| dc.description.abstract |
<p>Functional magnetic resonance imaging (fMRI) is a major neuroimaging methodology
and have greatly facilitate basic cognitive neuroscience research. However, there
are multiple statistical challenges in the analysis of fMRI data, including, dimension
reduction, multiple testing and inter-dependence of the MRI responses. In this thesis,
a spatial Bayesian variable selection (BVS) model is proposed for the analysis of
multi-subject fMRI data. The BVS framework simultaneously account for uncertainty
in model specific parameters as well as the model selection process, solving the multiple
testing problem. A spatial prior incorporate the spatial relationship of the MRI response,
accounting for their inter-dependence. Compared to the non-spatial BVS model, the
spatial BVS model enhances the sensitivity and accuracy of identifying activated voxels.</p>
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