Invasive and Non-Invasive Neural Decoding of Motor Performance in Parkinson's Disease for Personalized Deep Brain Stimulation

TL;DR

This study demonstrates neural decoding of motor performance in Parkinson's patients using EEG and ECoG, revealing DBS effects and guiding personalized adaptive stimulation strategies.

Contribution

It introduces a patient-specific, filterbank-based machine learning approach for neural decoding of motor performance in PD, applicable across multiple sessions and modalities.

Findings

Neural decoding achieved in 28 of 35 sessions with average Pearson's r=0.37.

DBS significantly modulated kinematics in 23 sessions.

Increased speed but decreased accuracy observed under DBS.

Abstract

Decoding motor performance from brain signals offers promising avenues for adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD). In a two-center cohort of 19 PD patients executing a drawing task, we decoded motor performance from electroencephalography (n=15) and, critically for clinical translation, electrocorticography (n=4). Within each session, patients performed the task under DBS on and DBS off. A total of 35 sessions were recorded. Instead of relying on single frequency bands, we derived patient-specific biomarkers using a filterbank-based machine-learning approach. DBS modulated kinematics significantly in 23 sessions. Significant neural decoding of kinematics was possible in 28 of the 35 sessions (average Pearson's $\text{r}= 0.37$). Our results further demonstrate modulation of speed-accuracy trade-offs, with increased drawing speed but reduced accuracy under DBS. Joint evaluation of behavioral and neural decoding outcomes revealed six prototypical scenarios, for which we provide guidance for future aDBS strategies.