Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, China.
Neuroimage. 2012 Jan 16;59(2):1846-58. doi: 10.1016/j.neuroimage.2011.08.047. Epub 2011 Aug 23.
We describe a new measure of Granger causality, componential Granger causality, and show how it can be applied to the identification of the directionality of influences between brain areas with functional neuroimaging data. Componential Granger causality measures the effect of y on x, but allows interaction effects between y and x to be measured. In addition, the terms in componential Granger causality sum to 1, allowing causal effects to be directly compared between systems. We show using componential Granger causality analysis applied to an fMRI investigation that there is a top-down attentional effect from the anterior dorsolateral prefrontal cortex to the orbitofrontal cortex when attention is paid to the pleasantness of a taste, and that this effect depends on the activity in the orbitofrontal cortex as shown by the interaction term. Correspondingly there is a top-down attentional effect from the posterior dorsolateral prefrontal cortex to the insular primary taste cortex when attention is paid to the intensity of a taste, and this effect depends on the activity of the insular primary taste cortex as shown by the interaction term. Componential Granger causality thus not only can reveal the directionality of effects between areas (and these can be bidirectional), but also allows the mechanisms to be understood in terms of whether the causal influence of one system on another depends on the state of the system being causally influenced. Componential Granger causality measures the full effects of second order statistics by including variance and covariance effects between each time series, thus allowing interaction effects to be measured, and also provides a systematic framework within which to measure the effects of cross, self, and noise contributions to causality. The findings reveal some of the mechanisms involved in a biased activation theory of selective attention.
我们描述了一种新的格兰杰因果关系度量方法,成分格兰杰因果关系,并展示了如何将其应用于使用功能神经影像学数据识别大脑区域之间的影响方向。成分格兰杰因果关系衡量的是 y 对 x 的影响,但允许衡量 y 和 x 之间的交互影响。此外,成分格兰杰因果关系中的各项之和为 1,允许在系统之间直接比较因果效应。我们使用应用于 fMRI 研究的成分格兰杰因果关系分析表明,当注意味觉的愉悦度时,前背外侧前额叶皮层对眶额皮层有自上而下的注意力影响,并且这种影响取决于眶额皮层的活动,正如交互项所示。相应地,当注意味觉的强度时,后背外侧前额叶皮层对岛叶初级味觉皮层有自上而下的注意力影响,并且这种影响取决于岛叶初级味觉皮层的活动,正如交互项所示。因此,成分格兰杰因果关系不仅可以揭示区域之间的影响方向(这些方向可以是双向的),还可以根据一个系统对另一个系统的因果影响是否取决于被因果影响的系统的状态来理解机制。成分格兰杰因果关系通过包含每个时间序列之间的方差和协方差效应,测量二阶统计的全部效应,从而允许测量交互效应,并提供了一个系统的框架来测量交叉、自相关和噪声对因果关系的影响。这些发现揭示了选择性注意的有偏差激活理论所涉及的一些机制。