TY - JOUR
T1 - TwinMARM
T2 - Two-stage multiscale adaptive regression methods for twin neuroimaging data
AU - Li, Yimei
AU - Gilmore, John H.
AU - Wang, Jiaping
AU - Styner, Martin
AU - Lin, Weili
AU - Zhu, Hongtu
N1 - Funding Information:
Manuscript received December 01, 2011; accepted January 13, 2012. Date of publication January 24, 2012; date of current version May 02, 2012 The work of H. Zhu was supported in part by the National Institutes of Health (NIH) under Grant RR025747-01, Grant P01CA142538-01, Grant MH086633, Grant MH092335, and Grant AG033387. NIH grants MH064065, HD053000, and MH070890 to J. H. Gilmore, NIH Grant R01NS055754 and Grant R01EB5-34816 to W. Lin, Lilly Research Laboratories, the UNC NDRC HD 03110, Eli Lilly grant F1D-MC-X252, and NIH Roadmap Grant U54 EB005149-01, NAMIC to M. Styner. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Asterisk indicates corresponding author.
PY - 2012
Y1 - 2012
N2 - Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for multiple comparisons. However, conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this paper is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structures and their functions. In each stage, TwinMARM employs hierarchically nested spheres with increasing radii at each location and then captures spatial dependence among imaging observations via consecutively connected spheres across all voxels. Simulation studies show that our TwinMARM significantly outperforms conventional analyses of twin imaging data. Finally, we use our method to detect statistically significant effects of genetic and environmental variations on white matter structures in a neonatal twin study.
AB - Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for multiple comparisons. However, conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this paper is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structures and their functions. In each stage, TwinMARM employs hierarchically nested spheres with increasing radii at each location and then captures spatial dependence among imaging observations via consecutively connected spheres across all voxels. Simulation studies show that our TwinMARM significantly outperforms conventional analyses of twin imaging data. Finally, we use our method to detect statistically significant effects of genetic and environmental variations on white matter structures in a neonatal twin study.
KW - Multiscale adaptive regression model
KW - smooth
KW - structural equation model
KW - twin study
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U2 - 10.1109/TMI.2012.2185830
DO - 10.1109/TMI.2012.2185830
M3 - Article
C2 - 22287236
AN - SCOPUS:84860657714
SN - 0278-0062
VL - 31
SP - 1100
EP - 1112
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 5
M1 - 6138918
ER -