Characterization of Vagus Nerve Stimulation (VNS) Dose-Dependent Effects on EEG Power Spectrum and Synchronization.

This study investigates the dose-dependent EEG effects of Vagus Nerve Stimulation (VNS) in patients with drug-resistant epilepsy. This research examines how varying VNS intensities impacts EEG power spectrum and synchronization in a cohort of 28 patients. Patients were categorized into responders, partial-responders, and non-responders based on seizure frequency reduction. The methods involved EEG recordings at incremental VNS intensities, followed by spectral and synchronization analysis. The results reveal significant changes in EEG power, particularly in the delta and beta bands across different intensities. Notably, responders exhibited distinct EEG changes compared to non-responders. Our study has found that VNS intensity significantly influences EEG power topographic allocation and brain desynchronization, suggesting the potential use of acute dose-dependent effects to personalized VNS therapy in the treatment of epilepsy. The findings underscore the importance of individualized VNS dosing for optimizing therapeutic outcomes and highlight the use of EEG metrics as an effective tool for monitoring and adjusting VNS parameters. These insights offer a new avenue for developing individualized VNS therapy strategies, enhancing treatment efficacy in epilepsy.

Locus coeruleus features are linked to vagus nerve stimulation response in drug-resistant epilepsy.

The locus coeruleus-norepinephrine system is thought to be involved in the clinical effects of vagus nerve stimulation. This system is known to prevent seizure development and induce long-term plastic changes, particularly with the release of norepinephrine in the hippocampus. However, the requisites to become responder to the therapy and the mechanisms of action are still under investigation. Using MRI, we assessed the structural and functional characteristics of the locus coeruleus and microstructural properties of locus coeruleus-hippocampus white matter tracts in patients with drug-resistant epilepsy responding or not to the therapy. Twenty-three drug-resistant epileptic patients with cervical vagus nerve stimulation were recruited for this pilot study, including 13 responders or partial responders and 10 non-responders. A dedicated structural MRI acquisition allowed in vivo localization of the locus coeruleus and computation of its contrast (an accepted marker of LC integrity). Locus coeruleus activity was estimated using functional MRI during an auditory oddball task. Finally, multi-shell diffusion MRI was used to estimate the structural properties of locus coeruleus-hippocampus tracts. These characteristics were compared between responders/partial responders and non-responders and their association with therapy duration was also explored. In patients with a better response to the therapy, trends toward a lower activity and a higher contrast were found in the left medial and right caudal portions of the locus coeruleus, respectively. An increased locus coeruleus contrast, bilaterally over its medial portions, correlated with duration of the treatment. Finally, a higher integrity of locus coeruleus-hippocampus connections was found in patients with a better response to the treatment. These new insights into the neurobiology of vagus nerve stimulation may provide novel markers of the response to the treatment and may reflect neuroplasticity effects occurring in the brain following the implantation.

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

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.

Vagus nerve stimulation-induced laryngeal motor evoked potentials for response prediction and intensity titration in drug-resistant epilepsy.

OBJECTIVE: The objective of the study was to record Laryngeal Motor Evoked Potentials (LMEPs) in Vagus Nerve Stimulation (VNS)-implanted patients suffering from Drug-Resistant Epilepsy (DRE). Based on these recordings, LMEPs characteristics were evaluated and compared between responders (R) and non-responders (NR). Finally, possible under- or over-stimulation was assessed based on a physiological indicator of fiber engagement. METHODS: Mean dose-response curves were compared between R and NR. A Support Vector Machine (SVM) model was built based on both LMEP and dose-response curves features, to discriminate R from NR. For the exploration of possible under- or over-stimulation, a ratio between the clinically applied stimulation intensity and the intensity yielding to LMEP saturation was computed for each patient. RESULTS: A trend towards a greater excitability of the nerve was observed in R compared to NR. The SVM classifier discriminated R and NR with an accuracy of 80%. An ineffective attempt to overstimulate at current levels above what is usually necessary to obtain clinical benefits was suggested in NR. CONCLUSIONS: The SVM model built emphasizes a possible link between vagus nerve recruitment characteristics and treatment effectiveness. Most of the clinically responding patients receive VNS at a stimulation intensity 1-fold and 2-fold the intensity inducing LMEP saturation. SIGNIFICANCE: LMEP saturation could be a practical help in guiding the titration of the stimulation parameters using a physiological indicator of fiber engagement.

Automated electrical source imaging with scalp EEG to define the insular irritative zone: Comparison with simultaneous intracranial EEG.

OBJECTIVE: To evaluate the accuracy of automatedinterictallow-density electrical source imaging (LD-ESI) to define the insular irritative zone (IZ) by comparing the simultaneous interictal ESI localization with the SEEG interictal activity. METHODS: Long-term simultaneous scalp electroencephalography (EEG) and stereo-EEG (SEEG) with at least one depth electrode exploring the operculo-insular region(s) were analyzed. Automated interictal ESI was performed on the scalp EEG using standardized low-resolution brain electromagnetic tomography (sLORETA) and individual head models. A two-step analysis was performed: i) sublobar concordance betweencluster-based ESI localization and SEEG-based IZ; ii) time-locked ESI-/SEEG analysis. Diagnostic accuracy values were calculated using SEEG as reference standard. Subgroup analysis wascarried out, based onthe involvement of insular contacts in the seizure onset and patterns of insular interictal activity. RESULTS: Thirty patients were included in the study. ESI showed an overall accuracy of 53% (C.I. 29-76%). Sensitivity and specificity were calculated as 53% (C.I. 29-76%), 55% (C.I. 23-83%) respectively. Higher accuracy was found in patients with frequent and dominant interictal insular spikes. CONCLUSIONS: LD-ESI defines with good accuracy the insular implication in the IZ, which is not possible with classical interictalscalpEEG interpretation. SIGNIFICANCE: Automated LD-ESI may be a valuable additional tool to characterize the epileptogenic zone in epilepsies with suspected insular involvement.