Large-scale neural dynamics in a shared low-dimensionalstate space reflect cognitive and attentional dynamics

  1. Hayoung Song  Is a corresponding author
  2. Won Mok Shim  Is a corresponding author
  3. Monica D Rosenberg  Is a corresponding author
  1. University of Chicago, United States
  2. Sungkyunkwan University, Republic of Korea

Abstract

Cognition and attention arise from the adaptive coordination of neural systems in response to external and internal demands. The low-dimensional latent subspace that underlies large-scale neural dynamics and the relationships of these dynamics to cognitive and attentional states, however, are unknown. We conducted functional magnetic resonance imaging as human participants performed attention tasks, watched comedy sitcom episodes and an educational documentary, and rested. Whole-brain dynamics traversed a common set of latent states that spanned canonical gradients of functional brain organization, with global desynchronization among functional networks modulating state transitions. Neural state dynamics were synchronized across people during engaging movie watching and aligned to narrative event structures. Neural state dynamics reflected attention fluctuations such that different states indicated engaged attention in task and naturalistic contexts whereas a common state indicated attention lapses in both contexts. Together, these results demonstrate that traversals along large-scale gradients of human brain organization reflect cognitive and attentional dynamics.

Data availability

Raw fMRI data from the SitcOm, Nature documentary, Gradual-onset continuous performance task (SONG) dataset are available on OpenNeuro;https://openneuro.org/datasets/ds004592/versions/1.0.1. Behavioral data, processed fMRI output, and main analysis scripts are published on Github; https://github.com/hyssong/neuraldynamics

The following data sets were generated
    1. Song H
    2. Shim WM
    3. Rosenberg MD
    (2023) SONG dataset
    https://doi.org/10.18112/openneuro.ds004592.v1.0.1.
The following previously published data sets were used

Article and author information

Author details

  1. Hayoung Song

    Department of Psychology, University of Chicago, Chicago, United States
    For correspondence
    hyssong@uchicago.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5970-8076
  2. Won Mok Shim

    Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
    For correspondence
    wonmokshim@skku.edu
    Competing interests
    The authors declare that no competing interests exist.
  3. Monica D Rosenberg

    Department of Psychology, University of Chicago, Chicago, United States
    For correspondence
    mdrosenberg@uchicago.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6179-4025

Funding

Institute for Basic Science (R015-D1)

  • Won Mok Shim

National Research Foundation of Korea (NRF-2019M3E5D2A01060299)

  • Won Mok Shim

National Research Foundation of Korea (NRF-2019R1A2C1085566)

  • Won Mok Shim

National Science Foundation (BCS-2043740)

  • Monica D Rosenberg

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Shella Keilholz, Emory University and Georgia Institute of Technology, United States

Ethics

Human subjects: Informed consent and consent to publish were obtained from the participants prior to the experiments, and the possible consequences of the study were explained. The study was approved by the Institutional Review Board of Sungkyunkwan University.

Version history

  1. Preprint posted: November 5, 2022 (view preprint)
  2. Received: December 10, 2022
  3. Accepted: June 16, 2023
  4. Accepted Manuscript published: July 3, 2023 (version 1)
  5. Version of Record published: August 3, 2023 (version 2)

Copyright

© 2023, Song et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Hayoung Song
  2. Won Mok Shim
  3. Monica D Rosenberg
(2023)
Large-scale neural dynamics in a shared low-dimensionalstate space reflect cognitive and attentional dynamics
eLife 12:e85487.
https://doi.org/10.7554/eLife.85487

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https://doi.org/10.7554/eLife.85487

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