Mattioli, Pietro

Abstract Backgrounds Isolated REM sleep behaviour disorder (iRBD) is a prodromal marker of α-synucleinopathies. Although cognitive impairment is consistently reported in iRBD, its assessment varies considerably across studies. This study characterized mild cognitive impairment (MCI) patterns in iRBD using machine learning and identified the minimal neuropsychological test battery needed for accurate diagnosis. Methods Comprehensive neuropsychological data from 216 assessments (162 with MCI, 54 without) in 110 iRBD patients were analysed. Four supervised classifiers (logistic regression, support vector machine, random forest, gradient boosting) were trained across three frameworks (raw scores, raw scores plus demographics, binarized scores) to distinguish MCI status. A multi-objective hyperparameter optimization approach then identified the minimal test subset preserving diagnostic accuracy. Results Of MCI cases, 30% were single-domain and 70% multi-domain, with attention/executive functions and memory most frequently impaired. Binarized neuropsychological scores yielded the best classification performance, with support vector machine achieving 94.7% accuracy. Feature importance analyses consistently highlighted episodic memory, executive, and visuospatial measures. The optimization framework identified a mean of 8 informative tests spanning these domains, achieving comparable diagnostic accuracy to the full battery. Conclusions Cognitive impairment is common in iRBD and predominantly multidomain. Machine learning approaches applied to neuropsychological data can accurately classify MCI status and identify a concise, clinically feasible set of tests with high diagnostic value. These findings support comprehensive, multidomain neuropsychological assessment in iRBD and may inform optimized risk stratification in prodromal α-synucleinopathies.

Abstract Isolated/idiopathic rapid-eye-movement (REM)-Sleep Behavior Disorder (iRBD) is characterized by dream enactment behaviors associated with loss of REM atonia. iRBD is in most cases a prodromal synucleinopathy, and emerging evidence suggests associations between RBD and other neurological and psychiatric conditions. In this study, we performed pathway-based polygenic risk score (PRS) and rare variant burden analyses to examine these potential associations. Pathway-specific PRS were constructed from genome-wide association study summary statistics of five neurodegenerative and seven psychiatric traits across 10 biologically relevant pathway categories, including a total of 279 pathways, in 1,573 iRBD cases and 16,022 controls from the International RBD Study Group and UK Biobank. Rare variant burden tests were performed in 1,264 iRBD cases and 2,581 controls. We identified multiple potential pathways indicating shared polygenic risk between RBD and both neurodegenerative and psychiatric disorders. Lewy body diseases and post-traumatic stress disorder had the most shared polygenic risk pathways in neurological and psychiatric disorders, respectively. Two pathways, the serotonin transport pathway and the chaperone-mediated autophagy pathway, showed the strongest association with iRBD, and gene-based rare variants analyses revealed five genes associated with iRBD: GBA1 , PLEKHM1 , LRP2 , P2RX1 , and HAP1. Subsequent analysis of these genes in Parkinson’s disease and dementia with Lewy bodies replicated several associations. Together, these findings provide novel insights into the shared genetic architecture underlying iRBD, neurodegenerative disorders, and psychiatric traits, with implications for early identification and mechanistic understanding.

Objective To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). Method s In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography‐confirmed RBD who eventually phenoconverted to overt alpha‐synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non‐phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. Results Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD‐converters from DLB‐converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z ‐score cutoff of −1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of −1.0 at the most affected hemisphere putamen/caudate ratio characterized PD‐converters. Interpretation Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease‐modifying trials. ANN NEUROL 2024;95:1178–1192