Spatial Generalized Linear Mixed Effect Models to an fMRI Study of Simon Task

  • 林 璟

Student thesis: Master's Thesis

Abstract

A spatial Bayesian hierarchical model is proposed to analyze functional magnetic resonance imaging (fMRI) data Typical fMRI experiments generate massive datasets with complex spatial and temporal structures Several studies have found that the spatialdependence not only appears in signal changes but also in temporal correlations among voxels; however current statistical approaches ignore the spatial dependence of temporal correlations to gain computational efficiency We incorporated the spatial random effects model to simultaneously model spatial dependence arising from both signal changes and temporal correlations Through simulation studies to demonstrate that the proposed approach increases the accuracy of the detection of brain activities while keeping computationally feasible Finally we apply a real event-related fMRI data to further illustrate the usefulness of the proposed model
Date of Award2016 Jul 6
Original languageEnglish
SupervisorKuo-Jung Lee (Supervisor)

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