Dataset Information

Value-Based Choice, Contingency Learning, and Suicidal Behavior in Mid- and Late-Life Depression.

ABSTRACT: BACKGROUND:Suicidal behavior is associated with impaired decision making in contexts of uncertainty. Existing studies, however, do not definitively address whether suidice attempers have 1) impairment in learning from experience or 2) impairment in choice based on comparison of estimated option values. Our reinforcement learning model-based behavioral study tested these hypotheses directly in middle-aged and older suicide attempters representative of those who die by suicide. METHODS:Two samples (sample 1, n = 135; sample 2, n = 125) of suicide attempters with depression (nattempters = 54 and 39, respectively), suicide ideators, nonsuicidal patients with depression, and healthy control participants completed a probabilistic three-choice decision-making task. A second experiment in sample 2 experimentally dissociated long-term learned value from reward magnitude. Analyses combined computational reinforcement learning and mixed-effects models of decision times and choices. RESULTS:With regard to learning, suicide attempters (vs. all comparison groups) were less sensitive to one-back reinforcement, as indicated by a reduced effect on both choices and decision times. Learning deficits scaled with attempt lethality and were partially explained by poor cognitive control. With regard to value-based choice, suicide attempters (vs. all comparison groups) displayed abnormally long decision times when choosing between similarly valued options and were less able to distinguish between the best and second-best options. Group differences in value-based choice were robust to controlling for cognitive performance, comorbidities, impulsivity, psychotropic exposure, and possible brain damage from attempts. CONCLUSIONS:Serious suicidal behavior is associated with impaired reward learning, likely undermining the search for alternative solutions. Attempted suicide is associated with impaired value comparison during the choice process, potentially interfering with the consideration of deterrents and alternatives in a crisis.

SUBMITTER: Dombrovski AY

PROVIDER: S-EPMC6380943 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Value-Based Choice, Contingency Learning, and Suicidal Behavior in Mid- and Late-Life Depression.

Dombrovski Alexandre Y AY Hallquist Michael N MN Brown Vanessa M VM Wilson Jonathan J Szanto Katalin K

Biological psychiatry 20181018 6

<h4>Background</h4>Suicidal behavior is associated with impaired decision making in contexts of uncertainty. Existing studies, however, do not definitively address whether suidice attempers have 1) impairment in learning from experience or 2) impairment in choice based on comparison of estimated option values. Our reinforcement learning model-based behavioral study tested these hypotheses directly in middle-aged and older suicide attempters representative of those who die by suicide.<h4>Methods< ...[more]

PMID: 30502081

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Project description:OBJECTIVE:Clinical heterogeneity is a key challenge to understanding suicidal risk, as different pathways to suicidal behavior are likely to exist. We aimed to identify such pathways by uncovering latent classes of late-life depression cases and relating them to prior and future suicidal behavior. METHODS:Data were collected from June 2010 to September 2015. In this longitudinal study we examined distinct associations of clinical and cognitive/decision-making factors with suicidal behavior in 194 older (50+ years) nondemented, depressed patients; 57 nonpsychiatric healthy controls provided benchmark data. The DSM-IV was used to establish diagnostic criteria. We identified multivariate patterns of risk factors, defining clusters based on personality traits, perceived social support, cognitive performance, and decision-making in an analysis blinded to participants' history of suicidal behavior. We validated these clusters using past and prospective suicidal ideation and behavior. RESULTS:Of 5 clusters identified, 3 were associated with high risk for suicidal behavior: (1) cognitive deficits, dysfunctional personality, low social support, high willingness to delay future rewards, and overrepresentation of high-lethality attempters; (2) high-personality pathology (ie, low self-esteem), minimal or no cognitive deficits, and overrepresentation of low-lethality attempters and ideators; (3) cognitive deficits, inability to delay future rewards, and similar distribution of high- and low-lethality attempters. There were significant between-cluster differences in number (P < .001) and lethality (P = .002) of past suicide attempts and in the likelihood of future suicide attempts (P = .010, 30 attempts by 22 patients, 2 fatal) and emergency psychiatric hospitalizations to prevent suicide (P = .005, 31 participants). CONCLUSIONS:Three pathways to suicidal behavior in older patients were found, marked by (1) very high levels of cognitive and dispositional risk factors suggesting a dementia prodrome, (2) dysfunctional personality traits, and (3) impulsive decision-making and cognitive deficits.

Project description:Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using differing combinations of sociodemographic characteristics, baseline clinical self-reports, cognitive tests, and structural magnetic resonance imaging (MRI) features to predict treatment outcomes in late-life depression (LLD). Methods: Data were combined from two clinical trials conducted with depressed adults aged 60 and older, including response to escitalopram (N = 32, NCT01902004) and Tai Chi (N = 35, NCT02460666). Remission was defined as a score of 6 or less on the 24-item Hamilton Rating Scale for Depression (HAMD) at the end of 24 weeks of treatment. Features subsets were constructed from baseline sociodemographic and clinical features, gray matter volumes (GMVs), or both. Three classification algorithms were compared: (1) Support Vector Machine-Radial Bias Function (SVMRBF), (2) Random Forest (RF), and (3) Logistic Regression (LR). A repeated 5-fold cross-validation approach with a wrapper-based feature selection method was used for model fitting. Model performance metrics included Area under the ROC Curve (AUC) and Matthews correlation coefficient (MCC). Cross-validated performance significance was tested by permutation analysis. Classifiers were compared by Cochran's Q and post-hoc pairwise comparisons using McNemar's Chi-Square test with Bonferroni correction. Results: For the RF and SVMRBF algorithms, the combined feature set outperformed the clinical and GMV feature sets with a final cross-validated AUC of 0.83 ± 0.11 and 0.80 ± 0.11, respectively. Both classifiers passed permutation analysis. The LR algorithm performed best using GMV features alone (AUC 0.79 ± 0.14) but failed to pass permutation analysis using any feature set. Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores. Conclusion: This preliminary exploration into the use of ML and multi-modal data to identify predictors of general treatment response in LLD indicates that integration of clinical and structural MRI features significantly increases predictive capability. Identified features are among those previously implicated in geriatric depression, encouraging future work in this arena.

Project description:BackgroundTwo treatments for smoking cessation--varenicline and bupropion--carry Boxed Warnings from the U.S. Food and Drug Administration (FDA) about suicidal/self-injurious behavior and depression. However, some epidemiological studies report an increased risk in smoking or smoking cessation independent of treatment, and differences between drugs are unknown.MethodologyFrom the FDA's Adverse Event Reporting System (AERS) database from 1998 through September 2010 we selected domestic, serious case reports for varenicline (n = 9,575), bupropion for smoking cessation (n = 1,751), and nicotine replacement products (n = 1,917). A composite endpoint of suicidal/self-injurious behavior or depression was defined as a case with one or more Preferred Terms in Standardized MedDRA Query (SMQ) for those adverse effects. The main outcome measure was the ratio of reported suicide/self-injury or depression cases for each drug compared to all other serious events for that drug.ResultsOverall we identified 3,249 reported cases of suicidal/self-injurious behavior or depression, 2,925 (90%) for varenicline, 229 (7%) for bupropion, and 95 (3%) for nicotine replacement. Compared to nicotine replacement, the disproportionality results (OR (95% CI)) were varenicline 8.4 (6.8-10.4), and bupropion 2.9 (2.3-3.7). The disproportionality persisted after excluding reports indicating concomitant therapy with any of 58 drugs with suicidal behavior warnings or precautions in the prescribing information. An additional antibiotic comparison group showed that adverse event reports of suicidal/self-injurious behavior or depression were otherwise rare in a healthy population receiving short-term drug treatment.ConclusionsVarenicline shows a substantial, statistically significant increased risk of reported depression and suicidal/self-injurious behavior. Bupropion for smoking cessation had smaller increased risks. The findings for varenicline, combined with other problems with its safety profile, render it unsuitable for first-line use in smoking cessation.

Dataset Information

Value-Based Choice, Contingency Learning, and Suicidal Behavior in Mid- and Late-Life Depression.

Publications

Value-Based Choice, Contingency Learning, and Suicidal Behavior in Mid- and Late-Life Depression.

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