# Examination of resting state functional connectivity as a marker of acute suicide risk

> **NIH VA I01** · NORTHPORT VA MEDICAL CENTER · 2020 · —

## Abstract

This Merit Award resubmission in response to the RFA CX-18-023 addresses one of the top VA priorities,
suicide prevention. Recognizing those at the highest risk of suicidal behavior with an imminent need for acute
medical intervention remains a fallible subjective decision based on known risk and protective factors.
Unfortunately, the contribution of each of these risk factors is small. Thus, there is an urgent need to develop
adequate algorithms to predict imminent suicide risk. The overall objective of this application is to test the
value of intrinsic brain activity as a marker of acute suicidal behavior and examine potential clinical correlates.
Our central hypothesis is that a neural pattern classifier based on resting state functional connectivity will
identify acute risk for suicidal behavior, by discriminating recent suicide attempters from current suicidal
ideators, in a reproducible and specific fashion. This application is the progression of our pilot work that used
machine learning to show that neural pattern classification of resting state-fMRI data allowed a specific
differentiation of recent suicidal attempters (within three days of the attempt) from patients currently endorsing
suicidal ideation with 79% accuracy. We plan to test our central hypothesis by using resting state functional
connectivity to discriminate depressed Veterans who recently attempted suicide (n=80) from depressed
Veterans with suicidal ideation (n=80), and non-suicidal stress controls (n=40). We will build on our previous
work, replicating the same strategy that resulted in a trained classifier in a larger independent and more
heterogeneous sample, and test whether the addition of demographic, clinical, cognitive and biological
variables associated with suicide may improve the classifier accuracy (AIM 1). We will examine the temporal
specificity of our classifier testing its ability to discriminate: a) clinically stable suicide attempters: attempters
rescanned 5-8 days later when symptom severity had subsided, from suicidal ideators, and b) depressed
patients with and without lifetime history of suicide attempts. We will also scan a stress-control cohort of age-,
sex-matched non-suicidal controls hospitalized in medical-surgical units and attempt to distinguish them from
suicidal ideators (AIM 2). Exploratory AIM1 will be a step towards translation, we will examine resting state
functional connectivity obtained in 1.5T and 3T scanners. In exploratory AIM2 we will attempt to identify a
responsible mechanism by using regression analysis between the most discriminating connectivity pathways
between recent attempters and ideators and suicide attempt intent and lethality. We aim to test the
reproducibility and specificity of a neural pattern classifier to discriminate recent suicide attempters from
current suicidal ideators as a proxy measure of acute suicide risk. This neural pattern classifier, directly based
on the function of the ultimate agent of human behavi...

## Key facts

- **NIH application ID:** 9780783
- **Project number:** 1I01CX001847-01A1
- **Recipient organization:** NORTHPORT VA MEDICAL CENTER
- **Principal Investigator:** RICARDO CACEDA
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2020-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9780783

## Citation

> US National Institutes of Health, RePORTER application 9780783, Examination of resting state functional connectivity as a marker of acute suicide risk (1I01CX001847-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9780783. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*