Functional connectivity in resting-state fMRI: is linear correlation sufficient?

ABSTRACT: Functional connectivity (FC) analysis is a prominent approach to analyzing fMRI data, especially acquired under the resting state condition. The commonly used linear correlation FC measure bears an implicit assumption of Gaussianity of the dependence structure. If only the marginals, but not all the bivariate distributions are Gaussian, linear correlation consistently underestimates the strength of the dependence. To assess the suitability of linear correlation and the general potential of nonlinear FC measures, we present a framework for testing and estimating the deviation from Gaussianity by means of comparing mutual information in the data and its Gaussianized counterpart. We apply this method to 24 sessions of human resting state fMRI. For each session, matrix of connectivities betwee

SUBMITTER: Hlinka J 

PROVIDER: S-EPMC4139498 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature