4.8 Article

Neuroscout, a unified platform for generalizable and reproducible fMRI research

期刊

ELIFE
卷 11, 期 -, 页码 -

出版社

eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.79277

关键词

naturalistic; fMRI; generalizability; neuroinformatics; reproducibility; open source; Human

类别

资金

  1. National Institute of Mental Health (NIMH) of the National Institute of Health (NIH) [R01MH109682]
  2. NIMH [P41EB019936, R24MH117179, R01MH096906]
  3. Canada First Research Excellence Fund
  4. Brain Canada Foundation
  5. Unifying Neuroscience and Artificial Intelligence - Quebec

向作者/读者索取更多资源

Neuroscout is an end-to-end platform for analyzing naturalistic fMRI data, which automatically annotates stimuli from multiple ecologically-valid datasets and reduces the burden of reproducible research. Through validating the automatic feature extraction approach, it has the potential to support more robust fMRI research and democratize fMRI research.
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.

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