  The ability to effectively control brain dynamics holds great promise for the
enhancement of cognitive function in humans, and the betterment of their
quality of life. Yet, successfully controlling dynamics in neural systems is
challenging, in part due to the immense complexity of the brain and the large
set of interactions that can drive any single change. While we have gained some
understanding of the control of single neurons, the control of large-scale
neural systems -- networks of multiply interacting components -- remains poorly
understood. Efforts to address this gap include the construction of tools for
the control of brain networks, mostly adapted from control and dynamical
systems theory. Informed by current opportunities for practical intervention,
these theoretical contributions provide models that draw from a wide array of
mathematical approaches. We present intriguing recent developments for
effective strategies of control in dynamic brain networks, and we also describe
potential mechanisms that underlie such processes. We review efforts in the
control of general neurophysiological processes with implications for brain
development and cognitive function, as well as the control of altered
neurophysiological processes in medical contexts such as anesthesia
administration, seizure suppression, and deep-brain stimulation for Parkinson's
disease. We conclude with a forward-looking discussion regarding how emerging
results from network control -- especially approaches that deal with nonlinear
dynamics or more realistic trajectories for control transitions -- could be
used to directly address pressing questions in neuroscience.
