The brain is a biophysical system subject to information flows that may be thought of as a many-body architecture with a spatiotemporal dynamics described by its neuronal structures. The oscillatory nature of brain activity allows these structures (nodes) to be described as a set of coupled oscillators forming a network where the node dynamics and that of the network topology can be studied. Quantifying its dynamics at various scales is an issue that claims to be explored for several brain activities, e.g., activity at rest. The resting-state (RS) associates the underlying brain dynamics of healthy subjects that are not actively compromised with sensory or cognitive processes. Studying its dynamics is highly non-trivial but opens the door to understand the general principles of brain functioning, as well as to contrast a passive null condition vs the dynamics of pathologies or non-resting activities. Here, we hypothesize about how the spatiotemporal dynamics of cortical fluctuations could be for healthy subjects at RS. To do that, we retrieve the alphabet that reconstructs the dynamics (entropy-complexity) of magnetoencephalography (MEG) signals. We assemble the cortical connectivity to elicit the dynamics in the network topology. We depict an order relation between entropy and complexity for frequency bands that is ubiquitous for different temporal scales. We unveiled that the posterior cortex conglomerates nodes with both stronger dynamics and high clustering for α band. The existence of an order relation between dynamic properties suggests an emergent phenomenon characteristic of each band. Interestingly, we find the posterior cortex as a domain of dual character that plays a cardinal role in both the dynamics and structure regarding the activity at rest. To the best of our knowledge, this is the first study with MEG involving information theory and network science to better understand the dynamics and structure of brain activity at rest for different bands and scales.