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

BackgroundIdiopathic 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. ObjectivesTo assess the feasibility of morphology-based machine learning in the identification and subtyping of iRBD. MethodsFor the classification tasks [iRBD (n=48) vs controls (n=41); iRBD vs Parkinsons 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. ResultsA 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 Parkinsons 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). ConclusionsMorphology-based machine learning approaches may allow for detection and subtyping of iRBD, potentially enabling efficient preclinical identification of synucleinopathies.