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Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-varying Covariates

Institution:
1Division of Biostatistics, Yale University, New Haven, CT, USA. martha.skup@yale.edu
2Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Publication Date:
Dec-2012
Journal:
Biometrics
Volume Number:
68
Issue Number:
4
Pages:
1083-92
Citation:
Biometrics. 2012 Dec;68(4):1083-92.
PubMed ID:
22551084
PMCID:
PMC3767131
Keywords:
Generalized method of moments (GMM), Longitudinal neuroimaging data, Marginal modeling, Multiscale adaptive regression model (MARM), Smoothing, Time-varying covariates, Voxelwise method
Appears in Collections:
NA-MIC
Sponsors:
R21 AG033387/AG/NIA NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
K01 AG030514/AG/NIA NIH HHS/United States
R01 MH086633/MH/NIMH NIH HHS/United States
P01 CA142538/CA/NCI NIH HHS/United States
P30 AG010129/AG/NIA NIH HHS/United States
UL1 RR025747/RR/NCRR NIH HHS/United States
T32 MH014235/MH/NIMH NIH HHS/United States
U01 AG024904/AG/NIA NIH HHS/United States
Generated Citation:
Skup M., Zhu H., Zhang H. Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-varying Covariates. Biometrics. 2012 Dec;68(4):1083-92. PMID: 22551084. PMCID: PMC3767131.
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Neuroimaging data collected at repeated occasions are gaining increasing attention in the neuroimaging community due to their potential in answering questions regarding brain development, aging, and neurodegeneration. These datasets are large and complicated, characterized by the intricate spatial dependence structure of each response image, multiple response images per subject, and covariates that may vary with time. We propose a multiscale adaptive generalized method of moments (MA-GMM) approach to estimate marginal regression models for imaging datasets that contain time-varying, spatially related responses and some time-varying covariates. Our method categorizes covariates into types to determine the valid moment conditions to combine during estimation. Further, instead of assuming independence of voxels (the components that make up each subject's response image at each time point) as many current neuroimaging analysis techniques do, this method "adaptively smoothes" neuroimaging response data, computing parameter estimates by iteratively building spheres around each voxel and combining observations within the spheres with weights. MA-GMM's development adds to the few available modeling approaches intended for longitudinal imaging data analysis. Simulation studies and an analysis of a real longitudinal imaging dataset from the Alzheimer's Disease Neuroimaging Initiative are used to assess the performance of MA-GMM. Martha Skup, Hongtu Zhu, and Heping Zhang for the Alzheimer's Disease Neuroimaging Initiative.

Additional Material
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Skup-Biometrics2012-fig3.jpg (219.801kB)