# Disentangling the anatomical, functional and clinical heterogeneity of major depression, using machine learning methods

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $732,970

## Abstract

Abstract
Neuropsychiatric disorders are characterized by distinct as well as shared clinical features that present in
heterogeneous symptom profiles. Delineating the neurobiological etiology of clinical symptoms has been
a key overarching aim of over 20 years of neuropsychiatric research. From the earliest studies,
neuroimaging research has identified abnormalities in regional brain structure and function. However, we
know that there is significant heterogeneity in brain structure and function in neuropsychiatric disorders.
Imaging analytic and machine learning methods developed by our group provide the analytic approaches
needed to quantify heterogeneity in neuropsychiatric disorders. Herein we leverage these methods, along
with a broad international collaboration which provides a unique resource of large highly phenotyped
datasets, in order to quantify heterogeneity in major depressive disorder (MDD). In the proposed project,
we focus on neuroanatomy and neurofunctional connectivity in MDD. We aim to identify imaging signatures
and subtypes in MDD by applying state-of-the-art harmonization, pattern analysis and machine learning
methods to structural and resting state functional MRI. The analytic methods allow us to quantify the
neuroanatomical and neurofunctional connectivity patterns that comprise MDD to provide powerful
predictive markers at the individual level. Our goal is to arrive at a new neuroanatomical-neurofunctional
(NA-NF) dimensional coordinate system in MDD (MDD COORDINATES), whereby each dimension reflects
a different pattern of brain alterations, hence capturing the underlying NA-NF heterogeneity in quantifiable,
replicable, and neurobiologically-based metrics. We will leverage data from our pooled cohorts consists of
4,973 adults with first episode and recurrent MDD, in a current depressive episode, that is not treatment
resistant, all medication-free, and respective healthy controls. Assembling these large and powerful
datasets will allow us to test our first hypothesis, namely that neuroanatomical and neurofunctional
phenotypes will display high heterogeneity, which will allow us to define NA-NF dimensions of pathology.
We then test the second hypothesis, namely that this heterogeneity will relate to disease-related
phenotypes in MDD and different patterns of clinical outcome. Our specific aims will 1) refine and apply
advanced harmonization methods in order to constructively pool and integrate this unique resource; 2)
dissect the heterogeneity of the neuroanatomy and function in MDD, thereby deriving a neuroimaging-
based coordinate system; 3) relate these imaging dimensions with clinical phenotypic measures, including
response to treatment.

## Key facts

- **NIH application ID:** 10891685
- **Project number:** 5R01MH134236-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Christos Davatzikos
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $732,970
- **Award type:** 5
- **Project period:** 2023-08-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10891685

## Citation

> US National Institutes of Health, RePORTER application 10891685, Disentangling the anatomical, functional and clinical heterogeneity of major depression, using machine learning methods (5R01MH134236-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10891685. Licensed CC0.

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