  Electrical brain stimulation is currently being investigated as a therapy for
neurological disease. However, opportunities to optimize such therapies are
challenged by the fact that the beneficial impact of focal stimulation on both
neighboring and distant regions is not well understood. Here, we use network
control theory to build a model of brain network function that makes
predictions about how stimulation spreads through the brain's white matter
network and influences large-scale dynamics. We test these predictions using
combined electrocorticography (ECoG) and diffusion weighted imaging (DWI) data
who volunteered to participate in an extensive stimulation regimen. We posit a
specific model-based manner in which white matter tracts constrain stimulation,
defining its capacity to drive the brain to new states, including states
associated with successful memory encoding. In a first validation of our model,
we find that the true pattern of white matter tracts can be used to more
accurately predict the state transitions induced by direct electrical
stimulation than the artificial patterns of null models. We then use a targeted
optimal control framework to solve for the optimal energy required to drive the
brain to a given state. We show that, intuitively, our model predicts larger
energy requirements when starting from states that are farther away from a
target memory state. We then suggest testable hypotheses about which structural
properties will lead to efficient stimulation for improving memory based on
energy requirements. Our work demonstrates that individual white matter
architecture plays a vital role in guiding the dynamics of direct electrical
stimulation, more generally offering empirical support for the utility of
network control theoretic models of brain response to stimulation.
