Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-Varying Covariates
Article first published online: 2 MAY 2012
© 2012, The International Biometric Society No claim to original US government works
Volume 68, Issue 4, pages 1083–1092, December 2012
How to Cite
Skup, M., Zhu, H. and Zhang, H. (2012), Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-Varying Covariates. Biometrics, 68: 1083–1092. doi: 10.1111/j.1541-0420.2012.01767.x
- Issue published online: 21 DEC 2012
- Article first published online: 2 MAY 2012
- Received June 2011. Revised January 2012. Accepted March 2012.
- Generalized method of moments (GMM);
- Longitudinal neuroimaging data;
- Marginal modeling;
- Multiscale adaptive regression model (MARM);
- Time-varying covariates;
- Voxelwise method
Summary 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.