# Anti-depressant response in neurobiologically defined psychiatric veteran groups

> **NIH VA I01** · VA SAN DIEGO HEALTHCARE SYSTEM · 2020 · —

## Abstract

The soldiers that face combat are at high risk for the potentially significant repercussions of
combat stress. Combat stress can lead to a number of impactful emotional and cognitive
conditions, most notably Posttraumatic Stress Disorder (PTSD), Major Depressive Disorder
(MDD), Generalized Anxiety Disorder (GAD), and mild Traumatic Brain Injury (mTBI). While
there have been attempts to match a specific neurobiological pattern to a specific DSM
identified disorder, the pursuit has met limited success. Clinically, different DSM diagnoses are
often approached with similar treatments with similar response rates (~33-50%). Clinically
groups are identified my sets behaviors (DSM disorders) and research has aimed to find the
neurobiological underpinnings of these behavior defined groups. Our aim is to instead identify
neurobiological groups in the context of underlying neurobiological and with the long term goal
of improving response rates to medical trials be clustering of relevant features. However, due
to the complex relationship between the neurobiological variables a simple linear relationship or
risk score is not appropriate. Here we present a novel approach in which we define
neurobiologically distinct subgroups — based on the most feasible, most robust, and most likely
to relate to treatment outcomes — in these Veterans with combat related psychiatric distress.
We have selected a set of brain imaging, molecular biology, and physiological markers such
that measures will not be influenced by current clinical models. We will then seek to determine
robust subgroups from this model-based hierarchical clustering approach. Next, we contrast our
neurobiologically defined groups with traditional groups or general response. Finally, to help
best understand the available data and feed forward for future studies, we will run a supervised
machine learning (random forest) to determine the optimal variables and groups to predict
treatment response. We have opted to solely test sertraline, as this is the most commonly
prescribed medication in this population at the San Diego VA mental health clinics (FDA
approved for MDD and PTSD). The model-based clustering approach allows us to look at the
non-linear relationship between variables of interest and foster an attempt to better link clinical
research and clinical practice to best benefit our Veteran population.

## Key facts

- **NIH application ID:** 9814123
- **Project number:** 5I01CX001542-03
- **Recipient organization:** VA SAN DIEGO HEALTHCARE SYSTEM
- **Principal Investigator:** ALAN N SIMMONS
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2017-10-01 → 2021-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9814123

## Citation

> US National Institutes of Health, RePORTER application 9814123, Anti-depressant response in neurobiologically defined psychiatric veteran groups (5I01CX001542-03). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9814123. Licensed CC0.

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