. 5. The discussion and conclusion section seems to be weak. It should be elaborated more. Further, the authors should mention the drawbacks and challenges of the current manuscript along with the implementation of ML in PD diagnosis. Reviewer #2: The Manuscript (PONE-D-23-28754) entitled Comparing Cognitive Assessment Scales for Predicting Cognitive Decline in Parkinson's Disease: A Hybrid Machine Learning Approach Using DAT SPECT and Clinical Biomarkers” aims to evaluate the combination of cognitive assessment scales and biomarkers which may provide an accurate prediction of cognitive decline in Parkinson's disease (PD) patients. Authors indicated that the combination of clinical biomarkers and imaging data improved the accuracy of cognitive decline prediction in PD. In general, the Manuscript is not well written and clear to understand, consequently it requires some major revisions. In addition, objectives and the rationale are not clearly stated in the Manuscript. The organization of the Manuscript is confusing, not very rational, and inappropriate in some sections. Specific comments: Cognitive impairment is a common non-motor symptom in PD which is frequently associated with olfactory deficit as indicated in previous studies (Solla et al., 2023, https://doi.org/10.3390/biology12010112; Baba et al., 2012; Fang et al., 2021; Cecchini et al., 2019, https://doi.org/10.1007/s00702-019-01996-z). Authors should include this finding in the Introduction section. The Introduction section is too long and should be summary and focused on the specific topic. It is not necessary to describe the MoCA test, the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPRDS-I) and other scales in the Introduction. These sections should be moved in the Methods section. In the Participant section Authors should indicate the exact number of men and women enrolled. The main limitations of this Manuscript should be included at the end of the Discussion section. The conclusions need to be implemented with future perspectives. ****** 6. 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Revision 1
28 Apr 2024 Author Response Response to Reviewers Reviewer #1: In the manuscript entitled “Comparing Cognitive Assessment Scales for Predicting Cognitive Decline in Parkinson's Disease: A Hybrid Machine Learning Approach Using DAT SPECT and Clinical Biomarkers”, the authors incorporated a special technique to visualize the dopamine levels in the brain region, especially striatum along with the machine learning. However, the manuscript does not seem to be novel as there are lots of publications that are available on PUBMED related to the same approach. For instance, Mahdi Hosseinzadeh et al., 2023, Hannes Almgren et al., 2023, Hojoong M Kim et al., 2019, Mohammad Salmanpour et al., 2020, and many others. Additionally, the manuscript hold the space for improvement as it contains several flaws, which should be rectified before any decision. The writing and arrangement of the manuscript is also not up to the mark, and thus, the authors need to improve it. - Thank you so much for your consideration. In 2023, Mahdi Hosseinzadeh and colleagues utilized the MoCA cognitive scale in the fourth year to forecast cognitive deterioration. In a similar timeframe, Hannes Almgren and his team also employed MoCA for anticipating cognitive shifts over four years. Earlier, in 2019, Hojoong M. Kim and associates conducted a comparative analysis of MoCA, DRS-2, and MMSE as cognitive assessments over an average span of 3.8 years. Subsequently, in 2020, Mohammad Salmanpour and his group undertook a clustering task that centered on the motor outcomes of patients, applying the HMLS method. Contrasting with these studies, our study predicts cognitive decline based on MoCA and the MDS-UPDRS-I score. Moreover, to enhance the precision of predicting Parkinson's Disease-Cognitive Decline (PD-CD), our study incorporated assessments over various durations. 1. The authors in the manuscript mentioned that they involved the PD patients in a 4-year interval. This is hard to understand why they use 4-year intervals and what is the rationale behind this selection. They should use other time intervals. It is noteworthy to mention that if they use different time intervals then it will be a good comparative study. - We appreciate your comment. As previously stated in our last paper, the four-year mark was selected based on prior findings indicating it as the pivotal year when cognitive abilities typically diminish, allowing for more accurate predictions. However, your idea about the concept of comparing various time intervals was fascinating, as it promised a more in-depth analysis of performance for each cognitive scale. As a result, in our revised study, we assessed different time frames, specifically 2, 3, 4, and 5 years, which adds another novelty to our work. We decided against including additional years because they would significantly reduce our data sample size, hindering the effective application of machine learning algorithms. 2. In the introduction section, more focus should be on HMLSs and its relationship with DAT SPECT. Here, the authors need to elaborate the previous studies that involved theses two techniques and how theses techniques contribute to PD diagnosis. - Thank you so much for your insightful comments. To distinguish our research from earlier studies, we shifted the emphasis of our paper from HMLS to an analysis of cognitive scales and timeframes. We employed machine learning techniques in place of HMLS to simplify the analysis of trajectories. Additionally, we referenced prior research that utilized ML or HMLS for forecasting cognitive outcomes based on DAT SPECT or clinical data in the introduction section of our paper. 3. In the section clinical data, the authors mentioned that they involved only 5 clinical biomarkers. The most important question is why they neglect other important biomarkers, such as gait abnormalities, which is one of the most important characteristic features of PD. The authors should mention the inclusion and exclusion criteria in the entire manuscript in a different subsection to increase the readability of the manuscript. - In our study, we initially selected five clinical biomarkers to enhance performance, as suggested by the referenced research (Predictors of cognitive impairment in Parkinson’s disease: a systematic review and meta‑analysis of prospective cohort studies). Based on your comment and upon further examination of additional literature and a deeper consideration of your feedback, we opted to incorporate additional clinical biomarkers, including gait abnormalities and fifteen other characteristics. Furthermore, we have detailed inclusion and exclusion criteria for participants in the participants section of our paper. 4. Further, the authors incorporated APOE genetic variation. However, studies demonstrated that genetic variation in α-Synuclein is related to PD more extensively. Thus, the authors should use genetic variation in α-Synuclein against the genetic variation in APOE. - We appreciate your comments. While cerebrospinal fluid α-Synuclein is more extensively connected to the risk of Parkinson's disease development, APOE variations have been shown to be more predictive of cognitive decline, as indicated by research (Alpha-Synuclein and Cognitive Decline in Parkinson's Disease). In light of your feedback and our study's evolution towards a multi-omics methodology and cognitive-related clinical biomarkers, we have included α-Synuclein in our clinical features to enhance the predictive accuracy of our models. 5. The discussion and conclusion section seems to be weak. It should be elaborated more. Further, the authors should mention the drawbacks and challenges of the current manuscript along with the implementation of ML in PD diagnosis. - Based on your valuable comments, we have significantly overhauled our manuscript. The discussion section has been thoroughly revised to provide an in-depth analysis of various cognitive scales and time intervals. Additionally, we have comprehensively outlined the principal challenges and limitations encountered in our research. Furthermore, with the shift in emphasis away from HMLS, we have succinctly referenced prior studies that have employed ML to predict PD-CD in the introduction section. Reviewer #2: The Manuscript (PONE-D-23-28754) entitled Comparing Cognitive Assessment Scales for Predicting Cognitive Decline in Parkinson's Disease: A Hybrid Machine Learning Approach Using DAT SPECT and Clinical Biomarkers” aims to evaluate the combination of cognitive assessment scales and biomarkers which may provide an accurate prediction of cognitive decline in Parkinson's disease (PD) patients. Authors indicated that the combination of clinical biomarkers and imaging data improved the accuracy of cognitive decline prediction in PD. In general, the Manuscript is not well written and clear to understand, consequently it requires some major revisions. In addition, objectives and the rationale are not clearly stated in the Manuscript. The organization of the Manuscript is confusing, not very rational, and inappropriate in some sections. Specific comments: Cognitive impairment is a common non-motor symptom in PD which is frequently associated with olfactory deficit as indicated in previous studies (Solla et al., 2023, https://doi.org/10.3390/biology12010112; Baba et al., 2012; Fang et al., 2021; Cecchini et al., 2019, https://doi.org/10.1007/s00702-019-01996-z). Authors should include this finding in the Introduction section. - Thank you for your comment. We have included the mentioned references and used their findings. The Introduction section is too long and should be summary and focused on the specific topic. It is not necessary to describe the MoCA test, the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-I) and other scales in the Introduction. These sections should be moved in the Methods section. - We greatly appreciate your insightful comments. Following your suggestions, we have refined the introduction to better reflect our unique approach, which emphasizes the comparative analysis of cognitive scales and temporal intervals. To enhance the readability of the introduction, we have moved the comprehensive details of the scales into the supplementary materials. In the Participant section Authors should indicate the exact number of men and women enrolled. - We added the exact number of men and women. The main limitations of this Manuscript should be included at the end of the Discussion section. - We have included a discussion of the limitations in the relevant section. The conclusions need to be implemented with future perspectives. - We have included a discussion of the future perspectives in the relevant section. Attachments Attachment Submitted filename: Response to reviewers.docx https://doi.org/10.1371/journal.pone.0304355.r002
9 May 2024 Decision Letter - Stephen D. Ginsberg, Editor Machine Learning for Predicting Cognitive Decline within Five Years in Parkinson's Disease: Comparing Cognitive Assessment Scales with DAT SPECT and Clinical Biomarkers PONE-D-23-28754R1 Dear Dr. Fathi Jouzdani, 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. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. 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, Stephen D. Ginsberg, Ph.D. Section Editor PLOS ONE 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: All comments have been addressed ******** 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 ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: 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 ****** 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: No ****** 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: Accepted in its current form. The manuscript significantly contribute to the scientific faterenity and may open a door for future studies. ****** 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: Yes:** Rohan Gupta https://doi.org/10.1371/journal.pone.0304355.r003
Formally Accepted
12 Jun 2024 Acceptance Letter - Stephen D. Ginsberg, Editor PONE-D-23-28754R1 PLOS ONE Dear Dr. Fathi Jouzdani, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Stephen D. Ginsberg Section Editor PLOS ONE https://doi.org/10.1371/journal.pone.0304355.r004

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