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A rapid theta network mechanism for flexible information encoding

Author

Listed:
  • Elizabeth L. Johnson

    (Northwestern University)

  • Jack J. Lin

    (University of California)

  • David King-Stephens

    (California Pacific Medical Center
    Yale School of Medicine)

  • Peter B. Weber

    (California Pacific Medical Center)

  • Kenneth D. Laxer

    (California Pacific Medical Center)

  • Ignacio Saez

    (University of California
    Ichan School of Medicine at Mt. Sinai)

  • Fady Girgis

    (University of California
    University of Calgary)

  • Mark D’Esposito

    (University of California)

  • Robert T. Knight

    (University of California)

  • David Badre

    (Brown University)

Abstract

Flexible behavior requires gating mechanisms that encode only task-relevant information in working memory. Extant literature supports a theoretical division of labor whereby lateral frontoparietal interactions underlie information maintenance and the striatum enacts the gate. Here, we reveal neocortical gating mechanisms in intracranial EEG patients by identifying rapid, within-trial changes in regional and inter-regional activities that predict subsequent behavioral outputs. Results first demonstrate information accumulation mechanisms that extend prior fMRI (i.e., regional high-frequency activity) and EEG evidence (inter-regional theta synchrony) of distributed neocortical networks in working memory. Second, results demonstrate that rapid changes in theta synchrony, reflected in changing patterns of default mode network connectivity, support filtering. Graph theoretical analyses further linked filtering in task-relevant information and filtering out irrelevant information to dorsal and ventral attention networks, respectively. Results establish a rapid neocortical theta network mechanism for flexible information encoding, a role previously attributed to the striatum.

Suggested Citation

  • Elizabeth L. Johnson & Jack J. Lin & David King-Stephens & Peter B. Weber & Kenneth D. Laxer & Ignacio Saez & Fady Girgis & Mark D’Esposito & Robert T. Knight & David Badre, 2023. "A rapid theta network mechanism for flexible information encoding," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38574-7
    DOI: 10.1038/s41467-023-38574-7
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    References listed on IDEAS

    as
    1. Matthew F. Panichello & Timothy J. Buschman, 2021. "Shared mechanisms underlie the control of working memory and attention," Nature, Nature, vol. 592(7855), pages 601-605, April.
    2. E. A. Solomon & J. E. Kragel & M. R. Sperling & A. Sharan & G. Worrell & M. Kucewicz & C. S. Inman & B. Lega & K. A. Davis & J. M. Stein & B. C. Jobst & K. A. Zaghloul & S. A. Sheth & D. S. Rizzuto & , 2017. "Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition," Nature Communications, Nature, vol. 8(1), pages 1-14, December.
    3. Nikos K. Logothetis & Jon Pauls & Mark Augath & Torsten Trinath & Axel Oeltermann, 2001. "Neurophysiological investigation of the basis of the fMRI signal," Nature, Nature, vol. 412(6843), pages 150-157, July.
    4. Andrew C. Murphy & Maxwell A. Bertolero & Lia Papadopoulos & David M. Lydon-Staley & Danielle S. Bassett, 2020. "Multimodal network dynamics underpinning working memory," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    5. Erin L. Rich & Joni D. Wallis, 2017. "Spatiotemporal dynamics of information encoding revealed in orbitofrontal high-gamma," Nature Communications, Nature, vol. 8(1), pages 1-13, December.
    6. Shi Gu & Fabio Pasqualetti & Matthew Cieslak & Qawi K. Telesford & Alfred B. Yu & Ari E. Kahn & John D. Medaglia & Jean M. Vettel & Michael B. Miller & Scott T. Grafton & Danielle S. Bassett, 2015. "Controllability of structural brain networks," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
    7. Ryoken Takase & Jared Boasen & Shinya Kuriki & Akira Toyomura & Koichi Yokosawa, 2022. "Processing time affects sequential memory performance beginning at the level of visual encoding," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-18, March.
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