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Activity Number: 487
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #319058 View Presentation
Title: HPRM: Hierarchical Principal Regression Model of Diffusion Tensor Bundle Statistics
Author(s): Jingwen Zhang* and Hongtu Zhu and Joseph G. Ibrahim
Companies: and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Keywords: diffusion tensor imaging ; hierarchical model ; principal component analysis ; varying-coefficient model ; latent factor model

In a typical diffusion tensor Imaging (DTI) study, diffusion properties are observed among multiple fiber bundles to understand the association between neurodevelopment and clinical variables, such as age, gender, biomarkers, etc. Most research focuses on individual tracts or use summary statistics to jointly study a group of tracts, which usually ignores the global and individual functional structures. To address this problem, we propose a hierarchical functional principal regression model, consisting of three components: (i) a multidimensional Gaussian process model to characterize functional data, (ii) a latent factor model to jointly analyze multiple fiber bundles and to capture common effect shared among tracts, and (iii) a multivariate regression model study tract-specific effect. A multilevel estimation procedure is proposed and a global statistic is introduced to test hypothesis of interest. Simulation is conducted to evaluate the performance of HPRM in estimating shared effect and individual effect. We also applied HPRM to a genome-wide association study (Gwas) of one-year twins to explore important genetic markers in brain development among young children.

Authors who are presenting talks have a * after their name.

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