Localization of epileptogenic zone based on time-varying effective networks

IF 2 4区医学 Q3 CLINICAL NEUROLOGY

Epilepsy Research Pub Date : 2024-07-02 DOI:10.1016/j.eplepsyres.2024.107409

Ning Yin (尹宁) , Yamei Han (韩雅美) , Le Wang (王乐) , Fan Yang (杨帆) , Jicheng Li (李济丞) , Guizhi Xu (徐桂芝)

{"title":"Localization of epileptogenic zone based on time-varying effective networks","authors":"Ning Yin (尹宁) , Yamei Han (韩雅美) , Le Wang (王乐) , Fan Yang (杨帆) , Jicheng Li (李济丞) , Guizhi Xu (徐桂芝)","doi":"10.1016/j.eplepsyres.2024.107409","DOIUrl":null,"url":null,"abstract":"<div><p>Surgical resection of the epileptogenic zone (EZ) is an effective method for treating drug-resistant epilepsy. At present, the accuracy of EZ localization needs to be further improved. The characteristics of graph theory based on partial directed coherence networks have been applied to the localization of EZ, but the application of network control theory to effective networks to locate EZ is rarely reported. In this study, the method of partial directed coherence analysis was utilized to construct the time-varying effective brain networks of stereo-electroencephalography (SEEG) signals from 20 seizures in 12 patients. Combined with graph theory and network control theory, the differences in network characteristics between epileptogenic and non-epileptogenic zones during seizures were analyzed. We also used dung beetle optimized support vector machine classification model to evaluate the localization effect of EZ based on brain network characteristics of graph theory and controllability. The results showed that the classification of the average controllability feature was the best, and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.9505, which is 1.32 % and 1.97 % higher than the traditional methods. The AUC value increased to 0.9607 after integrating the average controllability with other features. This study proved the effectiveness of controllability characteristic in identifying the EZ and provided a theoretical basis for the clinical application of network controllability in the EZ.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"205 ","pages":"Article 107409"},"PeriodicalIF":2.0000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epilepsy Research","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0920121124001244","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Surgical resection of the epileptogenic zone (EZ) is an effective method for treating drug-resistant epilepsy. At present, the accuracy of EZ localization needs to be further improved. The characteristics of graph theory based on partial directed coherence networks have been applied to the localization of EZ, but the application of network control theory to effective networks to locate EZ is rarely reported. In this study, the method of partial directed coherence analysis was utilized to construct the time-varying effective brain networks of stereo-electroencephalography (SEEG) signals from 20 seizures in 12 patients. Combined with graph theory and network control theory, the differences in network characteristics between epileptogenic and non-epileptogenic zones during seizures were analyzed. We also used dung beetle optimized support vector machine classification model to evaluate the localization effect of EZ based on brain network characteristics of graph theory and controllability. The results showed that the classification of the average controllability feature was the best, and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.9505, which is 1.32 % and 1.97 % higher than the traditional methods. The AUC value increased to 0.9607 after integrating the average controllability with other features. This study proved the effectiveness of controllability characteristic in identifying the EZ and provided a theoretical basis for the clinical application of network controllability in the EZ.

查看原文本刊更多论文

基于时变有效网络的致痫区定位。

手术切除致痫区（EZ）是治疗耐药性癫痫的有效方法。目前，EZ 定位的准确性有待进一步提高。基于部分有向相干网络的图论特征已被应用于 EZ 定位，但将网络控制理论应用于有效网络定位 EZ 的报道却很少。本研究利用部分有向相干分析方法，构建了12名患者20次癫痫发作的立体脑电图（SEEG）信号的时变有效脑网络。结合图论和网络控制理论，分析了癫痫发作时致痫区和非致痫区网络特征的差异。我们还根据图论和可控性的脑网络特征，使用蜣螂优化支持向量机分类模型来评估 EZ 的定位效果。结果表明，平均可控性特征的分类效果最好，接收者操作特征曲线下面积（ROC）（AUC）为 0.9505，比传统方法分别高出 1.32 % 和 1.97 %。将平均可控性与其他特征整合后，AUC 值增至 0.9607。该研究证明了可控性特征在识别 EZ 方面的有效性，并为网络可控性在 EZ 中的临床应用提供了理论依据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Epilepsy Research 医学-临床神经学

CiteScore

0.10

自引率

4.50%

发文量

143

审稿时长

62 days

期刊介绍： Epilepsy Research provides for publication of high quality articles in both basic and clinical epilepsy research, with a special emphasis on translational research that ultimately relates to epilepsy as a human condition. The journal is intended to provide a forum for reporting the best and most rigorous epilepsy research from all disciplines ranging from biophysics and molecular biology to epidemiological and psychosocial research. As such the journal will publish original papers relevant to epilepsy from any scientific discipline and also studies of a multidisciplinary nature. Clinical and experimental research papers adopting fresh conceptual approaches to the study of epilepsy and its treatment are encouraged. The overriding criteria for publication are novelty, significant clinical or experimental relevance, and interest to a multidisciplinary audience in the broad arena of epilepsy. Review articles focused on any topic of epilepsy research will also be considered, but only if they present an exceptionally clear synthesis of current knowledge and future directions of a research area, based on a critical assessment of the available data or on hypotheses that are likely to stimulate more critical thinking and further advances in an area of epilepsy research.