Canonical correlation analysis applied to functional connectivity in MEG

Abstract

We present a multivariate method based on canonical correlation analysis for the study of functional connectivity in the brain with MEG data. We obtain a time-frequency representation of the brain activity on the cortical surface, and use the signal power at specific frequency bands as inputs to our model. Our measure of interaction between two spatial locations is the canonical correlation, and the vectors associated with it indicate the contribution of each individual frequency band to the interaction. The resulting canonical correlation maps are thresholded for significance using false discovery rate. We further provide a novel way to control for linear mixing by testing whether the correlation vectors are collinear. We apply our method to simulations and experimental data from an MEG visuomotor study, and demonstrate that it is able to detect functional interactions across space as well as the frequency bands that contribute to these interactions.