Canonical correlation analysis for data fusion and group inferences

Data-driven analysis methods, such as blind source separation (BSS) based on independent component analysis (ICA), have proven very useful in the study of brain function, in particular when the dynamics are hard to model and underlying assumptions about the data have to be minimized. Many problems in medical data analysis involve the analysis of multiple data sets, either of the same type as in a group study where inferences are based on the same modality, e.g., group inferences from functional magnetic resonance imaging (fMRI) data collected from multiple subjects, or from different modalities as in the case of data fusion where inferences have to be drawn from data collected from multiple modalities such as fMRI, electroencephalography (EEG), and structural MRI (sMRI), for the same group of subjects. Canonical correlation analysis (CCA) [1], another data-driven approach, and its extension to multiple data setsmultiset CCA (M-CCA) [2]provide a natural framework for both types of study. In this article, we show how CCA and M-CCA can be used for the analysis of data from a single modality for group inferences as well as fusion of data from multiple modalities using a feature-based approach, discuss the advantages of the CCA-based approach, and compare its performance to ICA that has been successfully applied to both types of study.