Biomarkers of seizure response to vagus nerve stimulation: A scoping review

A predictive model combining connectomics and entropy biomarkers to discriminate long-term vagus nerve stimulation efficacy for pediatric patients with drug-resistant epilepsy

AIMS

To predict the vagus nerve stimulation (VNS) efficacy for pediatric drug-resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi-modal feature set with high prediction accuracy.

METHODS

Sixty-five pediatric DRE patients implanted with VNS were included and followed up. We explored the topological network and entropy features of preimplantation EEG signals to identify the biomarkers for VNS efficacy. A Support Vector Machine (SVM) integrated these biomarkers to distinguish the efficacy groups.

RESULTS

The proportion of VNS responders was 58.5% (38/65) at the last follow-up. In the analysis of parieto-occipital α band activity, higher synchronization level and nodal efficiency were found in responders. The central-frontal θ band activity showed significantly lower entropy in responders. The prediction model reached an accuracy of 81.5%, a precision of 80.1%, and an AUC (area under the receiver operating characteristic curve) of 0.838.

CONCLUSION

Our results revealed that, compared to nonresponders, VNS responders had a more efficient α band brain network, especially in the parieto-occipital region, and less spectral complexity of θ brain activities in the central-frontal region. We established a predictive model integrating both preimplantation clinical and EEG features and exhibited great potential for discriminating the VNS responders. This study contributed to the understanding of the VNS mechanism and improved the performance of the current predictive model.

Efficacy of vagus nerve stimulation in managing drug-resistant absence epilepsy syndromes

PURPOSE

Around 11% of patients with absence epilepsy develop drug-resistant absence epilepsy (DRAE), and are at increased risk for developing psychiatric and neurologic comorbidities. Current therapeutic options for DRAE are limited. The purpose of this study was to assess the efficacy of vagus nerve stimulation (VNS) in treating DRAE.

METHODS

Our institution maintains a database of patients who received VNS between 2010 and 2022. We identified DRAE patients who were <18 years of age at seizure onset, were electro-clinically diagnosed with an absence epilepsy syndrome (childhood absence, juvenile absence, or Jeavons Syndrome) by an epileptologist, and had normal brain imaging. The primary outcome measure was post-VNS absence seizure frequency.

RESULTS

Twenty-six patients (M/F:14/12) were identified. Median age at seizure onset was 7 years (IQR 4-10) and patients experienced seizures for 6 years (IQR 4.3-7.6) before VNS. After VNS, the median absence seizure frequency reduced to 1.5 days (IQR 0.1-3.5) per week from 7 days (IQR 7-7), a 66% reduction seizure frequency. VNS responder rate was 80%, and seven patients achieved seizure freedom. There was no significant effect on VNS efficacy between the time from DRAE diagnosis to VNS placement (p = 0.067) nor the time from first seizure onset to VNS implant (p = 0.80). The median follow-up duration was 4.1 years (IQR 2.4-6.7), without any significant association between follow-up duration and VNS efficacy (r2=0.023) CONCLUSIONS: VNS is effective in managing DRAE. The responder rate was 80%; seizure improvement was independent of age at both seizure onset and latency to VNS after meeting DRAE criteria.

Vagus nerve stimulation and heart rate variability: A scoping review of a somatic oscillatory signal

The goal of this review is to synthesize the literature on vagus nerve stimulator (VNS)-related changes in heart rate variability (HRV) in patients with drug-resistant epilepsy (DRE) and assess the role of these changes in seizure relief. A scoping literature review was performed with the following inclusion criteria: primary articles written in English, involved implantable VNS in humans, and had HRV as a primary outcome. Twenty-nine studies were retrieved, however with considerable heterogeneity in study methods. The overall depression in HRV seen in DRE patients compared to healthy controls persisted even after VNS implant, indicating that achieving "healthy" HRV is not necessary for VNS therapeutic success. Within DRE patients, changes in frequency domain parameters six months after VNS implant returned to baseline after a year. The mechanism of how VNS reduces seizure burden does not appear to be significantly related to alterations in baseline HRV. However, the subtlety of sympathetic/parasympathetic signaling likely requires a more structured approach to experimental and analytic techniques than currently found in the literature.

Neurostimulation treatments for epilepsy: Deep brain stimulation, responsive neurostimulation and vagus nerve stimulation

Epilepsy is a common and debilitating neurological disorder, and approximately one-third of affected individuals have ongoing seizures despite appropriate trials of two anti-seizure medications. This population with drug-resistant epilepsy (DRE) may benefit from neurostimulation approaches, such as vagus nerve stimulation (VNS), deep brain stimulation (DBS) and responsive neurostimulation (RNS). In some patient populations, these techniques are FDA-approved for treating DRE. VNS is used as adjuvant therapy for children and adults. Acting via the vagus afferent network, VNS modulates thalamocortical circuits, reducing seizures in approximately 50 ​% of patients. RNS uses an adaptive (closed-loop) system that records intracranial EEG patterns to activate the stimulation at the appropriate time, being particularly well-suited to treat seizures arising within eloquent cortex. For DBS, the most promising therapeutic targets are the anterior and centromedian nuclei of the thalamus, with anterior nucleus DBS being used for treating focal and secondarily generalized forms of DRE and centromedian nucleus DBS being applied for treating generalized epilepsies such as Lennox-Gastaut syndrome. Here, we discuss the indications, advantages and limitations of VNS, DBS and RNS in treating DRE and summarize the spatial distribution of neuroimaging observations related to epilepsy and stimulation using NeuroQuery and NeuroSynth.

Analysis of factors influencing the efficacy of vagus nerve stimulation for the treatment of drug-resistant epilepsy in children and prediction model for efficacy evaluation

Objective

Vagus nerve stimulation (VNS) has been widely used in the treatment of drug-resistant epilepsy (DRE) in children. We aimed to explore the efficacy and safety of VNS, focusing on factors that can influence the efficacy of VNS, and construct a prediction model for the efficacy of VNS in the treatment of DRE children.

Methods

Retrospectively analyzed 45 DRE children who underwent VNS at Qilu Hospital of Shandong University from June 2016 to November 2022. A ≥50% reduction in seizure frequency was defined as responder, logistic regression analyses were performed to analyze factors affecting the efficacy of VNS, and a predictive model was constructed. The predictive model was evaluated by receiver operating characteristic curve (ROC), calibration curves, and decision curve analyses (DCA).

Results

A total of 45 DRE children were included in this study, and the frequency of seizures was significantly reduced after VNS treatment, with 25 responders (55.6%), of whom 6 (13.3%) achieved seizure freedom. There was a significant improvement in the Quality of Life in Childhood Epilepsy Questionnaire (15.5%) and Seizure Severity Score (46.2%). 16 potential factors affecting the efficacy of VNS were included, and three statistically significant positive predictors were ultimately screened: shorter seizure duration, focal seizure, and absence of intellectual disability. We developed a nomogram for predicting the efficacy of VNS in the treatment of DRE children. The ROC curve confirmed that the predictive model has good diagnostic performance (AUC = 0.864, P < 0.05), and the nomogram can be further validated by bootstrapping for 1,000 repetitions, with a C-index of 0.837. Besides, this model showed good fitting and calibration and positive net benefits in decision curve analysis.

Conclusion

VNS is a safe and effective treatment for DRE children. We developed a predictive nomogram for the efficacy of VNS, which provides a basis for more accurate selection of VNS patients.

Acute Effect of Vagus Nerve Stimulation in Patients with Drug-Resistant Epilepsy: A Preliminary Exploration via Stereoelectroencephalogram

As the pathophysiological mechanisms of vagus nerve stimulation (VNS) causing individual differences in the vagal ascending network remains unclear, stereoelectroencephalography (SEEG) provides a unique platform to explore the brain networks affected by VNS and helps to understand the anti-seizure mechanism of VNS more comprehensively. This study presents a preliminary exploration of the acute effect of VNS. SEEG signals were collected to assess the acute effect of VNS on neural synchronization in patients with drug-resistant epilepsy, especially in epileptogenic networks. The results show that the better the efficacy of VNS, the wider the spread of desynchronization assessed by weighted phase lag index at a high frequency band caused by VNS. Future studies should focus on the association between the change in synchronization and the efficacy of VNS, exploring the possibility of synchronization as a biomarker for patient screening and parameter programming.

Brain functional connectivity-based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy

OBJECTIVE

Although vagus nerve stimulation (VNS) is a common and widely used therapy for pharmacoresistant epilepsy, the reported efficacy of VNS in pediatric patients varies, so it is unclear which children will respond to VNS therapy. This study aimed to identify functional brain network features associated with VNS action to distinguish VNS responders from nonresponders using scalp electroencephalogram (EEG) data.

METHODS

Twenty-three children were included in this study, 16 in the discovery cohort and 7 in the test cohort. Using partial correlation value as a measure of whole-brain functional connectivity, we identified the differential edges between responders and nonresponders. Results derived from this were used as input to generate a support vector machine-learning classifier to predict VNS outcomes.

RESULTS

The postcentral gyrus in the left and right parietal lobe regions was identified as the most significant differential brain region between VNS responders and nonresponders (p < 0.001). The resultant classifier demonstrated a mean AUC value of 0.88, a mean sensitivity rate of 91.4%, and a mean specificity rate of 84.3% on fivefold cross-validation in the discovery cohort. In the testing cohort, our study demonstrated an AUC value of 0.91, a sensitivity rate of 86.6%, and a specificity rate of 79.3%. Furthermore, for prediction accuracy, our model can achieve 81.4% accuracy at the epoch level and 100% accuracy at the patient level.

SIGNIFICANCE

This study provides the first treatment response prediction model for VNS using scalp EEG data with ictal recordings and offers new insights into its mechanism of action. Our results suggest that brain functional connectivity features can help predict therapeutic response to VNS therapy. With further validation, our model could facilitate the selection of targeted pediatric patients and help avoid risky and costly procedures for patients who are unlikely to benefit from VNS therapy.

Entropy in scalp EEG can be used as a preimplantation marker for VNS efficacy

Vagus nerve stimulation (VNS) is a therapeutic option in drug-resistant epilepsy. VNS leads to ≥ 50% seizure reduction in 50 to 60% of patients, termed "responders". The remaining 40 to 50% of patients, "non-responders", exhibit seizure reduction < 50%. Our work aims to differentiate between these two patient groups in preimplantation EEG analysis by employing several Entropy methods. We identified 59 drug-resistant epilepsy patients treated with VNS. We established their response to VNS in terms of responders and non-responders. A preimplantation EEG with eyes open/closed, photic stimulation, and hyperventilation was found for each patient. The EEG was segmented into eight time intervals within four standard frequency bands. In all, 32 EEG segments were obtained. Seven Entropy methods were calculated for all segments. Subsequently, VNS responders and non-responders were compared using individual Entropy methods. VNS responders and non-responders differed significantly in all Entropy methods except Approximate Entropy. Spectral Entropy revealed the highest number of EEG segments differentiating between responders and non-responders. The most useful frequency band distinguishing responders and non-responders was the alpha frequency, and the most helpful time interval was hyperventilation and rest 4 (the end of EEG recording).

A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management

Epilepsy is a complex neurological disorder affecting millions worldwide, with a substantial number of patients facing drug-resistant epilepsy. This comprehensive review explores innovative therapies for epilepsy management, focusing on their principles, clinical evidence, and potential applications. Traditional antiseizure medications (ASMs) form the cornerstone of epilepsy treatment, but their limitations necessitate alternative approaches. The review delves into cutting-edge therapies such as responsive neurostimulation (RNS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS), highlighting their mechanisms of action and promising clinical outcomes. Additionally, the potential of gene therapies and optogenetics in epilepsy research is discussed, revealing groundbreaking findings that shed light on seizure mechanisms. Insights into cannabidiol (CBD) and the ketogenic diet as adjunctive therapies further broaden the spectrum of epilepsy management. Challenges in achieving seizure control with traditional therapies, including treatment resistance and individual variability, are addressed. The importance of staying updated with emerging trends in epilepsy management is emphasized, along with the hope for improved therapeutic options. Future research directions, such as combining therapies, AI applications, and non-invasive optogenetics, hold promise for personalized and effective epilepsy treatment. As the field advances, collaboration among researchers of natural and synthetic biochemistry, clinicians from different streams and various forms of medicine, and patients will drive progress toward better seizure control and a higher quality of life for individuals living with epilepsy.

Functional connectomic profile correlates with effective anterior thalamic stimulation for refractory epilepsy

BACKGROUND

Deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) is an effective treatment for refractory epilepsy; however, seizure outcome varies among individuals. Identifying a reliable noninvasive biomarker to predict good responders would be helpful.

OBJECTIVES

To test whether the functional connectivity between the ANT-DBS sites and the seizure foci correlates with effective seizure control in refractory epilepsy.

METHODS

We performed a proof-of-concept pilot study of patients with focal refractory epilepsy receiving ANT-DBS. Using normative human connectome data derived from 1000 healthy participants, we investigated whether intrinsic functional connectivity between the seizure foci and the DBS site was associated with seizure outcome. We repeated this analysis controlling for the extent of seizure foci, distance between the seizure foci and DBS site, and using functional connectivity of the ANT instead of the DBS site to test the contribution of variance in DBS sites.

RESULTS

Eighteen patients with two or more seizure foci were included. Greater functional connectivity between the seizure foci and the DBS site correlated with more favorable outcome. The degree of functional connectivity accounted for significant variance in clinical outcomes (DBS site: |r| = 0.773, p < 0.001 vs ANT-atlas: |r| = 0.715, p = 0.001), which remained significant when controlling for the extent of the seizure foci (|r| = 0.773, p < 0.001) and the distance between the seizure foci and DBS site (|r| = 0.777, p < 0.001). Significant correlations were independent of variance in the DBS sites (|r| = 0.148, p = 0.57).

CONCLUSION

These findings suggest that functional connectomic profile is a potential reliable non-invasive biomarker to predict ANT-DBS outcomes. Accordingly, the identification of ANT responders could decrease the surgical risk for patients who may not benefit and optimize the cost-effective allocation of health care resources.

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.

Dynamic flexibility and controllability of network communities in juvenile myoclonic epilepsy

Juvenile myoclonic epilepsy (JME) is the most common syndrome within the idiopathic generalized epilepsy spectrum, manifested by myoclonic and generalized tonic-clonic seizures and spike-and-wave discharges (SWDs) on electroencephalography (EEG). Currently, the pathophysiological concepts addressing SWD generation in JME are still incomplete. In this work, we characterize the temporal and spatial organization of functional networks and their dynamic properties as derived from high-density EEG (hdEEG) recordings and MRI in 40 JME patients (25.4 ± 7.6 years, 25 females). The adopted approach allows for the construction of a precise dynamic model of ictal transformation in JME at the cortical and deep brain nuclei source levels. We implement Louvain algorithm to attribute brain regions with similar topological properties to modules during separate time windows before and during SWD generation. Afterwards, we quantify how modular assignments evolve and steer through different states towards the ictal state by measuring characteristics of flexibility and controllability. We find antagonistic dynamics of flexibility and controllability within network modules as they evolve towards and undergo ictal transformation. Prior to SWD generation, we observe concomitantly increasing flexibility (F(1,39) = 25.3, corrected p < 0.001) and decreasing controllability (F(1,39) = 55.3, p < 0.001) within the fronto-parietal module in γ-band. On a step further, during interictal SWDs as compared to preceding time windows, we notice decreasing flexibility (F(1,39) = 11.9, p < 0.001) and increasing controllability (F(1,39) = 10.1, p < 0.001) within the fronto-temporal module in γ-band. During ictal SWDs as compared to prior time windows, we demonstrate significantly decreasing flexibility (F(1,14) = 31.6; p < 0.001) and increasing controllability (F(1,14) = 44.7, p < 0.001) within the basal ganglia module. Furthermore, we show that flexibility and controllability within the fronto-temporal module of the interictal SWDs relate to seizure frequency and cognitive performance in JME patients. Our results demonstrate that detection of network modules and quantification of their dynamic properties is relevant to track the generation of SWDs. The observed flexibility and controllability dynamics reflect the reorganization of de-/synchronized connections and the ability of evolving network modules to reach a seizure-free state, respectively. These findings may advance the elaboration of network-based biomarkers and more targeted therapeutic neuromodulatory approaches in JME.

Development of localized interictal epileptiform discharges following vagus nerve stimulation for lennox-gastaut syndrome: a case report

Background

Lennox-gastaut syndrome (LGS) is an epileptic encephalopathy often associated with behavioral and psychiatric disorders. Vagus nerve stimulation (VNS) has been approved effective for LGS treatment. Surgical resection is also an option for LGS patients with focal pathology, offering a high probability of seizure control. However, it is challenging to accurately localize the seizure focus.

Case presentation

The case presented here is a 19-year-old male with a 16-year history of epilepsy with comorbid severe cognitive and psychiatric disorders. He was diagnosed with LGS due to generalized slow spike-wave discharges and multiple seizure types. He was treated with VNS in 2017 at the age of 15. After that, the frequency of the short tonic seizures decreased from 4–5 times per day to 2–5 times per year, and the generalized tonic–clonic seizure pattern did not recur, which had a frequency of 2–4 times per month before the surgery. In 2019, the generalized abnormal interictal epileptiform discharges changed to be localized in the right frontal–temporal lobe at the age of 17 years (2019).

Conclusions

This case report suggested that the generalized epileptiform discharges evolve into localized discharges after VNS treatment, which may help reveal the primary seizure focus for resection surgery in patients with LGS.

Brain-clinical signatures for vagus nerve stimulation response

AIM

Vagus nerve stimulation (VNS) is a valuable treatment for drug-resistant epilepsy (DRE) without the indication of surgical resection. The clinical heterogeneity of DRE has limited the optimal indication of choice and diagnosis prediction. The study aimed to explore the correlations of brain-clinical signatures with the clinical phenotype and VNS responsiveness.

METHODS

A total of 89 DRE patients, including VNS- (n = 44) and drug-treated (n = 45) patients, were retrospectively recruited. The brain-clinical signature consisted of demographic information and brain structural deformations, which were measured using deformation-based morphometry and presented as Jacobian determinant maps. The efficacy and presurgical differences between these two cohorts were compared. Then, the potential of predicting VNS response using brain-clinical signature was investigated according to the different prognosis evaluation approaches.

RESULTS

The seizure reduction was higher in the VNS-treated group (42.50%) as compared to the drug-treated group (12.09%) (p = 0.11). Abnormal imaging representation, showing encephalomalacia (p_corrected  = 0.03), was commonly observed in the VNS-treated group (p = 0.04). In the patients treated with VNS, the mild/subtle brain abnormalities indicated higher seizure frequency (p = 0.03) and worse VNS response (p = 0.04). The partial least square regression analysis showed a moderate prediction potential of brain-clinical signature for VNS response (p < 0.01). The increase in the pre-VNS seizure frequency and structural etiology could indicate a worse prognosis (higher McHugh classification).

CONCLUSION

The brain-clinical signature illustrated its clinical potential in predicting the VNS response, which might allow clinicians to personalize treatment decisions for DRE patients.

Towards network-guided neuromodulation for epilepsy

Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.

Quantitative electroencephalographic analysis as a potential biomarker of response to treatment with cannabidiol

PURPOSE

Pharmaceutical grade cannabidiol (CBD) is one of the newest anti-seizure medications for refractory epilepsy, and the effects of CBD on EEG have not been fully described.

METHODS

Patients enrolled in a CBD expanded access study had EEGs prior to and 12 weeks after initiation of CBD treatment for their refractory epilepsy. In addition to evaluating the clinical EEG reports, a nonbiased quantitative EEG (qEEG) analysis of background EEG was performed to determine whether consistent changes occur in the EEG in response to administration of CBD.

RESULTS

No significant qualitative changes were seen, nor changes in quantitative markers of EEG amplitude (RMS amplitude, standard deviation of the amplitude, skewness, or kurtosis), frequency (relative delta, theta, or alpha power), Spearman correlation, or coherence between brain regions. However, relative beta power and 1/f slope, a measure of signal noise increased with the addition of CBD. When patients were separated into responders and nonresponders based on seizure reduction with CBD, responders also had decreased Spearman correlation between the frontopolar and occipital regions after addition of CBD, suggesting that responders may have quantitatively improved EEG background organization after CBD initiation. The differences in beta and 1/f slope were also seen more robustly in CBD responders compared with nonresponders after CBD initiation. These differences disappeared when analyzing only patients not taking benzodiazepines, suggesting that the effect of CBD on seizures was related to the ability of the brain to further increase beta in response to CBD in patients already taking benzodiazepines. We noted that even before initiation of CBD, 1/f slope was also significantly different in responders compared to nonresponders. Therefore, to explore the baseline EEG in responders and nonresponders, we utilized a variable selection procedure to identify baseline EEG features that could predict whether a patient's seizures would improve with CBD. In the optimal multivariable logistic model, baseline coherence, Spearman correlation, and patient sex jointly predicted whether a patient in this cohort would respond to CBD (defined as a seizure reduction of 40% or greater) with 74% accuracy. This model performed less well on a data set of reduced duration and variability, highlighting the importance of real-world testing of any clinically relevant model.

CONCLUSION

These results suggest that there are subtle changes in certain metrics detected by qEEG even at baseline that may not be perceived during qualitative EEG analysis and that could be used in the future as a biomarker to predict a patient's clinical response to CBD administration. Development of such a predictive EEG biomarker, especially before the initiation of a medication trial, could reduce unnecessary ASM exposure and improve outcomes for patients with epilepsy facing new medication selection.

Latest Views on the Mechanisms of Action of Surgically Implanted Cervical Vagal Nerve Stimulation in Epilepsy

BACKGROUND

Vagus nerve stimulation (VNS) is approved as an adjunctive treatment for drug-resistant epilepsy. Although there is a substantial amount of literature aiming at unraveling the mechanisms of action of VNS in epilepsy, it is still unclear how the cascade of events triggered by VNS leads to its antiepileptic effect.

OBJECTIVE

In this review, we integrated available peer-reviewed data on the effects of VNS in clinical and experimental research to identify those that are putatively responsible for its therapeutic effect. The topic of transcutaneous VNS will not be covered owing to the current lack of data supporting the differences and commonalities of its mechanisms of action in relation to invasive VNS.

SUMMARY OF THE MAIN FINDINGS

There is compelling evidence that the effect is obtained through the stimulation of large-diameter afferent myelinated fibers that project to the solitary tract nucleus, then to the parabrachial nucleus, which in turn alters the activity of the limbic system, thalamus, and cortex. VNS-induced catecholamine release from the locus coeruleus in the brainstem plays a pivotal role. Functional imaging studies tend to point toward a common vagal network that comes into play, made up of the amygdalo-hippocampal regions, left thalamus, and insular cortex.

CONCLUSIONS

Even though some crucial pieces are missing, neurochemical, molecular, cellular, and electrophysiological changes occur within the vagal afferent network at three main levels (the brainstem, the limbic system [amygdala and hippocampus], and the cortex). At this final level, VNS notably alters functional connectivity, which is known to be abnormally high within the epileptic zone and was shown to be significantly decreased by VNS in responders. The effect of crucial VNS parameters such as frequency or current amplitude on functional connectivity metrics is of utmost importance and requires further investigation.

A prediction model integrating synchronization biomarkers and clinical features to identify responders to vagus nerve stimulation among pediatric patients with drug-resistant epilepsy

Aims

Vagus nerve stimulation (VNS) is a neuromodulation therapy for children with drug‐resistant epilepsy (DRE). The efficacy of VNS is heterogeneous. A prediction model is needed to predict the efficacy before implantation.

Methods

We collected data from children with DRE who underwent VNS implantation and received regular programming for at least 1 year. Preoperative clinical information and scalp video electroencephalography (EEG) were available in 88 children. Synchronization features, including phase lag index (PLI), weighted phase lag index (wPLI), and phase‐locking value (PLV), were compared between responders and non‐responders. We further adapted a support vector machine (SVM) classifier selected from 25 clinical and 18 synchronization features to build a prediction model for efficacy in a discovery cohort (n = 70) and was tested in an independent validation cohort (n = 18).

Results

In the discovery cohort, the average interictal awake PLI in the high beta band was significantly higher in responders than non‐responders (p < 0.05). The SVM classifier generated from integrating both clinical and synchronization features had the best prediction efficacy, demonstrating an accuracy of 75.7%, precision of 80.8% and area under the receiver operating characteristic (AUC) of 0.766 on 10‐fold cross‐validation. In the validation cohort, the prediction model demonstrated an accuracy of 61.1%.

Conclusion

This study established the first prediction model integrating clinical and baseline synchronization features for preoperative VNS responder screening among children with DRE. With further optimization of the model, we hope to provide an effective and convenient method for identifying responders before VNS implantation.

Prediction of the Responsiveness to Vagus-Nerve Stimulation in Patients with Drug-Resistant Epilepsy via Directed-Transfer-Function Analysis of Their Perioperative Scalp EEGs

This study aims to compare directed transfer function (DTF), which is an effective connectivity analysis, derived from scalp EEGs between responder and nonresponder groups implanted with vagus-nerve stimulation (VNS). Twelve patients with drug-resistant epilepsy (six responders and six nonresponders) and ten controls were recruited. A good response to VNS was defined as a reduction of ≥50% in seizure frequency compared with the presurgical baseline. DTF was calculated in five frequency bands (delta, theta, alpha, beta, and broadband) and seven grouped electrode regions (left and right frontal, temporal, parieto-occipital, and midline) in three different states (presurgical, stimulation-on, and stimulation-off states). Responders showed presurgical nodal strength close to the control group in both inflow and outflow, whereas nonresponders exhibited increased inward and outward connectivity measures. Nonresponders also had increased inward and outward connectivity measures in the various brain regions and various frequency bands assessed compared with the control group when the stimulation was on or off. Our study demonstrated that the presurgical DTF profiles of responders were different from those of nonresponders. Moreover, a presurgical normal DTF profile may predict good responsiveness to VNS.

Transcutaneous Auricular Vagus Nerve Stimulation Differently Modifies Functional Brain Networks of Subjects With Different Epilepsy Types

Epilepsy types differ by pathophysiology and prognosis. Transcutaneous auricular vagus nerve stimulation (taVNS) is a non-invasive treatment option in epilepsy. Nevertheless, its mode of action and impact on different types of epilepsy are still unknown. We investigated whether short-term taVNS differently affects local and global characteristics of EEG-derived functional brain networks in different types of epilepsy. Thirty subjects (nine with focal epilepsy, 11 with generalized epilepsy, and 10 without epilepsy or seizures) underwent a 3-h continuous EEG-recording (1 h pre-stimulation, 1 h taVNS stimulation, 1 h post-stimulation) from which we derived evolving functional brain networks. We assessed—in a time-resolved manner—important global (topological, robustness, and stability properties) and local (centralities of vertices and edges) network characteristics. Compared to the subjects with focal epilepsies and without epilepsy, those with generalized epilepsies clearly presented with different topological properties of their functional brain network already at rest. Furthermore, subjects with focal and generalized epilepsies reacted differently to the stimulation, expressed as different taVNS-induced immediate and enduring reorganization of global network characteristics. On the local network scale, no discernible spatial pattern could be detected, which points to a rather unspecific and generalized modification of brain activity. Assessing functional brain network characteristics can provide additional information for differentiating between focal and generalized epilepsy. TaVNS-related modifications of global network characteristics clearly differ between epilepsy types. Impact of such a non–pharmaceutical intervention on clinical decision-making in the treatment of different epilepsy types needs to be assessed in future studies.

The optimal target and connectivity for DBS in Lennox‐Gastaut syndrome

Objective

Deep brain stimulation (DBS) can reduce seizures in Lennox–Gastaut syndrome (LGS). However, little is known about the optimal target and whether efficacy depends on connectivity of the stimulation site. Using outcome data from the ESTEL trial, we aimed to determine the optimal target and connectivity for DBS in LGS.

Methods

A total of 20 patients underwent bilateral DBS of the thalamic centromedian nucleus (CM). Outcome was percentage seizure reduction from baseline after 3 months of DBS, defined using three measures (monthly seizure diaries, 24‐hour scalp electroencephalography [EEG], and a novel diary‐EEG composite). Probabilistic stimulation mapping identified thalamic locations associated with higher/lower efficacy. Two substitute diffusion MRI datasets (a normative dataset from healthy subjects and a “disease‐matched” dataset from a separate group of LGS patients) were used to calculate structural connectivity between DBS sites and a map of areas known to express epileptic activity in LGS, derived from our previous EEG‐fMRI research.

Results

Results were similar across the three outcome measures. Stimulation was most efficacious in the anterior and inferolateral “parvocellular” CM border, extending into the ventral lateral nucleus (posterior subdivision). There was a positive association between diary‐EEG composite seizure reduction and connectivity to areas of a priori EEG‐fMRI activation, including premotor and prefrontal cortex, putamen, and pontine brainstem. In contrast, outcomes were not associated with baseline clinical variables.

Interpretation

Efficacious CM‐DBS for LGS is linked to stimulation of the parvocellular CM and the adjacent ventral lateral nucleus, and is associated with connectivity to, and thus likely modulation of, the “secondary epileptic network” underlying the shared electroclinical manifestations of LGS. ANN NEUROL 2022;92:61–74

Connectomic profiling and Vagus nerve stimulation Outcomes Study (CONNECTiVOS): a prospective observational protocol to identify biomarkers of seizure response in children and youth

INTRODUCTION

Vagus nerve stimulation (VNS) is a neuromodulation therapy that can reduce the seizure burden of children with medically intractable epilepsy. Despite the widespread use of VNS to treat epilepsy, there are currently no means to preoperatively identify patients who will benefit from treatment. The objective of the present study is to determine clinical and neural network-based correlates of treatment outcome to better identify candidates for VNS therapy.

METHODS AND ANALYSIS

In this multi-institutional North American study, children undergoing VNS and their caregivers will be prospectively recruited. All patients will have documentation of clinical history, physical and neurological examination and video electroencephalography as part of the standard clinical workup for VNS. Neuroimaging data including resting-state functional MRI, diffusion-tensor imaging and magnetoencephalography will be collected before surgery. MR-based measures will also be repeated 12 months after implantation. Outcomes of VNS, including seizure control and health-related quality of life of both patient and primary caregiver, will be prospectively measured up to 2 years postoperatively. All data will be collected electronically using Research Electronic Data Capture.

ETHICS AND DISSEMINATION

This study was approved by the Hospital for Sick Children Research Ethics Board (REB number 1000061744). All participants, or substitute decision-makers, will provide informed consent prior to be enrolled in the study. Institutional Research Ethics Board approval will be obtained from each additional participating site prior to inclusion. This study is funded through a Canadian Institutes of Health Research grant (PJT-159561) and an investigator-initiated funding grant from LivaNova USA (Houston, TX; FF01803B IIR).

Brainstem Associated Somatosensory Evoked Potentials and Response to Vagus Nerve Stimulation: An Investigation of the Vagus Afferent Network

Despite decades of clinical usage, selection of patients with drug resistant epilepsy who are most likely to benefit from vagus nerve stimulation (VNS) remains a challenge. The mechanism of action of VNS is dependent upon afferent brainstem circuitry, which comprises a critical component of the Vagus Afferent Network (VagAN). To evaluate the association between brainstem afferent circuitry and seizure response, we retrospectively collected intraoperative data from sub-cortical recordings of somatosensory evoked potentials (SSEP) in 7 children with focal drug resistant epilepsy who had failed epilepsy surgery and subsequently underwent VNS. Using multivariate linear regression, we demonstrate a robust negative association between SSEP amplitude (p < 0.01), and seizure reduction. There was no association between SSEP latency and seizure outcomes. Our findings provide novel insights into the mechanism of VNS and inform our understanding of the importance of brainstem afferent circuitry within the VagAN for seizure responsiveness following VNS.

Evolution of the Vagus Nerve Stimulation (VNS) Therapy System Technology for Drug-Resistant Epilepsy

The vagus nerve stimulation (VNS) Therapy® System is the first FDA-approved medical device therapy for the treatment of drug-resistant epilepsy. Over the past two decades, the technology has evolved through multiple iterations resulting in software-related updates and implantable lead and generator hardware improvements. Healthcare providers today commonly encounter a range of single- and dual-pin generators (models 100, 101, 102, 102R, 103, 104, 105, 106, 1000) and related programming systems (models 250, 3000), all of which have their own subtle, but practical differences. It can therefore be a daunting task to go through the manuals of these implant models for comparison, some of which are not readily available. In this review, we highlight the technological evolution of the VNS Therapy System with respect to device approval milestones and provide a comparison of conventional open-loop vs. the latest closed-loop generator models. Battery longevity projections and an in-depth examination of stimulation mode interactions are also presented to further differentiate amongst generator models.

Efficacy and potential predictors of vagus nerve stimulation therapy in refractory postencephalitic epilepsy

BACKGROUND

Vagus nerve stimulation (VNS) is a therapeutic approach for patients with refractory postencephalitic epilepsy (PEE), which is characterized by drug resistance and disappointing surgical outcomes. However, the efficacy of VNS has not yet been studied in patients with refractory PEE. The present study aimed to demonstrate the efficacy of VNS and evaluate potential clinical predictors in patients with refractory PEE.

METHODS

We retrospectively collected the outcomes of VNS with at least a 1-year follow-up in all patients with refractory PEE. Subgroups were classified as responders and non-responders according to the efficacy of VNS (⩾50% or < 50% reduction in seizure frequency). Preoperative data were analyzed to screen for potential predictors of VNS responsiveness.

RESULTS

A total of 42 refractory PEE patients who underwent VNS therapy were enrolled, with an average age of 21.13 ± 9.70 years. Seizure frequency was reduced by more than 50% in 64.25% of patients, and 7.14% of patients achieved seizure-free events after VNS therapy. In addition, the response rates increased over time, with 40.5%, 50.0% and 57.1%, respectively at 6 months, 12 months, and 24 months after VNS therapy. Preoperative duration of epilepsy, monthly seizure frequency, and spatial distribution of interictal epileptic discharges (IEDs) were correlated with responders (p < 0.05) in the univariate analysis. Further multivariate regression analysis demonstrated that refractory PEE patients with high monthly seizure frequency or Focal IEDs (focal or multifocal epileptiform discharges) achieved better efficacy on VNS (p = 0.010, p = 0.003, respectively).

CONCLUSION

VNS is an effective palliative therapy for patients with refractory PEE. Focal IEDs (focal or multifocal epileptiform discharges) and high seizure frequency were potential preoperative predictors of effectiveness after VNS therapy.

Patient Characteristics Associated with Seizure Freedom after Vagus Nerve Stimulation in Pediatric Intractable Epilepsy: An Analysis of “Super-Responders”

Clinical responses to vagus nerve stimulation (VNS) therapy for intractable epilepsy can be unpredictable, and factors that predict response to therapy are elusive. Minority of children undergoing VNS achieve seizure freedom. The current study aimed to characterize this exceptional patient population, defined as “super-responders” (SRs). Retrospective data were collected from 150 children who underwent VNS at a single pediatric institution. The patients' mean age at VNS device implantation was 12.0 years (range, 3.09–17.9 years). Ten SRs (6.7%) were identified who achieved and maintained seizure freedom for longer than 1 year following implantation. The interval between epilepsy onset and VNS device implantation was significantly shorter in SRs than in the other children (mean epilepsy duration 5.72 vs. 8.44 years, respectively; p = 0.032). SRs also had a significantly shorter proportion of life with epilepsy compared with the other children (mean ratio of epilepsy duration to age at implantation 0.52 vs. 0.71, respectively; p = 0.023). SRs reported their seizure freedom relatively early (six patients within 6 months and all patients within 12 months after implantation) at relatively low device settings (mean output current 0.81 mA at their last follow-up). Compared with conventional models, responsive VNS models with autostimulation features did not increase the ratio of SRs. No other clinical or imaging characteristic difference between SRs and the other children was found in this cohort. The current study showed a significant association between shorter epilepsy duration and shorter proportion of life with epilepsy and seizure freedom after VNS.

Severe autonomic nervous system imbalance in Lennox-Gastaut syndrome patients demonstrated by heart rate variability recordings

OBJECTIVE

Patients diagnosed with Lennox Gastaut syndrome (LGS), an epileptic encephalopathy characterized by usually drug resistant generalized and focal seizures, are often considered as candidates for vagus nerve stimulation (VNS). Recent research shows that heart rate variability (HRV) differs in epilepsy patients and is related to VNS treatment response. This study investigated pre-ictal HRV in generalized onset seizures of patients with LGS in correlation with their VNS response.

METHODS

In drug resistant epilepsy (DRE) patients diagnosed with LGS video-electroencephalography recording was performed during their pre-surgical evaluation. Six HRV parameters (time and-, frequency domain, non-linear parameters) were evaluated for every seizure in epochs of 10 min at baseline (60 to 50 min before seizure onset) and pre-ictally (10 min prior to seizure onset). The results were correlated to VNS response after one year of VNS therapy.

RESULTS

Seven patients and 31 seizures were included, two patients were classified as VNS responders (≥ 50 % seizure reduction). No difference in pre-ictal HRV parameters between VNS responders and VNS non-responders could be found, but high frequency (HF) power, reflecting the parasympathetic tone increased significantly in the pre-ictal epoch in both VNS responders and VNS non-responders (p = 0.017, p = 0.004).

SIGNIFICANCE

In this pilot data pre-ictal HRV did not differ in VNS responders compared to VNS non-responders, but showed a significant increase in HF power - a parasympathetic overdrive - in both VNS responders and VNS non-responders. This sudden autonomic imbalance might have an influence on the cardiovascular system in the ictal period. Generalized tonic-clonic seizures are regarded as the main risk factor for SUDEP and severe seizure-induced autonomic imbalance may play a role in the pathophysiological pathway.

Vagus Nerve Stimulation Elicits Sleep EEG Desynchronization and Network Changes in Responder Patients in Epilepsy

Neural desynchronization was shown as a key mechanism of vagus nerve stimulation (VNS) action in epilepsy, and EEG synchronization measures are explored as possible response biomarkers. Since brain functional organization in sleep shows different synchrony and network properties compared to wakefulness, we aimed to explore the effects of acute VNS on EEG-derived measures in the two different states of vigilance. EEG epochs were retrospectively analyzed from twenty-four VNS-treated epileptic patients (11 responders, 13 non-responders) in calm wakefulness and stage N2 sleep. Weighted Phase Lag Index (wPLI) was computed as connectivity measure of synchronization, for VNS OFF and VNS ON conditions. Global efficiency (GE) was computed as a network measure of integration. Ratios OFF/ON were obtained as desynchronization/de-integration index. Values were compared between responders and non-responders, and between EEG states. ROC curve and area-under-the-curve (AUC) analysis was performed for response classification. In responders, stronger VNS-induced theta desynchronization (p < 0.05) and decreased GE (p < 0.05) were found in sleep, but not in wakefulness. Theta sleep wPLI Ratio OFF/ON yielded an AUC of 0.825, and 79% accuracy as a response biomarker if a cut-off value is set at 1.05. Considering all patients, the VNS-induced GE decrease was significantly more important in sleep compared to awake EEG state (p < 0.01). In conclusion, stronger sleep EEG desynchronization in theta band distinguishes responders to VNS therapy from non-responders. VNS-induced reduction of network integration occurs significantly more in sleep than in wakefulness.

Electrophysiological Biomarkers in Genetic Epilepsies

Precision treatments for epilepsy targeting the underlying genetic diagnoses are becoming a reality. Historically, the goal of epilepsy treatments was to reduce seizure frequency. In the era of precision medicine, however, outcomes such as prevention of epilepsy progression or even improvements in cognitive functions are both aspirational targets for any intervention. Developing methods, both in clinical trial design and in novel endpoints, will be necessary for measuring, not only seizures, but also the other neurodevelopmental outcomes that are predicted to be targeted by precision treatments. Biomarkers that quantitatively measure disease progression or network level changes are needed to allow for unbiased measurements of the effects of any gene-level treatments. Here, we discuss some of the promising electrophysiological biomarkers that may be of use in clinical trials of precision therapies, as well as the difficulties in implementing them.