Individual cases of Parkinson's disease can be robustly classified using magnetoencephalography.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that causes debilitating symptoms in both the motor and cognitive domains. The neurophysiological markers of PD include 'oscillopathies' such as diffuse neural oscillatory slowing, dysregulated beta band activity, and changes in interhemispheric functional connectivity; however, the relative importance of these markers as determinants of disease status is unclear. In this case-control study, we used resting state magnetoencephalography (MEG) data (n = 199 participants, 78 PD, 121 controls) from the OMEGA repository to investigate changes in spectral power and functional networks in PD. Using a Contrast of Parameter Estimates approach, we modelled the effects of PD while controlling for population-level confounds. Permutation testing revealed significant increases in theta (p = 0.0001) and decreases in gamma band spectral power (p = 0.0001). We also used a partial least squares-based classifier to find linear combinations of MEG features which independently predict PD. We found MEG-based predictions to be highly sensitive and specific, reaching an optimal AUC-ROC of 0.87 ± 0.04. Interpretation of the model indicates oscillatory slowing can be separated into components that can robustly identify individual cases of PD. This suggests MEG can reveal dissociable, complementary neural processes which contribute to PD.