VNS Responders versus Non-Responders: Unveiling EEG Functional Connectivity Signatures
Abstract number :
2.214
Submission category :
3. Neurophysiology / 3E. Brain Stimulation
Year :
2025
Submission ID :
1003
Source :
www.aesnet.org
Presentation date :
12/7/2025 12:00:00 AM
Published date :
Authors :
Presenting Author: Irena Doležalová, MD, PhD – Brno Epilepsy Center, First Department of Neurology, St. Anne’s University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic
Jan Chladek, MSc, Ph.D – St. Anne's University Hospital and Masaryk university
Michal Macek, MSc, Ph.D. – Czech Academy of Science
Jan Chrastina, MD, Ph.D. – St. Anne's University Hospital and Masaryk University, Brno, Czech Republic
Stepan Erben, MD – St. Anne's University Hospital and Masaryk University
Milan Brazdil, PhD – Department of Neurology, St Anne's University Hospital, Brno, Czechia
Rationale: Vagal nerve stimulation (VNS) is a promising neurostimulation approach offering significant seizure reduction ( >50%) in approximately 50-60% of patients. However, a substantial subset does not experience benefits. Understanding the neurophysiological distinctions between responders and non-responders is crucial for optimizing patient selection and tailoring therapies. This study investigates whether EEG-derived functional connectivity metrics, specifically the weighted Phase Lag Index (wPLI), can differentiate these groups pre-implantation.
Methods: We analyzed pre-surgical EEG recordings from 59 patients undergoing VNS therapy—24 responders and 35 non-responders. EEG data were filtered into standard frequency bands (broad 0.5–48 Hz, theta, alpha, beta, gamma) and segmented into eight consecutive intervals encompassing stimulation and resting phases. wPLI was calculated across scalp electrodes within each interval and frequency band, aiming to identify distinct connectivity patterns associated with treatment response.
Results: Significant differences emerged predominantly within the broad frequency band—most notably during Rest_1 (initial recording), photic stimulation, hyperventilation, and Rest_4 (final interval). These connectivity variations suggest distinct neural circuit engagement between responders and non-responders at different states and stimuli.
Conclusions: EEG functional connectivity metrics, particularly the wPLI, hold promise as predictive biomarkers for VNS response. Our findings underscore the potential of advanced EEG analysis in personalizing epilepsy treatment strategies, ultimately enhancing clinical outcomes.
Funding: Funded by European Union, project Relieve (no. 101099481)
Neurophysiology