Causal Source Analysis of EEG

脑电图因果源分析

• 批准号: 6583136
• 负责人: Mark E Pflieger
• 金额: $ 10万
• 依托单位: SOURCE SIGNAL IMAGING, INC.
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-02-01 至 2004-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6583136
• 关键词: brain electrical activity; clinical research; computer data analysis; computer program /software; computer simulation; electroencephalography; evoked potentials; human data; method development; neural information processing; neuroimaging; statistics /biometry

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.

描述（由申请人提供）：我们建议开发，验证和商业化创新的软件工具，用于研究人类大规模大脑网络的因果动力学。这些工具可用于为神经科医生提供定位癫痫发作病灶的可靠诊断，并为神经科学家提供评估人脑中神经信息传输的新分析工具。在第一阶段，将开发和实施分析方法，以估计选定的大脑区域之间的因果连接。 这些方法基于使用REGAE（局部大脑活动估计）算法计算代表选定感兴趣区域的大脑活动的时间序列之间的因果信息。将开发统计显著性的非参数检验，并应用于因果估计。结果将使用因果图可视化，指示大脑区域之间的统计学显著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，并与癫痫患者的EEG数据以及正常受试者的诱发反应数据进行评估。在完成开发和测试后，新工具将集成到我们现有的EMSE套件产品中进行商业化。