Granger causality and Phase Slope Index (PSI) are recent approaches to measure how one signal depends on another, which gives an indication of information flow in complex systems. We show that the Granger causality and PSI mapping, voxel-by-voxel, for functional magnetic resonance imaging (fMRI) resting state data set. Slow fluctuations (< 0.1 Hz) in fMRI signal have been used to map several consistent resting state networks in the brain. The results demonstrate that PSI influence directions among reference regions and gray matter voxels were more consistent with the relevant previous studies compared with Granger causality. The PSI approach proposed is effective, computationally efficient, and easy to interpret.
Granger causality, Phase Slope Index, fMRI resting state, Information flow