为了提高脑功能效应连接网络检测的可靠性，提出了一种基于Granger因果关系检验和主成分分析（PCA）的功能磁共振（fMRI）数据效应连接方法．该方法首先通过PCA提取感兴趣区域内fMRI信号的时间主成分，以此特征作为时间参考信息，然后计算参考信息与大脑其余每个体素之间的Granger因果关系，并映射到全脑，形成Granger因果图（GCM）．理论推导阐明了所提方法的有效性．采用该方法研究人脑运动功能脑区在手动任务下的效应连接GCM，结果验证了运动功能神经网络理论

In order to improve the detection reliability of effective connectivity in brain network, an fMRI （Functional Magnetic Resonance Imaging） analytical approach of effective connectivity is proposed based on the Granger causality （GC） and the principle component analysis （PCA）. In this approach, first, temporal principal components are extracted via the PCA from the fMRI signals in the region of interest, and the patterns are considered as temporal reference information. Next, the Granger causality between the reference region and each of other voxels of the brain is calculated. Then, the results are mapped into the whole brain and a Granger causality map （GCM） is thus obtained. Moreover, a theoretical derivation is performed to verify the effectiveness of the proposed approach. The proposed approach is finally used t＇o analyze the GCM of a manual movement task-induced activation in the motor area, the results verifying the correctness of theory of motor-function neural network