Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone - Lidao Xu; Yongxin Yang; Qinghua Tan; Hongping Tan; Yifan Wang; Zeliang Hou; Jingxian Shen; Rihui Li; Yuxi Luo; Lizhang Zeng; Qiang Guo; Xuchu Weng; et al. (Neurobiology of Disease)
Background: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains abundant epileptogenic information, which aids in exploring the mechanism of seizures and localizing the epileptogenic zone (EZ). This study aims to investigate the regulatory mechanisms of seizure and localize EZ by analyzing the dynamic alterations of diverse characteristics during peri-ictal period based on SEEG. Methods: A total of 61 patients with refractory focal epilepsy were included, and each patient underwent SEEG electrodes implantation. The data in the peri-ictal period were selected, and the dynamic alterations of phase-amplitude coupling (MI), connectivity strength (wPLI, DTF), and sample entropy were calculated in each sliding window. Finally, machine learning models were utilized to predict the seizure onset zone (SOZ) and undergo performance evaluation. Results: The MI and inflow intensity of SOZ in each frequency band were significantly higher (p < 0.001) than those of nSOZ, and exhibited an initial increase followed by a decrease after onset. The outflow intensity and sample entropy (except delta band) of SOZ were significantly lower (p < 0.001) than those of nSOZ, which rose first and then fell after onset. The features of the propagation zone lay between those of the SOZ and non-involved zone. Integrating these features with machine learning models effectively localized SOZ, among which XGBoost model had the best performance, its AUC, accuracy, specificity, and sensitivity, and were 0.905, 87.0 %, 87.9 %, and 79.5 % respectively. Conclusions: This study explored the dynamic evolution during the peri-ictal period from multiple perspectives. There were strong control effects both inside and outside the SOZ before seizure onset but decreased later, confirming the existence of regulatory mechanism of seizures. Further subdivision revealed a hierarchical organization of the regulatory model. Combined with machine learning models, multiple features accurately localized the SOZ, providing a new sight for clinical treatment and serving as a reference model.