NDLI: Biological Parametric Mapping Accounting for Random Regressors with Regression Calibration and Model II Regression

Content Provider	PubMed Central
Author	Yang, Xue Lauzon, Carolyn B. Ciprian, Crainiceanu Caffo, Brian Resnick, Susan M. Landman, Bennett A.
Copyright Year	2012
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 multi-modal 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. Largely, current statistical methods in the imaging community 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. Herein, we discuss two unified regression and inference approaches, model II regression and regression calibration, for use in massively univariate inference with imaging data. These methods use the design matrix paradigm and account for both random and non-random imaging regressors. We characterize these methods in simulation and illustrate their use on an empirical dataset. Both methods have been made readily available as a toolbox plug-in for the SPM software.
Related Links	http://dx.doi.org/10.1016/j.neuroimage.2012.05.020
Ending Page	1768
Page Count	8
Starting Page	1761
File Format	PDF
ISSN	10538119
e-ISSN	10959572
Journal	NeuroImage
Issue Number	3
Volume Number	62
Language	English
Publisher Date	2012-09-01
Access Restriction	Open
Subject Keyword	Cognitive Neuroscience Neurology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Neurology Cognitive Neuroscience

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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