ArticleMovement disorders : official journal of the Movement Disorder Society2026
Electroencephalography-Based Clustering Reveals Robust Neurophysiological Subtypes in Parkinson's Disease.
Article in Movement disorders : official journal of the Movement Disorder Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundParkinson's disease (PD) is clinically heterogeneous, with substantial variability in motor and cognitive features. Conventional clinical scales provide limited insight into underlying neural mechanisms and show poor longitudinal stability. Scalp electroencephalography (EEG) offers a direct and scalable measure of brain activity that may support mechanistically informed stratification of PD, yet EEG-based subtyping remains largely unexplored.
objectivesTo determine whether EEG-derived features can identify neurophysiological subtypes of PD and to examine their clinical and genetic correlates.
methodsEEG recordings were obtained from 116 PD patients and 30 healthy controls during resting state and treadmill walking. Spectral power (theta-gamma), aperiodic components, and temporal complexity measures were extracted across cortical regions. Multiple dimensionality reduction and unsupervised clustering approaches were evaluated, with internal validity and stability assessed using leave-one-out resampling. Clinical, cognitive, gait, and genetic features were compared across clusters.
resultsA three-cluster solution demonstrated optimal separation and robustness across models. The clusters exhibited distinct neurophysiological profiles characterized by differences in spectral slowing, signal complexity, and aperiodic activity across both behavioral states. Clusters did not differ in motor severity, gait speed, or disease duration, but showed differences in cognitive performance and genetic composition. One cluster demonstrated preserved cognition and included more LRRK2-mutation carriers, whereas two cognitively impaired clusters displayed divergent electrophysiological signatures despite similar clinical profiles.
conclusionsEEG-based clustering reveals robust neurophysiological subtypes of PD that capture heterogeneity not reflected by standard clinical measures. These findings support EEG as a scalable tool for mechanism-based stratification, with implications for prognosis and precision clinical trials. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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