It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. In this study, we use a whole-brain approach combining data analysis and modelling of FC dynamics between 66 ROIs covering the entire cortex. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions ("communities") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features obtained from 24 healthy subjects, thereby ensuring that they generalize across subjects. First, we determine that at this resolution, four communities that resemble known RSNs can be clearly discerned in the empirical data: DMN, visual network, control networks, and sensorimotor network. Second, we use a noise-driven stationary mean field model which possesses simple node dynamics and realistic anatomical connectivity derived from DTI and fiber tracking. It has been shown to explain resting state FC as averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. Thus, it is unclear whether the same type of model can reproduce FC at different points in time. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings suggest that in resting state fMRI, FC patterns that occur over time are mostly derived from the average FC, are shaped by underlying structural connectivity, and that the activation of these patterns is limited to brief periods in time. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.