A seminar titled “Integrating Behavioral, Electrophysiological, and Computational Approaches for Improved Understanding of Brain Dynamics,” will take place on April 13 from 3:15 p.m.-4:30 p.m. Tahra L. Eissa, Ph.D., Postdoctoral Research Associate in the Department of Applied Mathematics at the University of Colorado Boulder will be the speaker. The seminar is co-sponsored by the Department of Chemical and Biological Engineering and the Department of Psychology.
Seminar title: Integrating Behavioral, Electrophysiological, and Computational Approaches for Improved Understanding of Brain Dynamics
When: April 13, 2022, 3:15 p.m. – 4:30 p.m.
Where: Perlstein Hall 108
Biography
Dr. Tahra Eissa completed a bachelor’s degree in bioengineering at Cornell University. For her doctoral work in neurobiology at the University of Chicago, Dr. Eissa studied the dynamics of seizures in focal epilepsy patients to identify novel biomarkers that can improve treatment. Following her PhD, she spent one year as a postdoc in Columbia University’s neurology department before becoming a research associate and instructor in the applied math department at University of Colorado Boulder. Her current work focuses on understanding the strategies humans use to perform cognitive processes in the brain, including decision making and working memory.
Abstract
Understanding how our brains process information about the world and how pathologies impact these processes is a non-trivial endeavor that requires a multifaceted approach, including conceiving possible mechanisms or strategies and comparing these theories to measurable metrics, such as behavioral responses or electrophysiological recordings. Here, I will present examples of how advanced data analytics can be combined with computational models to improve our understanding of the brain. First, using brain recordings from epilepsy patients and population-based models, I will show how the dynamics of focal seizures can be analyzed across spatial scales to reveal unique hallmarks of neural activity relevant for treatment of intractable epilepsy. Second, I will demonstrate how Bayesian inference models can be used to educate behavioral tasks as well as analyze the resulting data to identify suboptimalities and strategic tradeoffs humans apply to complex decision-making tasks. Finally, I will present evidence of how these approaches can be combined to correlate cognitive processes with brain dynamics and educate our understanding of physiological and pathological brain activity.