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Nat Neurosci. 2015 May;18(5):744-51. doi: 10.1038/nn.3993. Epub 2015 Apr 6.

Learning-induced autonomy of sensorimotor systems.

Author information

  • 11] Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA. [2] Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • 21] Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA. [2] Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • 31] Department of Psychological and Brain Sciences and UCSB Brain Imaging Center, University of California, Santa Barbara, Santa Barbara, California, USA. [2] Human Brain Physiology and Stimulation Laboratory, Department of Physical Medicine and Rehabilitation, Johns Hopkins Medical Institution, Baltimore, Maryland, USA.
  • 4Department of Psychological and Brain Sciences and UCSB Brain Imaging Center, University of California, Santa Barbara, Santa Barbara, California, USA.

Abstract

Distributed networks of brain areas interact with one another in a time-varying fashion to enable complex cognitive and sensorimotor functions. Here we used new network-analysis algorithms to test the recruitment and integration of large-scale functional neural circuitry during learning. Using functional magnetic resonance imaging data acquired from healthy human participants, we investigated changes in the architecture of functional connectivity patterns that promote learning from initial training through mastery of a simple motor skill. Our results show that learning induces an autonomy of sensorimotor systems and that the release of cognitive control hubs in frontal and cingulate cortices predicts individual differences in the rate of learning on other days of practice. Our general statistical approach is applicable across other cognitive domains and provides a key to understanding time-resolved interactions between distributed neural circuits that enable task performance.

PMID:
25849989
DOI:
10.1038/nn.3993
[PubMed - indexed for MEDLINE]
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