围周期SEEG特征的动态变化及癫痫发作区定位

IF 5.6 2区 医学 Q1 NEUROSCIENCES
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
{"title":"围周期SEEG特征的动态变化及癫痫发作区定位","authors":"Lidao Xu ,&nbsp;Yongxin Yang ,&nbsp;Qinghua Tan ,&nbsp;Hongping Tan ,&nbsp;Yifan Wang ,&nbsp;Zeliang Hou ,&nbsp;Jingxian Shen ,&nbsp;Rihui Li ,&nbsp;Yuxi Luo ,&nbsp;Lizhang Zeng ,&nbsp;Qiang Guo ,&nbsp;Xuchu Weng ,&nbsp;Jiuxing Liang","doi":"10.1016/j.nbd.2025.106998","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>The MI and inflow intensity of SOZ in each frequency band were significantly higher (<em>p</em> &lt; 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 (<em>p</em> &lt; 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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>","PeriodicalId":19097,"journal":{"name":"Neurobiology of Disease","volume":"213 ","pages":"Article 106998"},"PeriodicalIF":5.6000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone\",\"authors\":\"Lidao Xu ,&nbsp;Yongxin Yang ,&nbsp;Qinghua Tan ,&nbsp;Hongping Tan ,&nbsp;Yifan Wang ,&nbsp;Zeliang Hou ,&nbsp;Jingxian Shen ,&nbsp;Rihui Li ,&nbsp;Yuxi Luo ,&nbsp;Lizhang Zeng ,&nbsp;Qiang Guo ,&nbsp;Xuchu Weng ,&nbsp;Jiuxing Liang\",\"doi\":\"10.1016/j.nbd.2025.106998\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>The MI and inflow intensity of SOZ in each frequency band were significantly higher (<em>p</em> &lt; 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 (<em>p</em> &lt; 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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>\",\"PeriodicalId\":19097,\"journal\":{\"name\":\"Neurobiology of Disease\",\"volume\":\"213 \",\"pages\":\"Article 106998\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2025-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurobiology of Disease\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0969996125002141\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurobiology of Disease","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0969996125002141","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

背景癫痫发作的围痫期(从痫前期到痫期)的演变包含着丰富的致痫信息,有助于探索癫痫发作的机制和定位致痫区(EZ)。本研究旨在通过分析基于SEEG的癫痫发作周期不同特征的动态变化,探讨癫痫发作的调控机制和局部EZ的定位。方法对61例难治性局灶性癫痫患者进行SEEG电极植入。选取周期内的数据,计算各滑动窗口内的相幅耦合(MI)、连通性强度(wPLI)、DTF和样本熵的动态变化。最后,利用机器学习模型预测癫痫发作区(SOZ)并进行性能评估。结果各频带SOZ的MI和流入强度均显著增高(p <;0.001),发病后呈先升高后降低的趋势。SOZ的流出强度和样本熵(δ波段除外)显著降低(p <;0.001)高于nSOZ,发病后先上升后下降。传播带的特征介于SOZ和非卷入带之间。将这些特征与机器学习模型相结合,有效地对SOZ进行了定位,其中XGBoost模型的AUC、准确率、特异性和灵敏度表现最佳,分别为0.905%、87.0%、87.9%和79.5%。结论本研究从多个角度探讨了围产期的动态演变。发作前SOZ内外均有较强的控制作用,发作后控制作用减弱,证实了癫痫发作的调控机制的存在。进一步的细分揭示了监管模式的层次结构。结合机器学习模型,多种特征精确定位SOZ,为临床治疗提供新的视野,可作为参考模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Neurobiology of Disease
Neurobiology of Disease 医学-神经科学
CiteScore
11.20
自引率
3.30%
发文量
270
审稿时长
76 days
期刊介绍: Neurobiology of Disease is a major international journal at the interface between basic and clinical neuroscience. The journal provides a forum for the publication of top quality research papers on: molecular and cellular definitions of disease mechanisms, the neural systems and underpinning behavioral disorders, the genetics of inherited neurological and psychiatric diseases, nervous system aging, and findings relevant to the development of new therapies.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信