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 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.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

“…Due to increasing awareness of these issues, at least three sets of solutions have been proposed for future research: a) the use of stable, uniform and openly-annotated pipelines and platforms [426][427][428][429][430] ; b) benchmarking approaches to quantifying and reporting the residual degree of artefact and variability present in a given set of outputs [431][432][433][434] ; and c) performing "multiverse" analysis, which entails reporting results from a multiplicity of analytic approaches within a single paper 435,436 .…”

Gamer in the scanner : Event-related analysis of fMRI activity during retro videogame play guided by automated annotations of game content

“…Since the foundational work of Hasson et al (2004) using movie watching as stimulus for neuroimaging, movie content descriptions have taken diverse forms, from human raters who flag features of interest manually to automated tools (Wallenius, 2010). For instance, the Neuroscout platform enables automated movie annotations that capture elements like the presence of faces and buildings, word frequencies, and other audio-visual features (de la Vega et al, 2022). Sentiment analysis tools such as VADER (Hutto & Gilbert, 2014), TextBlob (Loria, n.d.), and FLAIR (Akbik et al, 2019) have been harnessed to extract positive, negative or neutral emotions from movie captions (Mahrukh et al, 2023).…”