Spatial Bayesian Variable Selection with Application to Functional Magnetic Resonance Imaging (fMRI)

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2011

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Abstract

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.

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Yang, Ying (2011). Spatial Bayesian Variable Selection with Application to Functional Magnetic Resonance Imaging (fMRI). Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/5620.

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