Causal Source Analysis of EEG

描述（由申请人提供）：为了促进大规模脑网络的因果动力学的研究，我们建议开发，验证和商业化创新的软件工具，用于测量人类神经生理学的有效连接。这些工具有可能被用来提供一个可靠的诊断定位癫痫发作病灶的神经学家，并提供系统和认知神经科学家与评估人类大脑中的神经信息传输的新技术。在第一阶段，开发了分析方法来估计选定的大脑区域之间的固定和随时间变化的有效连接。这些方法计算时间序列之间的滞后因果信息，这些时间序列代表了从头皮EEG估计的所选感兴趣区域中的大脑活动状态。与预测信息不同的是，因果信息是通过贴现已识别的非因果混淆来近似的。我们开发了线性和非线性措施，以及相关的统计显著性检验。该方法成功地应用于（a）模拟数据，（B）静息EEG，（c）认知事件相关EEG数据，和（d）发作发作头皮EEG。在第二阶段，我们将设计和开发一个有效的连接软件工具集，供认知和临床神经生理学家研究使用。我们将在两个阶段验证因果信息分析：第一，通过比较颅内EEG分析的结果与通过皮层电刺激记录获得的已知有效连接;第二，通过比较头皮与颅内EEG的连接分析。因果信息分析工具将用于测试认知神经科学应用中的连通性假设，并评估癫痫的潜在临床实用性。在整个过程中，新的措施将与传统的评估神经生理功能连接的措施进行比较，

DESCRIPTION (provided by applicant): To facilitate the study of causal dynamics of large-scale brain networks, we propose to develop, validate, and commercialize innovative software tools for measuring neurophysiological effective connectivity in humans. These tools potentially may be used to provide neurologists with a reliable diagnostic for locating epilepsy seizure foci, and to provide systems and cognitive neuroscientists with new techniques for assessing neural information transfer in the human brain. During Phase I, analysis methods were developed to estimate both stationary and time-varying effective connectivity among selected brain regions. These methods compute time-lagged causal information between time series which represent states of brain activity in selected regions of interest, estimated from scalp EEG. Causal information, as distinct from predictive information, is approximated by discounting identified non-causal confounds. We developed both linear and nonlinear measures, together with associated tests of statistical significance. The approach was applied successfully to (a) simulated data, (b) resting EEG, (c) cognitive event-related EEG data, and (d) ictal onset scalp EEG. In Phase II, we will design and develop an effective connectivity software toolset for research use by cognitive and clinical neurophysiologists. We will validate causal information analysis in two stages: first, by comparing the results of intracranial EEG analysis against known effective connectivities obtained via cortical electrostimulation recordings; and second, by comparing connectivity analyses of scalp versus intracranial EEG. Causal information analysis tools will be used to test connectivity hypotheses in a cognitive neuroscience application, and to evaluate potential clinical utility in epilepsy. Throughout, the new measures will be compared with traditional measures for assessing neurophysiological functional connectivity,