A graphical approach for evaluating effective connectivity in neural systems
A graphical approach for evaluating effective connectivity in neural systems
复制标题
DOI：
10.1098/rstb.2005.1641
复制
发表时间：
2005-05-29
影响因子：
6.3
通讯作者：
Eichler, M
中科院分区：
文献类型：
--
作者：
Eichler, M
从脑电图（EEG）或时间分辨功能磁共振成像（fMRI）记录等时间序列数据中识别有效连接是脑成像中的一个重要问题。一种常用的推断有效连通性的方法是基于向量自回归模型和格兰杰因果关系的概念。然而，这种因果关系的概率概念可能导致潜在变量存在的虚假因果关系。近年来，图形模型被用于讨论多变量数据的因果推理问题。在本文中，我们将这些概念扩展到时间序列的情况，并提出了一种讨论多个时间序列之间格兰杰因果关系的图解方法。特别是，我们提出了一种新的图形表示，允许虚假因果关系的表征，因此，可以用来调查虚假的因果关系。该方法通过并发脑电图和功能磁共振成像记录来证明，这些记录用于研究脑电图中的α节律与功能磁共振成像中血氧水平依赖（BOLD）反应之间的相互关系。该结果证实了先前关于脑电图α节律源位置的发现。
The identification of effective connectivity from time-series data such as electroencephalogram (EEG) or time-resolved function magnetic resonance imaging (fMRI) recordings is an important problem in brain imaging. One commonly used approach to inference effective connectivity is based on vector autoregressive models and the concept of Granger causality. However, this probabilistic concept of causality can lead to spurious causalities in the presence of latent variables. Recently, graphical models have been used to discuss problems of causal inference for multivariate data. In this paper, we extend these concepts to the case of time-series and present a graphical approach for discussing Granger-causal relationships among multiple time-series. In particular, we propose a new graphical representation that allows the characterization of spurious causality and, thus, can be used to investigate spurious causality. The method is demonstrated with concurrent EEG and fMRI recordings which are used to investigate the interrelations between the alpha rhythm in the EEG and blood oxygenation level dependent (BOLD) responses in the fMRI. The results confirm previous findings on the location of the source of the EEG alpha rhythm.