# Magnetoencephalography-based prediction of longitudinal symptom progression in Parkinson’s disease

## TL;DR

This study uses brain scans to predict how Parkinson’s disease symptoms will progress over time.

## Contribution

The study introduces a novel method to separate aperiodic neural activity from oscillatory patterns in MEG data to predict Parkinson’s progression.

## Key findings

- An increase in aperiodic neural activity in the left postcentral region was linked to worsening rigidity in Parkinson’s patients.
- Baseline peak beta power in certain brain regions correlated with less severe bradykinesia, but this relationship weakened over time.
- A model using baseline neurophysiological features predicted 19.5% of motor progression variability in an independent cohort.

## Abstract

Motor dysfunction in Parkinson’s disease (PD) has been linked to widespread oscillatory changes within the basal ganglia-thalamic-cortical network, particularly in the beta frequency range. However, the evolution of cortical neurophysiological alterations and their relationship to clinical progression remain poorly understood. We conducted a longitudinal resting-state magnetoencephalography (MEG) study in 27 persons with PD and 30 healthy individuals with a mean follow-up time of 4 years. Source-reconstructed MEG data were parcellated into cortical regions, from which power spectra were parameterized to separate oscillatory peaks from the aperiodic component. An increase in the aperiodic exponent in the left postcentral region was associated with progression of rigidity. Peak beta power in parieto-temporo-occipital regions was elevated at baseline, correlating with less severe bradykinesia. This negative relationship weakened over time in patients with progressive symptoms, suggesting an association with compensatory mechanisms. Using partial least squares regression to predict future disease course from baseline neurophysiological features, 19.5% of the variability in motor progression was explained in an independent validation cohort. Our results emphasize the importance of separating aperiodic neural activity from periodic oscillations as a progressive alteration of the aperiodic component represented the most prominent PD-related neurophysiological change. Further, our findings highlight the potential predictive value of resting-state neurophysiology for future disease progression.