Modulation of Brain Activity via Deep Brain Stimulation: an EEG-Based Assessment with Personalized Models in Parkinson’s Disease

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder marked by motor impairments, often accompanied by pathological oscillations in brain networks (notably excessive beta-band activity). Understanding these alterations is crucial for improving diagnosis and guiding therapies such as deep brain stimulation (DBS). In this study, we combined a computational model with source-reconstructed EEG to examine how dopaminergic modulation and DBS affect brain network activity in PD. We created a neural mass model that accounts for dopamine-driven modulation. The model suggests that increasing dopamine reduces pathological oscillations and promotes more stable brain activity. To test this prediction, we analyzed high-density EEG from a PD patient recorded before DBS (on/off medication) and six months after DBS implantation (on/off stimulator, on/off medication). EEG signals were source-reconstructed using the patient’s MRI, and functional connectivity metrics (amplitude envelope correlation, AEC; phase-locking value, PLV) were calculated across standard frequency bands. Our results showed that beta-band functional connectivity was markedly reduced after DBS – the largest change among all frequency bands. Beta-band coupling between cortical regions (especially frontal and temporal areas) significantly decreased postDBS, whereas changes in delta, theta, alpha, and gamma bands were minor. This reduction in pathological beta synchrony aligns with our model’s prediction that enhanced dopaminergic signaling suppresses excessive network oscillations and corresponds to improved motor function with DBS therapy. These findings highlight beta-band connectivity as a key biomarker of PD network changes and demonstrate that integrating computational modeling with EEG connectivity analysis yields mechanistic insight into DBS effects, informing personalized neuromodulation strategies. To further support clinical translation, these models will be embedded into The Virtual Brain platform to create individualized digital twins of Parkinson’s disease patients.