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• W9567103 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, the de facto standard 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." @default.
• W9567103 created "2016-06-24" @default.
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• W9567103 date "2011-01-01" @default.
• W9567103 modified "2023-09-27" @default.
• W9567103 title "Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging" @default.
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• W9567103 doi "https://doi.org/10.1007/978-3-642-24446-9_1" @default.
• W9567103 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4208720" @default.
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