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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.

Vagus Nerve Stimulation (VNS) is an adjunctive treatment for patients suffering from inoperable drug-resistant epilepsy. Although a complete understanding of the mediators involved in the antiepileptic effects of VNS and their complex interactions is lacking, VNS is known to trigger the release of neurotransmitters that have seizure-suppressing effects. In particular, norepinephrine (NE) is a neurotransmitter that has been associated with the clinical effects of VNS by preventing seizure development and by inducing longterm plastic changes that could restore a normal function of the brain circuitry. However, the biological requisites to become responder to VNS are still unknown. In this review, we report evidence of the critical involvement of NE in the antiepileptic effects of VNS in rodents and humans. Moreover, we emphasize the hypothesis that the functional integrity of the noradrenergic system could be a determining factor to obtain clinical benefits from the therapy. Finally, encouraging avenues of research involving NE in VNS treatment are discussed. These could lead to the personalization of the stimulation parameters to maximize the antiepileptic effects and potentially improve the response rate to the therapy.

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.