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The University of Michigan Department of Biostatistics Working Paper Series
Bayesian hierarchical model; Functional brain mapping; Multi-subject fMRI data analysis; Reversible jump Markov chain Monte Carlo; Spatial mixture model
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Bayesian Spatial Modeling Of Fmri Data: A Multiple-Subject Analysis, Lei Xu, Timothy Johnson, Thomas Nichols
Bayesian Spatial Modeling Of Fmri Data: A Multiple-Subject Analysis, Lei Xu, Timothy Johnson, Thomas Nichols
The University of Michigan Department of Biostatistics Working Paper Series
The aim of this work is to develop a spatial model for multi-subject fMRI data. While there has been much work on univariate modeling of each voxel for single- and multi-subject data, and some work on spatial modeling for single-subject data, there has been no work on spatial models that explicitly account for intersubject variability in activation location. We use a Bayesian hierarchical spatial model to fit the data. At the first level we model "population centers" that mark the centers of regions of activation. For a given population center each subject may have zero or more associated "individual components". …