# Meta-analysis in human brain mapping

> **NIH NIH R56** · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · 2020 · $543,396

## Abstract

This is the competing renewal of R01MH074457-13, which sustains the BrainMap Project (www.brainmap.org).
The overall goal of the BrainMap Project is to provide the human neuroimaging community with curated data
sets, metadata, computational tools, and related resources that enable coordinate-based meta-analyses
(CBMA), meta-analytic connectivity modeling (MACM), meta-data informed interpretation (“decoding”) of
imaging results, and meta-analytic priors for mining (including machine learning) primary (per-subject)
neuroimaging data. To date, the BrainMap Project has designed and populated two coordinate-based
databases: 1) a task-activation repository (TA DB); and, 2) a voxel-based morphometry repository (VBM DB).
The TA DB contains >17,200 experiments, collectively representing > 78,000 subjects and > 110 task-
activation paradigms. The VBM DB contains > 3,100 experiments, collectively representing > 81,000 subjects
with > 80 psychiatric, neurologic and developmental disorders with ICD-10 coding. The BrainMap Project has
created, optimized and validated an integrated pipeline of multi-platform (Javascript), open-access tools to
curate (Scribe), filter and retrieve (Sleuth), analyze (GingerALE), visualize (Mango) and interpret analysis
output (BrainMap meta-data plugins for Mango). Several network-modeling approaches have been applied to
BrainMap data -- MACM, independent components analysis (ICA), graph theory modeling (GTM), author-topic
modeling (ATM), structural equation modeling (SEM), and connectivity-based parcellation (CBP) – but none
are yet pipeline components. Utilization of these CBMA resources is substantial: BrainMap software, data and
meta-data have been used in > 825 peer-reviewed publications. Of these, > 350 were published within the
current funding period (April 2015-March 2019; brainmap.org/pubs). In this competing renewal, four tool-
development aims are proposed, each of which extends this high-impact research resource.
Aim 1. Database Expansion. BrainMap data repositories will be expanded.
Aim 2. Meta-analytic Network Modeling. Network modeling will be added to the BrainMap pipeline.
Aim 3. Large-Scale Simulations, Comparisons and Validations. Data simulations, characterizations and
validations will be performed.
Aim 4. Meta-data Inferential tools. Tools for mining BrainMap’s location-linked meta-data will be expanded.
Data Sharing Plan. BrainMap data, meta-data, pipeline tools, and templates created by whole-database
modeling (e.g., ICA and ATM network masks) are shared at BrainMap.org. Of all new data entries, more than
half are contributed by BrainMap users, i.e., community data sharing via BrainMap.org. For community-coded
entries, the BrainMap team provides curation and quality control. Comprehensive database images (database
dumps) are available to tool developers through Collaborative Use Agreements.

## Key facts

- **NIH application ID:** 10056029
- **Project number:** 2R56MH074457-14
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
- **Principal Investigator:** PETER Thornton FOX
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $543,396
- **Award type:** 2
- **Project period:** 2020-01-01 → 2020-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10056029

## Citation

> US National Institutes of Health, RePORTER application 10056029, Meta-analysis in human brain mapping (2R56MH074457-14). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10056029. Licensed CC0.

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