Presurgical thalamocortical connectivity is associated with response to vagus nerve stimulation in children with intractable epilepsy

Vagus nerve stimulation as a predictive coding modulator that enhances feedforward over feedback transmission

Vagus nerve stimulation (VNS) has emerged as a promising therapeutic intervention across various neurological and psychiatric conditions, including epilepsy, depression, and stroke rehabilitation; however, its mechanisms of action on neural circuits remain incompletely understood. Here, we present a novel theoretical framework based on predictive coding that conceptualizes VNS effects through differential modulation of feedforward and feedback neural circuits. Based on recent evidence, we propose that VNS shifts the balance between feedforward and feedback processing through multiple neuromodulatory systems, resulting in enhanced feedforward signal transmission. This framework integrates anatomical pathways, receptor distributions, and physiological responses to explain the influence of the VNS on neural dynamics across different spatial and temporal scales. Vagus nerve stimulation may facilitate neural plasticity and adaptive behavior through acetylcholine and noradrenaline (norepinephrine), which differentially modulate feedforward and feedback signaling. This mechanistic understanding serves as a basis for interpreting the cognitive and therapeutic outcomes across different clinical conditions. Our perspective provides a unified theoretical framework for understanding circuit-specific VNS effects and suggests new directions for investigating their therapeutic mechanisms.

Neuromodulation in pediatric drug-resistant epilepsy

This is a summary of the three commercially available neuromodulation devices for refractory epilepsy, highlighting their use in children. The article offers a high-level review of the proposed mechanisms of vagus nerve stimulation, responsive neurostimulation, and deep brain stimulation, the pivotal trials leading to their approval for use in the United States, as well as their efficacy and associated adverse effects.

Invasive Neurostimulation for the Treatment of Epilepsy

Although electricity has been used in medicine for thousands of years, bioelectronic medicine for treating epilepsy has become increasingly common in recent years. Invasive neurostimulation centers primarily around three approaches: vagus nerve stimulation (VNS), responsive neurostimulation (RNS), and deep brain stimulation (DBS). These approaches differ by target (e.g., cranial nerve, cortex, or thalamus) and stimulation parameters (e.g., triggered stimulation or continuous stimulation). Although typically noncurative, these approaches can dramatically reduce the seizure burden and offer patients new treatment options. There remains much to be understood about optimal targets and individualized stimulation protocols. Objective markers of seizure burden and biomarkers that quickly quantify neural excitability are still needed. In the future, bioelectronic medicine could become a curative approach that remodels neural networks to reduce pathological activity.

Vagus nerve stimulation for epilepsy: A narrative review of factors predictive of response

Vagus nerve stimulation (VNS) is an established therapy for drug-resistant epilepsy. However, there is a lack of reliable predictors of VNS response in clinical use. The identification of factors predictive of VNS response is important for patient selection and stratification as well as tailored stimulation programming. We conducted a narrative review of the existing literature on prognostic markers for VNS response using clinical, demographic, biochemical, and modality-specific information such as from electroencephalography (EEG), magnetoencephalography, and magnetic resonance imaging (MRI). No individual marker demonstrated sufficient predictive power for individual patients, although several have been suggested, with some promising initial findings. Combining markers from underresearched modalities such as T1-weighted MRI morphometrics and EEG may provide better strategies for treatment optimization.

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.

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.

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history

BACKGROUND

Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, there is significant value in computational methods capable of assisting in the selection of AEDs, aiming to minimize unnecessary medication and improve treatment efficacy.

RESULTS

In this study, we collected 7,507 medical records from 1,000 pediatric epilepsy patients and developed a computational clinical decision-supporting system for AED selection. This system leverages three multi-channel convolutional neural network (CNN) models tailored to three specific AEDs (vigabatrin, prednisolone, and clobazam). Each CNN model predicts whether a respective AED is effective on a given patient or not. The CNN models showed AUROCs of 0.90, 0.80, and 0.92 in 10-fold cross-validation, respectively. Evaluation on a hold-out test dataset further revealed positive predictive values (PPVs) of 0.92, 0.97, and 0.91 for the three respective CNN models, representing that suggested AEDs by our models would be effective in controlling epilepsy with a high accuracy and thereby reducing unnecessary medications for pediatric patients.

CONCLUSION

Our CNN models in the system demonstrated high PPVs for the three AEDs, which signifies the potential of our approach to support the clinical decision-making by assisting doctors in recommending effective AEDs within the three AEDs for patients based on their medical history. This would result in a reduction in the number of unnecessary ad hoc attempts to find an effective AED for pediatric epilepsy patients.

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.

Vagus Nerve Stimulation Therapy for Drug-Resistant Epilepsy in Children-A Literature Review

Vagus nerve stimulation (VNS) is a palliative treatment for drug-resistant epilepsy (DRE) that has been in use for over two decades. VNS suppresses epileptic seizures, prevents emotional disorders, and improves cognitive function and sleep quality, a parallel effect associated with the control of epileptic seizures. The seizure suppression rate with VNS increases monthly to annually, and the incidence of side effects reduces over time. This method is effective in treating DRE in children as well as adults, such as epilepsy associated with tuberous sclerosis, Dravet syndrome, and Lennox-Gastaut syndrome. In children, it has been reported that seizures decreased by >70% approximately 8 years after initiating VNS, and the 50% responder rate was reported to be approximately 70%. VNS regulates stimulation and has multiple useful systems, including self-seizure suppression using magnets, additional stimulation using an automatic seizure detection system, different stimulation settings for day and night, and an automatic stimulation adjustment system that reduces hospital visits. VNS suppresses seizures and has beneficial behavioral effects in children with DRE. This review describes the VNS system, the mechanism of the therapeutic effect, the specific stimulation adjustment method, antiepileptic effects, and other clinical effects in patients with childhood DRE.

Graph Theory and Modeling of Network Topology in Clinical Neurosurgery

The last several decades have seen a shift in understanding many neurological disorders as abnormalities in brain networks rather than specific brain regions. This conceptual revolution, coupled with advancements in computing capabilities and resources, has enabled a wealth of research on delineating and treating aberrant brain networks. One approach to network neuroscience, graph theory, involves modeling network topologies as mathematical graphs and computing various metrics that describe its characteristics. Using graph theory, researchers have derived new insights into the pathophysiology of many neuropsychiatric disorders and even developed treatments targeted at restoring network disturbances. In this chapter, we provide an overview of the principles of graph theory and how to implement it, specific applications of graph theory within clinical neurosurgery, and a discussion on the advantages and limitations of these approaches.

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.

Vagus nerve stimulation in refractory idiopathic generalised epilepsy: An Irish retrospective observational study

OBJECTIVE

Refractory idiopathic generalised epilepsy (IGE; also known as genetic generalised epilepsy) is a clinical challenge due to limited available therapeutic options. While vagus nerve stimulation (VNS) is approved as an adjunctive treatment for drug-resistant focal epilepsy, there is limited evidence supporting its efficacy for refractory IGE.

METHODS

We conducted a single-centre retrospective analysis of adult IGE patients treated with VNS between January 2003 and January 2022. We analysed the efficacy, safety, tolerability, stimulation parameters and potential clinical features of VNS response in this IGE cohort.

RESULTS

Twenty-three IGE patients were implanted with VNS between January 2003 and January 2022. Twenty-two patients (95.65%) were female. The median baseline seizure frequency was 30 per month (interquartile range [IQR]= 140), including generalised tonic-clonic seizures (GTCS), absences, myoclonus, and eyelid myoclonia with/without absences. The median number of baseline anti-seizure medications (ASM) was three (IQR= 2). Patients had previously failed a median of six ASM (IQR= 5). At the end of the study period, VNS therapy remained active in 17 patients (73.9%). amongst patients who continued VNS, thirteen (56.5% of the overall cohort) were considered responders (≥50% seizure frequency reduction). Amongst the clinical variables analysed, only psychiatric comorbidity correlated with poorer seizure outcomes, but was non-significant after applying the Bonferroni correction. Although 16 patients reported side-effects, none resulted in the discontinuation of VNS therapy.

SIGNIFICANCE

Over half of the patients with refractory IGE experienced a positive response to VNS therapy. VNS represents a viable treatment option for patients with refractory IGE, particularly for females, when other therapeutic options have been exhausted.

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).

Predicting seizure outcome after epilepsy surgery: Do we need more complex models, larger samples, or better data?

OBJECTIVE

The accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if (1) training more complex models, (2) recruiting larger sample sizes, or (3) using data-driven selection of clinical predictors would improve our ability to predict postoperative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict postoperative seizure outcome.

METHODS

We performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models-a logistic regression, a multilayer perceptron, and an XGBoost model-to predict 1-year postoperative seizure outcome on our data set. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up to N = 2000. Finally, we examined the impact of predictor selection on model performance.

RESULTS

Our logistic regression achieved an accuracy of 72% (95% confidence interval [CI] = 68%-75%, area under the curve [AUC] = .72), whereas our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP  = 67%-74%, AUCMLP  = .70; 95% CIXGBoost own  = 68%-75%, AUCXGBoost own  = .70). There was no significant difference in performance between our three models (all p > .4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI = 59%-67%, AUC = .62; pLR  = .005, pMLP  = .01, pXGBoost own  = .01) on our data. All models showed improved performance with increasing sample size, but limited improvements beyond our current sample. The best model performance was achieved with data-driven feature selection.

SIGNIFICANCE

We show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict postoperative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.

Predictive factors for successful vagus nerve stimulation in patients with refractory epilepsy: real-life insights from a multicenter study

Introduction

Vagus nerve stimulation (VNS) therapy is an established treatment for patients with drug-resistant epilepsy that reduces seizure frequency by at least 50% in approximately half of patients; however, the characteristics of the patients with the best response have not yet been identified. Thus, it is important to identify the profile of patients who would have the best response to guide early indications and better patient selection.

Methods

This retrospective study evaluated vagus nerve stimulation (VNS) as an adjuvant therapy for patients with drug-resistant epilepsy from six epilepsy centers in Brazil. Data from 192 patients aged 2-66 years were analyzed, and all patients received at least 6 months of therapy to be included.

Results

Included patients were aged 2-66 years (25.6 ± 14.3), 105 (54.7%) males and 87 (45.8%) females. Median follow-up interval was 5 years (range, 2005-2018). Overall, the response rate (≥50% seizure reduction) after VNS implantation was 65.6% (126/192 patients). Most patients had 50-90% seizure reduction (60.9%) and nine patients became seizure-free. There were no serious complications associated with VNS implantation. The rate of a ≥ 50% seizure reduction response was significantly higher in patients with no history of neurosurgery. The presence of focal without generalized seizures and focal discharges on interictal EEG was associated with better response. Overall, etiological predictors of a better VNS response profile were tumors while a worse response to VNS was related to the presence of vascular malformations and Lennox-Gastaut Syndrome.

Discussion

We observed an association between a better response to VNS therapy no history of neurosurgery, focal interictal epileptiform activity, and focal seizure pattern. Additionally, it is important to highlight that age was not a determinant factor of the response, as children and adults had similar response rates. Thus, VNS therapy should be considered in both adults and children with DRE.

Immediate Effects of Vagal Nerve Stimulation in Drug-Resistant Epilepsy Revealed by Magnetoencephalographic Recordings

Objective: Vagus nerve stimulation (VNS) has been a neuromodulatory option for treating drug-resistant epilepsy (DRE), but its mechanism remains unclear. To obtain insight into the mechanism by which VNS reduces epileptic seizures, the immediate effects of VNS in brain networks of DRE patients were investigated when the patients' vagal nerve stimulators were turned on. Methods: The brain network properties of 14 DRE patients with a vagal nerve stimulator and 14 healthy controls were evaluated using magnetoencephalography recordings for 6 main frequency bands. Results: Compared with healthy controls, DRE patients exhibited significant increases in functional connectivity in the theta, alpha, beta, and gamma bands and significant reductions in the small-world measure in the theta and beta bands. During periods when patients' vagal nerve stimulators were turned on, DRE patients showed significant reductions in functional connectivity in the theta and alpha bands and a significant increase in the small-world measure in the theta band when compared with periods when patients' vagal nerve stimulators were turned off. Conclusions: Our results indicate that the brain networks of DRE patients were pathologically hypersynchronous and instantaneous VNS can decrease the synchronization of brain networks of epileptic patients, which might play a key role in the mechanism by which VNS reduces epileptic seizures. In the theta band, instantaneous VNS can increase the network efficiency of DRE patients, and the increment in network efficiency may be helpful for improving brain cognitive function in epileptic patients. Impact statement For the first time, we investigated the immediate effects of vagus nerve stimulation (VNS) in the brain networks of drug-resistant epilepsy patients using magnetoencephalography. Our results show that instantaneous VNS can decrease the hypersynchronization of epileptic networks and increase the network efficiency of epileptic patients. Our results are helpful in understanding the mechanism of action by which VNS reduces epileptic seizures and improves the cognitive function in epileptic patients and the brain network reorganization caused by long-term VNS.

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.

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.

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.

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.

Neuroimaging and thalamic connectomics in epilepsy neuromodulation

Neuromodulation is an increasingly utilized therapy for the treatment of people with drug-resistant epilepsy. To date, the most common and effective target has been the thalamus, which is known to play a key role in multiple forms of epilepsy. Neuroimaging has facilitated rapid developments in the understanding of functional targets, surgical and programming techniques, and the effects of thalamic stimulation. In this review, the role of neuroimaging in neuromodulation is explored. First, the structural and functional changes of the thalamus in common epilepsy syndromes are discussed as the rationale for neuromodulation of the thalamus. Next, methods for imaging different thalamic nuclei are presented, as well as rationale for the need of direct surgical targeting rather than reliance on traditional stereotactic coordinates. Lastly, we discuss the potential role of neuroimaging in assessing the effects of thalamic stimulation and as a potential biomarker for neuromodulation outcomes.

Patient-Specific Characteristics Associated with Favorable Response to Vagus Nerve Stimulation

OBJECTIVE

The expansion in treatments for medically refractory epilepsy heightens the importance of identifying patients who are likely to benefit from vagus nerve stimulation (VNS). Here, we identify predictors with a positive VNS response.

METHODS

We present a retrospective analysis of 158 patients with medically refractory epilepsy. Patients were categorized as VNS responders or nonresponders. Baseline characteristics and time to VNS response were recorded. Univariate and multivariate Cox regression were used to identify predictors of response. Recursive partitioning analysis was used to identify likely VNS responders.

RESULTS

Eighty-nine (56.3%) patients achieved ≥50% seizure frequency reduction. Left-hand dominance (hazard ratio [HR] 1.703, P = 0.038), age at epilepsy onset ≥15 years (HR 2.029, P = 0.005), duration of epilepsy ≥8 years (HR 1.968, P = 0.007) and age at implantation ≥35 years (HR 1.809, P = 0.020), and baseline seizure frequency <5/month (HR 1.569, P = 0.044) were significant univariate predictors of VNS response. Following multivariate Cox regression, left-hand dominance, age at epilepsy onset ≥15 years, and duration of epilepsy ≥8 years remained significant. With recursive partitioning analysis, patients with either age at epilepsy onset ≥15 years, left-hand dominance, or baseline seizure frequency <5/month were stratified into Group A and had a 73.9% responder rate; the remaining patients stratified into Group B had a 43.8% responder rate.

CONCLUSIONS

Patients with age at epilepsy onset ≥15 years, left-hand dominance, or baseline seizure frequency <5/month are ideal candidates for VNS.

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.

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.

Norwegian population-based study of long-term effects, safety, and predictors of response of vagus nerve stimulation treatment in drug-resistant epilepsy: The NORPulse study

OBJECTIVE

This study was undertaken to evaluate the efficacy of vagus nerve stimulation (VNS) over time, and to determine which patient groups derive the most benefit.

METHODS

Long-term outcomes are reported in 436 epilepsy patients from a VNS quality registry (52.8% adults, 47.2% children), with a median follow-up of 75 months. Patients were stratified according to evolution of response into constant responders, fluctuating responders, and nonresponders. The effect was evaluated at 6, 12, 24, 36, and 60 months. Multivariate regression analysis was used to identify predictors of response.

RESULTS

The cumulative probability of ≥50% seizure reduction was 60%; however, 15% of patients showed a fluctuating course. Of those becoming responders, 89.5% (230/257) did so within 2 years. A steady increase in effect was observed among constant responders, with 48.7% (19/39) of those becoming seizure-free and 29.3% (39/133) with ≥75% seizure reduction achieving these effects within 2-5 years. Some effect (25%-<50%) at 6 months was a positive predictor of becoming a responder (odds ratio [OR] = 10.18, p < .0001) and having ≥75% reduction at 2 years (OR = 3.34, p = .03). Patients without intellectual disability had ORs of 3.34 and 3.11 of having ≥75% reduction at 2 and 5 years, respectively, and an OR of 6.22 of being seizure-free at last observation. Patients with unchanged antiseizure medication over the observation period showed better responder rates at 2 (63.0% vs. 43.1%, p = .002) and 5 years (63.4% vs. 46.3%, p = .031) than patients whose antiseizure medication was modified. Responder rates were higher for posttraumatic (70.6%, p = .048) and poststroke epilepsies (75.0%, p = .05) than other etiologies (46.5%).

SIGNIFICANCE

Our data indicate that the effect of VNS increases over time and that there are important clinical decision points at 6 and 24 months for evaluating and adjusting the treatment. There should be better selection of candidates, as certain patient groups and epilepsy etiologies respond more favorably.

Auricular transcutaneous vagus nerve stimulation modulates the heart-evoked potential

BACKGROUND

There is active interest in biomarker discovery for transcutaneous auricular vagus nerve stimulation (taVNS). However, greater understanding of the neurobiological mechanisms is needed to identify candidate markers. Accumulating evidence suggests that taVNS influences activity in solitary and parabrachial nuclei, the primary brainstem relays for the transmission of visceral sensory afferents to the insula. The insula mediates interoception, which concerns the representation and regulation of homeostatic bodily states. Consequently, interoceptive pathways may be relevant to taVNS mechanisms of action.

HYPOTHESES

We hypothesized that taVNS would modulate an EEG-derived marker of interoceptive processing known as the heart-evoked potential (HEP). We also hypothesized that taVNS-induced HEP effects would be localizable to the insula.

METHODS

Using a within-subject, sham-controlled design in 43 healthy adults, we recorded EEG and ECG concurrent to taVNS. Using ECG and EEG data, we extracted HEPs. Estimation of the cortical sources of the taVNS-dependent HEP responses observed at the scalp were computed using the Boundary Element Method and weighted Minimum Norm Estimation. Statistics were calculated using cluster-based permutation methods.

RESULTS

taVNS altered HEP amplitudes at frontocentral and centroparietal electrode sites at various latencies. The taVNS-dependent HEP effect was localized to the insula, operculum, somatosensory cortex, and orbital and ventromedial prefrontal regions.

CONCLUSION

The results support the hypothesis that taVNS can access the insula as well as functionally and anatomically connected ventral prefrontal regions. HEPs may serve as an objective, non-invasive outcome parameter for the cortical effects of taVNS.

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.

Spectral changes following resective epilepsy surgery and neurocognitive function in children with epilepsy

Decelerated resting cortical oscillations, high-frequency activity, and enhanced cross-frequency interactions are features of focal epilepsy. The association between electrophysiological signal properties and neurocognitive function, particularly following resective surgery, is, however, unclear. In the current report, we studied intraoperative recordings from intracranial electrodes implanted in seven children with focal epilepsy and analyzed the spectral dynamics both before and after surgical resection of the hypothesized seizure focus. The associations between electrophysiological spectral signatures and each child's neurocognitive profiles were characterized using a partial least squares analysis. We find that extent of spectral alteration at the periphery of surgical resection, as indexed by slowed resting frequency and its acceleration following surgery, is associated with baseline cognitive deficits in children. The current report provides evidence supporting the relationship between altered spectral properties in focal epilepsy and neuropsychological deficits in children. In particular, these findings suggest a critical role of disrupted thalamocortical rhythms, which are believed to underlie the spectral alterations we describe, in both epileptogenicity and neurocognitive function.NEW & NOTEWORTHY Spectral alterations marked by decelerated resting oscillations and ectopic high-frequency activity have been noted in focal epilepsy. We leveraged intraoperative recordings from chronically implanted electrodes pre- and postresection to understand the association between these electrophysiological phenomena and neuropsychological function. We find that the extent of spectral alteration, indexed by slowed resting frequency and its acceleration following resection, is associated with baseline cognitive deficits. These findings provide novel insights into neurocognitive impairments in focal epilepsy.

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.

Vagus nerve stimulation in brain diseases: Therapeutic applications and biological mechanisms

Brain diseases, including neurodegenerative, cerebrovascular and neuropsychiatric diseases, have posed a deleterious threat to human health and brought a great burden to society and the healthcare system. With the development of medical technology, vagus nerve stimulation (VNS) has been approved by the Food and Drug Administration (FDA) as an alternative treatment for refractory epilepsy, refractory depression, cluster headaches, and migraines. Furthermore, current evidence showed promising results towards the treatment of more brain diseases, such as Parkinson's disease (PD), autistic spectrum disorder (ASD), traumatic brain injury (TBI), and stroke. Nonetheless, the biological mechanisms underlying the beneficial effects of VNS in brain diseases remain only partially elucidated. This review aims to delve into the relevant preclinical and clinical studies and update the progress of VNS applications and its potential mechanisms underlying the biological effects in brain diseases.

From vision to action: Canadian leadership in ethics and neurotechnology

This chapter explores the complex neuroethical aspects of neurosurgery and neuromodulation in the context of Canadian healthcare and innovation, as seen through the lens of the Pan Canadian Neurotechnology Ethics Consortium (PCNEC). Highlighted are key areas of ethical focus, each with its own unique challenges: technical advances, readiness and risk, vulnerable populations, medico-legal issues, training, and research. Through an exploration of Canadian neurotechnological practice from these various clusters, we provide a critical review of progress, describe opportunities to address areas of debate, and seek to foster ethical innovation. Underpinning this comprehensive review are the fundamental principles of solution-oriented, practical neuroethics, with beneficence and justice at the core. In our view, it is a moral imperative that neurotechnological advancements include a delineation of ethical priorities for future guidelines, oversight, and interactions.

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.

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.

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.

Pre-ictal heart rate variability alterations in focal onset seizures and response to vagus nerve stimulation

PURPOSE

Vagus nerve stimulation (VNS) is an effective and well-known treatment for drug resistant epilepsy (DRE) patients since 1997, yet prediction of treatment response before implantation is subject of ongoing research. Neuroimaging and neurophysiological studies investigating the vagal afferent network in resting state documented that differences in between epilepsy patients were related to treatment response. This study investigated whether an event-related parameter, pre-ictal heart rate variability (HRV) is associated with response to VNS therapy.

METHODS

DRE patients underwent video-electroencephalography (EEG) recording before VNS implantation. HRV parameters (time, non-linear and frequency domain) were assessed for every seizure during two 10 min timeframes: baseline (60 min before seizure onset) and pre-ictal (10 min before seizure onset). Pre-ictal HRV parameter alterations were correlated with VNS response after one year of VNS therapy and seizure characteristics (temporal/extratemporal, left/right or bilateral).

RESULTS

104 seizures from 22 patients were evaluated. Eleven patients were VNS responders with a seizure frequency reduction of ≥ 50 % after one year of VNS. In VNS responders no changes in HRV parameters were found while in VNS non-responders the time domain and non-linear HRV variables decreased significantly (p = 0.024, p = 0.005, p = 0.005) during the pre-ictal time frame. 10/11 VNS non-responders had a seizure lateralization to the left compared to 4/11 VNS responders.

CONCLUSION

VNS non-responders were characterized by a significant decrease of pre-ictal HRV (time domain/non-linear variables) suggesting a sudden autonomic imbalance probably due to an impaired central autonomic function that makes it at the same time unlikely to respond to VNS.

Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine

INTRODUCTION

Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM.

METHODS

PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019.

RESULTS

From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease.

CONCLUSION

This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.

The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization

Introduction: VNS is an adjunctive neuromodulation therapy for patients with drug-refractory epilepsy. The antiseizure effect of VNS is thought to be related to a diffuse modulation of functional connectivity but remains to be confirmed.

Aim: To investigate electroencephalographic (EEG) metrics of functional connectivity in patients with drug-refractory epilepsy treated by vagus nerve stimulation (VNS), between VNS-stimulated “ON” and nonstimulated “OFF” periods and between responder (R) and nonresponder (NR) patients.

Methods: Scalp-EEG was performed for 35 patients treated by VNS, using 21 channels and 2 additional electrodes on the neck to detect the VNS stimulation. Patients were defined as VNS responders if a reduction of seizure frequency of ∼50% was documented. We analyzed the synchronization in EEG time series during “ON” and “OFF” periods of stimulation, using average phase lag index (PLI) in signal space and phase-locking value (PLV) between 10 sources. Based on graph theory, we computed brain network models and analyzed minimum spanning tree (MST) for responder and nonresponder patients.

Results: Among 35 patients treated by VNS for a median time of 7 years (range 4 months to 22 years), 20 were R and 15 were NR. For responder patients, PLI during ON periods was significantly lower than that during OFF periods in delta (p = 0.009), theta (p = 0.02), and beta (p = 0.04) frequency bands. For nonresponder patients, there were no significant differences between ON and OFF periods. Moreover, variations of seizure frequency with VNS correlated with the PLI OFF/ON ratio in delta (p = 0.02), theta (p = 0.04), and beta (p = 0.03) frequency bands. Our results were confirmed using PLV in theta band (p < 0.05). No significant differences in MST were observed between R and NR patients.

Conclusion: The correlation between VNS-induced interictal EEG time-series desynchronization and decrease in seizure frequency suggested that VNS therapeutic impact might be related to changes in interictal functional connectivity.