Year: 2011
Type: article
Abstract: Neuroimaging studies aim to analyze imaging data with complex spatial patterns in a large number of locations (called voxels) on a two-dimensional (2D) surface or in a 3D volume. Conventional analyses of imaging data include two sequential steps: spatially smoothing imaging data and then independently fitting a statistical model at each voxel. However, conventional analyses suffer from the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and low statistical power in detecting spatial patterns. We propose a multiscale adaptive regression model (MARM) to integrate the propagation-separation (PS) approach (Polzehl and Spokoiny, 2000, 2006) with statistical modeling at each voxel for spatial and adaptive analysis of neuroimaging data from multiple subjects. MARM has three features: being spatial, being hierarchical, and being adaptive. We use a multiscale adaptive estimation and testing procedure (MAET) to utilize imaging observations from the neighboring voxels of the current voxel to adaptively calculate parameter estimates and test statistics. Theoretically, we establish consistency and asymptotic normality of the adaptive parameter estimates and the asymptotic distribution of the adaptive test statistics. Our simulation studies and real data analysis confirm that MARM significantly outperforms conventional analyses of imaging data.
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Funders: National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke, National Institute of Environmental Health Sciences, National Center for Research Resources, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Early Brain Development in One and Two Year Olds, Early Brain Development in Twins, Prospective Studies of the Pathogenesis of Schizophrenia, QUANTITATIVE BRAIN OXIMETRY USING MRI, Statistical Methods for Cancer Clinical Trials, MR Measured Oxygen Metabolic Index in Ischemic Stroke, Statistical Analysis of Biomedical Imaging Data in Curved Space, UNC Clinical and Translational Science Award, Characterization of Normal Brain Development Using Parallel MRI, Longitudinal Analysis of Biomedical Imaging Data, Early Brain Development in Twins, Inference in Regression Models with Missing Covariates, Bayesian Approaches to Model Selection for Survival Data, G9000081443, G9000147613, G9000187772, G9000198256, G9000247604, G9000307525, UNC Center for Environmental Health and Susceptibility
OpenAlex ID: W2127889169