# Combining Voice and Genetic Information to Detect Heterogeneity in Major Depressive Disorder

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $667,757

## Abstract

PROJECT SUMMARY
This application aims to advance our understanding of major depressive disorder (MDD) by combining genetic
information and analyzing speech patterns of those with MDD to identify subtypes. MDD is the leading cause
of disability throughout the world, yet, relative to other common disorders, less is known about its origins.
There are less effective treatments and much less is spent on trying to understand how it arises and how to
cure it. Current treatments are relatively ineffective, with up 50% of patients refractory and many suffering
severe recurrence. Understanding the mechanisms underlying MDD has been recognized as a grand
challenge in global mental health. Thus, developing new treatments for MDD is a major priority for public health.
A major challenge for MDD research is the presence of heterogeneity. The existence of multiple subtypes of
MDD has been suspected for a long time, and likely confounds the ability to treat the disorder appropriately
with existing treatments, as well as making it hard to identify the causes of MDD as a prelude to developing
new treatments. However finding subtypes has been hard. Given that the way people talk can reflect
alterations in mood, we expect voice to be able to predict mood, and hence potentially be used as biomarker to
recognize heterogeneity. In preliminary data show that in combination with genetic data high-dimensional vocal
features extracted from recordings can be used to identify subtypes. Furthermore, the use of genetic data
allows us to impute voice features into large biobanks where no recordings exist, making it possible to explore
the relationship between vocal features and a rich array of clinically important indicators. We explore the power
of voice to make a diagnosis of MDD, to predict severity and other clinical features. Applying our approach to
will inform clinical management, improving diagnosis, refine treatment and aid the development of new
treatments

## Key facts

- **NIH application ID:** 10656229
- **Project number:** 5R01MH122569-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** JONATHAN FLINT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $667,757
- **Award type:** 5
- **Project period:** 2020-08-14 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10656229

## Citation

> US National Institutes of Health, RePORTER application 10656229, Combining Voice and Genetic Information to Detect Heterogeneity in Major Depressive Disorder (5R01MH122569-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10656229. Licensed CC0.

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