FUNCTIONAL CONNECTIVITY ANALYSIS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 复杂的大脑功能被认为是通过大规模神经网络的编排来完成的，该大规模神经网络由不同的大脑功能位点和这些区域之间的通信组成。作为传统来源分析的结果，脑磁图和EEG结合解剖学和功能磁共振，提供了在给定任务中激活的大脑轨迹的位置以及它们的时间进程的估计。为了了解这些领域是如何协调行动的，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，相关分析是在信号空间中执行的，并且基于激活之间的时间延迟来隐含因果关系。这个项目的目标是将相关性分析扩展到震源时间进程，并使用明确的措施来建立基于fMRI、MEG和EEG数据的震源估计中的因果关系。

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 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.