title: Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging.
abstract: Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, thede factostandard "design matrix"-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multiple three-dimensional assessments of the same individuals - e.g., structural, functional and quantitative magnetic resonance imaging alongside functional and ligand binding maps with positron emission tomography. Current statistical methods assume that the regressors are non-random. For more realistic multi-parametric assessment (e.g., voxel-wise modeling), distributional consideration of all observations is appropriate (e.g., Model II regression). Herein, we describe a unified regression and inference approach using the design matrix paradigm which accounts for both random and non-random imaging regressors.
authors.affiliation: Electrical Engineering, Vanderbilt University, Nasvhille TN, 37235 USA.
authors.forename: Xue
authors.initials: X
authors.lastname: Yang
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore MD, 21205 USA.
authors.forename: Carolyn B
authors.initials: CB
authors.lastname: Lauzon
authors.affiliation: National Institute on Aging, National Institutes of Health, Baltimore MD, 21224.
authors.forename: Ciprian
authors.initials: C
authors.lastname: Crainiceanu
authors.forename: Brian
authors.initials: B
authors.lastname: Caffo
authors.forename: Susan M
authors.initials: SM
authors.lastname: Resnick
authors.forename: Bennett A
authors.initials: BA
authors.lastname: Landman
keywords: Biological parametric mapping; Inference; Model II regression; Statistical parametric mapping; model fitting