Modulation of Brain Activity via Deep Brain Stimulation in Parkinson’s Disease Patients: Individualized EEG Based Assessment

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised by motor and non-motor symptoms, and is associated with altered brain network dynamics. Understanding large-scale functional changes is crucial for improving diagnosis, treatment planning, and monitoring of disease progression or intervention effects. Electroencephalography (EEG) combined with source reconstruction and magnetic resonance imaging (MRI) data facilitates the analysis of cortical dynamics with greater anatomical precision. The present study investigates differences in preoperative and postoperative brain activity in a PD patient using source-constructed EEG. The objective of this study is to extract and compare the connectivity and dynamical features of brain networks to identify functional alterations associated with the deep brain stimulation (DBS) treatment. The resting state EEG dataset of PD patients was collected before and after DBS electrode implantation. The patients were treated at the Department of Neurosurgery, Lithuanian University of Health Sciences Hospital in Kaunas, Lithuania, 2024–2025. Preoperatively, EEG recordings were obtained when the patients were off L-DOPA medication, and postoperatively with the DBS electrode activated and off L-DOPA medication. Post-surgery EEGs were recorded six months later after the surgical intervention. All EEG recordings were acquired using a 64-channel ANT Neuro device. The study protocol was approved by the Kaunas Regional Biomedical Research Ethics Committee (No. BE-2-115), informed consent was obtained from every study participant. EEG preprocessing was performed using Matlab EEGLab and Makoto’s Miyakoshi pipeline. Brain sources were reconstructed utilising processed EEGs in Brainstorm, using patient MRI data to create Boundary Element Method (BEM) surfaces and an individualized head model via OpenMEEG. Electrode positions were manually adjusted, EEG data was concatenated, and downsampled to the Desikan-Killiany atlas. Source scouts were then extracted and resegmented to the 3-second epochs. The EEG features Amplitude Envelope Correlation (AEC) and Phase Lock Value (PLV) were calculated. A Wilcoxon permutation test (1000 permutations) was used to assess the statistical significance at the edge level, focusing on connections between brain regions. The resulting p-values were MaxT corrected to control the family-wise error rate, and the effect size was calculated. All statistically significant edges exhibited an effect size greater than 0.5. The most significant regions were selected by ranking PLV and AEC metrics. The top 15 of both features were compared to find regions with most statistically significant edges. A normalised sum of all statistically significant edges per EEG band was also calculated for comparison. The most significant changes were found in the Beta band for both AEC and PLV features, as reported in previous studies [1]. Specifically, the normalised number of the significant AEC features in the following bands were as follows: Delta: 0.00, Theta: 1.18, Alpha: 2.71, Beta: 15.82, Gamma: 5.85; the number of the PLV features were: Delta: 0.00, Theta: 1.56, Alpha: 3.53, Beta: 13.62, Gamma: 9.68. The regions with the most significant edges were the Fusiform Gyrus on both left and right side, Pericalcarine Cortex, Parahippocampal Gyrus, Supramarginal Gyrus, Temporal Pole, Pars Opercularis, Precentral Gyrus, and Superior Frontal Gyrus on the left hemisphere, and Caudal Middle Frontal Gyrus and Superior Temporal Gyrus on the right hemisphere. The findings of this study indicate that the most significant alterations in brain connectivity occurred in the Beta frequency band for both AEC and PLV metrics [1]. In contrast, other frequency bands showed comparatively minor alterations. These results suggest that DBS-induced modulation predominantly affects the Beta band connectivity within these critical brain regions, which may have implications for understanding the neural mechanisms underlying DBS efficacy in Parkinson’s disease