Effective connectivity in FMRI from mutual prediction approach

Effective connectivity aims to quantify how a neural system influences another. Estimation of effective connectivity in neurophysiological signals has gained great popularity in recent years. Lag-based methods, such as Granger causality, depend strongly on the amplitudes of the signals and assume the signals are linear and stationary. In this paper, we extend a previously proposed model-free method for estimation of directionality of coupling. Mutual prediction approach is implemented by estimating the instantaneous phases from the Reduced Interference Rihaczek time-frequency distribution and calculating the directionality index as function of frequency. The proposed method is evaluated on both simulated signal models and resting state fMRI time series.