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Electrical brain stimulation is currently being investigated as a therapy forneurological disease. However, opportunities to optimize such therapies arechallenged by the fact that the beneficial impact of focal stimulation on bothneighboring and distant regions is not well understood. Here, we use networkcontrol theory to build a model of brain network function that makespredictions about how stimulation spreads through the brain's white matternetwork and influences large-scale dynamics. We test these predictions usingcombined electrocorticography (ECoG) and diffusion weighted imaging (DWI) datawho volunteered to participate in an extensive stimulation regimen. We posit aspecific model-based manner in which white matter tracts constrain stimulation,defining its capacity to drive the brain to new states, including statesassociated 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 moreaccurately predict the state transitions induced by direct electricalstimulation than the artificial patterns of null models. We then use a targetedoptimal control framework to solve for the optimal energy required to drive thebrain to a given state. We show that, intuitively, our model predicts largerenergy requirements when starting from states that are farther away from atarget memory state. We then suggest testable hypotheses about which structuralproperties will lead to efficient stimulation for improving memory based onenergy requirements. Our work demonstrates that individual white matterarchitecture plays a vital role in guiding the dynamics of direct electricalstimulation, more generally offering empirical support for the utility ofnetwork control theoretic models of brain response to stimulation.

White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions