个体化的高分辨结构网络与局灶性癫痫致痫区域的关联性研究

The utility of the brain network theory to guide individual therapy has always been a problem to be solved. While the white matter network has been recognized as the basis of intracranial electrical signals, it is still not clear about the consistency between the white matter network and electrophysiological network. As the intracranial EEG has only limited coverage of mapping which might frequently result in underestimation, we carried out a preliminary study by using quantified mapping method of white matter developed by the human connectome project(HCP), and discovered the white matter network might be predictive to the activity of epileptic discharge. We further postulated that: “In the epileptic network, white matter network properties and electrical properties might be consistent, as a result, the epileptogenic zone might be perceptible by mastering the connective properties of the white matter only”. This application would further verify this hypothesis. The properties of the structural network would be measured by the connectivity matrix based on diffusion spectrum imaging(DSI) and the results would be referred to both the quantified intracranial EEG and the pathological findings. This project might settle the problem of the low coverage rate of the intracranial EEG and improve the accuracy of localization of the epileptogenesis. The individualization of the brain network theory might also be primarily solved according to this study.

癫痫的脑网络学说一直无法用于个体化的诊疗；在癫痫评估过程中，个体化的白质网络作为结构基础，与癫痫活动的传递是否具有一致性目前尚不清楚。由于颅内电生理信号的采集覆盖率过低，较容易漏诊，为解决此问题，在前期工作中，我们利用人脑连接组计划（HCP）中产生的定量化白质网络分析方法，初步发现通过分析白质网络的属性，似乎可对痫性电活动的起源作出预判。我们提出假说“癫痫网络的白质网络属性和电信号属性应具有一致性，通过监测白质网络即有可能掌握痫性电活动的传导特性”。本项目将进一步验证这一假说，通过弥散谱成像（DSI）技术测绘结构连接矩阵，来评估癫痫的白质网络连接属性；再结合定量化的颅内电信号分析手段，如时频分析、痫性指数等方法，以及神经病理，来衡量癫痫白质网络和电生理网络的一致性。本项目有望能弥补颅内电极监测覆盖范围有限的不足，提升癫痫定位的准确性，解决癫痫网络学说诞生以后一直没回答的个体化问题。

癫痫的脑网络学说一直无法用于个体化的诊疗; 在癫痫评估过程中，个体化的白质网络作为结构基础，与癫痫活动的传递是否具有一致性目前尚不清楚。为解决此问题，我们利用人脑连接组计划(HCP)中产生的定量化白质网络分析方法，发现通过分析白质网络的属性，可对痫性电活动的起源作出预判。通过本研究，创建了国人DSI脑连接组模板，探索高级脑功能环路；探索了局灶性癫痫中，致痫灶与连接组学的一致性；通过人工智能的方法，分析了致痫灶颅内电生理特征，开发出了针对关键节点的微创干预机制。并用单细胞测序，对致痫灶的细胞图谱进行了预研。本研究探讨以脑网络学说指导临床治疗，提升了癫痫定位的准确性，减少了外科干预的创伤，具有进一步的应用价值。