Application of Spatial Bayesian Hierarchical Model with Variable Selection to fMRI data
碩士 === 國立成功大學 === 統計學系 === 103 === We propose a spatial Bayesian hierarchical model to analyze functional magnetic resonance imaging data with complex spatial and temporal structures. Several studies have found that the spatial dependence not only appear in signal changes but also in temporal correl...
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| Format: | Others |
| Language: | en_US |
| Published: |
2015
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| Online Access: | http://ndltd.ncl.edu.tw/handle/18746413115126691555 |
| Summary: | 碩士 === 國立成功大學 === 統計學系 === 103 === We propose a spatial Bayesian hierarchical model to analyze functional magnetic resonance imaging data with complex spatial and temporal structures. Several studies have found that the spatial dependence not only appear in signal changes but also in temporal correlations among voxels. However, currently existing statistical approaches ignore the spatial dependence of temporal correlations for the computational efficiency. We consider the spatial random effect models to simultaneously model spatial dependences in both signal changes and temporal correlations, but keep computationally feasible. Through simulation, the proposed approach improves the accuracy of identifying the activations. We study the properties of the model through its performance on simulations and a real event-related fMRI data set.
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