# Integration of brain imaging and multi-omics data for improved diagnosis and prediction of mental disorders

> **NIH NIH R56** · TULANE UNIVERSITY OF LOUISIANA · 2022 · $547,297

## Abstract

Title: Integration of brain imaging and multi-omics data for improved diagnosis and prediction
 of mental disorders
Project Summary
An overarching goal of this project is to incorporate multiscale omics and brain imaging into clinical studies
towards a nosology of psychiatric disorders that are biologically defined, and to uncover their specific genetic
architectures. This will transform the current practice of mental disease diagnosis and prognosis, leading to
precision psychiatry. Our recent work has demonstrated the value of combining multiscale brain imaging such
as fMRI with knowledge of genomics, networks, and biology to detect risk genes and biomarkers. We also
developed a set of integrative approaches for joint fMRI and genomics (e.g., SNPs) analysis. Despite our initial
success, the following significant challenges remain: 1) how to uncover previously hidden (e.g., nonlinear)
relationships among multiple data types for the detection of interaction networks both within and across omics,
revealing the specific genetic architecture of psychiatric disorders; 2) how to incorporate phenotype-relevant
multi-omics profiling to redefine and differentiate multiple psychiatric disorders with clinically overlapping
symptoms such as schizophrenia (SZ), bipolar disorder (BI), and unipolar depression (UD); 3) how to link multi-
omics and brain imaging data with phenotypical and cognitive measurements for the prediction of clinical
outcomes or disease states (predictome); and 4) how to validate the detected biomarkers with newly collected
large cohort studies by incorporating additional brain and omics data (e.g., DTI, methylations).
 In this proposal we will address the above remaining challenges in neurosciences and clinical psychiatry.
We will continue to build on the productivity of our multidisciplinary research team comprising an image analyst
and bioinformatician (Wang), an MRI imaging scientist (Calhoun), a geneticist and biostatistician (Deng), and a
psychiatrist (Pearlson). To leverage our past success, we propose to accomplish the following specific aims: 1)
to detect complex disease-specific non-linear relationships between multi-modal brain imaging and genomics
data and further identify interaction networks both within and across omics levels; 2) to incorporate phenotype-
specific network and structure information into our integration models for the detection of biomarkers and further
validate them on large datasets for the classification of multiple mental disorders and their genetic make-ups; 3)
to link multi-omics and brain imaging, including their interactions with behavioral and cognitive measurements,
for the prediction of psychiatric disorders (predictome); and 4) to disseminate integrative multi-omics imaging
analysis tools featuring non-linear analysis in open source software to the neuroimaging research community.
 These approaches will lead to better differentiation of mental disorders with overlapping symptomatology and
more acc...

## Key facts

- **NIH application ID:** 10415228
- **Project number:** 5R56MH124925-02
- **Recipient organization:** TULANE UNIVERSITY OF LOUISIANA
- **Principal Investigator:** YU-PING WANG
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $547,297
- **Award type:** 5
- **Project period:** 2021-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10415228

## Citation

> US National Institutes of Health, RePORTER application 10415228, Integration of brain imaging and multi-omics data for improved diagnosis and prediction of mental disorders (5R56MH124925-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10415228. Licensed CC0.

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