Wang Yingwei, Li Zhongjie, Zhang Yujin, Long Yingming, Xie Xinyan, Wu Ting
Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.
College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China.
Front Neuroinform. 2022 Aug 18;16:934480. doi: 10.3389/fninf.2022.934480. eCollection 2022.
Temporal lobe epilepsy (TLE) is a chronic neurological disorder that is divided into two subtypes, complex partial seizures (CPS) and simple partial seizures (SPS), based on clinical phenotypes. Revealing differences among the functional networks of different types of TLE can lead to a better understanding of the symbology of epilepsy. Whereas Although most studies had focused on differences between epileptic patients and healthy controls, the neural mechanisms behind the differences in clinical representations of CPS and SPS were unclear. In the context of the era of precision, medicine makes precise classification of CPS and SPS, which is crucial. To address the above issues, we aimed to investigate the functional network differences between CPS and SPS by constructing support vector machine (SVM) models. They mainly include magnetoencephalography (MEG) data acquisition and processing, construction of functional connectivity matrix of the brain network, and the use of SVM to identify differences in the resting state functional connectivity (RSFC). The obtained results showed that classification was effective and accuracy could be up to 82.69% (training) and 81.37% (test). The differences in functional connectivity between CPS and SPS were smaller in temporal and insula. The differences between the two groups were concentrated in the parietal, occipital, frontal, and limbic systems. Loss of consciousness and behavioral disturbances in patients with CPS might be caused by abnormal functional connectivity in extratemporal regions produced by post-epileptic discharges. This study not only contributed to the understanding of the cognitive-behavioral comorbidity of epilepsy but also improved the accuracy of epilepsy classification.
颞叶癫痫（TLE）是一种慢性神经疾病，根据临床表型可分为两个亚型，即复杂部分性发作（CPS）和简单部分性发作（SPS）。揭示不同类型颞叶癫痫功能网络之间的差异有助于更好地理解癫痫的症状学。尽管大多数研究聚焦于癫痫患者与健康对照之间的差异，但CPS和SPS临床表现差异背后的神经机制尚不清楚。在精准医学时代背景下，对CPS和SPS进行精准分类至关重要。为解决上述问题，我们旨在通过构建支持向量机（SVM）模型来研究CPS和SPS之间的功能网络差异。主要包括脑磁图（MEG）数据采集与处理、脑网络功能连接矩阵构建以及使用SVM识别静息态功能连接（RSFC）差异。所得结果表明分类有效，准确率在训练时可达82.69%，测试时可达81.37%。CPS和SPS在颞叶和脑岛的功能连接差异较小。两组之间的差异集中在顶叶、枕叶、额叶和边缘系统。CPS患者的意识丧失和行为障碍可能是由癫痫发作后放电在颞外区域产生的异常功能连接所致。本研究不仅有助于理解癫痫的认知 -行为共病，还提高了癫痫分类的准确性。
