# Large-scale meta-analysis of functional MRI data

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2024 · $779,816

## Abstract

PROJECT SUMMARY
The human functional neuroimaging literature has experienced sustained and explosive growth, making it
difficult to effectively glean insights from this vast knowledge base. Previously, we introduced Neurosynth, a
web-based framework for automated fMRI synthesis that supports meta-analysis at scale across over 12,000
studies. We subsequently extended the ecosystem by developing a powerful platform for expert-driven data
annotation, enabling researchers to define and execute custom image-based meta-analyses. Although the
Neurosynth framework has become a widely used resource in the neuroimaging community, the laborious
challenge of annotating studies at scale remains. We previously addressed this problem by using
frequency-based text-mining techniques to extract meaning from the abstracts of articles, but these methods
were unable to make fine-grained distinctions between neuroscientific concepts or encode methodological
details. Recent breakthroughs in natural language processing and artificial intelligence promise to extract
accurate information from unstructured texts with little to no labeled data using Zero Shot Learning. However,
the application of state-of-the-art language models to the neuroimaging literature requires a systematic
framework to ensure machine comprehension accurately captures known concepts, and robust infrastructure
to retrieve information from the literature at scale. The overarching goal of the present project period is to
bridge the gap between manual annotation and fully automated meta-analysis, by extending the Neurosynth
framework to leverage state-of-the-art natural language processing models to enable precise large-scale
automated meta-analysis. In Aim 1, we will develop an extensible framework for information retrieval and
validation from neuroimaging articles, with a focus on features relevant to neuroimaging meta-analysis such as
study participant demographics (e.g. sample size, age, phenotype), methodological details, and semantic
concepts mapped onto expert-defined ontologies. In Aim 2, we will develop and deploy infrastructure for
continuous retrieval and processing of the neuroimaging literature, ensuring the timely acquisition of new
articles and transforming our meta-analytic study database into a comprehensive knowledge base. In Aim 3,
we will develop an interactive researcher-in-the-loop platform meta-analysis, enabling users to use extracted
study information to find and identify studies, create gold-standard meta-analyses, and browse hundreds of
pre-generated large-scale meta-analyses, enabling discovery-driven science. Meta-analyses will be
automatically updated, transforming them from a static to a “living” scientific product that remains relevant and
up-to-date. Realizing these objectives will introduce powerful and flexible tools for synthesizing the
neuroimaging literature at a large scale and with unprecedented precision and ease. These tools will be freely
and publicly available ...

## Key facts

- **NIH application ID:** 10982264
- **Project number:** 2R01MH096906-10
- **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:** 2024
- **Award amount:** $779,816
- **Award type:** 2
- **Project period:** 2012-08-10 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10982264

## Citation

> US National Institutes of Health, RePORTER application 10982264, Large-scale meta-analysis of functional MRI data (2R01MH096906-10). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10982264. Licensed CC0.

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