Electrophysiological signatures predict the therapeutic window of deep brain stimulation electrode contacts.

ABSTRACT: Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease. Identifying the optimal parameters is a complex task. Here, we investigated whether electrophysiology, combined with machine learning, can support contact selection. We applied tree learning to resting-state magnetoencephalographic and local field potential recordings from the subthalamic nucleus (STN). STN power and STN-cortex coherence in various frequency bands served to predict the therapeutic window. The model successfully predicted therapeutic windows in the original (r = 0.45, p < 0.001, N = 45) and in an independent cohort (r = 0.30, p < 0.001, N = 8). It relied mostly on fast (>35 Hz) subthalamic activity and on STN-cortex coherence in several bands. Furthermore, it was able to order contacts such that

SUBMITTER: Rassoulou F 

PROVIDER: S-EPMC12572235 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature