Neuroscout, a unified platform for generalizable and reproducible fMRI research

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 naturalistic fMRI studies, 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.

Integrating media content analysis, reception analysis, and media effects studies

“…Or researchers have used NLP techniques to study moral language in media ( Weber et al, 2018 ). Perhaps the most systematic yet still young approach in this area is the NeuroScout platform and the related pliers python package ( McNamara et al, 2017 ; De la Vega et al, 2022 ). NeuroScout provides easy access to machine-learning methods capable of automatically extracting hundreds of features that range from the very concrete (like root mean squared amplitude of the sound signal) to more abstract ones (like concept-level image tags from Google’s or Clarif.ai’s computer vision systems).…”