Title | Multiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates. |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Skup, Martha, Hongtu Zhu, and Heping Zhang |
Journal | Biometrics |
Volume | 68 |
Issue | 4 |
Pagination | 1083-92 |
Date Published | 2012 Dec |
ISSN | 1541-0420 |
Keywords | Algorithms, Alzheimer Disease, Brain, Computer Simulation, Data Interpretation, Statistical, Humans, Image Interpretation, Computer-Assisted, Longitudinal Studies, Models, Statistical, Neuroimaging, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity |
Abstract | 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. |
DOI | 10.1111/j.1541-0420.2012.01767.x |
Alternate Journal | Biometrics |
Original Publication | Multiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates. |
PubMed ID | 22551084 |
PubMed Central ID | PMC3767131 |
Grant List | K01 AG030514 / AG / NIA NIH HHS / United States AG033387 / AG / NIA NIH HHS / United States T32 MH014235 / MH / NIMH NIH HHS / United States U54 EB005149 / EB / NIBIB NIH HHS / United States EB005149â01 / EB / NIBIB NIH HHS / United States P01CA142538â01 / CA / NCI NIH HHS / United States P30 AG010129 / AG / NIA NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States R21 AG033387 / AG / NIA NIH HHS / United States T32âMH014235 / MH / NIMH NIH HHS / United States U01 AG024904 / AG / NIA NIH HHS / United States RR025747â01 / RR / NCRR NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States MH086633 / MH / NIMH NIH HHS / United States UL1 RR025747 / RR / NCRR NIH HHS / United States |
Multiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates.
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