Network neuroscience is a rapidly growing field that uses tools from network theory to investigate brain networks across multiple scales and modalities. While the use of network analysis in neuroscience has led to many important insights about brain structure and function, properly mapping neuroimaging data to a graph presents multiple difficulties. In this talk, I’ll discuss some of the challenges of building networks from neuroimaging data and why the structure of these networks can be difficult to properly interpret. Further, because many network metrics were designed for other systems such as social networks, typical network measures may not be appropriate for detecting and analyzing structure in brain networks. I’ll present work that aims to modify existing network metrics and/or design new metrics that are specifically tailored to the features of brain networks, both at large at small scales.
Dr. Sarah Muldoon is an Associate Professor in the Mathematics Department, core faculty in the Computational and Data-Enabled Sciences and Engineering Program, and member of the Neuroscience Program at the University at Buffalo, SUNY. Her research interests lie at the intersection of experiment and theory with a focus on applications of network theory to neuroscience data. She has spent extensive time working in experimental neurobiology labs and now runs a research group that couples theoretical advancement, computational modeling, and data-intensive analysis to study the relationship between structure and function in brain networks.