For our third episode, we bring you a birds-eye view of modelling messy biologic systems, namely the brain. Peter Bandettini (@fmri_today) talks to Danielle Bassett about the challenges of measurement accuracy and what scale might be most informative for modelling, including how to make to do with what we have. From the clinical perspective, they talk about network control theory for modulating networks for therapy and discuss limitations in technology. They also talk about the limits of network modelling and the search for the equivalent of an idea as powerful as 'natural selection' for the brain. In the second part of the podcast, they discuss bias in science and what Danielle is doing to help increase transparency to combat the bias.

Danielle Bassett PhD, is currently the J. Peter Skirkanich Professor at the University of Pennsylvania with a primary appointment in the Department of Bioengineering and a Secondary appointment in the Departments of Physics and Astronomy, Electrical and Systems Engineering, Neurology, and Psychiatry. Dr. Bassett received her B.S. in 2004 in Physics from Penn State University. She received a Ph.D. in physics in 2009 from the University of Cambridge, UK as a Churchill Scholar, and an NIH Health Sciences Scholar. Following a postdoctoral position at UC Santa Barbara, she was a Junior Research Fellow at the Sage Center for the Study of the Mind. In 2013, she joined the University of Pennsylvania as an assistant professor, and in 2019, was promoted to full professor. She is also founding director of the Penn Network Visualization Program, a combined undergraduate art internship and K-12 outreach program bridging network science and the visual arts. Her primary work is towards developing network models towards deriving principles of brain function.