Dynamic functional connectivity (dFC) measured by functional magnetic resonance imaging (fMRI) shows evidence of large-scale networks with highly dynamic (re) configurations. We propose a novel approach to extract traces of human brain function by the construction of a trajectory in a meaningful low-dimensional space. This allows studying dFC in more detail and identify possible meaningful brain states from the moment-to-moment fluctuations of the brain signals during resting state or naturalistic conditions such as passive movie watching. Specifically, we explored dynamic organization of sub-networks derived from the time-dependent graph Laplacian in combination with Riemannian manifold distance to measure dissimilarity over time of dFC and to subsequently build the trajectory of brain activity. As a proof-of-principle, we show results for an fMRI dataset containing both rest and movie epochs in 15 healthy participants. The movie condition varied (i.e., fearful, joyful, and neutral movie excerpts) and clearly influenced the subsequent resting-state period in terms of FC brain state.