TY - JOUR
T1 - FMEM
T2 - Functional mixed effects modeling for the analysis of longitudinal white matter Tract data
AU - Yuan, Ying
AU - Gilmore, John H.
AU - Geng, Xiujuan
AU - Martin, Styner
AU - Chen, Kehui
AU - Wang, Jane ling
AU - Zhu, Hongtu
N1 - Funding Information:
This work was partially supported by NIH grants R01ES17240 , MH091645 , U54 EB005149 , P30 HD03110 , RR025747-01 , P01CA142538-01 , MH086633 , AG033387 , MH064065 , HD053000 , and MH070890 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The readers are welcome to request reprints from Dr. Hongtu Zhu.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
KW - Diffusion properties
KW - Functional mixed effects model
KW - Longitudinal
KW - Spatial-temporal correlation
KW - White matter fiber tract
UR - http://www.scopus.com/inward/record.url?scp=84885751298&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885751298&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2013.09.020
DO - 10.1016/j.neuroimage.2013.09.020
M3 - Article
C2 - 24076225
AN - SCOPUS:84885751298
SN - 1053-8119
VL - 84
SP - 753
EP - 764
JO - NeuroImage
JF - NeuroImage
ER -