FUNCTIONAL CONNECTIVITY ANALYSIS

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 复杂的脑功能被认为是通过大规模神经网络的协调来完成的，该网络由不同的功能脑位点和这些区域之间的通信组成。作为传统源分析的结果，MEG和EEG，结合解剖和功能MRI，提供在给定任务中激活的大脑位点的位置以及它们的时间过程的估计。为了了解这些领域如何协同行动，有必要调查估计的时间进程之间的相关性，并应用连接模型来推断它们的因果关系。传统上，相关性分析已经在信号空间中进行，并且因果关系已经基于激活之间的时间延迟而被暗示。本项目的目标是将相关性分析扩展到源时间过程，并使用指定的措施来建立基于fMRI，MEG和EEG数据的源估计的因果关系。

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 the 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.