EEG-Based Evaluation of Brain Activity Modulation Through Deep Brain Stimulation in Parkinson’s Disease

Abstract

Introduction Parkinson’s disease (PD) is a neurodegenerative disorder affecting motor and cognitive function. While dopaminergic medications provide symptom relief, deep brain stimulation (DBS) is often used in advanced cases to manage motor dysfunction. However, the response to DBS varies between individuals, and optimal stimulation settings are typically determined through clinical trial and error. This highlights the need for biomarkers to monitor DBS-induced changes in brain function. Methods In this pilot study, we explored EEG-based connectivity features as potential biomarkers for DBS effects in two PD patients. EEG was recorded before and after DBS implantation. Preoperative EEGs were collected while patients were off medication; postoperative EEGs were obtained six months later with DBS turned on. Preprocessing followed Makoto’s pipeline in EEGLAB, and source reconstruction was conducted in Brainstorm using MRI-based Boundary Element Method head models generated via Open-MEEG. EEG signals were projected onto the Desikan-Killiany atlas and segmented into 3-second epochs. We extracted three connectivity metrics from source- space data: Amplitude Envelope Correlation (AEC), Phase Locking Value (PLV), and Avalanche Transition Matrix (ATM). A Wilcoxon permutation test (1000 iterations) with max-statistic correction was used to assess changes between conditions. Results ATM showed no significant differences. In contrast, AEC and PLV both revealed reductions in beta- band connectivity after DBS activation (Fig 1). Subject specific patterns were observed: subject 1 showed stronger AEC changes, while subject 2 exhibited more pronounced PLV effects. These results suggest that beta-band connectivity is sensitive to neuromodulation and that EEG-based metrics can capture individualized brain dynamics. Conclusions This pilot study supports the use of source-space EEG connectivity analysis for tracking DBS- elated changes and lays the groundwork for future integration into modeling frameworks such as The Virtual Brain (TVB).