Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, D-04103, Leipzig, Germany.
Neuroimage. 2010 Jan 15;49(2):1372-84. doi: 10.1016/j.neuroimage.2009.09.056. Epub 2009 Oct 6.
We propose a new exploratory method for the discovery of partially directed functional networks from fMRI meta-analysis data. The method performs structure learning of Bayesian networks in search of directed probabilistic dependencies between brain regions. Learning is based on the co-activation of brain regions observed across several independent imaging experiments. In a series of simulations, we first demonstrate the reliability of the method. We then present the application of our approach in an extensive meta-analysis including several thousand activation coordinates from more than 500 imaging studies. Results show that our method is able to automatically infer Bayesian networks that capture both directed and undirected probabilistic dependencies between a number of brain regions, including regions that are frequently observed in motor-related and cognitive control tasks.
我们提出了一种新的探索性方法，用于从 fMRI 元分析数据中发现部分有向功能网络。该方法在搜索脑区之间有向概率依赖关系时执行贝叶斯网络的结构学习。学习是基于在几个独立的成像实验中观察到的脑区的共同激活。在一系列模拟中，我们首先证明了该方法的可靠性。然后，我们将我们的方法应用于一个广泛的元分析中，其中包括来自 500 多个成像研究的数千个激活坐标。结果表明，我们的方法能够自动推断出贝叶斯网络，这些网络能够捕捉到包括运动相关和认知控制任务中经常观察到的区域在内的多个脑区之间的有向和无向概率依赖关系。
