- Xue Yang20,
- Carolyn B. Lauzon20,
- Ciprian Crainiceanu21,
- Brian Caffo21,
- Susan M. Resnick22 &
- …
- Bennett A. Landman20
Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 7012))
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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.
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Authors and Affiliations
Electrical Engineering, Vanderbilt University, Nasvhille, TN, 37235, USA
Xue Yang, Carolyn B. Lauzon & Bennett A. Landman
Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205, USA
Ciprian Crainiceanu & Brian Caffo
National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224
Susan M. Resnick
- Xue Yang
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- Carolyn B. Lauzon
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- Ciprian Crainiceanu
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Editors and Affiliations
Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, 30602, Athens, GA, USA
Tianming Liu
School of Medicine, Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, 130 Mason Farm Road, 27599, Chapel Hill, NC, USA
Dinggang Shen
Kitware Inc., 28 Corporate Drive, 12065, Clifton Park, NY, USA
Luis Ibanez
General Electric Research Center, 1 Research Circle, 12309, Niskayuna, NY, USA
Xiaodong Tao
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Yang, X., Lauzon, C.B., Crainiceanu, C., Caffo, B., Resnick, S.M., Landman, B.A. (2011). Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging. In: Liu, T., Shen, D., Ibanez, L., Tao, X. (eds) Multimodal Brain Image Analysis. MBIA 2011. Lecture Notes in Computer Science, vol 7012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24446-9_1
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