1. Neuroscience

Neuroscout, a unified platform for generalizable andreproducible fMRI research

  1. Alejandro de la Vega  Is a corresponding author
  2. Roberta Rocca
  3. Ross W Blair
  4. Christopher J Markiewicz
  5. Jeff Mentch
  6. James D Kent
  7. Peer Herholz
  8. Satrajit S Ghosh
  9. Russell A Poldrack
  10. Tal Yarkoni
  1. The University of Texas at Austin, United States
  2. Aarhus University, Denmark
  3. Stanford University, United States
  4. Massachusetts Institute of Technology, United States
  5. McGill University, Canada
https://doi.org/10.7554/eLife.79277
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Cite this article
  1. Alejandro de la Vega
  2. Roberta Rocca
  3. Ross W Blair
  4. Christopher J Markiewicz
  5. Jeff Mentch
  6. James D Kent
  7. Peer Herholz
  8. Satrajit S Ghosh
  9. Russell A Poldrack
  10. Tal Yarkoni
(2022)
Neuroscout, a unified platform for generalizable andreproducible fMRI research
eLife 11:e79277.
https://doi.org/10.7554/eLife.79277
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Abstract

Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli-such as movies and narratives-allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.

Data availability

All code from our processing pipeline and core infrastructure is available online (https://www.github.com/neuroscout/neuroscout). An online supplement including all analysis code and resulting images is available as a public GitHub repository (https://github.com/neuroscout/neuroscout-paper).All analysis results are made publicly available in a public GitHub repository

The following previously published data sets were used
    1. Hanke M
    2. et al
    (2014) studyforrest
    OpenNeuro, doi:10.18112/ openneuro.ds000113 .v1.3.0.
    https://doi.org/10.18112/openneuro.ds000113.v1.3.0
    1. Nastase SA
    2. et al
    (2021) Narratives
    OpenNeuro, doi:10.18112/openneuro.ds002345 .v1.1.4.
    https://doi.org/10.18112/openneuro.ds002345.v1.1.4

Article and author information

Author details

  1. Alejandro de la Vega

    Department of Psychology, The University of Texas at Austin, Austin, United States
    For correspondence
    delavega@utexas.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9062-3778

  2. Roberta Rocca

    Interacting Minds Centre, Aarhus University, Aarhus, Denmark
    Competing interests
    The authors declare that no competing interests exist.

  3. Ross W Blair

    Department of Psychology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Christopher J Markiewicz

    Department of Psychology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6533-164X

  5. Jeff Mentch

    McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. James D Kent

    Department of Psychology, The University of Texas at Austin, Austin, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Peer Herholz

    Montreal Neurological Institute, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9840-6257

  8. Satrajit S Ghosh

    McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5312-6729

  9. Russell A Poldrack

    Department of Psychology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  10. Tal Yarkoni

    Department of Psychology, The University of Texas at Austin, Austin, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institute of Mental Health (R01MH109682)

  • Alejandro de la Vega
  • Roberta Rocca
  • Ross W Blair
  • Christopher J Markiewicz
  • Jeff Mentch
  • James D Kent
  • Peer Herholz
  • Satrajit S Ghosh
  • Russell A Poldrack
  • Tal Yarkoni

National Institute of Mental Health (R01MH096906)

  • Alejandro de la Vega
  • James D Kent
  • Tal Yarkoni

National Institute of Mental Health (R24MH117179)

  • Peer Herholz
  • Satrajit S Ghosh

National Institute of Mental Health (R24MH117179)

  • Ross W Blair
  • Christopher J Markiewicz
  • Russell A Poldrack

Canada First Research Excellence Fund

  • Peer Herholz

Brain Canada Fondation

  • Peer Herholz

Unifying Neuroscience and Artificial Intelligence - Québec

  • Peer Herholz

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

Reviewing Editor

  1. Laurence Tudor Hunt, University of Oxford, United Kingdom

Version history

  1. Received: April 6, 2022
  2. Preprint posted: April 8, 2022 (view preprint)
  3. Accepted: August 27, 2022
  4. Accepted Manuscript published: August 30, 2022 (version 1)
  5. Accepted Manuscript updated: August 31, 2022 (version 2)
  6. Version of Record published: September 20, 2022 (version 3)

Copyright

© 2022, de la Vega 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. Alejandro de la Vega
  2. Roberta Rocca
  3. Ross W Blair
  4. Christopher J Markiewicz
  5. Jeff Mentch
  6. James D Kent
  7. Peer Herholz
  8. Satrajit S Ghosh
  9. Russell A Poldrack
  10. Tal Yarkoni
(2022)
Neuroscout, a unified platform for generalizable andreproducible fMRI research
eLife 11:e79277.
https://doi.org/10.7554/eLife.79277

Share this article

https://doi.org/10.7554/eLife.79277

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