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Dynamic reconfiguration of functional brain networks during working memory training

Author

Listed:
  • Karolina Finc

    (Nicolaus Copernicus University)

  • Kamil Bonna

    (Nicolaus Copernicus University
    Nicolaus Copernicus University)

  • Xiaosong He

    (University of Pennsylvania)

  • David M. Lydon-Staley

    (University of Pennsylvania
    University of Pennsylvania)

  • Simone Kühn

    (Max Planck Institute for Human Development
    University Medical Center Hamburg–Eppendorf)

  • Włodzisław Duch

    (Nicolaus Copernicus University
    Nicolaus Copernicus University)

  • Danielle S. Bassett

    (University of Pennsylvania
    University of Pennsylvania
    University of Pennsylvania
    University of Pennsylvania)

Abstract

The functional network of the brain continually adapts to changing environmental demands. The consequence of behavioral automation for task-related functional network architecture remains far from understood. We investigated the neural reflections of behavioral automation as participants mastered a dual n-back task. In four fMRI scans equally spanning a 6-week training period, we assessed brain network modularity, a substrate for adaptation in biological systems. We found that whole-brain modularity steadily increased during training for both conditions of the dual n-back task. In a dynamic analysis,we found that the autonomy of the default mode system and integration among task-positive systems were modulated by training. The automation of the n-back task through training resulted in non-linear changes in integration between the fronto-parietal and default mode systems, and integration with the subcortical system. Our findings suggest that the automation of a cognitively demanding task may result in more segregated network organization.

Suggested Citation

  • Karolina Finc & Kamil Bonna & Xiaosong He & David M. Lydon-Staley & Simone Kühn & Włodzisław Duch & Danielle S. Bassett, 2020. "Dynamic reconfiguration of functional brain networks during working memory training," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15631-z
    DOI: 10.1038/s41467-020-15631-z
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    Cited by:

    1. Zengmin Li & Dilsher Athwal & Hsu-Lei Lee & Pankaj Sah & Patricio Opazo & Kai-Hsiang Chuang, 2023. "Locating causal hubs of memory consolidation in spontaneous brain network in male mice," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. D. Jones & V. Lowe & J. Graff-Radford & H. Botha & L. Barnard & D. Wiepert & M. C. Murphy & M. Murray & M. Senjem & J. Gunter & H. Wiste & B. Boeve & D. Knopman & R. Petersen & C. Jack, 2022. "A computational model of neurodegeneration in Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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