Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis
Parametric statistical methods, such as Z-, t-, and F-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks....
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/627947 |
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| _version_ | 1832561898171662336 |
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| author | Anders Eklund Mats Andersson Hans Knutsson |
| author_facet | Anders Eklund Mats Andersson Hans Knutsson |
| author_sort | Anders Eklund |
| collection | DOAJ |
| description | Parametric statistical methods, such as Z-, t-, and F-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single-subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation-based approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level. |
| format | Article |
| id | doaj-art-252d0a413deb409e949e3fb8d0c2c8cc |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-252d0a413deb409e949e3fb8d0c2c8cc2025-02-03T01:23:53ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/627947627947Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI AnalysisAnders Eklund0Mats Andersson1Hans Knutsson2Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, SwedenDivision of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, SwedenDivision of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, SwedenParametric statistical methods, such as Z-, t-, and F-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single-subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation-based approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level.http://dx.doi.org/10.1155/2011/627947 |
| spellingShingle | Anders Eklund Mats Andersson Hans Knutsson Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis International Journal of Biomedical Imaging |
| title | Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis |
| title_full | Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis |
| title_fullStr | Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis |
| title_full_unstemmed | Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis |
| title_short | Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis |
| title_sort | fast random permutation tests enable objective evaluation of methods for single subject fmri analysis |
| url | http://dx.doi.org/10.1155/2011/627947 |
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