Network dynamics of sleep-wake states in epilepsy

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PROJECT SUMMARY/ABSTRACT Of 46 million people worldwide with active epilepsy, one third are drug-resistant. Emerging neuromodulation- based therapies have demonstrated great potential to reduce seizure frequency and improve the quality of life in patients with drug-resistant epilepsy over time. The mechanisms underlying such therapies are thought to relate to the progressive restructuring of the epileptogenic network toward dynamics that reduce epileptic activity. Yet, the network properties underlying low and high epileptic potential are poorly understood, and the management of neuromodulatory therapies thus remains largely empiric with variable outcomes. To move toward rational approaches rooted in mechanistic understanding, there is a critical need to first fundamentally understand how network dynamics influence epileptogenic activity. In this proposal, we turn to the rich relationship between sleep and epilepsy, as sleep-wake states offer a robust and systematic way to cycle through a wide range of network dynamics that are strongly associated with different epileptic potentials. By leveraging sleep-wake states as a portal to probing dynamic brain networks, the overall objective of this proposal is to identify salient network features that represent states of variable epileptogenic potential and to determine associated network mechanisms that indicate reconfiguration into epileptogenic states. Using a combination of magnetoencephalography (MEG) imaging and diffusion tensor imaging (DTI)/tractography, I will first identify physiologic network dynamics of sleep-wake states in patients with focal epilepsy (Aim 1). I will then identify state-dependent network predictors and develop biophysical models of pathologic states predictive of interictal epileptiform activity (Aim 2). The expected outcome of this work is to gain a deeper understanding of key network features that augment epileptic potential and insight into their underlying mechanisms. This proposal combines an innovative research project with translational implications and a rigorous training and career development plan, which are highly complementary and together will facilitate my transition into an independent physician-scientist. I have assembled a leading, multidisciplinary mentorship team that has a constellation of expertise aligned with my research and training goals, including in epilepsy, sleep, MEG imaging, structural-function network analysis, neural computation, and biostatistics. In addition, through formal training, coursework, and directed mentorship, I will advance my skills in the areas of signal processing, machine learning, dynamical models, sleep electrophysiology, and clinical trials, which I will continue to use throughout my scientific career. The knowledge and training obtained during this award period will enable me to establish a robust independent research program that leverages multimodal electrophysiology and imaging in humans and insights from the rich relationship between sleep and epilepsy to improve therapeutic tools for patients with medically refractory epilepsy.