Multisubject fMRI data analysis via two dimensional multi-set canonical correlation analysis

Multisubject analysis helps to jointly analyze themedical data from multiple subjects, to make insightful inferences. Multi set canonical correlation analysis (MCCA), which extends the application of canonical correlation analysis to more than two datasets, is one such statistical technique to perform multisubject analysis. MCCA aims to compute optimal data transformations such that overall correlation of transformed datasets is maximized. But, the conventional approach is directly applicable to vector data, which requires the image data to be reshaped into vectors. Vectorization of images disturbs their spatial structure and increases computational complexity. We propose a new two dimensional MCCA approach that operates directly on the image data. Experiments are performed against fMRI data sets acquired through block-paradigm right finger tapping task.