静息态下基于格兰杰因果分析的ACT-R网络有效连接变化研究
Change of Effective Connective of the ACT-R Network in the Resting State Based on Granger Causality Analysis

DOI: 10.12677/AP.2015.53024, PP. 173-179

Keywords: 格兰杰因果分析，静息态，ACT-R脑网络，有效连接
Granger Causality Analysis, Resting States, ACT-R Model Network, Effective Connectivity

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Abstract:

近年来，脑功能网络的组织促进了人类大脑的理解。为进一步探究人类大脑功能网络的变化，本研究主要使用有效连接方法对比任务前、后静息态fMRI数据变化。区别于以往大脑整体网络的改变，研究使用基于格兰杰因果分析(GCA)的有效连接方法针对ACT-R脑网络内部开展。结果表明：短暂认知任务前后，ACT-R网络模型内部有效连接发生较大改变；在后静息态，程序性模块(Cad)作为一个主要信息接收点接收来自其他模块的因果影响。
Recently, the organization of functional network promotes the understanding of the human brain. To further explore the functional reorganization affected by a short-time cognitive performance in human brain, we used the method of Granger causality analysis (GCA) to compare two resting fMRI data before and after a problem solving task. Distinguished from the view of the brain network as a whole in previous studies, GCA focused on the internal organization within a brain network. The re-sults showed that taking the ACT-R network as an example, the effective connectivity within the ACT-R network significantly changed after the brief cognitive task. In the post-resting state, proce-dural module (Cad) acted as a main information receiver received influence from other modules.

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