In recent years, the analysis of brain network connectivity has become increasingly popular in neuroscience research. Investigating brain functional connectome as well as its change across time provides insights into the dynamic nature of brain organizations. However, such analyses often face some major challenges including the high dimensionality of brain networks, unknown latent sources underlying the observed connectivity, and the large number of brain connections leading to spurious findings. In our research, we aim to characterize various neural circuits underlying dynamic brain functional connectivity using a novel blind source separation (BSS) framework with a low-rank structure and an angle-based sparsity regularization. Our proposed method captures the dynamic profiles of different neural circuits, reveals key brain regions or nodes that drive each of these circuits and identifies neural circuits associated with disease phenotypes. In this talk, we will introduce the methodology of our dynamic connectome source separation method and also present our findings on dynamic neurocircuitry traits extracted from a resting-state fMRI study.