FUNCTIONAL CONNECTIVITY ANALYSIS

功能连接分析

• 批准号: 7957665
• 负责人: MATTI HAMALAINEN
• 金额: $ 11.45万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7957665
• 关键词: Area; Biological Neural Networks; Brain; Communication; Complex; Computer Retrieval of Information on Scientific Projects Database; Data; Electroencephalography; Etiology; Functional Imaging; Functional Magnetic Resonance Imaging; Funding; Goals; Grant; Imaging technology; Institution; Location; Measures; Modeling; Research; Research Personnel; Resources; Signal Transduction; Source; Specific qualifier value; Time; United States National Institutes of Health; base

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Complex brain functions are suggested to be completed by the orchestration of the large-scale neural network, consisting of distinct functional brain loci and the communication between these areas. As a result of conventional source analysis, MEG and EEG, combined with anatomical and functional MRI, provide estimates of the locations of brain loci activated in a given task as well as their time courses. To understand how these areas act in concert it is necessary to investigate the correlations between the estimated time courses and to apply connectivity models to infer their causal relationships. Traditionally, correlation analysis has been perfomed in the signal space and causal relationships have been implied on the basis of time delays between the activations. The goal of this project is to extend correlation analysis to the source time courses and to use well-specified measures to establish causality in source estimates based on fMRI, MEG, and EEG data.

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