# Establishing Multimodal Brain Biomarkers Using Data-driven Analyticsfor Treatment Selection in Depression

> **NIH NIH R01** · LEHIGH UNIVERSITY · 2024 · $670,872

## Abstract

Project Abstract
Major depression is the leading cause of ill health and disability worldwide according to the World Health
Organization. Although significant progress has been made in understanding the disease and developing
treatments, antidepressants, as the treatment mainstay, are effective for only about 50% of patients, in part due
to the neurobiological and clinical heterogeneity in depression. Developing advanced data-driven techniques by
leveraging machine learning with large-scale multimodal neuroimaging data from randomized clinical trials
provides us a unique opportunity to explore brain biomarkers to identify treatment-predictive neurobiological
phenotypes. Establishing such biomarkers is crucial for reducing the need for multiple drug trials and expediting
remission by sharpening the search for treatment targets. However, integrative analysis of multimodal data for
identifying biomarkers and differentiating individual responses to treatment in depression remains highly
challenging and underexplored. In this proposal, we will develop new data-driven analytical tools to quantify
multimodal moderators and signatures jointly from pre-treatment functional magnetic resonance imaging (fMRI)
and electroencephalography (EEG) data for the prediction of treatment response to antidepressant medication.
In Aim 1, we will identify multimodal moderators of treatment effect using data from the Establishing Moderators
and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial. A canonical correlation
analysis-based data-driven model will be designed to extract combined features that fuse together
complementary information from both fMRI and EEG modalities. Intent-to-treat prediction linear mixed models
will be used to probe multimodal moderators of antidepressant sertraline versus placebo treatment response. In
Aim 2, we will build a supervised latent space model that unifies the feature fusion and predictive modeling and
apply it to quantify multimodal brain signatures that can predict individual treatment responses to sertraline
versus placebo medication. In Aim 3, we will recruit 50 depressed patients as an independent cohort undergoing
sertraline treatment to optimize and validate the identified multimodal biomarkers. Both fMRI and EEG will be
collected at baseline followed by treatment with the antidepressant medication sertraline (in a manner paralleling
EMBARC procedures) and clinical assessment of outcomes. We will release the developed software tools and
collected data to be publicly available to the research community to facilitate multimodal neuroimaging studies
in other mental disorders.

## Key facts

- **NIH application ID:** 10833696
- **Project number:** 5R01MH129694-02
- **Recipient organization:** LEHIGH UNIVERSITY
- **Principal Investigator:** Yu Zhang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $670,872
- **Award type:** 5
- **Project period:** 2023-05-01 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10833696

## Citation

> US National Institutes of Health, RePORTER application 10833696, Establishing Multimodal Brain Biomarkers Using Data-driven Analyticsfor Treatment Selection in Depression (5R01MH129694-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10833696. Licensed CC0.

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