SPATIAL BAYESIAN VARIABLE SELECTION AND GROUPING FOR HIGH-DIMENSIONAL SCALAR-ON-IMAGE REGRESSION
SPATIAL BAYESIAN VARIABLE SELECTION AND GROUPING FOR HIGH-DIMENSIONAL SCALAR-ON-IMAGE REGRESSION
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DOI：
10.1214/15-aoas818
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发表时间：
2015-06-01
影响因子：
1.8
通讯作者：
Coan, James A.
中科院分区：
文献类型：
--
作者：
Li, Fan;Zhang, Tingting;Coan, James A.
多受试者功能磁共振成像(FMRI)数据越来越多地被用于研究人类大脑活动与个人生物或行为特征之间的全人群关系。一种常见的方法是对成像预测器上的标量个体响应进行回归，称为图像上标量(SI)回归。分析和计算这些具有复杂时空相关结构的海量和噪声数据是具有挑战性的。本文基于fMRI对人类情感心理的研究，在贝叶斯随机搜索变量选择的框架下，提出了一种联合Ising和Dirichlet过程(Ising-DP)先验来选择高维SI回归中的脑体素。先验的Ising分量利用了体素之间的空间信息，而DP分量将大量体素的系数分组到一个较小的值集合，从而大大减少了后验计算负担。为了解决Ising先验的相变现象，我们提出了一种新的解析方法来求出超参数的界，并在二维和三维晶格上进行了说明。通过仿真将该方法与几种替代方法进行了比较，并将该方法应用于Kliff握手实验中采集的fMRI数据。
Multi-subject functional magnetic resonance imaging (fMRI) data has been increasingly used to study the population-wide relationship between human brain activity and individual biological or behavioral traits. A common method is to regress the scalar individual response on imaging predictors, known as a scalar-on-image (SI) regression. Analysis and computation of such massive and noisy data with complex spatio-temporal correlation structure is challenging. In this article, motivated by a psychological study on human affective feelings using fMRI, we propose a joint Ising and Dirichlet Process (Ising-DP) prior within the framework of Bayesian stochastic search variable selection for selecting brain voxels in high-dimensional SI regressions. The Ising component of the prior makes use of the spatial information between voxels, and the DP component groups the coefficients of the large number of voxels to a small set of values and thus greatly reduces the posterior computational burden. To address the phase transition phenomenon of the Ising prior, we propose a new analytic approach to derive bounds for the hyperparameters, illustrated on 2- and 3-dimensional lattices. The proposed method is compared with several alternative methods via simulations, and is applied to the fMRI data collected from the KLIFF hand-holding experiment.