# Large-scale image-based meta-analysis of functional MRI data

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $632,301

## Abstract

PROJECT SUMMARY
The human functional neuroimaging literature has experienced explosive growth over the last two decades. In
an effort to make sense of this literature, neuroimaging researchers have developed quantitative meta-analysis
methods that can aggregate and synthesize the results of hundreds or thousands of studies. In the previous
period of this project, we introduced a web-based platform called Neurosynth that supports automated meta-
analysis of the fMRI literature at large scale. Neurosynth has become a widely used resource within then
neuroimaging community; however, like other meta-analysis approaches to fMRI, it currently supports analysis
only of sparse, discrete activations previously reported in published studies. This coordinate-based meta-
analysis (CBMA) approach is inferior in many respects to image-based meta-analysis (IBMA) approaches that
operate over continuous whole-brain statistical maps. A community-wide shift from CBMA to IBMA would
considerably improve sensitivity and specificity, and allow a much broader range of mixed-effects meta-
analysis models to be fit to fMRI data. Our overarching goal in the present project period is to contribute to
such a shift by extending the existing Neurosynth platform into a turnkey solution for image-based meta-
analysis. In Aim 1, we will create a centralized database of whole-brain statistical maps, providing a rich data
source for large-scale image-based meta-analyses. In Aim 2, we will add new web-based interfaces to
Neurosynth that enable users to easily (i) edit, validate, and annotate data from individual studies, and (ii)
organize data from hundreds or thousands of studies into sophisticated image-based meta-analyses that can
be readily executed on local or cloud computing resources. In Aim 3, we will develop a reference open-source
software package (PyCIBMA) for efficient mixed-effects meta-analysis of fMRI data, providing the community
with a uniform interface for fMRI meta-analysis that complies with current open standards and specifications.
Realizing these objectives will introduce powerful new tools for synthesizing the neuroimaging literature at a
large scale and with unprecedented resolution. These tools will be freely and publicly available to anyone with
an internet connection, enabling rapid and efficient application to a broad range of clinical and basic research
applications.

## Key facts

- **NIH application ID:** 10075314
- **Project number:** 5R01MH096906-07
- **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:** 2021
- **Award amount:** $632,301
- **Award type:** 5
- **Project period:** 2012-08-10 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10075314

## Citation

> US National Institutes of Health, RePORTER application 10075314, Large-scale image-based meta-analysis of functional MRI data (5R01MH096906-07). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10075314. Licensed CC0.

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