State Key Laboratory of Reliability and Intelligentization of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China.
State Key Laboratory of Reliability and Intelligentization of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China.
Epilepsy Res. 2024 Sep;205:107409. doi: 10.1016/j.eplepsyres.2024.107409. Epub 2024 Jul 2.
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.
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。基于偏导相干网络的图论特征已被应用于 EZ 的定位，但将网络控制理论应用于有效网络以定位 EZ 的报道很少。在这项研究中，利用偏导相干分析方法，从 12 名患者的 20 次癫痫发作中构建了立体脑电图（SEEG）信号的时变有效脑网络。结合图论和网络控制理论，分析了癫痫发作期间致痫区和非致痫区网络特征的差异。我们还使用蜣螂优化支持向量机分类模型，基于图论和可控性的脑网络特征评估 EZ 的定位效果。结果表明，平均可控性特征的分类效果最好，受试者工作特征曲线（ROC）下面积（AUC）为 0.9505，比传统方法高 1.32%和 1.97%。将平均可控性与其他特征相结合后，AUC 值增加到 0.9607。这项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。
