ArticleCNS neuroscience & therapeutics2025
Effective Connectivity Predicts Surgical Outcomes in Temporal Lobe Epilepsy: A SEEG Study.
Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multimodal machine learning for surgical decision support in epilepsy: Current evidence and translational gaps.Epilepsia · 2026Pooled it
- Effective Connectivity Predicts Surgical Outcomes in Temporal Lobe Epilepsy: A SEEG Study.CNS neuroscience & therapeutics · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
introductionTemporal lobe epilepsy (TLE), the most common type of drug-resistant epilepsy (DRE), has a postoperative seizure-free rate of ~70%. Furthermore, precisely localizing the epileptogenic zone and determining the surgical resection area have been established as the key factors influencing surgical outcomes. Herein, we innovatively coupled the surgical resection area with characteristics of effective connectivity via intracranial electroencephalography (iEEG) to predict patients' surgical prognosis.
methodsThis study involved 56 patients who underwent TLE surgery and were followed up for over 1 year. All patients underwent stereo-electroencephalography (SEEG) electrode implantation and single-pulse electrical stimulation (SPES) tests. After comparing patients' RMS value of N1/N2 (Z-score standardized) from cortico-cortical evoked potentials (CCEP) with different surgical outcomes, an interpretable machine learning (ML) model based on support vector machine (SVM) for predicting patients' surgical prognosis was constructed.
resultsPatients with various surgical outcomes exhibited differences in effective connectivity. Furthermore, compared to the seizure-free group (Engel I), patients in the nonseizure-free group (Engel II-IV) exhibited stronger connectivity between the seizure onset zone (SOZ) and regions outside the surgical resection area. The nonseizure-free group also exhibited stronger connectivity between the surgical resection area and regions outside the resection area. Our prediction model demonstrated high-accuracy performance, with accuracy and area under the curve (AUC) values of 0.800 and 0.893, respectively.
conclusionsThis study confirmed the potential value of integrating the surgical resection area and effective connectivity characteristics in predicting patients' surgical outcomes; offering a novel approach that could be leveraged to precisely determine the surgical resection area and improve TLE patients' surgical prognosis.
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