Department of Psychology, Stanford University, Stanford, United States
Jeff Mentch
Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, United States; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States
James D Kent
Department of Psychology, The University of Texas at Austin, Austin, United States
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States; Department of Otolaryngology, Harvard Medical School, Boston, United States
Russell A Poldrack
Department of Psychology, Stanford University, Stanford, United States
Tal Yarkoni
Department of Psychology, The University of Texas at Austin, Austin, United States
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.
