Neuroscout, a unified platform for generalizable and reproducible fMRI research
- The University of Texas at Austin, United States
- Aarhus University, Denmark
- Stanford University, United States
- Massachusetts Institute of Technology, United States
- McGill University, Canada
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
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Article and author information
Author details
Alejandro de la Vega
Christopher J Markiewicz
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.
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Neuroscout, a unified platform for generalizable and reproducible fMRI research
eLife 11:e79277.
https://doi.org/10.7554/eLife.79277
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