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Improving CCA based fMRI Analysis by Covariance Pooling - Using the GPU for Statistical Inference
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
2011 (English)Conference paper, Published paper (Other academic)
Abstract [en]

Canonical correlation analysis (CCA) is a statistical methodthat can be preferable to the general linear model (GLM) for analysisof functional magnetic resonance imaging (fMRI) data. There are,however, two problems with CCA based fMRI analysis. First, it is notfeasible to use a parametric approach to calculate an activity thresholdfor a certain signi cance level. Second, two covariance matrices need tobe estimated in each voxel, from a rather small number of time samples.We recently solved the rst problem by doing random permutation testson the graphics processing unit (GPU), such that the null distribution ofany maximum test statistics can be estimated in the order of minutes. Inthis paper we consider the second problem. We extend the idea of variancepooling, that previously has been used for the GLM, to covariancepooling to improve the estimates of the covariance matrices. Our GPUimplementation of random permutation tests is used to calculate signicance thresholds, which are needed to compare the di erent activitymaps in an objective way. The covariance pooling results in more robustestimates of the covariance matrices. The number of signi cantly activevoxels that are detected (thresholded at p = 0.05, corrected for multiplecomparisons) is increased with 40 - 120% (if 8 mm smoothing is appliedto the covariance estimates). Too much covariance pooling can howeverresult in a loss of small activity clusters, 7-10 mm of smoothing givesthe best results. The calculations that were made in order to generatethe results in this paper would have taken a total of about 65 days witha Matlab implementation and about 10 days with a multithreaded Cimplementation, with our multi-GPU implementation they took about 2hours. By using fast random permutation tests, suggested improvementsof existing methods for fMRI analysis can be evaluated in an objective way.

Place, publisher, year, edition, pages
2011.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-71281OAI: oai:DiVA.org:liu-71281DiVA: diva2:446875
Conference
Joint MICCAI Workshop on High Performance and Distributed Computing for Medical Imaging, HP-MICCAI, September 22nd, 2011, Toronto, Canada
Available from: 2011-10-10 Created: 2011-10-10 Last updated: 2013-08-28

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Eklund, AndersAndersson, MatsKnutsson, Hans

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CiteExportLink to record
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