Li Xiang, Li Kaiming, Guo Lei, Lim Chulwoo, Liu Tianming
Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):251-9. doi: 10.1007/978-3-642-23629-7_31.
Granger causality analysis (GCA) has been well-established in the brain imaging field. However, the structural underpinnings and functional dynamics of Granger causality remain unclear. In this paper, we present fiber-centered GCA studies on resting state fMRI and natural stimulus fMRI datasets in order to elucidate the structural substrates and functional dynamics of GCA. Specifically, we extract the fMRI BOLD signals from the two ends of a white matter fiber derived from diffusion tensor imaging (DTI) data, and examine their Granger causalities. Our experimental results showed that Granger causalities on white matter fibers are significantly stronger than the causalities between brain regions that are not fiber-connected, demonstrating the structural underpinning of functional causality seen in resting state fMRI data. Cross-session and cross-subject comparisons showed that our observations are reproducible both within and across subjects. Also, the fiber-centered GCA approach was applied on natural stimulus fMRI data and our results suggest that Granger causalities on DTI-derived fibers reveal significant temporal changes, offering novel insights into the functional dynamics of the brain.
格兰杰因果分析(GCA)在脑成像领域已得到广泛应用。然而,格兰杰因果关系的结构基础和功能动态仍不清楚。在本文中,我们展示了以纤维为中心的关于静息态功能磁共振成像(fMRI)和自然刺激fMRI数据集的格兰杰因果分析研究,以阐明格兰杰因果分析的结构基础和功能动态。具体而言,我们从扩散张量成像(DTI)数据得出的白质纤维两端提取fMRI血氧水平依赖(BOLD)信号,并检验它们之间的格兰杰因果关系。我们的实验结果表明,白质纤维上的格兰杰因果关系显著强于非纤维连接的脑区之间的因果关系,这表明了在静息态fMRI数据中所观察到的功能因果关系的结构基础。跨时段和跨受试者的比较表明,我们的观察结果在受试者内部和受试者之间都是可重复的。此外,以纤维为中心的格兰杰因果分析方法应用于自然刺激fMRI数据,我们的结果表明,基于DTI得出的纤维上的格兰杰因果关系揭示了显著的时间变化,为大脑的功能动态提供了新的见解。