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 language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 1-9 |
| Number of pages | 9 |
| Volume | 7012 LNCS |
| DOIs | |
| State | Published - 2011 |
| Event | 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 Duration: Sep 18 2011 → Sep 18 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 7012 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 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 |
|---|---|
| Country | Canada |
| City | Toronto, ON |
| Period | 9/18/11 → 9/18/11 |
Fingerprint
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
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 proceeding › Conference contribution
}
TY - GEN
T1 - Accounting for random regressors
T2 - A unified approach to multi-modality imaging
AU - Yang, Xue
AU - Lauzon, Carolyn B.
AU - Crainiceanu, Ciprian M
AU - Caffo, Brian S
AU - Resnick, Susan M.
AU - Landman, Bennett A.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Biological parametric mapping
KW - Inference
KW - model fitting
KW - Model II regression
KW - Statistical parametric mapping
UR - http://www.scopus.com/inward/record.url?scp=80053479607&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053479607&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24446-9_1
DO - 10.1007/978-3-642-24446-9_1
M3 - Conference contribution
C2 - 25346952
AN - SCOPUS:80053479607
SN - 9783642244452
VL - 7012 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 9
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -