Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone
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 an...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Neurobiology of Disease |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0969996125002141 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850067454812749824 |
|---|---|
| author | Lidao Xu Yongxin Yang Qinghua Tan Hongping Tan Yifan Wang Zeliang Hou Jingxian Shen Rihui Li Yuxi Luo Lizhang Zeng Qiang Guo Xuchu Weng Jiuxing Liang |
| author_facet | Lidao Xu Yongxin Yang Qinghua Tan Hongping Tan Yifan Wang Zeliang Hou Jingxian Shen Rihui Li Yuxi Luo Lizhang Zeng Qiang Guo Xuchu Weng Jiuxing Liang |
| author_sort | Lidao Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1917f7d8fc1d4cac8a1f61bd460b5656 |
| institution | DOAJ |
| issn | 1095-953X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neurobiology of Disease |
| spelling | doaj-art-1917f7d8fc1d4cac8a1f61bd460b56562025-08-20T02:48:18ZengElsevierNeurobiology of Disease1095-953X2025-09-0121310699810.1016/j.nbd.2025.106998Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zoneLidao Xu0Yongxin Yang1Qinghua Tan2Hongping Tan3Yifan Wang4Zeliang Hou5Jingxian Shen6Rihui Li7Yuxi Luo8Lizhang Zeng9Qiang Guo10Xuchu Weng11Jiuxing Liang12Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China; Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, ChinaKey Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, ChinaEpilepsy Center, Guangdong 999 Brain Hospital, Guangzhou, ChinaEpilepsy Center, Guangdong 999 Brain Hospital, Guangzhou, ChinaKey Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, ChinaKey Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, ChinaTUM-Neuroimaging Center, Technical University of Munich, Munich, GermanyCentre for Cognitive and Brain Sciences, University of Macau, ChinaSchool of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaKey Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, ChinaEpilepsy Center, Guangdong 999 Brain Hospital, Guangzhou, China; Corresponding authors.Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China; Corresponding authors.Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S0969996125002141Seizure onset zoneSEEGBrain networkPhase-amplitude couplingPeri-ictal periodRegulatory mechanism |
| spellingShingle | Lidao Xu Yongxin Yang Qinghua Tan Hongping Tan Yifan Wang Zeliang Hou Jingxian Shen Rihui Li Yuxi Luo Lizhang Zeng Qiang Guo Xuchu Weng Jiuxing Liang Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone Neurobiology of Disease Seizure onset zone SEEG Brain network Phase-amplitude coupling Peri-ictal period Regulatory mechanism |
| title | Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone |
| title_full | Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone |
| title_fullStr | Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone |
| title_full_unstemmed | Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone |
| title_short | Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone |
| title_sort | dynamic alterations of seeg characteristics during peri ictal period and localization of seizure onset zone |
| topic | Seizure onset zone SEEG Brain network Phase-amplitude coupling Peri-ictal period Regulatory mechanism |
| url | http://www.sciencedirect.com/science/article/pii/S0969996125002141 |
| work_keys_str_mv | AT lidaoxu dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT yongxinyang dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT qinghuatan dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT hongpingtan dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT yifanwang dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT zelianghou dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT jingxianshen dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT rihuili dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT yuxiluo dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT lizhangzeng dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT qiangguo dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT xuchuweng dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone AT jiuxingliang dynamicalterationsofseegcharacteristicsduringperiictalperiodandlocalizationofseizureonsetzone |