Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression (VAR) hierarchical model for analyzing brain connectivity in a resting-state fMRI data set with autism spectrum disorder (ASD) patients and healthy controls. Our approach models functional and effective connectivity simultaneously, which is new in the VAR literature for brain connectivity, and allows for both group- and single-subject inference as well as group comparisons. We combine analytical marginalization with Hamiltonian Monte Carlo (HMC) to obtain highly efficient posterior sampling. The results from more simplified covariance settings are, in general, overly optimistic about functional connectivity between regions compared to our results. In addition, our modeling of heterogeneous subject-specific covariance matrices is shown to give smaller differences in effective connectivity compared to models with a common covariance matrix to all subjects.
翻译:对大脑连通性的分析对于了解大脑如何处理信息十分重要。 我们提议了一种新型贝叶西亚矢量自动递减(VAR)级模型,用于分析休息状态FMRI数据集中的大脑连通性,该数据集带有自闭症谱谱系障碍(ASD)病人和健康控制。我们的方法模型同时运行和有效的连通性,这是VAR大脑连通性文献中新的,可以同时进行群体和单一主体推论以及群体比较。我们把分析边缘化与汉密尔顿·蒙特卡洛(HMC)结合起来,以获得高效的后方取样。一般而言,更简化的共变式环境的结果与我们的结果相比,对区域间的功能连通性过于乐观。此外,我们对不同主题的相异性共变矩阵的模型显示,有效连通性小于所有主体具有共同共变量矩阵模型的模型。