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.

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.

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.