|FMEM: functional mixed effects modeling for the analysis of longitudinal white matter Tract data.
|Year of Publication
|Yuan, Ying, John H. Gilmore, Xiujuan Geng, Styner Martin, Kehui Chen, Jane-ling Wang, and Hongtu Zhu
|2014 Jan 01
|Algorithms, Brain, Child, Preschool, Diffusion Tensor Imaging, Female, Humans, Image Interpretation, Computer-Assisted, Infant, Infant, Newborn, Male, Models, Neurological, Nerve Fibers, Myelinated, Neural Pathways
Many longitudinal imaging studies have collected repeated diffusion tensor magnetic resonance imaging data to understand white matter maturation and structural connectivity pattern in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for the analysis of diffusion properties along fiber tracts and clinical data obtained from longitudinal studies. Jointly analyzing repeated fiber-tract diffusion properties and covariates (e.g., age or gender) 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 functional mixed effects modeling (FMEM) framework to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest and the structure of the variability of these white matter tract properties in various longitudinal studies. Our FMEM consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial-temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FMEM and to demonstrate that FMEM significantly outperforms the standard pointwise mixed effects modeling approach. We apply FMEM to study the spatial-temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment.
|FMEM: Functional mixed effects modeling for the analysis of longitudinal white matter Tract data.
|PubMed Central ID
|P41 RR005959 / RR / NCRR NIH HHS / United States
HD053000 / HD / NICHD NIH HHS / United States
R41 NS059095 / NS / NINDS NIH HHS / United States
P01CA142538-01 / CA / NCI NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
U54 HD079124 / HD / NICHD NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R21 AG033387 / AG / NIA NIH HHS / United States
AG033387 / AG / NIA NIH HHS / United States
MH070890 / MH / NIMH NIH HHS / United States
R42 NS059095 / NS / NINDS NIH HHS / United States
R01ES17240 / ES / NIEHS NIH HHS / United States
P01 DA022446 / DA / NIDA NIH HHS / United States
U54 EB005149 / EB / NIBIB NIH HHS / United States
P30 HD03110 / HD / NICHD NIH HHS / United States
MH086633 / MH / NIMH NIH HHS / United States
R01 MH091645 / MH / NIMH NIH HHS / United States
MH091645 / MH / NIMH NIH HHS / United States
MH064065 / MH / NIMH NIH HHS / United States
FMEM: functional mixed effects modeling for the analysis of longitudinal white matter Tract data.