Abstract

Each area of the human brain plays a unique role in processing information gleaned from the external world and in driving our responses to that external world via behavior. However, the brain is far from a set of disconnected building blocks. Instead, parts of the brain communicate with one another in complex spatiotemporal patterns that enable human behavior. Understanding this spatio-temporal complexity requires a paradigmatic shift in our conceptual approaches, empirical thrusts, and quantitative methods. In this talk, I will describe the recent use of tools from network science to understand the structure and function of the human brain. With these novel approaches, we can begin to characterize the ``connectome’’, a model representation of neurobiological data that encapsulates both constituent elements of the brain (network nodes) and their interactions with one another (network edges). In a critical innovation, we imbue network edges with temporal dependence to capture the dynamics of the ever-reconfiguring brain communication patterns that support cognition. I will recount the utility of dynamic network approaches in not only understanding, but also predicting individual differences in adaptive functions such as learning, and in delineating healthy versus diseased brain communication dynamics. An emerging frontier, dynamic network neuroscience provides a powerful new conceptual and mathematical framework with which to understand adaptive human capabilities because it embraces the inherently evolving, interconnected nature of neurophysiological phenomena underlying human thought.