A Multivariate Regression Framework for the Analysis of fMRI Data Accounting for Spatial Correlation

Rajesh Ranjan Nandy¹

Local canonical correlation analysis (CCA) is a multivariate method that simultaneously analyzes the timecourses of a group of neighboring voxels and is more sensitive than the conventional univariate GLM approach. However, unlike the general linear model (GLM), an arbitrary linear contrast of the temporal regressors has not been so far incorporated in the CCA formalism. To address the first problem, a multivariate regression model is presented. Multivariate regression model is equivalent to CCA, but easier to interpret. Arbitrary contrasts can be used in the multivariate regression model (MRM) approach including contrasts on voxels which is impossible in the univariate framework.