Losa, Mattia

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.