CAREER: Mathematical Modeling and Computational Studies of Human Seizure Initiation and Spread

Trustees Of Boston University, Boston

Investigators

Abstract

Epilepsy - the condition of recurrent unprovoked seizures - is a brain disorder that affects 3 million people in the United States. Although the symptoms of epilepsy have been observed for millennia, the brain processes that support human seizures remain poorly understood. This lack of understanding has a profound clinical impact; in one-third of patients with epilepsy, seizures are not adequately controlled. Animal studies provide powerful methods to uncover the potential mechanisms for epilepsy, yet how the results from these studies relate to human epilepsy remains unclear. Although some mechanisms of epilepsy may be consistent in animal models and humans, differences occur, and these differences are critical to understanding and treating human epilepsy. The PI's goal is to improve understanding of the mechanisms that drive human seizures and thereby advance therapeutic management of this disease. To do so, brain voltage recordings made directly from human patients will be analyzed. Motivated by these patient data, mathematical models will be developed that describe the activity of individual neurons and small populations of interacting neurons. The mathematical models will then be used to study the biological mechanisms that support the different brain voltage rhythms that appear during seizure, and how these rhythms move across the surface of the brain. Ultimately, these mathematical models will provide new insights into human epilepsy, and help identify novel approaches to improve patient care. The PI will also include integration of research data and methods into an undergraduate course in computational neuroscience, publish a textbook and online course in neuronal data analysis, and provide undergraduate and graduate research training in computational neuroscience, with a specific emphasis on clinical data and computational modeling. The PI aims to improve understanding of the ionic and neuronal mechanisms that govern the brain's stereotyped spatiotemporal dynamics during human seizure. To do so, a computational modeling framework will be developed that incorporates individual neuron dynamics in cortical and subcortical structures and ion concentration dynamics in the extracellular space. Model behavior will be explored through simulation and dynamical systems techniques, and model features will be constrained to match microelectrode array recordings of seizures in human patients. The modeling framework will be used to test the hypothesized scenario that a class of cortical interneurons serve as the first line of defense against the outbreak of seizure, but eventually fails upon entering depolarization block. Concomitant with this failure, another circuit activates to the support large amplitude, spike-and-wave dynamics, which appear as traveling waves that sweep across the cortical surface. Two main research goals are the focus of the project. First the modeling of human seizure data will provide new insights into the mechanisms of medically refractory epilepsy, and help identify biological targets for novel pharmacological approaches to improve patient care. Second, to understand brain function and dysfunction, a deeper knowledge of cortical and subcortical neuronal dynamics combined with ion concentration dynamics is required. In this project, the stereotyped dynamical state of seizure motivates models that implement these dynamics to examine principles that support spatiotemporal patterns in the human brain. Educationally the PI will develop new interdisciplinary training in computational neuroscience. This will be done through integration of research data, analysis methods and computational technology in the undergraduate classroom, publication of a textbook and development of an online course describing cases studies in neural data analysis, and directed graduate and undergraduate research in computational neuroscience.