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

Abstract

Electrical brain stimulation is currently being investigated as a potential therapy for neurological disease. However, opportunities to optimize and personalize such therapies are challenged by the fact that the beneficial impact (and potential side effects) of focal stimulation on both neighboring and distant regions is not well understood. Here, we use network control theory to build a formal model of brain network function that makes explicit 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 from patients with medically refractory epilepsy undergoing evaluation for resective surgery, and 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 a topological or spatial network null model. 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. We show that the strength and homogeneity of edges between controlled and uncontrolled nodes, as well as the persistent modal controllability of the stimulated region, predict 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.

Network controllability in transmodal cortex predicts psychosis spectrum symptoms

“…The structural connectivity correlates of the PS are increasingly well studied For instance, external neurostimulation techniques, such as transcranial magnetic stimulation, are increasingly being investigated as treatment modalities for PSrelated conditions, and candidate stimulation sites typically occupy transmodal cortex (74,75). Indeed, the analysis of neurostimulation data with NCT has begun to show promise (76).…”

Context-dependent architecture of brain state dynamics is explained by white matter connectivity and theories of network control

“…The novel approaches in this study pave the way for many future studies to continue to elucidate how a static structural connectome can give rise to complex, timeevolving activity patterns important for cognition. An intuitive and important application of our approach lies in the field of neurostimulation, where clinicians aim to implement targeted changes in the temporal evolution of brain activity patterns 16,19,89 to alleviate symptoms of neuropsychiatric illness. In particular, network control theory and data-driven estimation of brain states are a powerful combination for this purpose.…”

“…Such studies provide evidence for the existence of recurrent patterns of coactivation, consisting of different combinations of RSN components 9,11,12 . The study of time point level progression of coactivation patterns is intu-itively complementary to the goals of brain stimulation; that is, to alleviate symptoms of neuropsychiatric illness by modulating the current and future states of specific networks [16][17][18][19] .…”

Structural control energy of resting‐state functional brain states reveals less cost‐effective brain dynamics in psychosis vulnerability

“…While the results presented here were obtained for fMRI brain states and thus specific to resting‐state fMRI, the network control framework is not specific to fMRI and could also be applied to other types of data. In this regard, the framework has already used to study brain states derived from electrocorticography (Stiso et al, 2019), and could also be imagined for electroencephalography data.…”