Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

  1. Sydney M Bailes
  2. Daniel EP Gomez
  3. Beverly Setzer
  4. Laura D Lewis  Is a corresponding author
  1. Boston University, United States
  2. Massachusetts Institute of Technology, United States

Abstract

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

Data availability

The data used in this paper has been deposited on OpenNeuro (https://doi.org/10.18112/openneuro.ds004645.v1.0.0).

The following data sets were generated

Article and author information

Author details

  1. Sydney M Bailes

    Department of Biomedical Engineering, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1712-9233
  2. Daniel EP Gomez

    Department of Biomedical Engineering, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Beverly Setzer

    Department of Biomedical Engineering, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Laura D Lewis

    Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, United States
    For correspondence
    ldlewis@mit.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4003-0277

Funding

National Institutes of Health (R00-MH111748)

  • Laura D Lewis

National Institutes of Health (U19-NS123717)

  • Laura D Lewis

National Institutes of Health (R01-AG070135)

  • Laura D Lewis

Searle Scholars Program

  • Laura D Lewis

Pew Charitable Trusts (Pew Biomedical Scholars Program)

  • Laura D Lewis

Sloan School of Management, Massachusetts Institute of Technology (Sloan Fellowship)

  • Laura D Lewis

One Mind (One Mind Rising Star Award)

  • Laura D Lewis

National Institutes of Health (T32-GM008764)

  • Sydney M Bailes

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: All experimental procedures were approved by the Massachusetts General Hospital Institutional Review Board and all subjects provided informed consent. (protocol number: 2014P001068),

Version history

  1. Preprint posted: January 26, 2023 (view preprint)
  2. Received: January 27, 2023
  3. Accepted: August 10, 2023
  4. Accepted Manuscript published: August 11, 2023 (version 1)
  5. Version of Record published: September 18, 2023 (version 2)

Copyright

© 2023, Bailes 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. Sydney M Bailes
  2. Daniel EP Gomez
  3. Beverly Setzer
  4. Laura D Lewis
(2023)
Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing
eLife 12:e86453.
https://doi.org/10.7554/eLife.86453

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https://doi.org/10.7554/eLife.86453

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