Causal Source Analysis of EEG

脑电图因果源分析

• 批准号: 7259369
• 负责人: Mark E Pflieger
• 金额: $ 37.04万
• 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 2009-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7259369
• 关键词: Abbreviations; Area; Arts; Brain; Brain region; Clinical; Clinical Research; Cognitive; Computer software; Data; Data Set; Developmental Therapeutics Program; Diagnostic; Electrocorticogram; Electroencephalography; Electromagnetics; Electrophysiology (science); Epilepsy; Etiology; Evaluation; Event; Evoked Potentials; Functional Magnetic Resonance Imaging; Human; Image; Language; Magnetic Resonance Imaging; Maps; Market Research; Marketing; Measures; Methodology; Methods; Modeling; Neurologist; Neurosciences Research; Operative Surgical Procedures; Partial Epilepsies; Pathway interactions; Patients; Phase; Process; Receiver Operator Characteristics; Research; Research Personnel; Rest; Scalp structure; Seizures; Series; Signal Transduction; Simulate; Small Business Funding Mechanisms; Small Business Innovation Research Grant; Software Tools; Source; Spectrum Analysis; Staging; System; Techniques; Temporal Lobe Epilepsy; Testing; Time; Time Series Analysis; Today; Trademark; base; cognitive neuroscience; cognitive system; commercialization; design; discounting; innovation; interest; neurophysiology; pre-clinical; relating to nervous system; research study; tool; transmission process; vector

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,

描述（由申请人提供）：为了促进大规模大脑网络因果动力学的研究，我们建议开发、验证和商业化用于测量人类神经生理有效连接的创新软件工具。这些工具有可能为神经科医生提供定位癫痫发作病灶的可靠诊断，并为系统和认知神经科学家提供评估人脑神经信息传递的新技术。在第一阶段，开发了分析方法来估计选定大脑区域之间的平稳和时变有效连通性。这些方法计算时间序列之间的时滞因果信息，这些时间序列代表了从头皮EEG估计的选定感兴趣区域的大脑活动状态。因果信息，不同于预测信息，是通过贴现确定的非因果混淆近似。我们开发了线性和非线性测量方法，以及相关的统计显著性检验。该方法成功应用于(a)模拟数据，(b)静息脑电图，(c)认知事件相关脑电图数据和(d)突发头皮脑电图。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。我们将分两个阶段验证因果信息分析：首先，将颅内脑电图分析结果与通过皮质电刺激记录获得的已知有效连通性进行比较；第二，通过比较头皮和颅内脑电图的连通性分析。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效用。总之，新方法将与评估神经生理功能连通性的传统方法进行比较，