Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.
Neuroimage. 2011 Feb 14;54(4):2695-705. doi: 10.1016/j.neuroimage.2010.10.054. Epub 2010 Nov 1.
Most of the current methods to assess effective connectivity from functional magnetic resonance imaging (fMRI) rely on the assumption that all relevant brain regions are entered into the analysis. If this assumption is untenable, which we believe is most often the case, then spurious connections between brain regions can appear. In this paper we propose to use an ancestral graph to model connectivity, which provides a way to avoid spurious connections. The ancestral graph is determined from trial-by-trial variation and not from the time series. A random effects model is defined for ancestral graphs which allows for individual differences in terms of graph parameters (e.g., connection strength). Procedures for model selection, model fit, and hypothesis testing of ancestral graphs are proposed. The hypothesis test can be used to find differences in connection strength between, for example, conditions. Monte Carlo simulations show that the ancestral graph is appropriate to model connectivity from fMRI condition specific trial data. To assess the accuracy further, the proposed method is applied to real fMRI data to determine how brain regions interact during speech monitoring.
目前大多数评估功能磁共振成像（fMRI）有效连接的方法都依赖于一个假设，即所有相关的脑区都被纳入到分析中。如果这个假设站不住脚，我们认为这是最常见的情况，那么就会出现脑区之间的虚假连接。在本文中，我们建议使用祖先图来对连接性进行建模，这为避免虚假连接提供了一种方法。祖先图是从逐次试验的变化中确定的，而不是从时间序列中确定的。为祖先图定义了一个随机效应模型，允许在图参数（例如，连接强度）方面存在个体差异。提出了用于祖先图的模型选择、拟合和假设检验的程序。该假设检验可用于发现例如条件之间的连接强度差异。蒙特卡罗模拟表明，祖先图适合于对 fMRI 特定条件的试验数据进行连接建模。为了进一步评估准确性，将所提出的方法应用于真实的 fMRI 数据，以确定在语音监测过程中大脑区域如何相互作用。
