Assessing and compensating for zero-lag correlation effects in time-lagged Granger causality analysis of FMRI.
Assessing and compensating for zero-lag correlation effects in time-lagged Granger causality analysis of FMRI.
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DOI：
10.1109/tbme.2009.2037808
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发表时间：
2010-06
期刊：
影响因子：
--
通讯作者：
Hu X
中科院分区：
文献类型：
--
作者：
Deshpande G;Sathian K;Hu X
大脑网络的有效连通性可以通过基于时间优先的格兰杰因果分析来研究，而功能连通性通常使用零滞后相关来推导。由于功能性磁共振成像（fMRI）采集过程中固有的血流动力学响应平滑神经元活动，通常从fMRI数据计算的格兰杰因果关系可能受到零滞后相关的污染。在这项工作中进行的模拟表明，零滞后相关性确实“泄漏”到对时间滞后因果关系的估计中。为了消除这种泄漏，我们引入了一种方法，其中零滞后影响在向量自回归模型中显式建模，但在计算格兰杰因果关系时忽略。这种方法的有效性通过从执行口头工作记忆任务的健康人身上获得的功能磁共振成像数据得到了证明。
Effective connectivity in brain networks can be studied using Granger causality analysis which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this work showed that the zero-lag correlation does “leak” into estimates of time-lagged causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.