Roebroeck Alard, Formisano Elia, Goebel Rainer
Department of Cognitive Neuroscience, Faculty of Psychology, University of Maastricht, The Netherlands.
Neuroimage. 2005 Mar;25(1):230-42. doi: 10.1016/j.neuroimage.2004.11.017. Epub 2005 Jan 12.
We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity.
我们提出格兰杰因果映射（GCM）作为一种探索功能磁共振成像（fMRI）数据中神经元群体之间定向影响（有效连接性）的方法。该方法不依赖于预先指定一个包含预选区域及其之间连接的模型。这使其有别于其他旨在检验或对比关于神经元相互作用的特定假设的fMRI有效连接性方法。相反，GCM依赖于格兰杰因果关系的概念，根据数据中的信息来定义影响的存在和方向。利用时间先后信息来计算格兰杰因果关系图，以识别对于任何选定的感兴趣区域而言是定向影响源或目标的体素。我们通过模拟以及将其应用于一项复杂视觉运动任务的fMRI数据来研究该方法。所提出的绘制感兴趣区域与大脑其他部分之间影响的探索性方法可以对现有的有效连接性模型形成有益的补充。
