Zhou Zhenyu, Ding Mingzhou, Chen Yonghong, Wright Paul, Lu Zuhong, Liu Yijun
Pediatric Brain Imaging Laboratory, Department of Psychiatry, Columbia University, New York, NY 10032, USA.
Brain Res. 2009 Sep 15;1289:22-9. doi: 10.1016/j.brainres.2009.06.096. Epub 2009 Jul 9.
An fMRI connectivity analysis approach combining both principal component analysis (PCA) and Granger causality method (GCM) is proposed to study directional influence between functional brain regions. Both simulated data and human fMRI data obtained during behavioral tasks were used to validate this method. If PCA is first used to reduce number of fMRI time series, then more energy and information features in the signal can be preserved than using averaged values from brain regions of interest. Subsequently, GCM can be applied to principal components extracted in order to further investigate effective connectivity. The simulation demonstrated that by using GCM with PCA, between-region causalities were better represented than using GCM with average values. Furthermore, after localizing an emotion task-induced activation in the anterior cingulate cortex, inferior frontal sulcus and amygdala, the directional influences among these brain regions were resolved using our new approach. These results indicate that using PCA may improve upon application of existing GCMs in study of human brain effective connectivity.
提出了一种结合主成分分析（PCA）和格兰杰因果关系方法（GCM）的功能磁共振成像（fMRI）连通性分析方法，以研究大脑功能区域之间的定向影响。使用模拟数据和行为任务期间获得的人类fMRI数据来验证该方法。如果首先使用PCA来减少fMRI时间序列的数量，那么与使用感兴趣脑区的平均值相比，可以保留信号中更多的能量和信息特征。随后，可以将GCM应用于提取的主成分，以进一步研究有效连通性。模拟表明，与使用平均值的GCM相比，使用带有PCA的GCM能更好地表示区域间的因果关系。此外，在定位到前扣带回皮质、额下回沟和杏仁核中由情绪任务诱发的激活后，使用我们的新方法解析了这些脑区之间的定向影响。这些结果表明，在研究人类大脑有效连通性时，使用PCA可能会改进现有GCM的应用。
