# Reward Learning in Late-Life Suicidal Behavior

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $772,613

## Abstract

ABSTRACT
The US is facing rising suicide rates. Yet, we have only a limited understanding of why some people, but not
others, progress from contemplating to attempting suicide. In the past funding period, we have shown that
depressed older adults whose decision-making is impaired are more likely to progress from suicidal ideation to
action. Specifically, using decision experiments, computational modeling, and fMRI, we have found replicable
deficits in learning and choice processes paralleled by altered ventromedial and dorsolateral prefrontal abstract
learning signals. In this renewal application, we propose to extend these findings by examining how people at
risk for suicide make decisions under cognitive and emotional demands that are more representative of the
suicidal crisis. In our computational framework these demands include (i) a high information load and (ii)
constraints on information processing imposed by time pressure and impending threats. We have developed
and validated new experimental and computational methods for studying information-processing bottlenecks
during decision-making. Specifically, our reinforcement learning computational model applied to behavioral and
neuroimaging data, enables us to examine how people use their limited neurocomputational resources to make
good decisions under high information load. Our preliminary studies show that decision-making in this context
(i) relies on resource-rational strategies for managing information load, (ii) is subserved by dorsal attention and
cingulo-opercular networks, (iii) is likely disrupted in attempted suicide, (iv) a deficit paralleled by abnormal
dorsal attention network responses to information load. We thus propose to test the general hypothesis that
people at risk for suicide are prone to information-processing bottlenecks arising from alterations in these
cortical networks. We will perform decision experiments and cognitive computational models (Aim 1) in a
discovery sample and a non-overlapping replication sample (n = 200 each) to ensure that findings are robust to
the clinical and cognitive heterogeneity of suicidal behavior. Both samples will include individuals maximally
representative of suicide victims, namely older depressed suicide attempters, about half of whom survived
near-lethal attempts. Functional neuroimaging experiments manipulating information load will interrogate the
neurocomputational dynamics of the dorsal attention network and cingulo-opercular network during decision-
making in one sample (n = 200, Aim 2). A careful characterization of psychopathology, personality, cognition,
psychotropic exposure and brain damage from suicide attempts will allow us to control for key confounds. The
interdisciplinary team has expertise in mechanisms of suicidal behavior (Dombrovski), decision neuroscience
(Dombrovski, McGuire, Hallquist), imaging methods (Hallquist), and suicide risk management (Szanto,
Dombrovski). This work aligns with a key objective of the NI...

## Key facts

- **NIH application ID:** 9970968
- **Project number:** 2R01MH100095-06A1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Alexandre Y. Dombrovski
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $772,613
- **Award type:** 2
- **Project period:** 2014-08-01 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9970968

## Citation

> US National Institutes of Health, RePORTER application 9970968, Reward Learning in Late-Life Suicidal Behavior (2R01MH100095-06A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9970968. Licensed CC0.

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