Manuscripts

Purpose: The Locus Coeruleus (LC) plays a vital role by releasing norepinephrine, which contributes to the antiepileptic effects of Vagus Nerve Stimulation (VNS). LC activity also influences pupil dilation. Investigating
VNS dose-dependent Pupillary Dilation Response (PDR) may provide novel neurophysiological insights into therapeutic response and allow for an objective and personalized optimization of stimulation parameters.
Methods: Fourteen VNS-implanted patients (9 responders, 5 non-responders) treated for at least 6 months were retrospectively recruited. VNS intensities were adjusted from 0.25 mA to 2.25 mA, or to the highest tolerable level. Concurrently, we tracked pupil size in the left eye and gathered patients’ subjective perception scores. Individual curve fitting was used to explore the relationship between VNS intensity and PDR.
Results: PDR increased with stimulation intensity, particularly in responders. In 6 patients, an inverted U-shaped relationship between intensity and PDR was observed 2–3 s after stimulation onset. A significant interaction was found between VNS intensity and responder status, independent of subjective perception.
Conclusions: VNS induces a dose-dependent PDR, which differs between responders and non-responders. In nearly half the patients, the dose–response relationship was characterized by an inverted U-shape with a maximal VNS effect.
Significance: We propose VNS-induced PDR as a novel biomarker of VNS response.

Objectives: No reliable biomarkers exist for predicting and assessing vagus nerve stimulation (VNS) response. While VNS induces acute electroencephalography (EEG) desynchronization after implantation, longitudinal evaluations of EEG synchronization changes are lacking. This study constitutes the first prospective investigation evaluating EEG synchronization before and after VNS device implantation and correlating it with the clinical response to VNS.
Materials and Methods: High-density EEG recordings were obtained from 12 adults with drug-resistant epilepsy before and after VNS device implantation (one, three, and six months). EEG resting state (180 seconds), with eyes open and eyes closed (EC), was recorded in VNS ON and OFF conditions. The global weighted phase lag index (wPLI) was computed as an EEG phasesynchronization measure and correlated with the VNS response using various assessment methods, including binary classification (>50% or <50% seizure frequency reduction), percentage of seizure reduction, and the newly developed Clinical-Research Response Scale (CRRS).
Results: We observed a progressive decrease of wPLI in the delta band during the EC VNS OFF condition, which correlated with the VNS response over time, particularly when assessed using the new CRRS compared with other assessment methods. Additionally, a higher preimplant global wPLI predicted a better outcome of VNS, as did an early magnet response.
Conclusions: Overall, VNS may positively influence specific brain states, with a time-dependent evolution of EEG synchronization reflecting therapeutic efficacy. Preimplantation EEG synchronization and an early magnet response may predict VNS response. Moreover, the CRRS could constitute a more sensitive method for characterizing VNS response compared with traditional assessment methods.

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.

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.