# Integrative analysis of genomics and imaging data from the BRAIN Initiative and other public data sources

> **NIH NIH RF1** · YALE UNIVERSITY · 2021 · $1,309,909

## Abstract

Constructing an integrated picture of human brain function requires understanding how the effects of molecular
and genetic factors propagate upwards, through many intervening layers of structure and interaction, to
influence behavioral, psychiatric and cognitive traits. Projects such as the BRAIN Initiative (BI) recognize that
building such a picture requires the convergent efforts of experts across genetics, genomics, neuroscience,
and clinical studies, and have created resources to aid the integration of data from these disciplines. However,
the challenge of combining experimental methods and theoretical models spanning vast length/time scales
remains significant. One of the more promising avenues of addressing this challenge is the use of interpretable
deep-learning approaches to learn high-dimensional structure inherent in data. By embedding constraints from
known biological structure, investigators can relate the models’ internal representations to identifiable factors
from neuroscience. This proposal will draw on the extensive resources in BI archives, along with other public
resources, to integrate data from genetics, functional genomics, and neuroimaging. Through secondary
analysis on this data we will build deep, multilevel polygenic models of high-level traits, such as cognitive,
affective and psychiatric traits. We will trace the mechanisms underlying such traits to specific regions, cell
types, functional connectivity patterns and structural imaging features. Additionally, by embedding biological
structure at intermediate levels (tissue and cell-type gene regulatory networks; structural/functional constraints
from MRI data), we will build models that improve on additive heritability measures of polygenic risk. In the
process, we will harmonize BI data with other publicly available brain omics and imaging datasets. We will
deposit all resources and models into relevant BI archives. The proposal is framed as follows. First, we will
combine genetics with genomics-based networks from multiple brain regions and cell types, and develop
predictive models of region- and cell-type-specific omics variation. These will be included in an interpretable
deep model of cognitive and psychiatric traits (Aim 1). Second, we will learn predictive models of structural and
functional imaging features from genetic predictors, which will likewise be embedded in interpretable deep
models of high-level traits (Aim 2). Third, an integrated, polygenic model will be built by combining both
functional-genomics- and neuroimaging-based features, allowing the impact of both subcomponents to be
assessed. Furthermore, we will extend our previous work to develop compression-based interpretability
methods, which allow a network to be coarse-grained and interpreted at varying levels of resolution. Such
interpretation will include the exploration of subphenotypic structure in psychiatric disorders and interactions
between traits (Aim 3). We expect the proposed approach to ...

## Key facts

- **NIH application ID:** 10190025
- **Project number:** 1RF1MH123245-01A1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Mark Bender Gerstein
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,309,909
- **Award type:** 1
- **Project period:** 2021-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10190025

## Citation

> US National Institutes of Health, RePORTER application 10190025, Integrative analysis of genomics and imaging data from the BRAIN Initiative and other public data sources (1RF1MH123245-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10190025. Licensed CC0.

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