  The brain's functional connectivity fluctuates over time instead of remaining
steady in a stationary mode even during the resting state. This fluctuation
establishes the dynamical functional connectivity that transitions in a
non-random order between multiple modes. Yet it remains unexplored how the
transition facilitates the entire brain network as a dynamical system and what
utility this mechanism for dynamic reconfiguration can bring over the widely
used graph theoretical measurements. To address these questions, we propose to
conduct an energetic analysis of functional brain networks using resting-state
fMRI and behavioral measurements from the Human Connectome Project. Through
comparing the state transition energy under distinct adjacent matrices, we
justify that dynamic functional connectivity leads to 60% less energy cost to
support the resting state dynamics than static connectivity when driving the
transition through default mode network. Moreover, we demonstrate that
combining graph theoretical measurements and our energy-based control
measurements as the feature vector can provide complementary prediction power
for the behavioral scores. Our approach integrates statistical inference and
dynamical system inspection towards understanding brain networks.
