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There are currently no established biomarkers for predicting the therapeutic effectiveness of Vagus Nerve Stimulation (VNS). Given that neural desynchronization is a pivotal mechanism underlying VNS action, EEG synchronization measures could potentially serve as predictive biomarkers of VNS response. Notably, an increased brain synchronization in delta band has been observed during sleep–potentially due to an activation of thalamocortical circuitry, and interictal epileptiform discharges are more frequently observed during sleep. Therefore, investigation of EEG synchronization metrics during sleep could provide a valuable insight into the excitatory-inhibitory balance in a pro-epileptogenic state, that could be pathological in patients exhibiting a poor response to VNS. A 19-channel-standard EEG system was used to collect data from 38 individuals with Drug-Resistant Epilepsy (DRE) who were candidates for VNS implantation. An EEG synchronization metric–the Weighted Phase Lag Index (wPLI)—was extracted before VNS implantation and compared between sleep and wakefulness, and between responders (R) and non-responders (NR). In the delta band, a higher wPLI was found during wakefulness compared to sleep in NR only. However, in this band, no synchronization difference in any state was found between R and NR. During sleep and within the alpha band, a negative correlation was found between wPLI and the percentage of seizure reduction after VNS implantation. Overall, our results suggest that patients exhibiting a poor VNS efficacy may present a more pathological thalamocortical circuitry before VNS implantation. EEG synchronization measures could provide interesting insights into the prerequisites for responding to VNS, in order to avoid unnecessary implantations in patients showing a poor therapeutic efficacy.

Objective: During the presurgical evaluation, manual electrical source imaging (ESI) provides clinically useful information in one-third of the patients but it is time-consuming and requires specific expertise. This prospective study aims to assess the clinical added value of a fully automated ESI analysis in a cohort of patients with MRI-negative epilepsy and describe its diagnostic performance, by evaluating sublobar concordance with stereo-electroencephalography (SEEG) results and surgical resection and outcome.

Methods: All consecutive patients referred to the Center for Refractory Epilepsy (CRE) of St-Luc University Hospital (Brussels, Belgium) for presurgical evaluation between 15/01/2019 and 31/12/2020 meeting the
inclusion criteria, were recruited to the study. Interictal ESI was realized on low-density long-term EEG monitoring (LD-ESI) and, whenever available, high-density EEG (HD-ESI), using a fully automated analysis (Epilog PreOp, Epilog NV, Ghent, Belgium). The multidisciplinary team (MDT) was asked to formulate hypotheses about the epileptogenic zone (EZ) location at sublobar level and make a decision on further management for each patient at two distinct moments: i) blinded to ESI and ii) after the presentation and clinical interpretation of ESI. Results leading to a change in clinical management were considered contributive. Patients were followed up to assess whether these changes lead to concordant results on stereo-EEG (SEEG) or successful epilepsy surgery.

Results: Data from all included 29 patients were analyzed. ESI led to a change in the management plan in 12/29 patients (41%). In 9/12 (75%), modifications were related to a change in the plan of the invasive
recording. In 8/9 patients, invasive recording was performed. In 6/8 (75%), the intracranial EEG recording confirmed the localization of the ESI at a sublobar level. So far, 5/12 patients, for whom the management
plan was changed after ESI, were operated on and have at least one-year postoperative follow-up. In all cases, the EZ identified by ESI was included in the resection zone. Among these patients, 4/5 (80%) are
seizure-free (ILAE 1) and one patient experienced a seizure reduction of more than 50% (ILAE 4).

Conclusions: In this single-center prospective study, we demonstrated the added value of automated ESI in the presurgical evaluation of MRI-negative cases, especially in helping to plan the implantation of depth electrodes for SEEG, provided that ESI results are integrated into the whole multimodal evaluation and clinically interpreted.

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.

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.

Seizures produce autonomic symptoms, mainly sympathetic but also parasympathetic in origin. Within this context, the vagus nerve is a key player as it carries information from the different organs to the brain and vice versa. Hence, exploiting vagal neural traffic for seizure detection might be a promising tool to improve the efficacy of closed-loop Vagus Nerve Stimulation. This study developed a VENG detection algorithm that effectively detects seizures by emphasizing the loss of spontaneous rhythmicity associated with respiration in acute intrahippocampal Kainic Acid rat model. Among 20 induced seizures in six anesthetized rats, 13 were detected (sensitivity: 65%, accuracy: 92.86%), with a mean VENG-detection delay of 25.3  ± 13.5  s after EEG-based seizure onset. Despite variations in detection parameters, 7 out of 20 seizures exhibited no ictal VENG modifications and remained undetected. Statistical analysis highlighted a significant difference in Delta, Theta and Beta band evolution between detected and undetected seizures, in addition to variations in the magnitude of HR changes. Binomial logistic regression analysis confirmed that an increase in delta and theta band activity was associated with a decreased likelihood of seizure detection. This results suggest the possibility of distinct seizure spreading patterns between the two groups which may results in differential activation of the autonomic central network. Despite notable progress, limitations, particularly the absence of respiration recording, underscore areas for future exploration and refinement in closed-loop stimulation strategies for epilepsy management. This study constitutes the initial phase of a longitudinal investigation, which will subsequently involve reproducing these experiments in awake conditions with spontaneous recurrent seizures

The mechanisms of action of Vagus Nerve Stimulation (VNS) and the biological prerequisites to respond to the treatment are currently under investigation. It is hypothesized that thalamocortical tracts play a central role in the antiseizure effects of VNS by disrupting the genesis of pathological activity in the brain. This pilot study explored whether in vivo microstructural features of thalamocortical tracts may differentiate Drug-Resistant Epilepsy (DRE) patients responding and not responding to VNS treatment.

Eighteen patients with DRE (37.11  10.13 years, 10 females), including 11 responders or partial responders and 7 non-responders to VNS, were recruited for this highgradient multi-shell diffusion Magnetic Resonance Imaging (MRI) study. Using Diffusion Tensor Imaging (DTI) and multi-compartment models – Neurite Orientation Dispersion and Density Imaging (NODDI) and Microstructure Fingerprinting (MF), we extracted microstructural features in 12 subsegments of thalamocortical tracts. These characteristics were compared between responders/partial responders and non-responders.

Subsequently, a Support Vector Machine (SVM) classifier was built, incorporating microstructural features and 12 clinical covariates (including age, sex, duration of VNS therapy, number of antiseizure medications, benzodiazepine intake, epilepsy duration, epilepsy onset age, epilepsy type – focal or generalized, presence of an epileptic syndrome – no syndrome or Lennox-Gastaut syndrome, etiology of epilepsy – structural, genetic, viral, or unknown, history of brain surgery, and presence of a brain lesion detected on structural MRI images).

Multiple diffusion metrics consistently demonstrated significantly higher white matter fiber integrity in patients with a better response to VNS (pFDR < 0.05) in different subsegments of thalamocortical tracts. The SVM model achieved a classification accuracy of 94.12%. The inclusion of clinical covariates did not improve the classification performance. The results suggest that the structural integrity of thalamocortical tracts may be linked to therapeutic effectiveness of VNS. This study reveals the great potential of diffusion MRI in improving our understanding of the biological factors associated with the response to VNS therapy.