Non-invasive preoperative prediction of surgical outcomes in drug-resistant epilepsy using seizure-related EEG network dynamics

Abstract

Stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation (RF-TC) has emerged as a minimally invasive treatment option for selected patients with drug-resistant epilepsy (DRE). However, its overall therapeutic efficacy remains suboptimal, with seizure-free rates typically falling below 70%. To facilitate precision management of DRE and reduce the potential risk of ineffective or high-risk surgical interventions, a reliable, robust, and non-invasive preoperative evaluation framework for accurately predicting postoperative surgical outcomes is critically needed. This study proposes a novel non-invasive computational framework based on EEG source imaging for preoperative outcome prediction by quantitatively characterizing seizure-related brain network reorganization. Scalp EEG recordings from 50 patients with DRE were reconstructed into source space and segmented into pre-ictal and ictal states. Functional brain networks were constructed based on clinically predefined epileptogenic zone (pEZ) and non-epileptogenic zone (pNEZ), preoperatively defined by clinical experts, and multidimensional seizure-related network features were then extracted for subsequent predictive modeling. The proposed framework achieved robust and stable performance across various network thresholds, with optimal results observed at intermediate sparsity levels. The best predictive performance was obtained using pEZ-derived features, achieving a maximum AUC of 0.936 and an accuracy of 0.860. Both support vector machine (SVM) and logistic regression (LR) models demonstrated competitive classification performance. pEZ-derived features achieved higher peak predictive performance, whereas pNEZ-derived features provided complementary information and demonstrated more stable performance across different thresholds. These findings collectively demonstrate that the proposed method enables accurate, reliable, and non-invasive preoperative prediction of surgical outcomes, thereby providing a practical decision-support tool for individualized surgical planning and effectively reducing the risk of potentially ineffective interventions.