ArticleEpilepsia2024
Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach.
Article in Epilepsia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 15 citations in OpenAlex.
- EEG CONNECT: A proposed reporting checklist for EEG-based connectivity studies in clinical epilepsy research.Epilepsia open · 2026Review
- The use of quantitative electroencephalography in evaluating epilepsy treatment.Acta epileptologica · 2026Review
- Development and temporal validation of a postoperative nomogram based on clinical, surgical, histopathological, and semi-quantitative EEG features for predicting seizure-free survival in children after focal epilepsy surgery.Frontiers in pediatrics · 2026Article
- Non-invasive preoperative prediction of surgical outcomes in drug-resistant epilepsy using seizure-related EEG network dynamics.Frontiers in neuroscience · 2026Article
- Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.Frontiers in neurology · 2026Review
- Mapping epileptogenic brain using a unified spatial-temporal-spectral source imaging framework.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- AI-based localization of the epileptogenic zone using intracranial EEG.Epilepsia open · 2025Review
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Advancements and Challenges of Artificial Intelligence-Assisted Electroencephalography in Epilepsy Management.Journal of clinical medicine · 2025Review
- Can brain network analyses guide epilepsy surgery?Current opinion in neurology · 2025Review
- Normative high-frequency oscillation phase-amplitude coupling and effective connectivity under sevoflurane.Brain communications · 2025Article
- Recent Advances in Wearable Healthcare Devices: From Material to Application.Bioengineering (Basel, Switzerland) · 2024Review
- Electroencephalography derived connectivity informing epilepsy surgical planning: Towards clinical applications and future perspectives.NeuroImage. Clinical · 2024Review
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Authors and funding
11 authors at 2 institutions in 1 country.
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Abstract
objectiveTo deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data.
methodsWe analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization.
resultsFC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes. SIGNIFICANCE: We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.
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