Multiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates.

TitleMultiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates.
Publication TypeJournal Article
Year of Publication2012
AuthorsSkup, Martha, Hongtu Zhu, and Heping Zhang
JournalBiometrics
Volume68
Issue4
Pagination1083-92
Date Published2012 Dec
ISSN1541-0420
KeywordsAlgorithms, 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.

DOI10.1111/j.1541-0420.2012.01767.x
Alternate JournalBiometrics
Original PublicationMultiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates.
PubMed ID22551084
PubMed Central IDPMC3767131
Grant ListK01 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
EB005149−01 / EB / NIBIB NIH HHS / United States
P01CA142538−01 / 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
T32−MH014235 / MH / NIMH NIH HHS / United States
U01 AG024904 / AG / NIA NIH HHS / United States
RR025747−01 / 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
Project: