Identification of Parkinson's Disease Subtypes from Resting-State Electroencephalography

BackgroundParkinson's disease (PD) patients present with a heterogeneous clinical phenotype, including motor, cognitive, sleep, and affective disruptions. However, this heterogeneity is often either ignored or assessed using only clinical assessments. ObjectivesWe aimed to identify different PD sub-phenotypes in a longitudinal follow-up analysis and their electrophysiological profile based on resting-state electroencephalography (RS-EEG) and to assess their clinical significance over the course of the disease. MethodsUsing electrophysiological features obtained from RS-EEG recordings and data-driven methods (similarity network fusion and source-space spectral analysis), we have performed a clustering analysis to identify disease sub-phenotypes and we examined whether their different patterns of disruption are predictive of disease outcome. ResultsWe showed that PD patients (n = 44) can be sub-grouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterized by different levels of disruptions in the somatomotor network (Delta and beta band), the frontotemporal network (alpha 2 band) and the default mode network (alpha 1 band), which consistently correlate with clinical profiles and disease courses. These clusters are classified into either moderate (only-motor) or mild-to-severe (diffuse) disease. We showed that EEG features can predict cognitive evolution of PD patients from baseline, when the cognitive clinical scores were overlapped. ConclusionsThe identification of novel PD subtypes based on electrical brain activity signatures may provide a more accurate prognosis in individual patients in clinical practice and help to stratify subgroups in clinical trials. Innovative profiling in PD can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption. (c) 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Automated summary

In this study, different sub-phenotypes of Parkinson's disease (PD) were identified based on electrophysiological profiles obtained from resting-state electroencephalography (RS-EEG). These sub-phenotypes showed distinct disruptions in various brain networks and were predictive of disease outcome. EEG features were also able to predict cognitive evolution of PD patients. The identification of these novel PD subtypes based on electrical brain activity signatures has important clinical implications and can support the development of brain-based therapeutic strategies.