# Developing Computational Nosologies of Posttraumatic Stress Disorder (PTSD)

> **NIH VA IK2** · PROVIDENCE VA  MEDICAL CENTER · 2021 · —

## Abstract

Posttraumatic Stress Disorder (PTSD) is a highly prevalent and chronic psychiatric disorder in Veterans
and the broader US population. It is often associated with significant stigma, diminished psychosocial
functioning, poor physical health, and lessened quality of life. Despite the impact of PTSD, a precise
diagnosis is often difficult. PTSD presents as a multi-faceted illness with variable clinical presentation. It
is highly comorbid with other psychiatric disorders, and Veterans and patients often express a myriad of
distinct symptoms. A better neurobiological and network-level understanding of PTSD can lead to
diagnostic clarity and more advanced, targeted, and individualized treatments. Despite this, the
biological mechanisms of PTSD are not fully understood. Also, finding a unitary biomarker of PTSD has
proven difficult. This is likely because of the diversity of presentation, and the potential that different
biological subtypes exist within the clinical symptom profile.
Recently, advanced computational tools have emerged that can parse this high level of complexity and
thus hold significant promise to develop individualized and neurobiologically-based and objective
biomarkers of PTSD. The primary research objective of this CDA-2 is to develop an objective brain-
based identification that can be used to individualize diagnosis and treatment for Veterans suffering
from PTSD. The first specific aim will evaluate whether a machine learning algorithm can link PTSD
symptoms with the information in individuals’ neuroimaging data. The second specific aim will test
whether a machine learning algorithm can be used to link brain networks to DSM-5 PTSD symptom
clusters in order to enable mapping of network abnormalities to commonly recognized DSM-5 domains
of PTSD. The candidate’s exploratory research objective will investigate the relationship between
individual PTSD symptoms and connectivity-based networks to determine if symptoms can be grouped
differently to make a modified and data-driven PTSD diagnostic tool.
The findings from this study will provide foundational data for future Merit-funded work and lay the
foundation for a programmatic, independent VA career bringing data science to research and clinical
care. The protected time funded by this CDA award will allow the candidate to participate in activities
imparting a unique combination of skills and perspectives that will allow him to bridge basic and clinical
science in the service of finding better treatment options for Veterans with PTSD. This CDA-2 will allow
for the time to gain the critical skills needed to integrate neuroimaging, machine learning, and advanced
analytic methods. The candidate is well-established within the VA system and currently holds a staff
physician (psychiatrist) position. He is also actively involved in clinical research with a successful track
record of conducting clinical and translational studies. The candidate’s mentorship team is comprised of
VA clinicians an...

## Key facts

- **NIH application ID:** 10260058
- **Project number:** 1IK2CX002115-01A2
- **Recipient organization:** PROVIDENCE VA  MEDICAL CENTER
- **Principal Investigator:** Amin Zand Vakili
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10260058

## Citation

> US National Institutes of Health, RePORTER application 10260058, Developing Computational Nosologies of Posttraumatic Stress Disorder (PTSD) (1IK2CX002115-01A2). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10260058. Licensed CC0.

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