Constrained CCA with Different Novel Linear Constraints and a Nonlinear Constraint in FMRI

Dietmar Cordes¹, Rajesh Nandy², Mingwu Jin¹

Multivariate statistical analysis has recently become popular in fMRI data analysis as such methods can capture better the spatial dependencies between neighboring voxels. One such method is local canonical correlation analysis (CCA) where one looks at the joint time courses of a group of neighboring voxels. It is known that CCA without any constraints can lead to significant artifacts and an increase in false activations. Here, we investigate different novel linear constraints and a nonlinear constraint for CCA and propose a method that rectifies the weakness of conventional CCA mentioned above.