Dataset Information

Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task.

ABSTRACT:

Background

Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide.

Method

136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences.

Results

On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window. In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA.

Conclusions

These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.

SUBMITTER: Myers CE

PROVIDER: S-EPMC9883589 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task.

Myers Catherine E CE Dave Chintan V CV Callahan Michael M Chesin Megan S MS Keilp John G JG Beck Kevin D KD Brenner Lisa A LA Goodman Marianne S MS Hazlett Erin A EA Niculescu Alexander B AB St Hill Lauren L Kline Anna A Stanley Barbara H BH Interian Alejandro A

Psychological medicine 20220728 9

<h4>Background</h4>Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide.<h4>Method</h4>136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, ...[more]

PMID: 35899406

Similar Datasets

Project description:BackgroundLearning from feedback - adapting behavior based on reinforcing and punishing outcomes - has been implicated in numerous psychiatric disorders, including substance misuse, post-traumatic stress disorder, and depression; an emerging literature suggests it may also play a role in suicidality. This study examined whether a feedback-based learning task with rewarding, punishing and ambiguous outcomes, followed by computational modeling, could improve near-term prospective prediction of suicide attempt in a high-risk sample.MethodVeterans (N=60) at high-risk for suicide were tested on a task of reward- and punishment-based learning, at multiple sessions across a one-year period. Each session was coded according to whether the participant had (1) an actual suicide attempt (ASA); (2) another suicide-related event (OtherSE) such as suicidal behavior or suicidal ideation-related hospital admission (but not an ASA); or (3) neither (noSE) in the next 90 days. Computational modeling was used to estimate latent cognitive variables including learning rates from positive and negative outcomes, and the subjective value of ambiguous feedback.ResultsOptimal responding on the reward-based trials was positively associated with upcoming ASA, and remained predictive even after controlling for other standard clinical variables such as current suicidal ideation severity and prior suicide attempts. Computational modeling revealed that patients with upcoming ASA tended to view ambiguous outcomes as similar to weak punishment, while OtherSE and noSE both tended to view the ambiguous outcome as similar to weak reward. Differences in the reinforcement value of the neutral outcome remained predictive for ASA even after controlling for current suicidal ideation and prior suicide attempts.ConclusionA reinforcement learning task with ambiguous neutral outcomes may provide a useful tool to help predict near-term risk of ASA in at-risk patients. While most individuals interpret ambiguous feedback as mildly reinforcing (a "glass half full" interpretation), those with upcoming ASA tend to view it as mildly punishing (a "glass half empty" interpretation). While the current results are based on a very small sample with relatively few ASA events, and require replication in a larger sample, they provide support for the role of negative biases in feedback-based learning in the cognitive profile of suicide risk.

Project description:ImportanceSuicide attempts are strong predictors of suicide, a leading cause of adolescent mortality. Suicide attempts are highly familial, although the mechanisms of familial transmission are not understood. Better delineation of these mechanisms could help frame potential targets for prevention.ObjectiveTo examine the mechanisms and pathways by which suicidal behavior is transmitted from parent to child.Design, setting, and participantsIn this prospective study conducted from July 15, 1997, through June 21, 2012, a total of 701 offspring aged 10 to 50 years (mean age, 17.7 years) of 334 clinically referred probands with mood disorders, 191 (57.2%) of whom had also made a suicide attempt, were followed up for a mean of 5.6 years.Main outcomes and measuresThe primary outcome was a suicide attempt. Variables were examined at baseline, intermediate time points, and the time point proximal to the attempt. Participants were assessed by structured psychiatric assessments and self-report and by interview measures of domains hypothesized to be related to familial transmission (eg, mood disorder and impulsive aggression).ResultsAmong the 701 offspring, 44 (6.3%) had made a suicide attempt before participating in the study, and 29 (4.1%) made an attempt during study follow-up. Multivariate logistic regression revealed that proband suicide attempt was a predictor of offspring suicide attempt (odds ratio [OR], 4.79; 95% CI, 1.75-13.07), even controlling for other salient offspring variables: baseline history of mood disorder (OR, 4.20; 95% CI, 1.37-12.86), baseline history of suicide attempt (OR, 5.69; 95% CI, 1.94-16.74), and mood disorder at the time point before the attempt (OR, 11.32; 95% CI, 2.29-56.00). Path analyses were consistent with these findings, revealing a direct effect of proband attempt on offspring suicide attempt, a strong effect of offspring mood disorder at each time point, and impulsive aggression as a precursor of mood disorder.Conclusions and relevanceParental history of a suicide attempt conveys a nearly 5-fold increased odds of suicide attempt in offspring at risk for mood disorder, even after adjusting for the familial transmission of mood disorder. Interventions that target mood disorder and impulsive aggression in high-risk offspring may attenuate the familial transmission of suicidal behavior.

Dataset Information

Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task.

Background

Method

Results

Conclusions

Publications

Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets