Princeton University

School of Engineering & Applied Science

Linking structure and function in complex brain networks

Sarah Muldoon
E-Quad, B205
Tuesday, February 10, 2015 - 4:30pm

Understanding the brain as a complex system of interacting components can provide insight into cognitive function. Due to their dynamic nature, brain networks can be explored both in terms of the underlying anatomical network of connections that link neurons or brain regions in physical space, as well as in terms of the evolving functional networks defined by statistical relationships between the dynamics of network nodes. In this two-part talk, I will examine the relationship between network structure and function at both the micro-scale level of neuronal networks as well as at the larger macro-scale of human brain networks.  I will first present a novel algorithm and statistics developed to quantify neuronal network organization and provide examples of how these methods can be used to obtain insight into the relationship between micro-circuit structure and function at different levels of network complexity in both a simple model of network development and a reorganized epileptic network.  In the second part of the talk, I will focus on macro-scale human brain networks and the use of computational modeling to provide predictions about brain function and controllability.  Using a nonlinear model of brain activity built on structural networks derived from real-world human connectivity data, we can test the relationship between local network structure and the ability of regional stimulation to impart change in functional network configurations.  I will present preliminary findings relating differences in regional connectivity to network controllability and discuss the importance of differences in individuals by examining how small perturbations in the underlying connectivity can impact overall brain network behavior.

Sarah Feldt Muldoon is currently a Postdoctoral Researcher in the Department of Bioengineering at the University of Pennsylvania, and her research involves the development of novel techniques and measures to investigate and quantify the role of network organization in brain function.  Her initial training was in math and physics, and during her graduate studies she received a certificate in complex systems and a Ph.D. in physics from the University of Michigan. She then furthered her knowledge of experimental neuroscience and epilepsy first as an NIH Epilepsy Training Postdoctoral Fellow at the University of California, Irvine, and then as a Marie Curie Postdoctoral Fellow at the Institut de Neurobiologie de la Méditerranée in Marseille, France.  Her research lies at the intersection of theory, computation, and experiment, where she works across multiple spatial and temporal scales using network analysis to understand the relationship between the underlying structural connections in the brain, observed brain signals, and functional interactions between neurons/brain regions.  Additionally, she has a special interest in developing techniques to investigate how the spatial location of network elements relates to their role in overall network function and how this differs between healthy and pathological settings.