Considering brain functional connectivity (FC) as a graph network, we can identify the brain function hub nodes that have the most dense and heavy connections in the network. For a real-valued FC matrix (unsigned connections in a value range [0,1]), we can identify the hub nodes by a new method of eigencentrality mapping, which not only counts for the connections to other nodes but also the other nodes’ centrality values through the eigen decomposition of the FC matrix. In addition, there are two kinds of fMRI data, magnitude and phase, that can be used for brain FC and hub analysis. Although both magnitude and phase fMRI data are generated from the same magnetic source through different transformations, they are different in signal measurements, consequently leading to different inferences. We herein report on brain functional hub analysis by constructing the FC matrix from phase fMRI data and identifying the hub nodes by eigencentrality mapping. In our experiment, we collected a cohort of 160 complex-valued fMRI dataset (consisting of magnitude and phase in pairs), and performed independent component analysis (ICA), FC matrices calculation and FC matrices eigen decomposition; thereby obtained the node eigencentrality values in the largest eigenvalue-associated eigenvector. Our results showed that phase fMRI data analysis could determine the resting-state brain functional hubs primarily in the central subcortex and the posterior brain region (parieto-occipital lobes and cerebellum), which were different from the magnitude-inferred hubs in brain superior region (frontal and parietal lobes).