Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation

  1. Filip Sobczak  Is a corresponding author
  2. Patricia Pais-Roldán
  3. Kengo Takahashi
  4. Xin Yu  Is a corresponding author
  1. Max Planck Institute for Biological Cybernetics, Germany
  2. Massachusetts General Hospital and Harvard Medical School, United States

Abstract

Pupil dynamics serve as a physiological indicator of cognitive processes and arousal states of the brain across a diverse range of behavioral experiments. Pupil diameter changes reflect brain state fluctuations driven by neuromodulatory systems. Resting state fMRI (rs-fMRI) has been used to identify global patterns of neuronal correlation with pupil diameter changes, however, the linkage between distinct brain state-dependent activation patterns of neuromodulatory nuclei with pupil dynamics remains to be explored. Here, we identified four clusters of trials with unique activity patterns related to pupil diameter changes in anesthetized rat brains. Going beyond the typical rs-fMRI correlation analysis with pupil dynamics, we decomposed spatiotemporal patterns of rs-fMRI with principal components analysis (PCA) and characterized the cluster-specific pupil-fMRI relationships by optimizing the PCA component weighting via decoding methods. This work shows that pupil dynamics are tightly coupled with different neuromodulatory centers in different trials, presenting a novel PCA-based decoding method to study the brain state-dependent pupil-fMRI relationship.

Data availability

All fMRI datasets, as well as the synchronized pupil-size vectors, reported in this paper have been deposited in Zenodo at https://zenodo.org/record/4670277 (DOI: 10.5281/zenodo.4670277).Source data for all figures have been uploaded in the system.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Filip Sobczak

    Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
    For correspondence
    fsobczak@tue.mpg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9169-0243
  2. Patricia Pais-Roldán

    Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Kengo Takahashi

    Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Xin Yu

    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, United States
    For correspondence
    XYU9@mgh.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9890-5489

Funding

Max-Planck-Gesellschaft (internal funding)

  • Xin Yu

National Institutes of Health (RF1NS113278-01,R01MH111438-01,S10 MH124733-01)

  • Xin Yu

Deutsche Forschungsgemeinschaft (YU215/2-1,Yu215/3-1)

  • Xin Yu

Bundesministerium für Bildung und Forschung (01GQ1702)

  • Filip Sobczak
  • Xin Yu

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

Ethics

Animal experimentation: All experimental procedures were approved by the Animal Protection Committee of Tuebingen (Regierungsprasidium Tuebingen; protocol KY12-14) and performed following the guidelines. The rats were imaged under alpha-chloralose anesthesia.

Copyright

© 2021, Sobczak 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. Filip Sobczak
  2. Patricia Pais-Roldán
  3. Kengo Takahashi
  4. Xin Yu
(2021)
Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation
eLife 10:e68980.
https://doi.org/10.7554/eLife.68980

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https://doi.org/10.7554/eLife.68980

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