Date(s) - 11/30/2015
Reconfiguration of Brain Networks in Support of Cognition
The brain is an inherently dynamic system, and higher-order cognitive functions require dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization and prediction of these reconfiguration processes during cognitive function in humans has remained elusive. Dr. Bassett will describe the use of a series of techniques developed in the field of “dynamic network neuroscience” to investigate the dynamics of functional brain networks during working memory, skill learning, and linguistic processing. Collectively, these studies highlight the fact that higher-order cognitive functions require flexibility – particularly in cognitive control hubs in frontal and cingulate cortices – to adapt existing brain network function to the task at hand and precision in selecting new neurophysiological activities to drive desired behavior. What fundamental principles constrain the control of these dynamic network processes? In the final part of the talk, Dr. Bassett will discuss recent work based on diffusion spectrum imaging data to estimate hard-wired pathways connecting different areas of the human brain. Using the mathematical foundation of network control theory, we show that structural network differences between subgraphs in the brain dictate their distinct roles in controlling dynamic brain network function. While dynamic network neuroscience offers a characterization of reconfiguring brain networks supporting cognition, network control theory offers a mechanistic account of cognitive control affecting these reconfigurations.
Bassett’s group studies biological, physical, and social systems by using and developing tools from network science and complex systems theory. Our broad goal is to isolate problems at the intersection of basic science, engineering, and clinical medicine that can be tackled using systems-level approaches. Recent examples include predicting the extent of learning from human brain networks, resolving the evolution of the neuronal synapse via genetic interaction networks, determining bulk material properties from mesoscale force networks, and isolating individual drivers of collective social behavior during evacuations. In these contexts, we seek to develop new mathematical methods for the principled characterization of temporally dynamic, spatially embedded, and multiscale networked systems, with the goal of predicting system behavior and designing perturbations to affect a specific outcome. A current focal interest of the group lies in network neuroscience. We develop analytic tools to probe the hard-wired pathways and transient communication patterns inside of the brain in an effort to identify organizational principles, to develop novel diagnostics of disease, and to design personalized therapeutics for rehabilitation and treatment of brain injury, neurological disease, and psychiatric disorders.