Identifying directional influences in neural circuits from functional near infrared spectroscopy (fNIRS) recordings presents one of the main challenges for understanding brain dynamics. In this study a new strategy that combines Granger causality mapping (GCM) and independent component analysis (ICA) is proposed to reveal complex neural network dynamics underlying cognitive processes with fNIRS measurements. The GCM-ICA algorithm implements the following two procedures: (i) extraction of the region of interests (ROIs) of cortical activations by ICA, and (ii) estimation of the direct causal influences in local brain networks using Granger causality among voxels of ROIs. Our results show the use of GCM in conjunction with ICA is able to effectively capture the brain network dynamics in time-frequency domain with significantly reduced computational cost. We thus suggest that the GCM-ICA technique is a potentially valuable tool that could be used for the investigation of directional causality influences of brain network dynamics in biophotonics fields.