The analysis of time-varying connectivity has become an important part of neuroscience discussions. The majority of such studies have focused primarily on the temporal variations of functional connectivity among fixed regions of interest (ROIs) or brain networks. However, the brain reorganizes itself on both spatial and temporal scales. Approaches that capture spatial and temporal coupling variations are needed. Here, we describe a novel approach capable of identifying the base states of brain networks and capturing their spatial variations over time. It also provides a unique opportunity to characterize the temporal variations of the brain at both network and global scales.