基于主成分分析和Granger因果检验的手动任务脑功能效应连接

提出了一种基于Granger因果关系检验和主成分分析的fMRI数据效应连接方法，并将两种方法有效联合研究手动任务下激活皮层的因果效应连接。该方法首先由统计参数图法(SPM)组分析得到任务激活脑空间图，选取感兴趣区域(ROI)；其次通过主成分分析提取ROI内fMRI信号的时间主成分，以此特征作为时间参考信息；最后计算参考区域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(Granger Causality Map，GCM)。理论和试验结果阐明了提出方法的有效性及可靠性。运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，验证了运动功能神经网络理论。

Study of effective connectivity based on Granger causality and PCA in manual movement task

An fMRI effective connectivity analysis approach combining Granger causality (GC) and principle component analysis (PCA) is proposed, by which the causality of the selected regions in the manual movement task can be investigated simultaneously. By first using group analysis of statistic parameters mapping (SPM) to identify brain activation clusters, regions of interest (ROI) were selected. Then, temporal principal components were extracted by PCA from the fMRI signals in the ROI, and the patterns were considered as temporal reference function. Finally, Granger causality between the reference region and each of other voxels of the brain was calculated. The results were mapped into the whole brain and a Granger causality map (GCM) was obtained. The validity and reliability of the presented approach were confirmed by the theoretic and the practical fMRI results. After localizing a manual movement task-induced activation in the motor area, directional influences of physiological connectivity network between the reference and other brain regions were resolved using our new approach.