Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors

Abstract Objective . In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear. Approach . To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics. Main results . By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences ( p < 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann–Whitney U test with Benjamini–Hochberg correction procedure and use of a false discovery rate of 5%. Significance . Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.