  Distributed networks of brain areas interact with one another in a
time-varying fashion to enable complex cognitive and sensorimotor functions.
Here we use novel network analysis algorithms to test the recruitment and
integration of large-scale functional neural circuitry during learning. Using
functional magnetic resonance imaging (fMRI) data acquired from healthy human
participants, from initial training through mastery of a simple motor skill, we
investigate changes in the architecture of functional connectivity patterns
that promote learning. Our results reveal that learning induces an autonomy of
sensorimotor systems and that the release of cognitive control hubs in frontal
and cingulate cortices predicts individual differences in the rate of learning
on other days of practice. Our general statistical approach is applicable
across other cognitive domains and provides a key to understanding
time-resolved interactions between distributed neural circuits that enable task
performance.
