PCCA: A Projection CCA Method for Effective FMRI Data Analysis

Abstract

Canonical correlation analysis (CCA) is a data driven method that has been successfully used in functional magnetic resonance imaging (fMRI) data analysis. Standard CCA extracts meaningful information from a data set by seeking pairs of linear combinations from two sets of variables with maximum pairwise correlation. So far, however, this method has been used without incorporating prior information available for fMRI data. In this paper, we address this issue by proposing a new CCA method named PCCA (for projection CCA). PCCA is obtained by using the discrete cosine transform (DCT) to create a basis for a span that better characterizes the fMRI data set. Employing DCT guides the estimated ca..