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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dhillon, P.S. Avants, B. Ungar, L. Gee, J.C. |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Radiology, University of Pennsylvania, Philadelphia, PA U.S.A (Avants, B.; Gee, J.C.) || Department of Computer & Information Science, University of Pennsylvania, Philadelphia, PA U.S.A (Dhillon, P.S.; Ungar, L.) |
| Abstract | We propose a new multivariate method, partial sparse canonical correlation analysis (PSCCA), for computing the statistical comparisons needed by population studies in medical imaging. PSCCA is a multivariate generalization of linear regression that allows one to statistically parameterize imaging studies in terms of multiple views of the population (e.g., the full collection of measurements taken from an image set along with batteries of cognitive or genetic data) while controlling for nuisance variables. This paper develops the theory of PSCCA, provides an algorithm and illustrates PSCCA performance on both simulated and real datasets. We show, as a first application and evaluation of this new methodology, that PSCCA can improve detection power over mass univariate approaches while retaining the interpretability and biological plausibility of the estimated effects. We also discuss the strengths, limitations and future potential of this methodology. |
| Starting Page | 1132 |
| Ending Page | 1135 |
| File Size | 304880 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457718571 |
| ISSN | 19457928 |
| e-ISBN | 9781457718588 |
| DOI | 10.1109/ISBI.2012.6235759 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-02 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Correlation Vectors Standards Sparse matrices Biomedical imaging Neuroimaging Spectral Methods Multivariate modeling Medical Imaging |
| Content Type | Text |
| Resource Type | Article |
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