A network-based EEG source imaging framework for noninvasive localization of epileptogenic zones in MRI-negative focal drug-resistant epilepsy

OBJECTIVE: To develop and evaluate a dynamic brain network analysis framework integrating electroencephalography (EEG) source imaging (ESI) for non-invasive localization of the epileptogenic zone (EZ) in patients with magnetic resonance imaging (MRI)-negative focal drug-resistant epilepsy (DRE). METHODS: We retrospectively analyzed 15 patients with MRI-negative focal DRE who underwent resective surgery. Preictal scalp EEG data were processed using ESI, followed by power spectral density and directed transfer function analyses to construct epileptic brain networks. Network metrics, including seizure index (SI), degree centrality (DC), out-degree centrality (DCout), and in-degree centrality (DCin), were calculated and evaluated for spatial concordance with postoperative resection zones and clinical outcomes. RESULTS: Among the evaluated metrics, SI demonstrated the highest overall diagnostic performance, with a sensitivity of 60% and a specificity of 80%. DC and DCout showed high specificity (83.3%) but lower sensitivity, whereas DCin exhibited comparatively reduced sensitivity and accuracy. CONCLUSION: This non-invasive ESI-based network framework is capable of localizing epileptogenic regions in MRI-negative focal DRE. SI provided the most balanced performance across sensitivity and specificity, while DC and DCout contributed complementary information regarding epileptic network cores. SIGNIFICANCE: The proposed framework offers a clinically feasible and non-invasive approach to support preoperative EZ localization, with potential value for presurgical evaluation in patients with MRI-negative epilepsy.