# Identifying neural fingerprints of suicidality

> **NIH VA I21** · VA BOSTON HEALTH CARE SYSTEM · 2022 · —

## Abstract

Death by suicide has been steadily increasing in the last 20 years, and the social isolation and financial stress
associated with the current pandemic may unfortunately provide the perfect conditions for a dramatic
increase in suicidality. This risk is elevated among veterans, particularly those with traumatic brain injury
and psychiatric diagnoses, and the current public health crisis has alarming implications for mental health.
Current suicide prevention practices are largely informed by the evaluation of suicidal thoughts and
behaviors (STBs), and other clinical characteristics. One significant limitation in suicide risk and prevention
is the exclusive reliance on self-report, which is severely limited in its effectiveness to predict future suicide
attempts and deaths, and does not identify individuals who do not disclose thoughts or acts of self-harm. To
test an alternative to current modes of prevention, we propose that complementary neuroimaging-
based biomarkers of suicide risk can improve the identification of at-risk individuals.
DESIGN AND METHODS. Our lab is a leader in the application of cognitive neuroscience tools toward
precision psychiatry. We accomplish this by acquiring functional MRI, known to be consistently reproducible
within an individual but subject to great variability across individuals, making it unique to the person, as well
as their neuropsychiatric and neurocognitive profile. In this proposal, we will use these scans to parse out
brain connections across large-scale networks including emotional and inhibitory control circuitry that are
implicated in STBs. Then, by applying machine learning techniques, we will isolate the pattern of brain
activity that identifies suicidal individuals. Further, we will validate these neural markers of STBs by
collecting new fMRI data from veterans at risk for suicide while they perform the Suicide Implicit Association
Test (S-IAT), an objective behavioral measure known to predict future suicide attempt. Finally, we will
determine if these neural markers of STBs are also associated with impaired daily and social functioning, a
contributor to STBs. This will be one of the first studies to leverage these methods towards the goal of
identifying individuals at risk for suicide. The proposed study will accomplish these aims using both existing
neuroimaging and clinical data from the Translational Research Center for TBI and Stress Disorders, as well
as ongoing data collection in which 60 additional veterans will complete the S-IAT with concurrent fMRI.
OBJECTIVES. Aim 1: Develop a neuroimaging-based model to detect individuals with current suicidal
ideation and/or a history of suicide attempt(s). Hypothesis 1. Model will distinguish suicidal individuals
from those who are not suicidal but who have comparable mental health conditions, based on functional
connectivity between brain regions associated with emotional regulation and inhibitory control.
Aim 2: Determine if the cross-sectional mode...

## Key facts

- **NIH application ID:** 10358809
- **Project number:** 1I21RX003727-01A1
- **Recipient organization:** VA BOSTON HEALTH CARE SYSTEM
- **Principal Investigator:** Michael Esterman
- **Activity code:** I21 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2022-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10358809

## Citation

> US National Institutes of Health, RePORTER application 10358809, Identifying neural fingerprints of suicidality (1I21RX003727-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10358809. Licensed CC0.

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