Resting-State Electroencephalography Reveals Three Subphenotypes of Parkinson’s Disease
Sahar Yassine1, Ute Gschwandtner2, Manon Auffret3, Joan Duprez4, Marc Verin5, Peter Fuhr2, Mahmoud Hassan6
1Université Rennes 1, 2Hospitals of the University of Basel, 3Behavior & Basal Ganglia - CHU Rennes/ Universite de Rennes 1, 4University of Rennes 1, 5CHU Hopital Pontchaillou, 6Reykjavik University
Objective:
To identify different Parkinson’s disease (PD) sub-phenotypes and their electrophysiological profile based on resting-state electroencephalography (RS-EEG).
Background:

Parkinson’s disease is a complex brain disorder characterised mainly by motor signs, with clinical evidence of cognitive and behavioural deficits in up to 40% of cases in early stage. Clinical and biological manifestations are very heterogeneous among individuals with PD and subgrouping was mainly performed using clinical parameters. However, these subgroups do not take into account underlying disease physiopathology, and were shown not entirely predictive of disease prognosis.

Design/Methods:

Resting-state electroencephalography (EEG) is a powerful tool to identify abnormal patterns of motor and cognitive deficits in PD. These disruptions have previously been identified across multiple frequency bands using cortical spectral power and functional connectivity from longitudinal high-density EEG recordings (baseline, 3 years follow-up and 5 years follow-up). In this study, using data-driven methods (similarity network fusion and source-space spectral analysis), we have performed a clustering analysis to identify disease sub-phenotypes and to determine whether different patterns of disruption are predictive of disease outcome.

Results:
We showed that PD patients (N = 44) can be subgrouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterised by different levels of disruptions in the somatomotor network (delta and beta band), the frontotemporal network (alpha2 band) and the default mode network (alpha1 band), which consistently correlate with clinical profiles and disease courses. We showed that these clusters are statistically robust, and can predict the clinical trajectory and disease outcome.
Conclusions:

Our findings show that novel phenotyping using electric brain signal analysis can distinguish PD subtypes based exclusively on different patterns of brain oscillations. These patterns can reflect underlying disease neurobiology. Innovative profiling in PD has clear potential in patient’s stratification and can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption.

10.1212/WNL.0000000000202900