Multimodal quantitative analysis guides precise preoperative localization of epilepsy.
Authors
Affiliations (3)
Affiliations (3)
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou, Wuhan, 430030, Hubei, People's Republic of China.
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou, Wuhan, 430030, Hubei, People's Republic of China. [email protected].
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou, Wuhan, 430030, Hubei, People's Republic of China. [email protected].
Abstract
Epilepsy surgery efficacy is critically contingent upon the precise localization of the epileptogenic zone (EZ). However, conventional qualitative methods face challenges in achieving accurate localization, integrating multimodal data, and accounting for variations in clinical expertise among practitioners. With the rapid advancement of artificial intelligence and computing power, multimodal quantitative analysis has emerged as a pivotal approach for EZ localization. Nonetheless, no research team has thus far provided a systematic elaboration of this concept. This narrative review synthesizes recent advancements across four key dimensions: (1) seizure semiology quantification using deep learning and computer vision to analyze behavioral patterns; (2) structural neuroimaging leveraging high-field MRI, radiomics, and AI; (3) functional imaging integrating EEG-fMRI dynamics and PET biomarkers; and (4) electrophysiological quantification encompassing source localization, intracranial EEG, and network modeling. The convergence of these complementary approaches enables comprehensive characterization of epileptogenic networks across behavioral, structural, functional, and electrophysiological domains. Despite these advancements, clinical heterogeneity, limitations in algorithmic generalizability, and barriers to data sharing hinder translation into clinical practice. Future directions emphasize personalized modeling, federated learning, and cross-modal standardization to advance data-driven localization. This integrated paradigm holds promise for overcoming qualitative limitations, reducing medical costs, and improving seizure-free outcomes.