A Longitudinal Functional Analysis Framework

• Activity Number: 225
• Type: Topic Contributed
• Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Ying Yuan*+ and Hongtu Zhu and Jane-Ling wang and John Gilmore and Martin Styner and Xiujuan Geng
• Companies: St. Jude and UNC-Chapel Hill and University of California, Davis and UNC and UNC at Chapel Hill and UNC
• Keywords: Diffusion tensor imaging ; functional analysis ; longitudinal ; functional mixed effects model ; fiber tract

Abstract

Many longitudinal imaging studies have been/are being widely conducted to use diffusion tensor imaging (DTI) to better understand white matter maturation in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for analyzing diffusion properties along major fiber tracts obtained from longitudinal DTI studies. Jointly analyzing fiber-tract diffusion properties and covariates from longitudinal studies raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial-temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a longitudinal functional analysis framework (LFAF) to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest (e.g., age and group status) and the structure of the variability of these white matter tract properties in various longitudinal studies. Simulated data are used to evaluate the finite sample performance of LFAF and to demonstrate that LFAF significantly outperforms a voxel-wise mixed model method.