# Identifying neural signatures of current and future suicidal thoughts and behaviors

> **NIH VA IK1** · VA BOSTON HEALTH CARE SYSTEM · 2022 · —

## Abstract

Death by suicide has been steadily increasing in the last 20 years, and this risk is elevated among veterans,
particularly those with traumatic brain injury and psychiatric diagnoses. However, in the last 50 years,
improvements in identifying those at greatest risk for suicide, typically via self-report, have been limited.
Therefore, we propose that complementary and objective neurobiological markers of suicidal thoughts
and behaviors (STBs) can improve the identification of those at greatest risk. Preliminary brain markers
related to STBs have been identified in the cognitive control network (CCN), limbic network (LN), and the
default mode network (DMN). However, reliable and predictive brain markers of STBs remain elusive as there
are several methodological limitations in the previous literature. This study will address these limitations and
investigate neural markers of STBs using two different neuroimaging methods: resting-state fMRI and brain
activity during the suicide Implicit Association Task (s-IAT). Resting-state provides a stable and reliable
measure of intrinsic brain connectivity, whereas the behavior on the s-IAT (known as the d-score) measures
the strength of a participant’s implicit association between self and death. The d-score on the s-IAT is a better
predictor of future STBs than self-report, but little is known about neural activity related to the s-IAT.
DESIGN AND METHODS. This application utilizes a close collaboration with the Translational Research
Center for TBI and Stress Disorders (TRACTS), which has a comprehensive psychiatric and neuroimaging
database of over 800 post-9/11. This dataset provides the unique opportunity to compare STB groups with
control groups matched on psychiatric diagnoses, like depression and PTSD, that are differentiable only by the
absence of STBs (psychiatric controls; PCs). Using this existing dataset, resting-state fMRI will be used to
identify brain markers related to both a history of suicide attempt (SA) and current suicidal ideation (SI). Next,
we will determine if these brain markers predict future STBs using state-of-the-art machine learning
techniques. Lastly, an additional 100 veterans will complete the s-IAT with concurrent fMRI as part of their
participation in TRACTS. This will allow us to investigate the feasibility of detecting neural makers related to
implicit associations between self and death (d-score).
Aim 1: Identify neural signatures of previous suicide attempt and current suicidal ideation (n = 800, ~5% with
history of suicide attempt, ~10% with suicidal ideation). Hypothesis 1. We will identify neural markers in the LN,
CCN, and DMN, that differentiate those with STBs from PCs.
Aim 2: Determine if the STB neural markers identified in Aim 1 predict future STBs 1-2 years later at a follow-
up assessment (n=400; ~5% attempt suicide within the next 1-2 years and ~10% reporting current SI at follow-
up). Hypothesis 2: Models using the SA and SI neural markers identified in Ai...

## Key facts

- **NIH application ID:** 10478372
- **Project number:** 1IK1CX002541-01
- **Recipient organization:** VA BOSTON HEALTH CARE SYSTEM
- **Principal Investigator:** Audreyana Jagger-Rickels
- **Activity code:** IK1 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10478372

## Citation

> US National Institutes of Health, RePORTER application 10478372, Identifying neural signatures of current and future suicidal thoughts and behaviors (1IK1CX002541-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10478372. Licensed CC0.

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