  The human connectome has been widely studied over the past decade. A
principal finding is that it can be decomposed into communities of densely
interconnected brain regions. This result, however, may be limited
methodologically. Past studies have often used a flawed modularity measure in
order to infer the connectome's community structure. Also, these studies relied
on the intuition that community structure is best defined in terms of a
network's static topology as opposed to a more dynamical definition. In this
report we used the partition stability framework, which defines communities in
terms of a Markov process (random walk), to infer the connectome's multi-scale
community structure. Comparing the community structure to observed
resting-state functional connectivity revealed communities across a broad range
of dynamical scales that were closely related to functional connectivity. This
result suggests a mapping between communities in structural networks, models of
communication processes, and brain function. It further suggests that
communication in the brain is not limited to a single characteristic scale,
leading us to posit a heuristic for scale-selective communication in the
cerebral cortex.
