Localizing functional and pathological networks in epilepsy

描述（由申请人提供）：癫痫的最新进展源于这样一种概念，即与单一发作区相反，大脑区域网络可能负责癫痫发作的发生和维持。这是至关重要的，因为识别和完全切除癫痫发生区，同时保留健康的大脑皮层被认为是成功的癫痫手术治疗的基础。然而，定位和定量研究这些网络性质的方法还有待改进。神经科学的最新进展包括使用图论分析皮层网络的工具的开发，以及与神经元群放电相关的脑电图高频活动的识别。用图论量化大脑区域之间的连通性，可以比较正常和异常的大脑网络。传统的临床电极可以记录高度定位的神经元群活动标记物的可用性，为快速跟踪大脑活动提供了一种方法，因为它通过皮层传播。在这里，我们建议弥合目前临床实践导致手术结果不理想与更详细地了解皮层网络和癫痫传播之间的差距，确定神经成像和电生理技术衍生的网络之间的关系，以及2)使用多种记录技术的互补方法定位功能和病理网络。本研究的长期目标是将网络分析应用于多模态成像，以1)更好地理解非侵入性和侵入性成像之间的关系；2)开发更准确的技术来定位癫痫网络以改善手术结果，这两个将在本建议中得到解决。这项提议的研究有两个基本目标。

DESCRIPTION (provided by applicant): Recent advances in epilepsy stem from the notion that a network of brain areas, as opposed to a single onset zone, may be responsible for the onset and maintenance of seizures. This is critical as identifying and completely resecting the epileptogenic zone while preserving healthy eloquent cortex is considered to be the basis of the successful surgical treatment of epilepsy. However, methods to localize and quantitatively study the properties of these networks need to be improved. Recent advances in neuroscience include the development of tools for the analysis of cortical networks using graph theory and the identification of high frequency activity on EEG that correlates with neuronal population firing. Quantifying the connectivity between brain areas with graph theory allows comparison of normal and abnormal brain networks. Availability of a marker of highly localized neuronal population activity that can be recorded by conventional clinical electrodes offers a means to track brain activation rapidly as it propagates through cortex. Here, we propose to bridge the gap between current clinical practice that results in suboptimal surgical outcomes and a more detailed understanding of cortical networks and the spread of epileptic To determine the relationship between networks derived from neuroimaging and electrophysiology techniques, and 2) To localize functional and pathological networks using complementary methods from multiple recording techniques. The long term goal of this research is the application of network analysis to multimodal imaging to 1) better understand the relationship between non-invasive and invasive imaging and 2) develop more accurate techniques to localize epileptic networks to improve surgical outcome, both of which will be addressed in this proposal. This proposed study has two fundamental goals.