Information theoretic evaluation of brain connectivity compared to cross correlation using simulated resting state fMRI data

Cross correlation is the most common measure used in the literature to estimate functional connectivity between brain regions using resting state functional magnetic resonance imaging (rs-fMRI) data. However, nonlinearities in the BOLD signal along with intrinsic delay in the response of the remote brain regions deteriorate the performance of this measure. Unlike cross correlation, information theoretic measures are able to detect these nonlinear relations. In this paper, we propose using quadratic mutual information (QMI) for estimating connectivity matrix and compare it with cross correlation using simulated rs-fMRI data. The results show that the proposed measure outperforms cross correlation and other information theoretic measures in estimating the connectivity matrix.