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

2026-03-29

AI Summary

This study addresses the decoding of motor performance from neural signals in Parkinson’s disease patients to support personalized adaptive deep brain stimulation (aDBS). By integrating filter bank approaches with machine learning, it pioneers the simultaneous use of both invasive (ECoG) and non-invasive (EEG) signals in a clinically translatable setting to extract patient-specific neural biomarkers. These biomarkers successfully decoded kinematic parameters during a drawing task across 35 recording sessions, yielding significant decoding in 28 sessions (mean r = 0.37). Deep brain stimulation significantly increased movement speed in 23 sessions but concurrently reduced accuracy. Based on these findings, the study proposes six distinct neuro-behavioral response patterns, offering a foundation for individualized aDBS strategies tailored to patients’ unique neural and motor profiles.

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.

Problem

• Parkinson's disease
• motor performance
• neural decoding
• deep brain stimulation
• adaptive DBS

Innovation

• adaptive deep brain stimulation
• neural decoding
• filterbank-based machine learning
• electrocorticography
• personalized biomarkers