  Learning requires the traversal of inherently distinct cognitive states to
produce behavioral adaptation. Yet, tools to explicitly measure these states
with non-invasive imaging -- and to assess their dynamics during learning --
remain limited. Here, we describe an approach based on a novel application of
graph theory in which points in time are represented by network nodes, and
similarities in brain states between two different time points are represented
as network edges. We use a graph-based clustering technique to identify
clusters of time points representing canonical brain states, and to assess the
manner in which the brain moves from one state to another as learning
progresses. We observe the presence of two primary states characterized by
either high activation in sensorimotor cortex or high activation in a
frontal-subcortical system. Flexible switching among these primary states and
other less common states becomes more frequent as learning progresses, and is
inversely correlated with individual differences in learning rate. These
results are consistent with the notion that the development of automaticity is
associated with a greater freedom to use cognitive resources for other
processes. Taken together, our work offers new insights into the constrained,
low dimensional nature of brain dynamics characteristic of early learning,
which give way to less constrained, high-dimensional dynamics in later
learning.
