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Dynamic reconfiguration of human brain networks during learning.

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... Full description

Journal Title: Proceedings of the National Academy of Sciences of the United States of America May 3, 2011, Vol.108(18), pp.7641-7646
Main Author: Bassett, Danielle S
Other Authors: Wymbs, Nicholas F , Porter, Mason A , Mucha, Peter J , Carlson, Jean M , Grafton, Scott T
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1091-6490 ; DOI: 10.1073/pnas.1018985108
Link: http://search.proquest.com/docview/864964787/?pq-origsite=primo
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title: Dynamic reconfiguration of human brain networks during learning.
format: Article
creator:
  • Bassett, Danielle S
  • Wymbs, Nicholas F
  • Porter, Mason A
  • Mucha, Peter J
  • Carlson, Jean M
  • Grafton, Scott T
subjects:
  • Adaptation, Physiological–Physiology
  • Adult–Anatomy & Histology
  • Brain–Physiology
  • Female–Physiology
  • Humans–Anatomy & Histology
  • Learning–Physiology
  • Magnetic Resonance Imaging–Physiology
  • Male–Physiology
  • Models, Neurological–Physiology
  • Nerve Net–Physiology
  • Neuronal Plasticity–Physiology
  • Psychomotor Performance–Physiology
ispartof: Proceedings of the National Academy of Sciences of the United States of America, May 3, 2011, Vol.108(18), pp.7641-7646
description: 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. complex network | time-dependent network | FMRI | motor learning | community structure www.pnas.org/cgi/doi/10.1073/pnas.1018985108
language: eng
source:
identifier: E-ISSN: 1091-6490 ; DOI: 10.1073/pnas.1018985108
fulltext: fulltext
issn:
  • 10916490
  • 1091-6490
url: Link


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titleDynamic reconfiguration of human brain networks during learning.
creatorBassett, Danielle S ; Wymbs, Nicholas F ; Porter, Mason A ; Mucha, Peter J ; Carlson, Jean M ; Grafton, Scott T
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identifierE-ISSN: 1091-6490 ; DOI: 10.1073/pnas.1018985108
subjectAdaptation, Physiological–Physiology ; Adult–Anatomy & Histology ; Brain–Physiology ; Female–Physiology ; Humans–Anatomy & Histology ; Learning–Physiology ; Magnetic Resonance Imaging–Physiology ; Male–Physiology ; Models, Neurological–Physiology ; Nerve Net–Physiology ; Neuronal Plasticity–Physiology ; Psychomotor Performance–Physiology
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descriptionHuman 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. complex network | time-dependent network | FMRI | motor learning | community structure www.pnas.org/cgi/doi/10.1073/pnas.1018985108
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