Abstract #1713

A Novel Test Statistic Allowing a General Linear Contrast Vector for Local Canonical Correlation Analysis in FMRI

Mingwu Jin¹, Rajesh Nandy², Dietmar Cordes¹

¹Radiology, University of Colorado Denver, Aurora, CO, USA; ²Biostatistics and Psychology, UCLA, Los Angeles, CA, USA

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to determine more accurately activation patterns in fMRI data. One drawback of CCA is that, unlike the general linear model (GLM), an arbitrary linear contrast of the temporal regressors has not been incorporated in the CCA formalisms. In this research we show how to extent CCA so that an arbitrary linear contrast of the temporal regressors can be computed similar to a t-statistic in GLM.