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Fiber-Centered Granger Causality Analysis

机译:光纤中心的格兰杰因果关系分析

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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.
机译:格兰杰因果关系分析(GCA)在大脑成像领域已得到公认。但是,格兰杰因果关系的结构基础和功能动力学仍然不清楚。在本文中,我们提出了以纤维为中心的GCA对静止状态fMRI和自然刺激fMRI数据集的研究,以阐明GCA的结构底物和功能动力学。具体来说,我们从扩散张量成像(DTI)数据得出的白质纤维的两端提取fMRI BOLD信号,并检查其Granger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明显强于未与纤维相连的大脑区域之间的因果关系,这表明在静止状态fMRI数据中看到的功能因果关系的结构基础。跨会话和跨主题的比较表明,我们的观察结果在受试者内部和受试者之间均具有可重复性。此外,以纤维为中心的GCA方法已应用于自然刺激fMRI数据,我们的结果表明,DTI衍生纤维上的格兰杰因果关系显示出明显的时间变化,从而为大脑的功能动力学提供了新颖的见解。

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