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 canonical variates, yielding a more computationally efficient CCA procedure. The proposed method can be seen as a regularized CCA method where regularization is introduced via basis expansion. The advantages of the proposed PCCA algorithm over the standard CCA are illustrated on both simulated data and real fMRI data from a resting state experiment.