Past Event: CSEM Student Forum

Energy as a Limiting Factor in Neuronal Seizure Control: A Mathematical Model

Sophia Epstein, CSEM PhD Student, Oden Institute, UT Austin

Abstract

A seizure represents an uncontrolled, abnormal burst of electrical activity in the brain which alters an individual's level of consciousness,  memory, behavior, and feeling. This abnormal burst includes the widespread synchronization of excitatory neurons. Depending on the seizure type, the neuronal dynamical profile of a seizure will vary. For instance, a convulsive tonic-clonic or generalized seizure, affecting a widespread region of neurons, will self-terminate within a few minutes, while a small partial seizure can occur for years. The majority of seizures are self-limiting, meaning they spontaneously self-terminate. However, the termination mechanisms and dependencies are not yet understood. Understanding these mechanisms is critical to improving existing and developing novel therapeutic measures for seizure control. Published biological assays observe brain hypermetabolism followed by metabolic suppression during a seizure profile. These observations indicate that energy can be key in mediating seizure dynamics. In this research, we explore this hypothesis and propose a mathematical framework to model how energy may limit seizure propagation. Expanding upon existing models of neuronal spiking and energy consumption, the model accounts for changes in available energy over time. The results of this model indicate constrained energy consumption is a plausible mechanism for mediating seizure termination and may play a role in the generation of an epileptic spike in an electroencephalogram.

Biography

Sophia Epstein is a graduate student in the Computational Science, Engineering, and Math program at University of Texas at Austin. She received her B.A. from Claremont McKenna College, where she studied applied mathematics and neuroscience with a focus on computational neuroscience. Her current research interests include biological signal processing, optimization of stimulus waveforms, and model systems of neurological diseases.