  It has become increasingly popular to study the brain as a network due to the
realization that functionality cannot be explained exclusively by independent
activation of specialized regions. Instead, across a large spectrum of
behaviors, function arises due to the dynamic interactions between brain
regions. The existing literature on functional brain networks focuses mainly on
a battery of network properties characterizing the "resting state" using for
example the modularity, clustering, or path length among regions. In contrast,
we seek to uncover subgraphs of functional connectivity that predict or drive
individual differences in sensorimotor learning across subjects. We employ a
principled approach for the discovery of significant subgraphs of functional
connectivity, induced by brain activity (measured via fMRI imaging) while
subjects perform a motor learning task. Our aim is to uncover patterns of
functional connectivity that discriminate between high and low rates of
learning among subjects. The discovery of such significant discriminative
subgraphs promises a better data-driven understanding of the dynamic brain
processes associated with brain plasticity.
