Bayesian Time-Varying Tensor Vector Autoregressive Models for Dynamic Effective Connectivity
用于动态有效连接的贝叶斯时变张量向量自回归模型AI 解读 ↓
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用于动态有效连接的贝叶斯时变张量向量自回归模型
- 研究问题
- 在脑区数量较多时，如何降低时变VAR参数估计的计算负担，同时刻画脑区连接随时间变化的稀疏结构？
- 方法与思路
- 作者对VAR系数进行张量分解，以时间域Ising模型选择活跃分量，并用收缩先验确定分解秩及自回归滞后。
- 材料陈述的贡献
- 摘要提出计算高效的贝叶斯时变VAR框架，并以模拟和阅读实验的fMRI数据展示其动态连接建模表现。
- 阅读关注点
- 应核对张量表示、活跃分量切换和先验设定，秩与滞后的选择机制，以及计算成本和连接解释所需依据。
- 阅读边界
- 这是arXiv预印本，同行评审及接收状态未核实；摘要未给出效率数值或因果识别条件，不能把VAR连接直接视为干预效应。
原文摘要
In contemporary neuroscience, a key area of interest is dynamic effective connectivity, which is crucial for understanding the dynamic interactions and causal relationships between different brain regions. Dynamic effective connectivity can provide insights into how brain network interactions are altered in neurological disorders such as dyslexia. Time-varying vector autoregressive (TV-VAR) models have been employed to draw inferences for this purpose. However, their significant computational requirements pose challenges, 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 dealing with large-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 an innovative and parsimonious Ising model in the time-domain. Furthermore, we propose parsity-inducing priors to achieve global-local shrinkage of the VAR coefficients, determine automatically the rank of the tensor decomposition and 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.