  At rest, human brain functional networks display striking modular
architecture in which coherent clusters of brain regions are activated. The
modular account of brain function is pervasive, reliable, and reproducible.
Yet, a complementary perspective posits a core-periphery or rich-club account
of brain function, where hubs are densely interconnected with one another,
allowing for integrative processing. Unifying these two perspectives has
remained difficult due to the fact that the methodological tools to identify
modules are entirely distinct from the methodological tools to identify
core-periphery structure. Here we leverage a recently-developed model-based
approach -- the weighted stochastic block model -- that simultaneously uncovers
modular and core-periphery structure, and we apply it to fMRI data acquired at
rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate
that functional brain networks display rich meso-scale organization beyond that
sought by modularity maximization techniques. Moreover, we show that this
meso-scale organization changes appreciably over the course of
neurodevelopment, and that individual differences in this organization predict
individual differences in cognition more accurately than module organization
alone. Broadly, our study provides a unified assessment of modular and
core-periphery structure in functional brain networks, providing novel insights
into their development and implications for behavior.
