FMRI ANALYSIS THROUGH BAYESIAN VARIABLE SELECTION WITH A SPATIAL PRIOR

被引:9
|
作者
Xia, Jing [1 ]
Liang, Feng [1 ]
Wang, Yongmei Michelle [1 ]
机构
[1] Univ Illinois, Dept Stat, Champaign, IL 61820 USA
关键词
Bayesian variable selection; hemodynamic response function; activation detection; spatial prior; Markov chain Monte Carlo (MCMC);
D O I
10.1109/ISBI.2009.5193147
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This paper presents a novel spatial Bayesian method for simultaneous activation detection and hemodynamic response function (HRF) estimation of functional magnetic resonance imaging (fMRI) data. A Bayesian variable selection approach is used to induce shrinkage and sparsity, with a spatial prior on latent variables representing activated hemodynamic response components. Then, the activation map is generated from the full spectrum of posterior inference constructed through a Markov chain Monte Carlo scheme, and HRFs at different voxels are estimated non-parametrically with information pooling from neighboring voxels. By integrating functional activation detection and HRFs estimation in a unified framework, our method is more robust to noise and less sensitive to model mis-specification.
引用
收藏
页码:714 / 717
页数:4
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