Integration of Spatiotemporal Dynamics and Structural Connectivity for Automated Epileptogenic Zone Localization in Temporal Lobe Epilepsy

作者：Linxia Xiao, Qingqing Zheng, Sixian Li, Yanjie Wei, Weixin Si, Yi Pan · 发表于：IEEE Transactions on Neural Systems and Rehabilitation Engineering · 年份：2025 · DOI：10.1109/tnsre.2025.3595906 · 被引用次数：8 · 研究领域：EEG and Brain-Computer Interfaces、Neural dynamics and brain function、Complex Systems and Time Series Analysis

Accurate localization of the epileptogenic zone (EZ) is essential for surgical success in temporal lobe epilepsy. While stereoelectroencephalography (SEEG) and structural magnetic resonance imaging (MRI) provide complementary insights, existing unimodal methods fail to fully capture epileptogenic brain activity, and multimodal fusion remains challenging due to data complexity and surgeon-dependent interpretations. To address these issues, we proposed a novel multimodal framework to improve EZ localization with SEEG-drived electrophysiology with structural connectivity in temporal lobe epilepsy. By retrospectively analyzing SEEG, post-implant Computed Tomography (CT) and MRI (T1 & Diffusion Tensor Imaging (DTI)) data from 15 patients, we reconstructed SEEG electrode positions and obtained the SEEG and structural connectivity fusion features. We then proposed a spatiotemporal co-attention deep neural network (ST-CANet) to identify the fusion features, categorizing electrodes into seizure onset zone (SOZ), propagation zone (PZ), and non-involved zone (NIZ). Anatomical EZ boundaries were delineated by fusing the electrode position and classification information on brain atlas. The proposed method was evaluated based on the identification and localization performance of three epilepsy-related zones. The experiment results demonstrate that our method achieves 98.08% average accuracy and outperforms other identification methods, and improves the localization with Dice similarity coe...