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
Share this article
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
  2. Download BibTeX
  3. Download .RIS

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.

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.

Metrics

  • 1,328
    views
  • 233
    downloads
  • 6
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Categories and tags

Research organism

Further reading

    1. Neuroscience

    Aberrant auditory prediction patterns robustly characterize tinnitus

    Lisa Reisinger, Gianpaolo Demarchi ... Nathan Weisz
    Research Article

    Phantom perceptions like tinnitus occur without any identifiable environmental or bodily source. The mechanisms and key drivers behind tinnitus are poorly understood. The dominant framework, suggesting that tinnitus results from neural hyperactivity in the auditory pathway following hearing damage, has been difficult to investigate in humans and has reached explanatory limits. As a result, researchers have tried to explain perceptual and potential neural aberrations in tinnitus within a more parsimonious predictive-coding framework. In two independent magnetoencephalography studies, participants passively listened to sequences of pure tones with varying levels of regularity (i.e. predictability) ranging from random to ordered. Aside from being a replication of the first study, the pre-registered second study, including 80 participants, ensured rigorous matching of hearing status, as well as age, sex, and hearing loss, between individuals with and without tinnitus. Despite some changes in the details of the paradigm, both studies equivalently reveal a group difference in neural representation, based on multivariate pattern analysis, of upcoming stimuli before their onset. These data strongly suggest that individuals with tinnitus engage anticipatory auditory predictions differently to controls. While the observation of different predictive processes is robust and replicable, the precise neurocognitive mechanism underlying it calls for further, ideally longitudinal, studies to establish its role as a potential contributor to, and/or consequence of, tinnitus.

    1. Neuroscience

    Three-dimensional single-cell transcriptome imaging of thick tissues

    Rongxin Fang, Aaron Halpern ... Xiaowei Zhuang
    Tools and Resources

    Multiplexed error-robust fluorescence in situ hybridization (MERFISH) allows genome-scale imaging of RNAs in individual cells in intact tissues. To date, MERFISH has been applied to image thin-tissue samples of ~10 µm thickness. Here, we present a thick-tissue three-dimensional (3D) MERFISH imaging method, which uses confocal microscopy for optical sectioning, deep learning for increasing imaging speed and quality, as well as sample preparation and imaging protocol optimized for thick samples. We demonstrated 3D MERFISH on mouse brain tissue sections of up to 200 µm thickness with high detection efficiency and accuracy. We anticipate that 3D thick-tissue MERFISH imaging will broaden the scope of questions that can be addressed by spatial genomics.

    1. Neuroscience

    Learning enhances behaviorally relevant representations in apical dendrites

    Sam E Benezra, Kripa B Patel ... Randy M Bruno
    Research Article

    Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior. Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. Selective tufts emerged from initially unresponsive or low-selectivity populations. Animal movement and choice did not account for changes in stimulus selectivity. Enhanced selectivity persisted even after rewards were removed and animals ceased performing the task. We conclude that learning produces long-lasting realignment of apical dendrite tuft responses to behaviorally relevant dimensions of a task.