Causal Source Analysis of EEG

描述(由申请人提供)：我们建议开发、验证和商业化用于研究人类大规模脑网络因果动力学的创新软件工具。这些工具可用于为神经科医生定位癫痫发作灶提供可靠的诊断，并为神经学家提供新的分析工具来评估人脑中的神经信息传递。在第一阶段，将开发和实施分析方法，以估计选定大脑区域之间的因果联系。这些方法是基于使用REGAE(区域脑活动估计)算法来计算表示感兴趣区域中的大脑活动的时间序列之间的因果信息。将开发具有统计意义的非参数检验，并将其应用于临时估计。结果将用因果图可视化，表明大脑区域之间具有统计意义的因果路径及其特征滞后。这些方法将使用模拟数据进行验证，并使用癫痫患者的脑电数据以及正常受试者的诱发反应数据进行评估。开发和测试完成后，新工具将集成到我们现有的EMSE Suite产品中进行商业化。

DESCRIPTION (provided by applicant): We propose to develop, validate, and commercialize innovative software tools for the study of causal dynamics of large-scale brain networks in humans. These tools may be used to provide neurologists with a reliable diagnostic for locating epilepsy seizure foci, and to provide neuroscientists with new analytic tools for assessing neural information transmission in the human brain. During Phase I, analysis methods will be developed and implemented to estimate the causal connectivity among selected brain regions. These methods are based on the computing the causal information between time series that represent brain activity in selected regions of interest, using the REGAE (regional brain activity estimation) algorithm. A nonparametric test of statistical significance will be developed and applied to the casual estimates. The results will be visualized using a causal diagram, indicating the statistically significant causal paths between brain regions, and their characteristic lags. The methods will be verified using simulated data, and evaluated with EEG data from epilepsy patients, as well as with evoked response data from normal subjects. Upon completion of development and testing, the new tools will be integrated into our existing EMSE Suite product for commercialization.