Flexibility and selection precision’ predicts your susceptibility to learning

Dynamic reconfiguration of human brain networks during learning

Danielle S. Bassett ∗, Nicholas F. Wymbs †, Mason A. Porter ‡ §, Peter J. Mucha ¶ ￿, Jean M. Carlson ∗ , and Scott T.
Grafton †

∗Complex Systems Group, Department of Physics, University of California, Santa Barbara, CA 93106, USA,†Department of Psychology and UCSB Brain Imaging Center,
University of California, Santa Barbara, CA 93106, USA,‡Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX1
3LB, UK,§CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK,¶Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics,
University of North Carolina, Chapel Hill, NC 27599, USA, and ￿Institute for Advanced Materials, Nanoscience & Technology, University of North Carolina, Chapel Hill, NC
27599, USA

ABSTRACT
Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes — flexibility and selection — must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.

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