Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data
Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2012/574971 |
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| author | Mingwu Jin Rajesh Nandy Tim Curran Dietmar Cordes |
| author_facet | Mingwu Jin Rajesh Nandy Tim Curran Dietmar Cordes |
| author_sort | Mingwu Jin |
| collection | DOAJ |
| description | Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. |
| format | Article |
| id | doaj-art-2c8eb7f4dbca4b60bf0474f13826836c |
| institution | DOAJ |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-2c8eb7f4dbca4b60bf0474f13826836c2025-08-20T03:20:25ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962012-01-01201210.1155/2012/574971574971Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI DataMingwu Jin0Rajesh Nandy1Tim Curran2Dietmar Cordes3Department of Physics, University of Texas at Arlington, Arlington, TX 76019, USADepartments of Biostatistics and Psychology, UCLA, Los Angeles, CA 90095, USADepartment of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO 80309, USADepartment of Radiology, School of Medicine, University of Colorado Denver, Aurora, CO 80045, USALocal canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.http://dx.doi.org/10.1155/2012/574971 |
| spellingShingle | Mingwu Jin Rajesh Nandy Tim Curran Dietmar Cordes Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data International Journal of Biomedical Imaging |
| title | Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data |
| title_full | Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data |
| title_fullStr | Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data |
| title_full_unstemmed | Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data |
| title_short | Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data |
| title_sort | extending local canonical correlation analysis to handle general linear contrasts for fmri data |
| url | http://dx.doi.org/10.1155/2012/574971 |
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