Collaborative Research: Analysis and Control of Nonlinear Oscillatory Networks for the Design of Novel Cortical Stimulation Strategies

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An estimate of 1.2 percent of Americans have active epilepsy, and the annual cost for treating these cases is estimated at $12.5 billion. While brain stimulation is routinely used in clinical practice, stimulation signals and parameters are often applied and tuned empirically, and can be substantially improved by a quantitative model of the brain network dynamics. Indeed, in the human brain, function arises from complex dynamics between intricate and time-varying interconnection of dynamically rich neural components, and multiple neurological disorders are linked to disruptions of these network mechanisms. This project will develop new theories and tools to predict the onset and spreading of epileptic seizures and to inform the use of novel electrical brain stimulation strategies to treat neurological disorders. By constructing mathematical models that incorporate epileptic data and dynamical analysis, this work aims at uncovering the phenomena that underlie epileptic events and linking them to features of the anatomy of the brain. The intended outcome will be a novel theoretical basis to analyze and optimize practical noninvasive stimulation of brain networks. This project will also pursue educational initiatives at the graduate and undergraduate levels that will contribute to the growth of a large and diverse STEM workforce, outreach activities to engage the local community, and dissemination activities to promote multi-disciplinary approaches to problems in neuroscience.The project will develop novel methods to analyze the spreading of oscillations in complex networks and derive control mechanisms to regulate the spatiotemporal evolution of network dynamics, such as neurological oscillations. In particular, the research will aim to (i) characterize a novel set of dynamical biomarkers that explain qualitative changes in neurological recordings during epileptic events -- these biomarkers provide a quantitative link between the structure and parameters of nonlinear, networked, neural mass models and the features of epileptic recordings; (ii) reveal the structural properties that allow healthy, localized neural oscillations to cascade into brain-wide pathological seizures, and (iii) develop spatiotemporal control strategies to regulate the spreading of oscillatory dynamics over networks, which will provide a solid theoretical basis to analyze and optimize practical noninvasive brain stimulation. In addition to contributing to the fields of network control and dynamical systems, this project will also contribute to the integration of these disciplines with computational neuroscience and promote the translation of control-theoretic tools towards the design of novel, targeted, non-invasive, and highly effective treatments for neurological disorders.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.