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This study combines experimental and modeling approaches in order to investigate the temporal dynamics of the human brain at rest. The dynamics of the neuronal activity is modeled with FitzHugh-Nagumo oscillators and the blood-oxygen-level-dependent (BOLD) time series is inferred via the Balloon-Windkessel hemodynamic model. The simulations are based on structural connections that are derived from diffusion-weighted magnetic resonance imaging measurements yielding anatomical probabilities between the considered brain regions of interest. In addition, the length of the fiber tracks allows for inference of coupling delays due to finite signal propagation velocities. We aim (i) to investigate the network topology of our neuroimaging data and (ii) how randomization of structural connections influence dynamics on top of it. The network characteristics of the structural connectivity data are compared to density-matched Erdős-Rényi random graphs. Furthermore, the neuronal and BOLD activity are modeled on both real and random (Erdős-Rényi type) graphs. The simulated temporal dynamics on both graphs are compared statistically to capture whether the spatial organization of these network affects the modeled time series. Results supported that key topological network properties such as small-worldness of our neuroimaging data are distinguishable from random networks. Moreover, the simulated BOLD activity on real and random graphs are observed to be dissimilar. The difference of the modeled temporal dynamics on the brain and random graphs suggests that anatomical connections in the human brain together with dynamical self-organization are crucial for the temporal evolution of the resting-state activity.