Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation

Analysis of taVNS effects on autonomic and central nervous systems in healthy young adults based on HRV, EEG parameters

Objective.Transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive method of stimulating the vagus nerve, simultaneously affects the autonomic nervous system (ANS) and central nervous system (CNS) through efferent and afferent pathways. The purpose of this study is to analyze the effect of taVNS on the ANS and CNS through heart rate variability (HRV) and electroencephalography (EEG) parameters of identified responders.Approach.Two sets of data were collected from each of 10 healthy adult male subjects in their 20 s, and five HRV parameters from the time domain (RMSSD, pNN50, pNN30, pNN20, ppNNx) and two EEG parameters (power of alpha band, power of delta band) were extracted.Main results.Based on pNN50, responders to taVNS were identified; among them, pNN50 (p= 0.0041) and ppNNx (p= 0.0037) showed significant differences before and after taVNS. At the same time, for alpha power and delta power of EEG, significant difference (p< 0.05) was observed in most channels after taVNS compared to before stimulation.Significance.This study demonstrated the validity of identifying responders using pNN50 and the influence of taVNS on both the ANS and CNS. We conclude that taVNS can be used to treat a variety of diseases and as a tool to help control the ANS and CNS.

Identifying responders to vagus nerve stimulation based on microstructural features of thalamocortical tracts in drug-resistant epilepsy

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 high-gradient 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.

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.

Electroencephalogram synchronization measure as a predictive biomarker of Vagus nerve stimulation response in refractory epilepsy: A retrospective study

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.

Artificial intelligence in epilepsy - applications and pathways to the clinic

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

Neurostimulation targeting the epileptic focus: Current understanding and perspectives for treatment

For the one third of people with epilepsy whose seizures are not controlled with medications, targeting the seizure focus with neurostimulation can be an effective therapeutic strategy. In this focused review, we summarize a discussion of targeted neurostimulation modalities during a workshop held in Frankfurt, Germany in September 2023. Topics covered include: available devices for seizure focus stimulation; alternating current (AC) and direct current (DC) stimulation to reduce focal cortical excitability; modeling approaches to simulate DC stimulation; reconciling the efficacy of focal stimulation with the network theory of epilepsy; and the emerging concept of 'neurostimulation zones,' which are defined as cortical regions where focal stimulation is most effective for reducing seizures and which may or may not directly involve the seizure onset zone. By combining experimental data, modeling results, and clinical outcome analysis, rational selection of target regions and stimulation parameters is increasingly feasible, paving the way for a broader use of neurostimulation for epilepsy in the future.

Neurobehavioral and Clinical Comorbidities in Epilepsy: The Role of White Matter Network Disruption

Epilepsy is a common neurological disorder associated with alterations in cortical and subcortical brain networks. Despite a historical focus on gray matter regions involved in seizure generation and propagation, the role of white matter (WM) network disruption in epilepsy and its comorbidities has sparked recent attention. In this review, we describe patterns of WM alterations observed in focal and generalized epilepsy syndromes and highlight studies linking WM disruption to cognitive and psychiatric comorbidities, drug resistance, and poor surgical outcomes. Both tract-based and connectome-based approaches implicate the importance of extratemporal and temporo-limbic WM disconnection across a range of comorbidities, and an evolving literature reveals the utility of WM patterns for predicting outcomes following epilepsy surgery. We encourage new research employing advanced analytic techniques (e.g., machine learning) that will further shape our understanding of epilepsy as a network disorder and guide individualized treatment decisions. We also address the need for research that examines how neuromodulation and other treatments (e.g., laser ablation) affect WM networks, as well as research that leverages larger and more diverse samples, longitudinal designs, and improved magnetic resonance imaging acquisitions. These steps will be critical to ensuring generalizability of current research and determining the extent to which neuroplasticity within WM networks can influence patient outcomes.

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).

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.

Vagus nerve stimulation for pharmacoresistant epilepsy secondary to encephalomalacia: A single-center retrospective study

Objective

Vagus nerve stimulation (VNS) is an adjunctive treatment for pharmacoresistant epilepsy. Encephalomalacia is one of the most common MRI findings in the preoperative evaluation of patients with pharmacoresistant epilepsy. This is the first study that aimed to determine the effectiveness of VNS for pharmacoresistant epilepsy secondary to encephalomalacia and evaluate the potential predictors of VNS effectiveness.

Methods

We retrospectively analyzed the seizure outcomes of VNS with at least 1 year of follow-up in all patients with pharmacoresistant epilepsy secondary to encephalomalacia. Based on the effectiveness of VNS (≥50% or <50% reduction in seizure frequency), patients were divided into two subgroups: responders and non-responders. Preoperative data were analyzed to screen for potential predictors of VNS effectiveness.

Results

A total of 93 patients with epilepsy secondary to encephalomalacia who underwent VNS therapy were recruited. Responders were found in 64.5% of patients, and 16.1% of patients achieved seizure freedom at the last follow-up. In addition, the responder rate increased over time, with 36.6, 50.5, 64.5, and 65.4% at the 3-, 6-, 12-, and 24-month follow-ups, respectively. After multivariate analysis, seizure onset in adults (>18 years old) (OR: 0.236, 95%CI: 0.059-0.949) was found to be a positive predictor, and the bilateral interictal epileptic discharges (IEDs) (OR: 3.397, 95%CI: 1.148-10.054) and the bilateral encephalomalacia on MRI (OR: 3.193, 95%CI: 1.217-8.381) were found to be negative predictors of VNS effectiveness.

Conclusion

The results demonstrated the effectiveness and safety of VNS therapy in patients with pharmacoresistant epilepsy secondary to encephalomalacia. Patients with seizure onset in adults (>18 years old), unilateral IEDs, or unilateral encephalomalacia on MRI were found to have better seizure outcomes after VNS therapy.

Pediatric Vagus Nerve Stimulation: Case Series Outcomes and Future Directions

BACKGROUND

Vagus nerve stimulation (VNS) is a neuromodulatory procedure most extensively studied as an adjunct to medically refractory epilepsy. Despite widespread adoption and decades of clinical experience, clinical predictors of response to VNS remain unclear.

OBJECTIVE

To evaluate a retrospective cohort of pediatric patients undergoing VNS at our institution to better understand who may benefit from VNS and identify factors which may predict response to VNS.

METHODS

We conducted a retrospective cohort study examining pediatric patients undergoing VNS over nearly a 20-year span at a single institution. Presurgical evaluation, including demographics, clinical history, and diagnostic electroencephalogram, and imaging findings were examined. Primary outcomes included VNS response.

RESULTS

Two hundred ninety-seven subjects were studied. The mean age at surgery was 10.1 (SD = 4.9, range = 0.8-25.3) years; length of follow-up was a mean of 4.6 years (SD = 3.5, median = 3.9 years, range 1 day-16.1 years). There was no association between demographic factors, epilepsy etiology, or genetic basis and VNS outcomes. There was an association between reduction in main seizure type with positive MRI finding. Of all MRI findings analyzed, brain atrophy was significantly associated with worse VNS outcomes, whereas dysplastic hippocampus and chronic periventricular leukomalacia findings were found to be associated with improved outcomes. Increased seizure semiology variability and seizure type were also associated with improved seizure outcomes.

CONCLUSION

Predicting response to VNS remains difficult, leading to incompletely realized benefits and suboptimal resource utilization. Specific MRI findings and increased seizure semiology variability and type can help guide clinical decision making and patient counseling.

Cerebral cortex and hippocampus neural interaction during vagus nerve stimulation under in vivo large-scale imaging

Objective

The purpose of this study was to study mechanisms of VNS modulation from a single neuron perspective utilizing a practical observation platform with single neuron resolution and widefield, real-time imaging coupled with an animal model simultaneously exposing the cerebral cortex and the hippocampus.

Methods

We utilized the observation platform characterized of widefield of view, real-time imaging, and high spatiotemporal resolution to obtain the neuronal activities in the cerebral cortex and the hippocampus during VNS in awake states and under anesthesia.

Results

Some neurons in the hippocampus were tightly related to VNS modulation, and varied types of neurons showed distinct responses to VNS modulation.

Conclusion

We utilized such an observation platform coupled with a novel animal model to obtain more information on neuron activities in the cerebral cortex and the hippocampus, providing an effective method to further study the mechanisms of therapeutic effects modulated by VNS.

Effectiveness of vagus nerve stimulation therapy in refractory hypoxic-ischemic encephalopathy-induced epilepsy

Background

Epilepsy is one of the important long-term sequelae of neonatal hypoxic-ischemic encephalopathy (HIE) and is typically characterized by drug resistance and poor surgical outcomes. Vagus nerve stimulation (VNS) is a promising neuromodulation therapy for refractory epilepsy.

Objectives

The present study aimed to first evaluate the effectiveness of VNS in patients with refractory HIE-induced epilepsy and scrutinize potential clinical predictors.

Methods

We retrospectively collected the outcomes of VNS in all patients with refractory HIE-induced epilepsy and at least 2 years of follow-up. Subgroups were classified as responders and nonresponders according to the effectiveness of VNS (⩾50% or <50% reduction in seizure frequency). Preoperative data were analyzed to screen for potential predictors of VNS effectiveness.

Results

A total of 55 patients with refractory HIE-induced epilepsy who underwent VNS therapy were enrolled. Responders represented 56.4% of patients, and 12.7% of patients achieved seizure freedom at the last follow-up. In addition, the responder rate increased over time with rates of 23.6%, 38.2%, 50.9%, and 56.4% at the 3-, 6-, 12- and 24-month follow-ups, respectively. After multivariate analysis, neonatal seizure was identified as a negative predictor (OR: 4.640, 95% CI: 1.129-19.066), and a predominant seizure type of generalized onset was identified as a positive predictor (OR: 0.261, 95% CI: 0.078-0.873) of VNS effectiveness.

Conclusion

VNS therapy was effective in patients with refractory HIE-induced epilepsy and was well tolerated over a 2-year follow-up period. VNS therapy demonstrated better effectiveness in patients without neonatal seizures or with a predominant seizure type of generalized onset.

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.

Electroencephalogram and heart rate variability features as predictors of responsiveness to vagus nerve stimulation in patients with epilepsy: a systematic review

INTRODUCTION

Vagus nerve stimulation (VNS) is a mainstay treatment in people with medically refractive epilepsy with a growing interest to identify biomarkers that are predictive of VNS efficacy. In this review, we looked at electroencephalography (EEG) and heart rate variability (HRV) parameters as potential biomarkers.

METHODOLOGY

A comprehensive search of several databases limited to the English language and excluding animal studies was conducted. Data was collected from studies that specifically reviewed preoperative EEG and HRV characteristics as predictive factors of VNS outcomes.

RESULTS

Ten out of 1078 collected studies were included in this review, of which EEG characteristics were reported in seven studies; HRV parameters were reported in two studies, and one study reported both. For EEG, studies reported a lower global rate of synchronization in alpha, delta, and gamma waves as predictors of the VNS response. The P300 wave, an evoked response on EEG, had conflicting results. Two studies reported high P300 wave amplitudes in nonresponders and low amplitudes in responders, whereas another study reported high P300 wave amplitudes in responders. For HRV, one study reported high-frequency power as the only parameter to be significantly lower in responders. In contrast, two studies from the same authors showed that HRV parameters were not different between responders and nonresponders.

CONCLUSION

HRV parameters and EEG characteristics including focal seizures and P300 wave have been reported as potential biomarkers for VNS outcomes in people with medically refractive epilepsy. However, the contradictory findings imply a need for validation through clinical trials.

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.

Callosotomy vs Vagus Nerve Stimulation in the Treatment of Lennox-Gastaut Syndrome: A Systematic Review With Meta-Analysis

BACKGROUND

Lennox-Gastaut syndrome (LGS) is a severe drug-resistant epileptic syndrome. Palliative treatments such as corpus callosotomy (CC) and vagus nerve stimulation (VNS) have emerged as treatments to reduce the number of seizures in patients. The aim of this study is to compare the effectiveness of CC and VNS in patients with LGS studied in the last 30 years.

MATERIALS AND METHODS

We conducted a systematic review with meta-analysis and collected papers from PubMed (MEDLINE), Ovidsp, Web of Science, and Cochrane Library data bases. The articles analyzed were published between January 1990 and December 2020. Keywords were chosen based on internal and external validation in the PubMed data base (the analysis is available in the Supplementary Data Supplementary Appendix). Prospective or retrospective case reports (n ≥ 2), case series, cohort studies, or case-control studies involving patients with LGS were included in the analysis. We selected studies that had no age or sex restriction and that provided data on seizures before and after treatments. Studies not written in English, published without peer review, or not indexed in the data bases were excluded. Other exclusion criteria were the absence of seizure data and the impossibility of extracting this information from the studies. To analyze the results, we used the random-effects model based on the assessment of heterogeneity (I² statistics) in two scenarios. In scenario 1, we assessed the incidence of patients with a seizure reduction ≥ 50%; in scenario 2, we assessed the incidence of patients with a seizure reduction > 0%.

RESULTS

Of the 7418 articles found using the keywords, 32 were considered eligible. Of these, 18 articles were on VNS (175 patients) and 14 on CC (107 patients). For scenario 1 (seizure reduction ≥ 50%), CC had an incidence of 65% (95% CI, 37%-94%), with an I² value of 82.7%; VNS had an incidence of 34% (95% CI, 11%-57%), with an I² value of 80.7%. For scenario 2 (seizure reduction > 0%), CC had an incidence of 80% (95% CI, 58%-100%), with an I² value of 84.7%; VNS had an incidence of 64% (95% CI, 38%-89%), with an I² value of 90.8%. There was an overlap of confidence intervals, with no statistical difference between the treatments in both scenarios.

DISCUSSION

Our analysis of LGS showed that the CC and VNS treatments are significantly beneficial to reducing seizures, without superiority between them.

Vagus nerve stimulation for refractory posttraumatic epilepsy: Efficacy and predictors of seizure outcome

Background

Traumatic brain injury (TBI) has been recognized as an important and common cause of epilepsy since antiquity. Posttraumatic epilepsy (PTE) is usually associated with drug resistance and poor surgical outcomes, thereby increasing the burden of the illness on patients and their families. Vagus nerve stimulation (VNS) is an adjunctive treatment for medically refractory epilepsy. This study aimed to determine the efficacy of VNS for refractory PTE and to initially evaluate the potential predictors of efficacy.

Methods

We retrospectively collected the outcomes of VNS with at least a 1-year follow-up in all patients with refractory PTE. 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 efficacy.

Results

In total, forty-five patients with refractory PTE who underwent VNS therapy were enrolled. Responders were found in 64.4% of patients, and 15.6% of patients achieved seizure freedom at the last follow-up. In addition, the responder rate increased over time, with 37.8, 44.4, 60, and 67.6% at the 3-, 6-, 12-, and 24-month follow-ups, respectively. After multivariate analysis, generalized interictal epileptic discharges (IEDs) were found to be a negative predictor (OR: 4.861, 95% CI: 1.145–20.632) of VNS efficacy.

Conclusion

The results indicated that VNS therapy was effective in refractory PTE patients and was well tolerated over a 1-year follow-up period. Patients with focal or multifocal IEDs were recognized to have better efficacy after VNS therapy.

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.

Vagus Nerve Stimulation Modulates Phase-Amplitude Coupling in Thalamic Local Field Potentials

OBJECTIVE

The antiseizure effects of vagus nerve stimulation (VNS) are thought to be mediated by the modulation of afferent thalamocortical circuitry. Cross-frequency phase-amplitude coupling (PAC) is a mechanism of hierarchical network coordination across multiple spatiotemporal scales. In this study, we leverage local field potential (LFP) recordings from the centromedian (CM) (n = 3) and anterior (ATN) (n = 2) nuclei in five patients with tandem thalamic deep brain stimulation and VNS to study neurophysiological changes in the thalamus in response to VNS.

MATERIALS AND METHODS

Bipolar LFP data were recorded from contact pairs spanning target nuclei in VNS "on" and "off" states.

RESULTS

Active VNS was associated with increased PAC between theta, alpha, and beta phase and gamma amplitude in CM (q < 0.05). Within the ATN, PAC changes also were observed, although these were less robust. In both nuclei, active VNS also modulated interhemispheric bithalamic functional connectivity.

CONCLUSIONS

We report that VNS is associated with enhanced PAC and coordinated interhemispheric interactions within and between thalamic nuclei, respectively. These findings advance understanding of putative neurophysiological effects of acute VNS and contextualize previous animal and human studies showing distributed cortical synchronization after VNS.

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.

Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study

Objective

Acupuncture has been shown to be effective in the treatment of chronic pain. However, their neural mechanism underlying the effective acupuncture response to chronic pain is still unclear. We investigated whether metabolic patterns in the pain matrix network might predict acupuncture therapy responses in patients with primary dysmenorrhea (PDM) using a machine-learning-based multivariate pattern analysis (MVPA) on positron emission tomography data (PET).

Methods

Forty-two patients with PDM were selected and randomized into two groups: real acupuncture and sham acupuncture (three menstrual cycles). Brain metabolic data from the three special brain networks (the sensorimotor network (SMN), default mode network (DMN), and salience network (SN)) were extracted at the individual level by using PETSurfer in fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG-PET) data. MVPA analysis based on metabolic network features was employed to predict the pain relief after treatment in the pooled group and real acupuncture treatment, separately.

Results

Paired t-tests revealed significant alterations in pain intensity after real but not sham acupuncture treatment. Traditional mass-univariate correlations between brain metabolic and alterations in pain intensity were not significant. The MVPA results showed that the brain metabolic pattern in the DMN and SMN did predict the pain relief in the pooled group of patients with PDM (R2 = 0.25, p = 0.005). In addition, the metabolic pattern in the DMN could predict the pain relief after treatment in the real acupuncture treatment group (R2 = 0.40, p = 0.01).

Conclusion

This study indicates that the individual-level metabolic patterns in DMN is associated with real acupuncture treatment response in chronic pain. The present findings advanced the knowledge of the brain mechanism of the acupuncture treatment in chronic pain.

Vagus nerve stimulation for conservative therapy-refractive epilepsy and depression

Numerous studies confirm that the vagus nerve stimulation (VNS) is an efficient, indirect neuromodulatory therapy with electrically induced current for epilepsy that cannot be treated by epilepsy surgery and is therapy-refractory and for drug therapy-refractory depression. VNS is an established, evidence-based and in the long-term cost-effective therapy in an interdisciplinary overall concept.Long-term data on the safety and tolerance of the method are available despite the heterogeneity of the patient populations. Stimulation-related side effects like hoarseness, paresthesia, cough or dyspnea depend on the stimulation strength and often decrease with continuing therapy duration in the following years. Stimulation-related side effects of VNS can be well influenced by modifying the stimulation parameters. Overall, the invasive vagus nerve stimulation may be considered as a safe and well-tolerated therapy option.For invasive and transcutaneous vagus nerve stimulation, antiepileptic and antidepressant as well as positive cognitive effects could be proven. In contrast to drugs, VNS has no negative effect on cognition. In many cases, an improvement of the quality of life is possible.iVNS therapy has a low probability of complete seizure-freedom in cases of focal and genetically generalized epilepsy. It must be considered as palliative therapy, which means that it does not lead to healing and requires the continuation of specific medication. The functional principle is a general reduction of the neuronal excitability. This effect is achieved by a slow increase of the effectiveness sometimes over several years. Responders are those patients who experience a 50% reduction of the seizure incidence. Some studies even reveal seizure-freedom in 20% of the cases. Currently, it is not possible to differentiate between potential responders and non-responders before therapy/implantation.The current technical developments of the iVNS generators of the new generation like closed-loop system (cardiac-based seizure detection, CBSD) reduce also the risk for SUDEP (sudden unexpected death in epilepsy patients), a very rare, lethal complication of epilepsies, beside the seizure severity.iVNS may deteriorate an existing sleep apnea syndrome and therefore requires possible therapy interruption during nighttime (day-night programming or magnet use) beside the close cooperation with sleep physicians.The evaluation of the numerous iVNS trials of the past two decades showed multiple positive effects on other immunological, cardiological, and gastroenterological diseases so that additional therapy indications may be expected depending on future study results. Currently, the vagus nerve stimulation is in the focus of research in the disciplines of psychology, immunology, cardiology as well as pain and plasticity research with the desired potential of future medical application.Beside invasive vagus nerve stimulation with implantation of an IPG and an electrode, also devices for transdermal and thus non-invasive vagus nerve stimulation have been developed during the last years. According to the data that are currently available, they are less effective with regard to the reduction of the seizure severity and duration in cases of therapy-refractory epilepsy and slightly less effective regarding the improvement of depression symptoms. In this context, studies are missing that confirm high evidence of effectiveness. The same is true for the other indications that have been mentioned like tinnitus, cephalgia, gastrointestinal complaints etc. Another disadvantage of transcutaneous vagus nerve stimulation is that the stimulators have to be applied actively by the patients and are not permanently active, in contrast to implanted iVNS therapy systems. So they are only intermittently active; furthermore, the therapy adherence is uncertain.

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).

Prediction of Vagal Nerve Stimulation Efficacy in Drug-Resistant Epilepsy (PRECISE): Prospective Study for Pre-implantation Prediction/Study Design

Background

Vagal nerve stimulation (VNS) can be indicated in patients with drug-resistant epilepsy, who are not eligible for resective epilepsy surgery. In VNS therapy, the responder rate (i.e., percentage of subjects experiencing ≥50% seizure reduction) is ~50%. At the moment, there is no widely-accepted possibility to predict VNS efficacy in a particular patient based on pre-implantation data, which can lead to unnecessary surgery and improper allocation of financial resources. The principal aim of PRediction of vagal nerve stimulation EfficaCy In drug-reSistant Epilepsy (PRECISE) study is to verify the predictability of VNS efficacy by analysis of pre-implantation routine electroencephalogram (EEG).

Methods

PRECISE is designed as a prospective multicentric study in which patients indicated to VNS therapy will be recruited. Patients will be classified as predicted responders vs. predicted non-responders using pre-implantation EEG analyses. After the first and second year of the study, the real-life outcome (responder vs. non-responder) will be determined. The real-life outcome and predicted outcome will be compared in terms of accuracy, specificity, and sensitivity. In the meantime, the patients will be managed according to the best clinical practice to obtain the best therapeutic response. The primary endpoint will be the accuracy of the statistical model for prediction of response to VNS therapy in terms of responders and non-responders. The secondary endpoint will be the quantification of differences in EEG power spectra (Relative Mean Power, %) between real-life responders and real-life non-responders to VNS therapy in drug-resistant epilepsy and the sensitivity and specificity of the model.

Discussion

PRECISE relies on the results of our previous work, through which we developed a statistical classifier for VNS response (responders vs. non-responders) based on differences in EEG power spectra dynamics (Pre-X-Stim).

Trial Registration

www.ClinicalTrials.gov, identifier: NCT04935567.

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.

Vagus nerve stimulation alleviated cerebral ischemia and reperfusion injury in rats by inhibiting pyroptosis via α7 nicotinic acetylcholine receptor

Cumulative evidence suggests that pyroptosis, a new sort of programmed cell death, is closely related to cerebral ischemia/reperfusion (I/R) injury. Our previous studies have testified that vagus nerve stimulation (VNS) was involved in many different neuroprotective and neuroplasticity pathways via α7 nicotinic acetylcholine receptor (α7nAchR), a vital node of the cholinergic anti-inflammatory pathway during cerebral I/R injury. We aimed to determine the neuroprotective effects of VNS through α7nAchR-mediated inhibition of pyroptosis. Focal cerebral ischemic stroke rat models were obtained by middle cerebral artery occlusion for 120 min. Expression of the NLRP3 inflammasome was evaluated using western blotting and immunofluorescence (IF) staining. The neurological deficit score, infarct volume, TUNEL staining findings, transmission electron microscopy findings, and expression of inflammatory cytokines were assessed 3 days after I/R injury. Our findings suggested that the protein expression levels of NLRP3, GSDMD-N, cleaved caspase-1, and ASC gradually increased until they peaked on day 3 after I/R injury. VNS inhibited the expression of pyroptosis-related molecules and decreased the number of pyroptotic cells and membrane pores. Administration of α7nAchR-antagonist and agonist helped in further assessment of the role of α7nAchR in pyroptosis. α7nAchR-agonist mimicked VNS’s neuroprotective effects on the improvement of neurological deficits, the reduction of infarct volumes, and the inhibition of neuronal pyroptosis after cerebral I/R injury. Conversely, the neuroprotection provided by VNS could be reversed by the administration of α7nAchR-antagonist. In conclusion, VNS-induced neuroprotection via inhibition of neuronal pyroptosis was α7nAchR-dependent, highlighting the pivotal role of α7nAChR in suppressing cellular pyroptosis and neuroinflammation. These findings may allow a better understanding of treatment principles for cerebral I/R injury.

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.

Sex Hormone-Specific Neuroanatomy of Takotsubo Syndrome: Is the Insular Cortex a Moderator?

Takotsubo syndrome (TTS), a transient form of dysfunction in the heart’s left ventricle, occurs predominantly in postmenopausal women who have emotional stress. Earlier studies support the concept that the human circulatory system is modulated by a cortical network (consisting of the anterior cingulate gyrus, amygdala, and insular cortex (Ic)) that plays a pivotal role in the central autonomic nervous system in relation to emotional stressors. The Ic plays a crucial role in the sympathovagal balance, and decreased levels of female sex hormones have been speculated to change functional cerebral asymmetry, with a possible link to autonomic instability. In this review, we focus on the Ic as an important moderator of the human brain–heart axis in association with sex hormones. We also summarize the current knowledge regarding the sex-specific neuroanatomy in TTS.

Neuromodulation for Refractory Epilepsy

Three neuromodulation therapies, all using implanted device and electrodes, have been approved to treat adults with drug-resistant focal epilepsy, namely, the vagus nerve stimulation in 1995, deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) in 2018 (2010 in Europe), and responsive neurostimulation (RNS) in 2014. Indications for VNS have more recently extended to children down to age of 4. Limited or anecdotal data are available in other epilepsy syndromes and refractory/super-refractory status epilepticus. Overall, neuromodulation therapies are palliative, with only a minority of patients achieving long-term seizure freedom, justifying favoring such treatments in patients who are not good candidates for curative epilepsy surgery. About half of patients implanted with VNS, ANT-DBS, and RNS have 50% or greater reduction in seizures, with long-term data suggesting increased efficacy over time. Besides their impact on seizure frequency, neuromodulation therapies are associated with various benefits and drawbacks in comparison to antiseizure drugs. Yet, we lack high-level evidence to best position each neuromodulation therapy in the treatment pathways of persons with difficult-to-treat epilepsy.

A Network-Based Approach to Glioma Surgery: Insights from Functional Neurosurgery

Simple Summary

This manuscript details the literature and discussion around revolutionizing the neurosurgeon’s approach to surgery for brain tumors by conceptualizing these tumors as entities within functional networks. We hope that the work detailed herein will aid in establishing neurosurgical paradigms to optimize planning for brain tumor surgery to improve functional outcomes for all patients.

Abstract

The evaluation and manipulation of structural and functional networks, which has been integral to advancing functional neurosurgery, is beginning to transcend classical subspecialty boundaries. Notably, its application in neuro-oncologic surgery has stimulated an exciting paradigm shift from the traditional localizationist approach, which is lacking in nuance and optimization. This manuscript reviews the existing literature and explores how structural and functional connectivity analyses have been leveraged to revolutionize and individualize pre-operative tumor evaluation and surgical planning. We describe how this novel approach may improve cognitive and neurologic preservation after surgery and attenuate tumor spread. Furthermore, we demonstrate how connectivity analysis combined with neuromodulation techniques can be employed to induce post-operative neuroplasticity and personalize neurorehabilitation. While the landscape of functional neuro-oncology is still evolving and requires further study to encourage more widespread adoption, this functional approach can transform the practice of neuro-oncologic surgery and improve the care and outcomes of patients with intra-axial tumors.

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.

Neuromodulation in epilepsy: state-of-the-art approved therapies

Three neuromodulation therapies have been appropriately tested and approved in refractory focal epilepsies: vagus nerve stimulation (VNS), deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS), and closed-loop responsive neurostimulation of the epileptogenic zone or zones. These therapies are primarily palliative. Only a few individuals have achieved complete freedom from seizures for more than 12 months with these therapies, whereas more than half have benefited from long-term reduction in seizure frequency of more than 50%. Implantation-related adverse events primarily include infection and pain at the implant site. Intracranial haemorrhage is a frequent adverse event for ANT-DBS and responsive neurostimulation. Other stimulation-specific side-effects are observed with VNS and ANT-DBS. Biomarkers to predict response to neuromodulation therapies are not available, and high-level evidence to aid decision making about when and for whom these therapies should be preferred over other antiepileptic treatments is scant. Future studies are thus needed to address these shortfalls in knowledge, approve other forms of neuromodulation, and develop personalised closed-loop therapies with embedded machine learning. Until then, neuromodulation could be considered for individuals with intractable seizures, ideally after the possibility of curative surgical treatment has been carefully assessed and ruled out or judged less appropriate.

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.

Identifying juvenile myoclonic epilepsy via diffusion tensor imaging using machine learning analysis

The aim of this study was to evaluate the feasibility of using a machine learning approach based on diffusion tensor imaging (DTI) to identify patients with juvenile myoclonic epilepsy. We analyzed the usefulness of combining conventional DTI measures and structural connectomic profiles. This retrospective study was conducted at a tertiary hospital. We enrolled 55 patients with juvenile myoclonic epilepsy. All of the subjects underwent DTI from January 2017 to March 2020. We also enrolled 58 healthy subjects as a normal control group. We extracted conventional DTI measures and structural connectomic DTI profiles. We employed the support vector machines (SVM) algorithm to classify patients with juvenile myoclonic epilepsy and healthy subjects based on the conventional DTI measures and structural connectomic profiles. The SVM classifier based on conventional DTI measures had an accuracy of 68.1% and an area under the curve (AUC) of 0.682. Another SVM classifier based on the structural connectomic profiles demonstrated an accuracy of 72.7% and an AUC of 0.727. The SVM classifier based on combining the conventional DTI measures and structural connectomic profiles had an accuracy of 81.8% and an AUC of 0.818. DTI using machine learning is useful for classifying patients with juvenile myoclonic epilepsy and healthy subjects. Combining both the conventional DTI measures and structural connectomic profiles results in a better classification performance than using conventional DTI measures or the structural connectomic profiles alone to identify juvenile myoclonic epilepsy.

Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices. Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model. Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1). Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

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.

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.

Identification of focal epilepsy by diffusion tensor imaging using machine learning

OBJECTIVE

The aim of this study was to evaluate the feasibility of machine learning based on diffusion tensor imaging (DTI) measures to distinguish patients with focal epilepsy versus healthy controls and antiseizure medication (ASM) responsiveness.

METHODS

This was a retrospective study performed at a tertiary hospital. We enrolled 456 patients with focal epilepsy, who underwent DTI and were taking ASMs. We enrolled 100 healthy subjects as a control. We obtained the conventional DTI measures and structural connectomic profiles from the DTI.

RESULTS

The support vector machine (SVM) classifier based on the conventional DTI measures revealed an accuracy of 76.5% and an area under curve (AUC) of 0.604 (95% Confidence interval (CI), 0.506-0.695). Another SVM classifier combined with structural connectomic profiles demonstrated an accuracy of 82.8% and an AUC of 0.701 (95% CI, 0.606-0.784). Of the 456 patients with epilepsy, 242 patients were ASM good responders, whereas 214 patients were ASM poor responders. In the classification of the ASM responders, an SVM classifier based on the conventional DTI measures revealed an accuracy of 54.9% and an AUC of 0.551 (95% CI, 0.443-0.655). Another SVM classifier combined with structural connectomic profiles demonstrated an accuracy of 59.3% and an AUC of 0.594 (95% CI, 0.485-0.695).

CONCLUSION

DTI using a machine learning is useful for differentiating patients with focal epilepsy from healthy controls, but it cannot classify ASM responsiveness. Combining structural connectomic profiles results in a better classification performance than the use of conventional DTI measures alone for identifying focal epilepsy and ASM responsiveness.

A Novel Statistical Model for Predicting the Efficacy of Vagal Nerve Stimulation in Patients With Epilepsy (Pre-X-Stim) Is Applicable to Different EEG Systems

Background: Identifying patients with intractable epilepsy who would benefit from therapeutic chronic vagal nerve stimulation (VNS) preoperatively remains a major clinical challenge. We have developed a statistical model for predicting VNS efficacy using only routine preimplantation electroencephalogram (EEG) recorded with the TruScan EEG device (Brazdil et al., 2019). It remains to be seen, however, if this model can be applied in different clinical settings.

Objective: To validate our model using EEG data acquired with a different recording system.

Methods: We identified a validation cohort of eight patients implanted with VNS, whose preimplantation EEG was recorded on the BrainScope device and who underwent the EEG recording according to the protocol. The classifier developed in our earlier work, named Pre-X-Stim, was then employed to classify these patients as predicted responders or non-responders based on the dynamics in EEG power spectra. Predicted and real-world outcomes were compared to establish the applicability of this classifier. In total, two validation experiments were performed using two different validation approaches (single classifier or classifier voting).

Results: The classifier achieved 75% accuracy, 67% sensitivity, and 100% specificity. Only two patients, both real-life responders, were classified incorrectly in both validation experiments.

Conclusion: We have validated the Pre-X-Stim model on EEGs from a different recording system, which indicates its application under different technical conditions. Our approach, based on preoperative EEG, is easily applied and financially undemanding and presents great potential for real-world clinical use.

Effects of transcutaneous vagus nerve stimulation (tVNS) on beta and gamma brain oscillations

Physiological and behavioral effects induced through transcutaneous vagus nerve stimulation (tVNS) are under scrutiny in a growing number of studies, yet its mechanisms of action remain poorly understood. One candidate mechanism is a modulation of γ-aminobutyric acid (GABA) transmission through tVNS. Two recent behavioral studies suggest that such a GABAergic effect might occur in a lateralized fashion, i.e., the GABA modulation might be stronger in the left than in the right brain hemisphere after tVNS applied to the left ear. Using magnetoencephalography (MEG), we tested for GABA-associated modulations in resting and event-related brain oscillations and for a lateralization of those effects in a sample of 41 healthy young adults. Our data provide substantial evidence against all hypotheses, i.e., we neither find effects of tVNS on oscillatory power nor a lateralization of effects.

A Pediatric Patient With Seizures and Vagus Nerve Stimulation With Worsening Snoring and Apneas

An 11-year-old female presents to the sleep clinic for evaluation for possible sleep-disordered breathing (SDB). She has a history of frequently intractable seizures for which she was tried on multiple antiepileptic medications. She had vagus nerve stimulation (VNS) implantation two years ago to treat her focal seizures. Nine months later, her seizures were controlled, but the family raised concerns about louder snoring and more frequently witnessed apneas. She had polysomnography (PSG) that showed severe obstructive sleep apnea (OSA) related to her VNS. The patient was diagnosed with SDB secondary to electrical activations of the implanted VNS. We describe an epilepsy patient whose case illustrates the possible respiratory complications (primarily OSA) associated with VNS. We will discuss the possible mechanisms of VNS related SDB and the importance of screening for SDB and advocate for a PSG both before and after VNS implantation.

A review of magnetoencephalography use in pediatric epilepsy: an update on best practice

Introduction: Magnetoencephalography (MEG) is a noninvasive technique that is used for presurgical evaluation of children with drug-resistant epilepsy (DRE).Areas covered: The contributions of MEG for localizing the epileptogenic zone are discussed, in particular in extra-temporal lobe epilepsy and focal cortical dysplasia, which are common in children, as well as in difficult to localize epilepsy such as operculo-insular epilepsy. Further, the authors review current evidence on MEG for mapping eloquent cortex, its performance, application in clinical practice, and potential challenges.Expert opinion: MEG could change the clinical management of children with DRE by directing placement of intracranial electrodes thereby enhancing their yield. With improved identification of a circumscribed epileptogenic zone, MEG could render more patients as suitable candidates for epilepsy surgery and increase utilization of surgery.

Neuroimaging evaluation of deep brain stimulation in the treatment of representative neurodegenerative and neuropsychiatric disorders

Brain stimulation technology has become a viable modality of reversible interventions in the effective treatment of many neurological and psychiatric disorders. It is aimed to restore brain dysfunction by the targeted delivery of specific electronic signal within or outside the brain to modulate neural activity on local and circuit levels. Development of therapeutic approaches with brain stimulation goes in tandem with the use of neuroimaging methodology in every step of the way. Indeed, multimodality neuroimaging tools have played important roles in target identification, neurosurgical planning, placement of stimulators and post-operative confirmation. They have also been indispensable in pre-treatment screen to identify potential responders and in post-treatment to assess the modulation of brain circuitry in relation to clinical outcome measures. Studies in patients to date have elucidated novel neurobiological mechanisms underlying the neuropathogenesis, action of stimulations, brain responses and therapeutic efficacy. In this article, we review some applications of deep brain stimulation for the treatment of several diseases in the field of neurology and psychiatry. We highlight how the synergistic combination of brain stimulation and neuroimaging technology is posed to accelerate the development of symptomatic therapies and bring revolutionary advances in the domain of bioelectronic medicine.