TY - JOUR TI - Neuroscout, a unified platform for generalizable and reproducible fMRI research AU - de la Vega, Alejandro AU - Rocca, Roberta AU - Blair, Ross W AU - Markiewicz, Christopher J AU - Mentch, Jeff AU - Kent, James D AU - Herholz, Peer AU - Ghosh, Satrajit S AU - Poldrack, Russell A AU - Yarkoni, Tal A2 - Hunt, Laurence Tudor A2 - Makin, Tamar R A2 - Finn, Emily A2 - Baldassano, Christopher A2 - Duff, Eugene P VL - 11 PY - 2022 DA - 2022/08/30 SP - e79277 C1 - eLife 2022;11:e79277 DO - 10.7554/eLife.79277 UR - https://doi.org/10.7554/eLife.79277 AB - 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. KW - naturalistic KW - fMRI KW - generalizability KW - neuroinformatics KW - reproducibility KW - open source JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -