# Neurobiologically-Based Subtyping of Multi-Cohort Samples with MDD and PTSD Symptoms

> **NIH NIH R01** · DUKE UNIVERSITY · 2022 · $605,915

## Abstract

ABSTRACT
Significant symptom overlap and high rates of co-occurrence between syndromes of posttraumatic stress
disorder (PTSD) and major depressive disorder (MDD) call into question whether the two are distinct disorders.
The onset and course of both syndromes are strongly influenced by environmental variables. We hypothesize
that a continuum of life stress or adversity and an independent continuum of psychological trauma conspire to
influence the onset of PTSD and MDD (where at least one trauma exposure is required for PTSD). Our
overarching goal is to identify and compare neural signatures of MDD, PTSD, symptom features common to
PTSD and MDD, and heretofore unrecognized neurobiologically-defined syndromes. Therefore, we plan to
investigate neural signatures with supervised learning, and to identify biotypes that cut across disorders (PTSD
and MDD) with unsupervised learning, an approach that can better explain contributions of trauma, stressful
life events, and disease characteristics than possible with DSM-disorders. Rather than subtyping patients on
the basis of clinical symptoms or DSM-defined diagnoses, our goal is to identify distinct clusters of
neurobiological subtypes with disrupted neural signatures derived from resting-state fMRI. In Aim 1 we propose
to train algorithms with supervised learning to detect neural signatures from resting fMRI data that can classify
DSM diagnosis of comorbid PTSD and MDD, PTSD only, MDD only, and Controls (no psychiatric disorder).
The analysis will be performed separately with MDD and Control groups who experienced criterion-A trauma or
stressful life events, and those who did not. In Aim 2, we plan to use supervised learning in MDD and PTSD
patients to identify neural signatures from resting-state fMRI data associated with four trans-diagnostic
symptoms that include disrupted sleep, irritability, concentration difficulties, and loss of interest. In Aim 3, we
propose to apply unsupervised learning methods to identify novel biotypes associated with specific symptoms
or symptom clusters. The algorithms will employ rsfMRI features in patients with (1) PTSD only, (2) MDD only
and (2) across PTSD, MDD, and comorbid PTSD+MDD patients in order to identify potential trans-diagnostic
biotypes that cut across DSM boundaries. We will investigate associations of diagnosis-specific and trans-
diagnostic biotypes derived from unsupervised learning with stressful life events, trauma exposure,
developmental stage at time of exposure, psychiatric comorbidities, medical comorbidities, illness chronicity,
illness severity, gender, and age. The overlapping and intersecting patterns that maps circuit disruption to
psychiatric syndromes presents a daunting challenge in designing treatments that intervene at the circuit level.
Developing a neurobiologically-based nosology that maps to clinical symptoms and syndromes represents a
major advance in translational neuroscience. The advent of modern brain stimulation technology off...

## Key facts

- **NIH application ID:** 10422760
- **Project number:** 1R01MH129832-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** RAJENDRA A MOREY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $605,915
- **Award type:** 1
- **Project period:** 2022-04-15 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10422760

## Citation

> US National Institutes of Health, RePORTER application 10422760, Neurobiologically-Based Subtyping of Multi-Cohort Samples with MDD and PTSD Symptoms (1R01MH129832-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10422760. Licensed CC0.

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