# A Multivariate Mediation and Deep Learning Framework for Genome-Connectome -Substance Use Research

> **NIH NIH DP1** · UNIVERSITY OF MARYLAND BALTIMORE · 2021 · $463,500

## Abstract

Substance use and addiction are complex biopsychosocial disorders influenced by both genetic
and environmental factors. A key challenge in addiction genetics research is to understand how
multiple genetic variants interactively influence addiction traits through impacting the central
nervous system. To address this challenge, we propose a large-scale mediation analysis
framework to identify addiction-related gene-brain circuitry pathways, using nicotine addiction as
the targeted disorder, although the platform will be readily applicable for other addiction-related
disorders and phenotypes. We will fully leverage the complex and interactive interdependent
relationships between the imaging-genetics data and perform multivariate statistical inference
with simultaneously increased statistical power and reduce false positive rates. The results will
precisely identify multiple sets of genetic variants that interactively alter brain functional and
structural circuitries, and then influence nicotine addiction. We will further supplement the
mediation results with deep learning algorithms to study how genetic variants non-linearly and
interactively coordinate to influence nicotine addiction and explain the phenotypic variance.
Novel network topology based convolutional and pooling functions will be developed to achieve
optimal prediction accuracy of addiction traits using genome-connectome pathways. All models
and findings will be carefully validated through multiple independent large-sample data sets of
imaging-genetics studies for nicotine addiction for ensuring the replicability and reliability of our
findings derived from this framework. We plan to produce a freely available and user-friendly
software incorporating the mediation analysis framework and deep learning algorithms enabling
the complex whole genome - connectome analysis for addiction genetics research.

## Key facts

- **NIH application ID:** 10242826
- **Project number:** 5DP1DA048968-03
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** Shuo Chen
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $463,500
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10242826

## Citation

> US National Institutes of Health, RePORTER application 10242826, A Multivariate Mediation and Deep Learning Framework for Genome-Connectome -Substance Use Research (5DP1DA048968-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10242826. Licensed CC0.

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