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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ndltd-TW-103NCKU53370062016-05-22T04:40:56Z http://ndltd.ncl.edu.tw/handle/18746413115126691555 Application of Spatial Bayesian Hierarchical Model with Variable Selection to fMRI data 變數選取法於空間貝氏階層模型應用於功能性核磁共振影像之研究 Xin-HanHuang 黃信翰 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. Kuo-Jung Lee 李國榮 2015 學位論文 ; thesis 36 en_US |
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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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Kuo-Jung Lee |
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Kuo-Jung Lee Xin-HanHuang 黃信翰 |
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Xin-HanHuang 黃信翰 |
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Xin-HanHuang 黃信翰 Application of Spatial Bayesian Hierarchical Model with Variable Selection to fMRI data |
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Xin-HanHuang |
title |
Application of Spatial Bayesian Hierarchical Model with Variable Selection to fMRI data |
title_short |
Application of Spatial Bayesian Hierarchical Model with Variable Selection to fMRI data |
title_full |
Application of Spatial Bayesian Hierarchical Model with Variable Selection to fMRI data |
title_fullStr |
Application of Spatial Bayesian Hierarchical Model with Variable Selection to fMRI data |
title_full_unstemmed |
Application of Spatial Bayesian Hierarchical Model with Variable Selection to fMRI data |
title_sort |
application of spatial bayesian hierarchical model with variable selection to fmri data |
publishDate |
2015 |
url |
http://ndltd.ncl.edu.tw/handle/18746413115126691555 |
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