ArticleCNS neuroscience & therapeutics2022
A prediction model integrating synchronization biomarkers and clinical features to identify responders to vagus nerve stimulation among pediatric patients with drug-resistant epilepsy.
Article in CNS neuroscience & therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Prognostic factors in vagus nerve stimulation for drug-resistant epilepsy. Results from a systematic review and meta-analysis of the literature.Neurosurgical review · 2026Pooled it
- Factors predicting the response to vagus nerve stimulation (VNS) in pediatric patients with drug- resistant epilepsy: a systematic review and meta-analysis.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025Pooled it
- Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study.JMIR medical informatics · 2026Review
- High-gamma tACS may regulate brain network connectivity to alleviate symptoms in female adolescent non-suicidal self-injury: a preliminary TMS-EEG pilot study.BMC psychiatry · 2026Article
- Vagus Nerve Stimulation differentially modulates P3b in responders and non-responders: toward a biomarker of therapeutic efficacy.Frontiers in neuroscience · 2026Article
- Epilepsy prediction models for children and adolescents: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Predicting response to neuromodulation therapies in drug-resistant epilepsy using machine learning models: a meta-analysis and systematic review.Bioelectronic medicine · 2025Review
- Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity.Psychological medicine · 2025Article
- Vagus nerve stimulation for epilepsy: A narrative review of factors predictive of response.Epilepsia · 2024Review
- Identifying responders to vagus nerve stimulation based on microstructural features of thalamocortical tracts in drug-resistant epilepsy.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2024Article
- A predictive model combining connectomics and entropy biomarkers to discriminate long-term vagus nerve stimulation efficacy for pediatric patients with drug-resistant epilepsy.CNS neuroscience & therapeutics · 2024Article
- Characterization of Vagus Nerve Stimulation (VNS) Dose-Dependent Effects on EEG Power Spectrum and Synchronization.Biomedicines · 2024Article
- The absence of one's intimate partner promotes dyadic competition through enhanced interbrain synchronization between opponents.Frontiers in psychology · 2024Article
- Article
- Brain functional connectivity-based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy.CNS neuroscience & therapeutics · 2023Article
- A prediction model integrating synchronization biomarkers and clinical features to identify responders to vagus nerve stimulation among pediatric patients with drug-resistant epilepsy.CNS neuroscience & therapeutics · 2022Article
- An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
20 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsVagus 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.
methodsWe 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).
resultsIn 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%.
conclusionThis 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.