1. Computational and Systems Biology
  2. Neuroscience

Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture

  1. Richard Gao  Is a corresponding author
  2. Ruud L van den Brink
  3. Thomas Pfeffer
  4. Bradley Voytek
  1. University of California, San Diego, United States
  2. University Medical Center Hamburg-Eppendorf, Germany
https://doi.org/10.7554/eLife.61277
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  1. Richard Gao
  2. Ruud L van den Brink
  3. Thomas Pfeffer
  4. Bradley Voytek
(2020)
Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture
eLife 9:e61277.
https://doi.org/10.7554/eLife.61277
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Abstract

Complex cognitive functions such as working memory and decision-making require information maintenance over seconds to years, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings. Timescales increase along the principal sensorimotor-to-association axis across the entire human cortex, and scale with single-unit timescales within macaques. Cortex-wide transcriptomic analysis shows direct alignment between timescales and expression of excitation- and inhibition-related genes, as well as genes specific to voltage-gated transmembrane ion transporters. Finally, neuronal timescales are functionally dynamic: prefrontal cortex timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex, and are relevant for cognition in both short- and long-terms, bridging microcircuit physiology with macroscale dynamics and behavior.

Data availability

All raw data are previously published and taken from publicly available repositories (see Table 1), all intermediate data produced from this manuscript are available on Github, with the associated analysis and visualization code

The following previously published data sets were used
    1. Frauscher et al
    (2018) MNI Open iEEG
    MNI, https://mni-open-ieegatlas.research.mcgill.ca/.
    https://mni-open-ieegatlas.research.mcgill.ca/
    1. Glasser et al
    (2016) Human Connectome Project S1200 Release
    HCP, https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release.
    https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release
    1. Gryglewski et al
    (2018) Whole brain gene expression
    http://www.meduniwien.ac.at/neuroimaging/mRNA.html.
    http://www.meduniwien.ac.at/neuroimaging/mRNA.html

Article and author information

Author details

  1. Richard Gao

    Cognitive Science, University of California, San Diego, La Jolla, United States
    For correspondence
    r.dg.gao@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5916-6433

  2. Ruud L van den Brink

    Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3142-7248

  3. Thomas Pfeffer

    Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9561-3085

  4. Bradley Voytek

    Cognitive Science, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1640-2525

Funding

Natural Sciences and Engineering Research Council of Canada (CGSD3-488052-2016)

  • Richard Gao

Katzin Prize

  • Richard Gao

Alexander von Humboldt-Stiftung

  • Ruud L van den Brink

Alfred P. Sloan Foundation (FG-2015-66057)

  • Bradley Voytek

Whitehall Foundation (2017-12-73)

  • Bradley Voytek

National Science Foundation (BCS-1736028)

  • Bradley Voytek

National Institutes of Health (R01GM134363-01)

  • Bradley Voytek

Shiley-Marcos Alzheimer's Disease Research Center

  • Bradley Voytek

Halicioglu Data Science Institute Fellowship

  • Bradley Voytek

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

Reviewing Editor

  1. Martin Vinck, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Germany

Version history

  1. Received: July 21, 2020
  2. Accepted: November 22, 2020
  3. Accepted Manuscript published: November 23, 2020 (version 1)
  4. Version of Record published: December 22, 2020 (version 2)

Copyright

© 2020, Gao 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. Richard Gao
  2. Ruud L van den Brink
  3. Thomas Pfeffer
  4. Bradley Voytek
(2020)
Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture
eLife 9:e61277.
https://doi.org/10.7554/eLife.61277

Share this article

https://doi.org/10.7554/eLife.61277

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