Danielle S. Bassett, Ph.D.

BassettDanielle S. Bassett is the Skirkanich Assistant Professor of Innovation in the Department of Bioengineering at the University of Pennsylvania. She is most well-known for her work blending neural and systems engineering to identify fundamental mechanisms of cognition and disease in human brain networks. She received a B.S. in physics from the Pennsylvania State University and a Ph.D. in physics from the University of Cambridge, UK. 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 2012, she was named American Psychological Association’s `Rising Star’ and given an Alumni Achievement Award from the Schreyer Honors College at Pennsylvania State University for extraordinary achievement under the age of 35. In 2014, she was named an Alfred P Sloan Research Fellow and received the MacArthur Fellow Genius Grant. In 2015, she received the IEEE EMBS Early Academic Achievement Award, and was named an ONR Young Investigator. She is the 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 work has been supported by the National Science Foundation, the National Institutes of Health, the Army Research Office, the Army Research Laboratory, the Alfred P Sloan Foundation, the John D and Catherine T MacArthur Foundation, and the University of Pennsylvania. She lives with her husband and two sons in Wallingford, Pennsylvania.

Network Neuroscience

Abstract: Recent advances in noninvasive neuroimaging technologies have offered an unprecedented view into the large-scale organization of the human brain, and the differences in that architecture that make each of us who we are. However, this wealth of data must be met with complementary advances in mathematical methods and physical models to formalize our understanding of that architecture, and concretize a theoretical framework to generalize understanding and to predict patterns of thought and behavior. In this talk, I will discuss a family of such methods and models built on tools from network science, leading to the emerging field of network neuroscience (which can be thought of as a child of the older fields of neurophysics and neuroengineering).  I will highlight early successes in this field leading to fundamental understanding of the architecture, dynamics, and energetic constraints on healthy human thought, their development over childhood, and their alteration in psychiatric disease and neurological disorders. I will close by commenting on current frontiers and future potential in health care, business, and education sectors.