Extending the GLM for Analyzing fMRI Data to a Constrained Multivariate Regression Model

• Activity Number: 306
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Rajesh Ranjan Nandy*+
• Companies: University of California, Los Angeles
• Keywords: canonical correlation analysis ; fMRI ; multivariate regression ; GLM

Abstract

Local canonical correlation analysis (CCA) is a multivariate method that simultaneously analyzes the timecourses of a group of neighboring voxels and has been demonstrated to be 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 which is a direct extension of univariate GLM. Mathematically, multivariate regression model is equivalent to CCA, but easier to interpret since the framework is similar to GLM. Arbitrary contrasts can be used in the multivariate regression model (MRM) approach including multivariate contrasts. With multivariate contrasts, it is also possible to test for significance of contrasts on regression coefficients as well as contr