Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine\r\nactivation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI.\r\nA major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has\r\nnot been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using\r\nthe equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference\r\nof general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without\r\nreestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients\r\nof CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data\r\nthan the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from\r\nfMRI data were used to demonstrate the advantage of this novel test statistic.
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