Identification of REM Sleep Behavior Disorder by Magnetic Resonance Imaging and Machine Learning

Background Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a major risk factor for synucleinopathies, and patients often present with clinical signs and morphological brain changes. However, there is a heterogeneity in the presentation and progression of these alterations, and brain regions that are more vulnerable to neurodegeneration remain to be determined. Objectives To assess the feasibility of morphology-based machine learning in the identification and subtyping of iRBD. Methods For the classification tasks [iRBD (n=48) vs controls (n=41); iRBD vs Parkinson’s disease (n=29); iRBD with mild cognitive impairment (n=16) vs without mild cognitive impairment (n=32)], machine learning models were trained with morphometric measurements (thickness, surface area, volume, and deformation) extracted from T1-weighted structural magnetic resonance imaging. Model performance and the most discriminative brain regions were analyzed and identified. Results A high accuracy was reported for iRBD vs controls (79.6%, deformation of the caudal middle frontal gyrus and putamen, thinning of the superior frontal gyrus, and reduced volume of the inferior parietal cortex and insula), iRBD vs Parkinson’s disease (82%, smaller volume and surface area of the insula, lower thinning of the entorhinal cortex and lingual gyrus, and greater volume of the fusiform gyrus), and iRBD with vs without mild cognitive impairment (84.8%, thinning of the pars triangularis, superior temporal gyrus, transverse temporal cortex, larger surface area of the superior temporal gyrus, and deformation of isthmus of the cingulate gyrus). Conclusions Morphology-based machine learning approaches may allow for detection and subtyping of iRBD, potentially enabling efficient preclinical identification of synucleinopathies. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Shady Rahayel receives a scholarship from the Fonds de recherche du Québec – Santé. JFG holds a Canada Research Chair in Cognitive Decline in Pathological Aging. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All participants were part of research protocols approved by local ethics committees (CIUSSS-NÎM-HSCM) and CIUSSS du Centre-Sud-de-l'Île-de-Montréal-Comité d'éthique de la recherche vieillissement-neuroimagerie, Montreal, Canada) and provided written informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data sharing is not applicable to this article as no new data were created or analyzed in this study.