ArticleBrain communications2025
Leveraging interictal multimodal features and graph neural networks for automated planning of epilepsy surgery.
Article in Brain communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- AI enabled decision support systems in epilepsy surgery a scoping review.Research square · 2026Article
- Deep learning enabled decision support systems in epilepsy surgery: a scoping review.npj health systems · 2026Article
- Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.Frontiers in neurology · 2026Review
- Multimodal Imaging in Epilepsy Surgery for Personalized Neurosurgical Planning.Journal of personalized medicine · 2025Review
- An interpretable machine learning approach for predicting drug-resistant epilepsy in children with tuberous sclerosis complex.Frontiers in neurology · 2025Article
- Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applications.Frontiers in neuroscience · 2025Review
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precise localization of the epileptogenic zone is pivotal for planning minimally invasive surgeries in drug-resistant epilepsy. Here, we present a graph neural network (GNN) framework that integrates interictal intracranial EEG features, electrode topology, and MRI features to automate epilepsy surgery planning. We retrospectively evaluated the model using leave-one-patient-out cross-validation on a dataset of 80 drug-resistant epilepsy patients treated at St. Anne's University Hospital (Brno, Czech Republic), comprising 31 patients with good postsurgical outcomes (Engel I) and 49 with poor outcomes (Engel II-IV). The GNN predictions demonstrated a significantly better (
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