IRCS Conference Room
Department of Bioengineering
Department of Electrical and Systems Engineering
University of Pennsylvania
A Network Perspective on the Cognitive Neuroscience of Learning
Human learning is a complex phenomenon requiring network-wide flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the properties of brain network dynamics that predict individual differences in learning. Our results indicate that more flexibility during early practice sessions, which we measure by the allegiance of nodes to network modules, predicts more extensive learning in later practice sessions. Flexibility is greatest in a periphery of high-level cognitive control regions whose connectivity changes frequently, and is least in a relatively stiff core of motor and visual regions whose connectivity changes little in time. Our results indicate that network reconfiguration patterns enable the prediction of fundamental capacities, including the production of complex goal-directed behavior, in humans. More fundamentally, this body of work suggests that cognitive functions might be best characterized not only by a set of brain areas, but also by their functional allegiances under different task conditions and over different time scales.