Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China.
Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China.
J Neurosci Methods. 2023 Apr 15;390:109839. doi: 10.1016/j.jneumeth.2023.109839. Epub 2023 Mar 16.
Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions.
The location coordinates of SEEG in the brain were identified using CT and MRI. Based on undirected brain network connectivity, five functional connectivity measures and data feature vector centrality were calculated. Network connectivity was calculated from multiple perspectives of linear correlation, information theory, phase, and frequency, and the relative influence of nodes on network connectivity was considered. We investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the differences between patients with different surgical outcomes.
By comparing the centrality of brain network connectivity between epileptic and non-epileptic zones, we found significant differences in the distribution of brain networks between the two zones. There was a significant difference in brain network between patients with good surgical outcomes and those with poor surgical outcomes (p < 0.01). By combining support vector machines with static node importance, we predicted an AUC of 0.94 ± 0.08 for the epilepsy zone.
The results illustrated that nodes in epileptic zones are distinct from those in non-epileptic zones. Analysis of resting-state SEEG data and the importance of nodes in the brain network may contribute to identifying the epileptic zone and predicting the outcome.
大多数癫痫研究都是基于发作间期或发作期的功能连接。然而，长时间的电极植入可能会影响患者的健康状况，并且会降低癫痫灶识别的准确性。短暂的静息态 SEEG 记录通过减少电极植入和其他诱发癫痫发作的干预措施，减少了癫痫发作的观察。
使用 CT 和 MRI 确定了脑内 SEEG 的位置坐标。基于无向脑网络连接，计算了五种功能连接测量值和数据特征向量的中心度。从线性相关、信息论、相位和频率等多个角度计算了网络连接，并考虑了节点对网络连接的相对影响。我们通过比较癫痫区和非癫痫区之间、不同手术结果患者之间的差异，来研究静息态 SEEG 对癫痫灶识别的潜在价值。
通过比较癫痫区和非癫痫区脑网络连接的中心度，我们发现这两个区之间的脑网络分布存在显著差异。手术结果良好的患者和手术结果不佳的患者之间的脑网络也存在显著差异（p<0.01）。通过将支持向量机与静态节点重要性相结合，我们预测癫痫区的 AUC 为 0.94±0.08。
结果表明，癫痫区的节点与非癫痫区的节点不同。分析静息态 SEEG 数据和脑网络节点的重要性可能有助于识别癫痫区并预测手术结果。
