Dynamic reconfiguration of human brain networks during learning
- aComplex Systems Group, Department of Physics, University of California, Santa Barbara, CA 93106;
- bDepartment of Psychology and UCSB Brain Imaging Center, University of California, Santa Barbara, CA 93106;
- cOxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX1 3LB, United Kingdom;
- dComplex Agent-Based Dynamic Networks Complexity Centre, University of Oxford, Oxford OX1 1HP, United Kingdom;
- eCarolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599; and
- fInstitute for Advanced Materials, Nanoscience and Technology, University of North Carolina, Chapel Hill, NC 27599
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Edited by Marcus E. Raichle, Washington University in St. Louis, St. Louis, MO, and approved March 15, 2011 (received for review December 16, 2010)

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 investigate 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.
Footnotes
- ↵1To whom correspondence should be addressed. E-mail: dbassett{at}physics.ucsb.edu.
Author contributions: D.S.B., N.F.W., M.A.P., P.J.M., and S.T.G. designed research; D.S.B. and N.F.W. performed research; D.S.B., N.F.W., M.A.P., P.J.M., J.M.C., and S.T.G. contributed new reagents/analytic tools; D.S.B. and P.J.M. wrote the code; D.S.B. analyzed data; and D.S.B., N.F.W., and M.A.P. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1018985108/-/DCSupplemental.
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