Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity.
Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity.
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DOI：
10.3389/fncom.2013.00159
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发表时间：
2013
影响因子：
3.2
通讯作者：
Cramer S
中科院分区：
文献类型：
--
作者：
Gorrostieta C;Fiecas M;Ombao H;Burke E;Cramer S
向量自回归（VAR）模型通常形成用于构建有向图形模型的基础，所述有向图形模型用于研究以大脑感兴趣区域（ROI）作为节点的大脑网络中的连接性。标准VAR模型存在局限性。VAR模型中的参数数量随着ROI的数量呈二次方增加，并且随着模型的阶数呈线性增加，因此由于参数数量大，该模型可能会造成严重的估计问题。此外，当应用于成像数据时，标准VAR模型不能解释所有受试者之间连接结构的变异性。在本文中，我们开发了一种新的推广的VAR模型，克服了这些限制。为了处理高维度的参数空间，我们提出了一个贝叶斯层次框架的VAR模型，将占一个主题内的时间相关性和主题之间的变化。我们的方法使用先验分布，产生对应于弹性净惩罚的惩罚最小二乘准则的估计。我们应用所提出的模型，以调查在手抓实验中的有效连接健康对照组和中风后的残余运动障碍患者之间的差异。
Vector auto-regressive (VAR) models typically form the basis for constructing directed graphical models for investigating connectivity in a brain network with brain regions of interest (ROIs) as nodes. There are limitations in the standard VAR models. The number of parameters in the VAR model increases quadratically with the number of ROIs and linearly with the order of the model and thus due to the large number of parameters, the model could pose serious estimation problems. Moreover, when applied to imaging data, the standard VAR model does not account for variability in the connectivity structure across all subjects. In this paper, we develop a novel generalization of the VAR model that overcomes these limitations. To deal with the high dimensionality of the parameter space, we propose a Bayesian hierarchical framework for the VAR model that will account for both temporal correlation within a subject and between subject variation. Our approach uses prior distributions that give rise to estimates that correspond to penalized least squares criterion with the elastic net penalty. We apply the proposed model to investigate differences in effective connectivity during a hand grasp experiment between healthy controls and patients with residual motor deficit following a stroke.
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