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Abstract Details
Activity Number:
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310
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Graphics
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Abstract - #301638 |
Title:
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MSBLFFM: Multivariate Semiparametric Bayesian Local Factor Functional Models for Diffusion Tensor Tract Statistics
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Author(s):
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Zhaowei Hua*+ and David Dunson and Hongtu Zhu
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Companies:
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The University of North Carolina at Chapel Hill and Duke University and The University of North Carolina
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Address:
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, , 27517,
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Keywords:
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Bayes confidence band ;
Diffusion tensor imaging ;
Fiber bundle ;
local inference ;
Dirichlet process
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Abstract:
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Diffusion tensor imaging (DTI) is a modality to visualize and quantify the structure of white matters in human brain. In this article, we propose a multivariate semiparametric Bayesian local factor functional model to analyze fiber tract data. A local partition process is used to address the variability of multiple diffusion properties along major white fiber bundles and its association with a set of covariates of interests, such as gestational age. Two types of statistical inferences are provided: (1) global hypothesis testing to test the overall significance of a hypothesis of interest (2) local hypothesis testing to identify the region of significance. Posterior computation proceeds via an efficient MCMC algorithm using the exact block Gibbs sampler. A simulation study is performed to evaluate the performance of MSBLFFM. Our method is applied to analyze a fiber track data set of two fiber tracts from a clinical study of neurodevelopment: the splenium of the corpus callosum tract and the right internal capsule tract. The growth of white matter fiber diffusivities along these two tracts are addressed.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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