Recent developments in functional magnetic resonance imaging (fMRI) investigate how some brain regions directly influence the activity of other regions of the brain {\it dynamically} throughout the course of an experiment, namely dynamic effective connectivity. Time-varying vector autoregressive (TV-VAR) models have been employed to draw inferencesfor this purpose, but they are very computationally intensive, since the number of parameters to be estimated increases quadratically with the number of time series. In this paper, we propose a computationally efficient Bayesian time-varying VAR approach for modeling high-dimensional time series. The proposed framework employs a tensor decomposition for the VAR coefficient matrices at different lags. Dynamically varying connectivity patterns are captured by assuming that at any given time only a subset of components in the tensor decomposition is active. Latent binary time series select the active components at each time via a convenient Ising prior specification. The proposed prior structure encourages sparsity in the tensor structure and allows to ascertain model complexity through the posterior distribution. More specifically, sparsity-inducing priors are employed to allow for global-local shrinkage of the coefficients, to determine automatically the rank of the tensor decomposition and to guide the selection of the lags of the auto-regression. We show the performances of our model formulation via simulation studies and data from a real fMRI study involving a book reading experiment.
翻译:功能磁共振成像(fMRI)的近期发展动态,调查某些大脑区域在整个实验过程中,即动态有效连通性,如何直接影响到脑中其它区域的活动,即动态有效连通性。使用时间变化矢量自动递减模型(TV-VAR)来为此提取推论,但它们在计算上非常密集,因为估计的参数数随着时间序列的数量而增加。在本文中,我们提议一种计算高效的巴伊西亚时间变VAR方法,用于模拟高维度时间序列。拟议的框架在不同滞后点对VAR系数矩阵采用高压分解法。动态变化的连通模式是通过假设在任何特定时间只为此提取一个组件集,而这种模型非常密集,因为估计的参数数随着时间序列数的增加而增加。在高温尔图结构中鼓励恐慌,并能够通过后表分布来确定模型的复杂性。更具体地说,在不同的滞后点上对VAR系数进行快速分解。在前的实验中,将自动地显示数据选择的进度,从而可以进行全球数据排序的模拟研究。