Accounting for random regressors: A unified approach to multi-modality imaging

Xue Yang, Carolyn B. Lauzon, Ciprian M Crainiceanu, Brian S Caffo, Susan M. Resnick, Bennett A. Landman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages1-9
Number of pages9
Volume7012 LNCS
DOIs
StatePublished - 2011
Event1st International Workshop on Multimodal Brain Image Analysis, MBIA 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 18 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7012 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Multimodal Brain Image Analysis, MBIA 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period9/18/119/18/11

Fingerprint

Multimodality
Regression
Imaging
Imaging techniques
Neuroimaging
Positron Emission Tomography
Positron emission tomography
Functional Magnetic Resonance Imaging
Voxel
Linear Regression Model
Linear regression
Statistical method
Univariate
Brain
Statistical methods
Ligands
Paradigm
Three-dimensional
Modeling
Design

Keywords

  • Biological parametric mapping
  • Inference
  • model fitting
  • Model II regression
  • Statistical parametric mapping

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Yang, X., Lauzon, C. B., Crainiceanu, C. M., Caffo, B. S., Resnick, S. M., & Landman, B. A. (2011). Accounting for random regressors: A unified approach to multi-modality imaging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7012 LNCS, pp. 1-9). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7012 LNCS). https://doi.org/10.1007/978-3-642-24446-9_1

Accounting for random regressors : A unified approach to multi-modality imaging. / Yang, Xue; Lauzon, Carolyn B.; Crainiceanu, Ciprian M; Caffo, Brian S; Resnick, Susan M.; Landman, Bennett A.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7012 LNCS 2011. p. 1-9 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7012 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yang, X, Lauzon, CB, Crainiceanu, CM, Caffo, BS, Resnick, SM & Landman, BA 2011, Accounting for random regressors: A unified approach to multi-modality imaging. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7012 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7012 LNCS, pp. 1-9, 1st International Workshop on Multimodal Brain Image Analysis, MBIA 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011, Toronto, ON, Canada, 9/18/11. https://doi.org/10.1007/978-3-642-24446-9_1
Yang X, Lauzon CB, Crainiceanu CM, Caffo BS, Resnick SM, Landman BA. Accounting for random regressors: A unified approach to multi-modality imaging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7012 LNCS. 2011. p. 1-9. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-24446-9_1
Yang, Xue ; Lauzon, Carolyn B. ; Crainiceanu, Ciprian M ; Caffo, Brian S ; Resnick, Susan M. ; Landman, Bennett A. / Accounting for random regressors : A unified approach to multi-modality imaging. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7012 LNCS 2011. pp. 1-9 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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