# NeuroScout: A cloud-based platform for flexible re-analysis of naturalistic fMRI datasets

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2020 · $590,248

## Abstract

Project Summary
The widespread introduction of functional magnetic resonance imaging (fMRI) over the past two decades
has revolutionized the study of human brain and cognitive function in healthy and clinical populations. Yet
the potential utility of fMRI remains constrained by its resource-intensive nature. Because even small,
exploratory studies are expensive to conduct, biomedical researchers can collectively test only a small
fraction of the research hypotheses that are in principle amenable to fMRI investigation. There is an
urgent need for novel methodological approaches that enable rapid and efficient testing of novel
theoretical hypotheses by reusing existing fMRI datasets rather than acquiring new ones. To help achieve
this goal, we propose a new platform called NeuroScout that will support rapid and flexible cloud-based
analysis of existing functional fMRI datasets. Our approach differs importantly from previous infrastructure
projects in that, rather than developing a domain-general neuroimaging platform, we focus on extracting
maximum utility from a limited set of fMRI experiments--namely, those that use intrinsically high
dimensional stimuli such as movies and audio narratives. The proposed work encompasses three Specific
Aims. Aim 1 focuses on reducing the burden of re-analyzing existing fMRI by automating much of the
analysis process and allowing researchers to easily execute their analyses in the cloud. Aim 2 increases
analytical flexibility by developing highly extensible tools for multimodal stimulus annotation. Aim 3
focuses on incentivizing platform use by integrating NeuroScout outputs with existing data sharing,
visualization and interpretation platforms such as NeuroVault and Neurosynth. When fully deployed, the
NeuroScout platform will provide a turnkey solution for extremely rapid analysis and visualization of
existing fMRI data at a marginal cost very close to zero. Researchers will be able to iteratively test and
refine hypotheses in domains ranging from visual word recognition to social cognition, and interactively
visualize and share their results with the broader research community at the push of a button.

## Key facts

- **NIH application ID:** 9982125
- **Project number:** 5R01MH109682-05
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Alejandro De La Vega
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $590,248
- **Award type:** 5
- **Project period:** 2016-09-23 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9982125

## Citation

> US National Institutes of Health, RePORTER application 9982125, NeuroScout: A cloud-based platform for flexible re-analysis of naturalistic fMRI datasets (5R01MH109682-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9982125. Licensed CC0.

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