基于脑磁图的脑网络新方法研究及其在癫痫分类中的应用

Epilepsy diagnosis and correct classification is a prerequisite for rational drug use and prognosis. Currently it is mainly based on the patient's symptoms and the combined EEG / MEG for classification of epilepsy. In practice EEG/MEG role in the classification is not obvious. One reason is that the traditional EEG/MEG indicators ( such as one or several brain power spectrums ) or EEG/MEG waveform information analysis ( various spike and slow waves ) ignores the nature of the network in different epileptic brain differences. The brain works in a collaborative manner. Epilepsy is different because of the different networks of brain dysfunction caused by paroxysmal brain networks and such abnormal networks cause seizures. This paper intends to use non-invasive and high time resolution of MEG to analyzes epileptic brain networks and to build a new classification method. The steps as following, firstly by MEG inverse solution, get an reasonably accurate source locations and precise brain activated waveforms, and then build the brain epileptic brain networks by using causal network analysis, based on a delayed synchronous events, to analyze source waveforms. At last, from the perspective of neural electromagnetic physiological signal network study a more rational classification of epilepsy, closer, and lay the foundation for better diagnosis and treatment of epilepsy. Based on comprehensive analysis of the above results, further study underlying initiation and propagation mechanisms of epilepsy.

癫痫的确诊和正确分类是正确治疗、合理用药以及预后判断的先决条件。当前主要基于患者症状并结合EEG/MEG进行癫痫分类。实践中EEG/MEG在分类中的作用并不明显。一个原因是传统的EEG/MEG指标（比如脑区的功率谱）或者其波形信息分析（各种棘慢波）等忽略了不同癫痫差异的脑网络本质。大脑是以脑区间功能协作的方式来工作。不同的癫痫类型是因为不同的脑网络功能缺陷所致，这种脑网络的阵发性失调导致癫痫发作。本课题拟从癫痫网络的角度利用无创的高时间分辨率的MEG来研究癫痫并提出新的基于脑网络的癫痫分类方法。具体思路是通过MEG反演获取准确的脑内源位置和精确的源活动波形，然后用新的基于同步事件延迟的因果脑网络方法分析源波形，计算癫痫脑网络，从MEG脑网络角度提出一种更合理更接近其网络本质的癫痫分类方法，为更好地诊断和治疗癫痫打下基础。并综合分析结果，进一步研究癫痫内在脑网络机制。

癫痫类型主要是根据临床症状学、病程特点、脑电图、药物治疗反应和预后等进行区分。癫痫的确诊和发作类型的准确判断是正确治疗、合理用药以及预后判断的先决条件。目前的分类方法不是很完美，需要不断改进。我们尝试通过基于脑磁图脑网络的方法探索分类的方法。在本短期小额项目的资助下，我们做了这样的工作：1.发展了一种叫做PWINN的新的MEG定位和功能分析的方法，为做源空间的脑网络分析打下基础；2. 基于源的功能连通性，发展了一种MEG弱源定位方法；3.发现了脑磁图低频段（delta band）的半球能量可以用于颞叶癫痫的病灶定侧。4.MEG脑网络的节点度可以用于左右颞叶癫痫的分类。这些研究的科学意义：方法学方面，提供了更好的功能定位方法，建立脑网络的方法；临床上，可以用于癫痫定侧，方便治疗。也有后继的癫痫脑网络的聚类和分类等成果，有待下一步发表并上报。