# ENIGMA- Addiction: Pooling of Existing Datasets to Identify Brain and Genetic Correlates of Addiction, Next Steps

> **NIH NIH R01** · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · 2024 · $688,498

## Abstract

PROJECT SUMMARY
The ENIGMA consortium was established to investigate brain structure, function, and disease by combining
genomic and neuroimaging datasets from multiple sites. Its cost-effective goal is to maximize statistical power
and the yield from existing datasets through very large data pooling efforts. This goal has considerable
importance in light of growing concerns over the rigor and reproducibility of many neuroimaging and genomic
findings. ENIGMA has had many successes (including a genome wide association study on cortical thickness
with over 51,556 participants, Grasby et al., 2021 Science), and a number of working groups have been
established which leverage the standardized multi-site ENIGMA preprocessing pipelines and analytic methods
to focus on the neurobiology of specific diseases. This application’s PI co-created the ENIGMA Addiction working
group which now has access to datasets representing over 24,000 participants contributed by 103 PI members
from 71 different institutions in 16 countries spanning 6 continents. There have been 27 peer reviewed
publications since the start of the initial R01 with another 8 papers currently under review. Building on our initial
funding (R01 DA047119), we propose to further expand the Addiction working group to support the largest
international resource of its kind for the scientific community. Dataset users are encouraged to propose and lead
their own projects. Curation of the shared database by the ENIGMA Addiction team according to standardized
protocols for harmonization and quality control is a cost-effective means of maximizing heavy investments in
data collection and overcomes an insurmountable obstacle for younger investigators and investigators from low
or middle income countries. ENIGMA Addiction has approved 43 projects, 16 of which are completed and 27 are
ongoing, including a full 30 projects led by investigators from outside of the central research team at the
University of Vermont. These projects investigate topics ranging from sex differences to early markers of
addiction risk to machine learning classification. Research projects can be performed via guest access to a high
performance supercomputer hosted at the University of Vermont or through COINSTAC, a federated cloud-
based meta-analytic pipeline. Analyses have been completed on thousands of pooled participants with structural
MRI, DTI, resting state fMRI or genetic data and we have now expanded to include task-based fMRI. During the
next phase of funding, the team at UVM will engage in two main analytic efforts whose success is favored by the
large database afforded by this working group, namely comprehensive interrogation of psychiatric comorbidity
by joining efforts with other large ENIGMA working groups and, second, identification of biologically meaningful
sub-groups within the population of problematic substance users with distinct etiology.

## Key facts

- **NIH application ID:** 10903078
- **Project number:** 2R01DA047119-06A1
- **Recipient organization:** UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
- **Principal Investigator:** HUGH P. GARAVAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $688,498
- **Award type:** 2
- **Project period:** 2018-09-15 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10903078

## Citation

> US National Institutes of Health, RePORTER application 10903078, ENIGMA- Addiction: Pooling of Existing Datasets to Identify Brain and Genetic Correlates of Addiction, Next Steps (2R01DA047119-06A1). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10903078. Licensed CC0.

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