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

Xue Yang, Carolyn B. Lauzon, Ciprian Crainiceanu, Brian 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 publicationMultimodal Brain Image Analysis - First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Proceedings
Pages1-9
Number of pages9
DOIs
StatePublished - Oct 11 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)0302-9743
ISSN (Electronic)1611-3349

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

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Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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 Multimodal Brain Image Analysis - First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Proceedings (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