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

Exploration-Exploitation and Suicidal Behavior in Borderline Personality Disorder and Depression

Importance

Clinical theory and behavioral studies suggest that people experiencing suicidal crisis are often unable to find constructive solutions or incorporate useful information into their decisions, resulting in premature convergence on suicide and neglect of better alternatives. However, prior studies of suicidal behavior have not formally examined how individuals resolve the tradeoffs between exploiting familiar options and exploring potentially superior alternatives.

Objective

To investigate exploration and exploitation in suicidal behavior from the formal perspective of reinforcement learning.

Design, Setting, and Participants

Two case-control behavioral studies of exploration-exploitation of a large 1-dimensional continuous space and a 21-day prospective ambulatory study of suicidal ideation were conducted between April 2016 and March 2022. Participants were recruited from inpatient psychiatric units, outpatient clinics, and the community in Pittsburgh, Pennsylvania, and underwent laboratory and ambulatory assessments. Adults diagnosed with borderline personality disorder (BPD) and midlife and late-life major depressive disorder (MDD) were included, with each sample including demographically equated groups with a history of high-lethality suicide attempts, low-lethality suicide attempts, individuals with BPD or MDD but no suicide attempts, and control individuals without psychiatric disorders. The MDD sample also included a subgroup with serious suicidal ideation.

Main Outcomes and Measures

Behavioral (model-free and model-derived) indices of exploration and exploitation, suicide attempt lethality (Beck Lethality Scale), and prospectively assessed suicidal ideation.

Results

The BPD group included 171 adults (mean [SD] age, 30.55 [9.13] years; 135 [79%] female). The MDD group included 143 adults (mean [SD] age, 62.03 [6.82] years; 81 [57%] female). Across the BPD (χ23 = 50.68; P < .001) and MDD (χ24 = 36.34; P < .001) samples, individuals with high-lethality suicide attempts discovered fewer options than other groups as they were unable to shift away from unrewarded options. In contrast, those with low-lethality attempts were prone to excessive behavioral shifts after rewarded and unrewarded actions. No differences were seen in strategic early exploration or in exploitation. Among 84 participants with BPD in the ambulatory study, 56 reported suicidal ideation. Underexploration also predicted incident suicidal ideation (χ21 = 30.16; P < .001), validating the case-control results prospectively. The findings were robust to confounds, including medication exposure, affective state, and behavioral heterogeneity.

Conclusions and Relevance

The findings suggest that narrow exploration and inability to abandon inferior options are associated with serious suicidal behavior and chronic suicidal thoughts. By contrast, individuals in this study who engaged in low-lethality suicidal behavior displayed a low threshold for taking potentially disadvantageous actions.

Using Drift Diffusion and RL Models to Disentangle Effects of Depression On Decision-Making vs. Learning in the Probabilistic Reward Task

The Probabilistic Reward Task (PRT) is widely used to investigate the impact of Major Depressive Disorder (MDD) on reinforcement learning (RL), and recent studies have used it to provide insight into decision-making mechanisms affected by MDD. The current project used PRT data from unmedicated, treatment-seeking adults with MDD to extend these efforts by: (1) providing a more detailed analysis of standard PRT metrics-response bias and discriminability-to better understand how the task is performed; (2) analyzing the data with two computational models and providing psychometric analyses of both; and (3) determining whether response bias, discriminability, or model parameters predicted responses to treatment with placebo or the atypical antidepressant bupropion. Analysis of standard metrics replicated recent work by demonstrating a dependency between response bias and response time (RT), and by showing that reward totals in the PRT are governed by discriminability. Behavior was well-captured by the Hierarchical Drift Diffusion Model (HDDM), which models decision-making processes; the HDDM showed excellent internal consistency and acceptable retest reliability. A separate "belief" model reproduced the evolution of response bias over time better than the HDDM, but its psychometric properties were weaker. Finally, the predictive utility of the PRT was limited by small samples; nevertheless, depressed adults who responded to bupropion showed larger pre-treatment starting point biases in the HDDM than non-responders, indicating greater sensitivity to the PRT's asymmetric reinforcement contingencies. Together, these findings enhance our understanding of reward and decision-making mechanisms that are implicated in MDD and probed by the PRT.

Depression in older women who died by suicide: associations with other suicide contributors and suicide methods

Suicides among older women have received little research attention. In this study based on the 2017-2019 National Violent Death Reporting System data, we examined the prevalence of depression in older female suicide decedents (N = 3,061), associations between depression and other suicide precipitants, and the associations between suicide methods and depression. Descriptive statistics and generalized linear models (GLM) for a Poisson distribution with a log link were used to examine the research questions. Of the decedents, 15.0% had depressed mood without a reported diagnosis and 41.8% had a depression diagnosis. Nearly one-half of the decedents with reported depression were receiving mental health/substance use treatment at the time of injury. The likelihood of depression was lower among those who were age 85 and older compared to those were age 65-74, but higher among those who had anxiety disorder (IRR = 1.50, 95% CI = 1.33-1.69), history of suicidal ideation (IRR = 1.22, 95% CI = 1.10-1.35), history of suicide attempt (IRR = 1.27, 95% CI = 1.14-1.41), and bereavement problems (IRR = 1.45, 95% CI = 1.27-1.65). Those who had depression were less likely to have used firearms (IRR = 0.85, 95% CI = 0.75-0.97) but more likely to have used hanging/suffocation (IRR = 1.37, 95% CI = 1.13-1.67). The findings show that gun ownership was likely an important factor for firearm use. The high prevalence of depressed mood and/or depression diagnosis among older female suicide decedents at the time of their fatal injury underscores the importance of assessing depression and providing evidence-based depression treatment as an essential suicide prevention approach.

Validation and application of the Chinese version of the Columbia-Suicide Severity Rating Scale: Suicidality and cognitive deficits in patients with major depressive disorder

BACKGROUND

Depression is a significant risk factor for death by suicide. Additionally, patients with depression who have neurocognitive dysfunction are at a higher risk of exhibiting suicidal behaviors. We aimed to validate the Chinese version of the Columbia Suicide Severity Rating Scale (C-SSRS) and then employ it to examine the association between suicidality and cognitive deficits in patients with Major Depressive Disorder (MDD).

METHODS

Data from 456 patients with MDD who underwent baseline assessment and 3-month follow-up were used for psychometric validation of the C-SSRS. 430 patients were divided into a mild cognitive impairment group (N = 390) and a severe cognitive impairment group (N = 40) using cluster analysis and compared with healthy controls. The relationship between C-SSRS scores and the degree of cognitive impairment was analyzed.

RESULTS

1) The Chinese version of C-SSRS demonstrated good internal consistency (Cronbach's alpha = 0.884/0.842), convergent and divergent validity. 2) The severity of suicidal ideation (SI), the intensity of SI, and the lifetime history of suicide attempts were significant independent predictors of short-term suicide attempts. 3) Higher worst-point lifetime SI severity and intensity scores in patients with MDD were significantly associated with severe cognitive impairment.

LIMITATIONS

The analysis of cognition and suicide was based on cross-sectional studies. Hence, changes in SI and cognitive function over time could not be analyzed.

CONCLUSIONS

The Chinese C-SSRS is a reliable and valid assessment tool for suicidal ideation and behavior in patients with depression. Suicidal ideation in patients with MDD is associated with cognitive dysfunction. These findings provide a reference for suicide prevention in patients with depression.

Mild cognitive impairment is associated with passive suicidal ideation in older adults: A population-based study

OBJECTIVE

To investigate the association between MCI and passive/active suicidal ideation in a population-based sample of older adults.

METHOD

The sample included 916 participants without dementia acquired from the two population-based studies Prospective Population Study of Women (PPSW) and the H70-study. Cognitive status was assessed using a comprehensive neuropsychiatric examination and classified according to the Winblad et al. criteria: 182 participants were classified as cognitively intact, 448 had cognitive impairment but did not fulfill MCI criteria and 286 were diagnosed with MCI. Passive/active suicidal ideation was assessed using the Paykel questions.

RESULTS

Passive or active suicidal ideation (any level) was reported by 16.0% of those with MCI and 1.1% of those who were cognitively intact. MCI was associated with past year life-weariness (OR 18.32, 95% CI 2.44-137.75) and death wishes (OR 5.30, 95% CI 1.19-23.64) in regression models adjusted for covariates including major depression. Lifetime suicidal ideation was reported more frequently in MCI (35.7%) than in cognitively intact participants (14.8%). MCI was associated with lifetime life-weariness (OR 2.90, 95% CI 1.67-5.05). Among individuals with MCI, impairments in memory and visuospatial ability were associated with both past year and lifetime life-weariness.

CONCLUSION

Our findings suggest reports of past year as well as lifetime passive suicidal ideation to be more frequent among individuals with MCI compared to those cognitively intact, indicating that individuals with MCI may constitute a high-risk group for suicidal behavior.

Suicide prevention and ketamine: insights from computational modeling

Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.

Suicidal urges and attempted suicide at multiple time scales in borderline personality disorder

BACKGROUND

There is strong evidence for an enduring suicidal diathesis among individuals with a history of suicide attempts, particularly among people diagnosed with borderline personality disorder (BPD). However, the progression of suicidal crises among people predisposed to suicidal behavior remains poorly understood.

METHODS

Via multilevel structural equation modeling we tested the hypothesis that a history of attempted suicide predicts a stronger dynamic link between affect and impulsivity with suicidal ideation (i.e., suicidal urges) - both moment-to-moment and day-to-day. 153 patients diagnosed with BPD, 105 of whom had a history of medically serious suicide attempts completed a 21-day ecological momentary assessment protocol (17,926 total assessments).

RESULTS

Individuals with higher average levels of negative affect reported more suicidal thoughts. Moments characterized by more negative affect, hostility, impulsivity, and less positive affect were also characterized by elevated suicidal ideation. For hostility and positive affect, these significant links generalized to the daily level. At the same time, for negative affect and hostility the within-person coupling was stronger among attempters in comparison to non-attempters, and these effects did not significantly differ across timescales.

LIMITATIONS

Follow-up studies replicating our findings of the dysregulation-suicidality nexus in clinically more diverse samples are needed.

CONCLUSIONS

The diathesis for suicidal behavior manifests in tighter dynamic links between negative affect or hostility and suicidal ideation. Because these within-person links were amplified in attempters compared to non-attempters, differential coupling patterns may index potentially lethal processes that generalize beyond BPD reflecting distinct diathesis components.

Belief updating in psychosis, depression and anxiety disorders: A systematic review across computational modelling approaches

Alterations in belief updating are proposed to underpin symptoms of psychiatric illness, including psychosis, depression, and anxiety. Key parameters underlying belief updating can be captured using computational modelling techniques, aiding the identification of unique and shared deficits, and improving diagnosis and treatment. We systematically reviewed research that applied computational modelling to probabilistic tasks measuring belief updating in stable and volatile (changing) environments, across clinical and subclinical psychosis (n = 17), anxiety (n = 9), depression (n = 9) and transdiagnostic samples (n = 9). Depression disorders related to abnormal belief updating in response to the valence of rewards, evidenced in both stable and volatile environments. Whereas psychosis and anxiety disorders were associated with difficulties adapting to changing contingencies specifically, indicating an inflexibility and/or insensitivity to environmental volatility. Higher-order learning models revealed additional difficulties in the estimation of overall environmental volatility across psychosis disorders, showing increased updating to irrelevant information. These findings stress the importance of investigating belief updating in transdiagnostic samples, using homogeneous experimental and computational modelling approaches.

Dissociated deficits of anticipated and experienced regret in at-risk suicidal individuals

Backgrounds

Decision-making deficits have been reported as trans-diagnostic characteristics of vulnerability to suicidal behaviors, independent of co-existing psychiatric disorders. Individuals with suicidal behaviors often regret their decision to attempt suicide and may have impairments in future-oriented processing. However, it is not clear how people with suicidal dispositions use future-oriented cognition and past experience of regret to guide decision-making. Here, we examined the processes of regret anticipation and experience in subclinical youth with and without suicidal ideation during value-based decision-making.

Methods

In total, 80 young adults with suicidal ideation and 79 healthy controls completed a computational counterfactual thinking task and self-reported measures of suicidal behaviors, depression, anxiety, impulsivity, rumination, hopelessness, and childhood maltreatment.

Results

Individuals with suicidal ideation showed a reduced ability to anticipate regret compared to healthy controls. Specifically, suicidal ideators’ experience of regret/relief was significantly different from that of healthy controls upon obtained outcomes, while their disappointment/pleasure experience was not significantly different from healthy controls.

Conclusion

These findings suggest that young adults with suicidal ideation have difficulty predicting the consequences or the future value of their behavior. Individuals with suicidal ideation showed impairments in value comparison and flat affect to retrospective rewards, whereas individuals with high suicidality showed blunted affect to immediate rewards. Identifying the counterfactual decision-making characteristics of at-risk suicidal individuals may help to elucidate measurable markers of suicidal vulnerability and identify future intervention targets.

Depression and approach-avoidance decisions to emotional displays: The role of anhedonia

Depression is linked to dysfunctional appetitive and aversive motivational systems and effort-based decision-making, yet whether such deficits extend to social decisions remains unclear. Participants (23 non-depressed, 48 depressed - 24 with a past history of suicide attempt) completed a social decision-making task consisting in freely choosing whether to approach or avoid individuals displaying happy or angry expressions. Occasionally, participants had to make a further effort (change button press) to obtain the desired outcome. All participants preferentially avoided anger on their first choice. Yet, depressed patients less often chose to approach happy individuals, as a function of anhedonia severity. Depressed patients were also less inclined than controls to change their response when the anticipated outcome of their first choice was undesirable (approach angry and avoid happy). Again, such effect correlated with anhedonia severity. Our results support that both altered valuation and willingness to exert effort impact approach-avoidance decisions in social contexts in depression. On this basis, we propose a new integrating framework for reconciling different hypotheses on the effect of depression and anhedonia on motivational responses to emotional stimuli.

Decision-making biases in suicide attempters with major depressive disorder: A computational modeling study using the balloon analog risk task (BART)

BACKGROUND

In the last decade, suicidality has been increasingly theorized as a distinct phenomenon from major depressive disorder (MDD), with unique psychological and neural mechanisms, rather than being mostly a severe symptom of MDD. Although decision-making biases have been widely reported in suicide attempters with MDD, little is known regarding what components of these biases can be distinguished from depressiveness itself.

METHODS

Ninety-three patients with current MDD (40 with suicide attempts [SA group] and 53 without suicide attempts [NS group]) and 65 healthy controls (HCs) completed psychometric assessments and the balloon analog risk task (BART). To analyze and compare decision-making components among the three groups, we applied a five-parameter Bayesian computational modeling.

RESULTS

Psychological assessments showed that the SA group had greater suicidal ideation and psychological pain avoidance than the NS group. Computational modeling showed that both MDD groups had higher risk preference and lower ability to learn and adapt from within-task observations than HCs, without differences between the SA and NS patient groups. The SA group also had higher loss aversion than the NS and HC groups, which had similar loss aversion.

CONCLUSIONS

Our BART and computational modeling findings suggest that psychological pain avoidance and loss aversion may be important suicide risk factor that are distinguishable from depression illness itself.

Self-injurious thoughts and behaviors and alterations in positive valence systems: A systematic review of the literature

Self-injurious thoughts and behaviors (SITBs), which include suicidal ideation (SI), suicide attempts (SA), and nonsuicidal self-injury (NSSI), are prevalent and associated with impairments in functioning and elevated risk of suicide deaths. Preventing suicide is a complex problem, with numerous systems likely contributing to the onset and maintenance of SITBs, and there is a critical need to identify more precise predictors of risk. Positive valence systems (PVS) are an understudied domain with promise for improving understanding of risk processes underlying SITBs. In this systematic review, we evaluate the evidence for the potential role of altered PVS function in SI, SA, and/or NSSI, including alterations in reward responsiveness, learning, and valuation assessed through behavioral, physiological and circuit measures. Results provide preliminary support for associations between distinct aspects of PVS function and alterations in SITBs. Specifically, SI appears to be characterized by low reward responsiveness, whereas little research has examined reward responsiveness in SA, and NSSI has been characterized by hyper-responsiveness to rewards. Alterations in reward learning and valuation are commonly examined in SA, with some evidence that they may be more strongly associated with attempts than SI or NSSI. At the same time, the literature is limited in that some constructs are commonly examined in one form of SITBs but not others. Further, research is predominantly cross-sectional and focused on adults, raising questions about the role of PVS function in developmental pathways to SITBs. We conclude by integrating the research to date and highlighting promising directions for future research.

Correlation Between Sleep Electroencephalogram, Brain-Derived Neurotrophic Factor, AVPR1B Gene Polymorphism, and Suicidal Behavior in Patients with Depression

The purpose of this article was to investigate the level of serum brain-derived neurotrophic factor and its relationship with suicidal symptoms and severity in mentally ill suicide attempt patients, and to explore the possible role of serum brain-derived neurotrophic factor (BDNF) in the occurrence of psychotic suicidal behavior. A retrospective analysis was performed on patients with depression in the neurology department of a hospital. General physical examination, neurological specialist examination, and cranial magnetic resonance imaging (MRI) examination were performed on any selected group. We applied the 24-item Hamilton Depression Scale, Hamilton Anxiety Scale, and Mini-Intelligence Mental State Scale, and performed polysomnography and electroencephalography (EEG) monitoring to conduct statistical analysis on sleep indicators. The amplitude of low-frequency fluctuation (ALFF) values of the right frontal gyrus, left posterior cerebellar lobe, right anterior cerebellar lobe, and right occipital lingual gyrus of the patient group were significantly lower than those of the control group. The ReHo values of the right superior parietal lobule, the left precuneus, and the right occipital lingual gyrus were significantly higher than those of the control group. The genotype and allele frequency distribution of FKBP5, AVPR1B, and CRHR2 gene SNPs had no significant difference between the case group and the control group (P > 0.05). The ReHo value of the precuneus is significantly correlated with the proportion of N3 sleep, and the dysfunction of the precuneus or default network may be related to the altered sleep structure in patients with depression. The GT and TT genotypes at rs9324924 of the NR3C1 gene are associated with suicide attempts.

A Systematic Review of Behavior-Outcome Psychological Assessments as Correlates of Suicidality

AIM

Identifying correlates of suicidality is an important goal for suicide researchers because these correlates may predict suicidal behaviors. Psychological tasks that assess sensitivity to the outcomes of actions (i.e., consequence-based learning) have been commonly used by researchers seeking to identify correlates of suicidality. This is likely due to the straightforward integration of the tasks within most theoretical frameworks for understanding suicidality. Contextual factors have been shown to have a substantial effect on responding in behavior-outcome tasks. However, the direct relevance of these factors as determinants of behavior in suicide research is not clear. Thus, the purpose of this review was to assess the role of context in tasks involving behavior-outcome relations in suicide research.

METHODS

Four databases were searched using terms from general learning theory. Articles that featured evaluation of tasks with hypothetical or real outcomes to differentiate suicidality were included.

RESULTS

Eighty-two studies met inclusion criteria. Across studies there were 27 different tasks. Most instances of tasks across studies involved rewards (76.9%), while others emphasized punishment (15.7%), social (5.6%), or virtual suicide (1.8%) outcomes. Differentiation of suicidality was detected by 43.4%, 64.7%, 83.3%, and 50% of tasks featuring reward, punishment, social contexts, and virtual suicide respectively. All but five studies were retrospective.

CONCLUSION

Tasks that more closely mimic contexts and outcomes related to suicide appear to produce more pronounced differentiation of people with suicidality from people without suicidality. The lack of prospective designs is an important limitation of the literature.HIGHLIGHTSTasks that involve punishment or social outcomes better discriminate suicidality.Reward-based tasks are overused in suicide research.The conditioning hypothesis of suicidality is closely aligned with the literature.Only 5 of 82 studies incorporated prospective measures.

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

BACKGROUND

Previous studies suggest that deficits in cognition may increase the risk of suicide. Our study aims to develop a machine learning (ML) algorithm-based suicide risk prediction model using cognition in patients with major depressive disorder (MDD).

METHODS

Participants comprised 52 depressed suicide attempters (DSA) and 61 depressed non-suicide attempters (DNS), and 98 healthy controls (HC). All participants were required to complete a series of questionnaires, the Suicide Stroop Task (SST) and the Iowa Gambling Task (IGT). The performance in IGT was analyzed using repeated measures ANOVA. ML with extreme gradient boosting (XGBoost) classification algorithm and locally explanatory techniques assessed performance and relative importance of characteristics for predicting suicide attempts. Prediction performances were compared with the area under the curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI).

RESULTS

DSA and DNS preferred to select the card from disadvantageous decks (decks "A" + "B") under risky situation (p = 0.023) and showed a significantly poorer learning effect during the IGT (F = 2.331, p = 0.019) compared with HC. Performance of XGBoost model based on demographic and clinical characteristics was compared with that of the model created after adding cognition data (AUC, 0.779 vs. 0.819, p > 0.05). The net benefit of model was improved and cognition resulted in continuous reclassification improvement with NRI of 5.3%. Several clinical dimensions were significant predictors in the XGBoost classification algorithm.

LIMITATIONS

A limited sample size and failure to include sufficient suicide risk factors in the predictive model.

CONCLUSION

This study demonstrate that cognitive deficits may serve as an important risk factor to predict suicide attempts in patients with MDD. Combined with other demographic characteristics and attributes drawn from clinical questionnaires, cognitive function can improve the predictive effectiveness of the ML model. Additionally, explanatory ML models can help clinicians detect specific risk factors for each suicide attempter within MDD patients. These findings may be helpful for clinicians to detect those at high risk of suicide attempts quickly and accurately, and help them make proactive treatment decisions.

Negative affectivity and disinhibition as moderators of an interpersonal pathway to suicidal behavior in borderline personality disorder

This longitudinal study examined whether personality traits moderate the link between interpersonal dysfunction and suicidal behavior in a high-risk sample of 458 individuals diagnosed with borderline personality disorder (BPD). Participants were assessed annually for up to 30 years (mean number of follow-ups = 7.82). Using multilevel structural equation modeling, we examined i) longitudinal, within-person relationships among interpersonal dysfunction, suicidal ideation, and suicide attempts; and ii) moderation of these relationships by negative affectivity and disinhibition. Negative affectivity predicted a stronger within-person coupling between interpersonal dysfunction and suicidal ideation. Disinhibition predicted a stronger coupling between ideation and suicide attempts. Assessing negative affectivity and disinhibition in a treatment setting may guide clinician vigilance toward those at highest risk for interpersonally triggered suicidal behaviors.

Work Expectations, Depressive Symptoms, and Passive Suicidal Ideation Among Older Adults: Evidence From the Health and Retirement Study

BACKGROUND AND OBJECTIVES

Employment and work transitions (e.g., retirement) influence mental health. However, how psychosocial contexts such as anticipation and uncertainty about work transitions, irrespective of the transitions themselves, relate to mental health is unclear. This study examined the relationships of work expectations with depressive symptoms, major depression episodes (MDE), and passive suicidal ideation over a 10-year period among the "Baby Boom" cohort of the Health and Retirement Study.

RESEARCH DESIGN AND METHODS

Analysis was limited to 13,247 respondents aged 53-70 observed from 2008 to 2018. Past-year depressive symptoms, MDE, and passive suicidal ideation were indexed using the Composite International Diagnostic Interview-Short Form. Expectations regarding working full-time after age 62 were assessed using a probability scale (0%-100%). Mixed-effect logistic regressions with time-varying covariates were used to assess the relationship of work expectations with mental health, accounting for demographics, health status, and functioning, and stratified by baseline employment status.

RESULTS

At baseline, higher work expectations were inversely associated with depressive symptoms. Longitudinally, higher expectations were associated with lower odds of depressive symptoms (odds ratio [OR] = 0.93, 95% CI: 0.91, 0.94). This association was more pronounced among respondents not working at baseline (ORNot working = 0.93 vs ORWorking = 0.96). Greater uncertainty (i.e., expectations near 50%) was also inversely associated with depressive symptoms. Results were similar for past-year MDE and passive suicidal ideation.

DISCUSSION AND IMPLICATIONS

Expectations (overall likelihood and uncertainty), as indicators of psychosocial context, provide insight into the processes that link work transitions with depression risk.

Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges

In the past decade, decision neuroscience and neuroeconomics have developed many new insights in the study of decision making. This review provides an overarching update on how the field has advanced in this time period. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across economics and psychology. First, we review progress on topics including reward learning, explore-exploit decisions, risk and ambiguity, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choices and new exciting developments in the prediction and neuroforecasting of future decisions. Finally, we consider how trends in decision-neuroscience research reflect progress toward resolving past challenges, discuss new and exciting applications of recent research, and identify new challenges for the field. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Emotion and Motivation.

Reinforcement Learning in Patients With Mood and Anxiety Disorders vs Control Individuals: A Systematic Review and Meta-analysis

IMPORTANCE

Computational psychiatry studies have investigated how reinforcement learning may be different in individuals with mood and anxiety disorders compared with control individuals, but results are inconsistent.

OBJECTIVE

To assess whether there are consistent differences in reinforcement-learning parameters between patients with depression or anxiety and control individuals.

DATA SOURCES

Web of Knowledge, PubMed, Embase, and Google Scholar searches were performed between November 15, 2019, and December 6, 2019, and repeated on December 3, 2020, and February 23, 2021, with keywords (reinforcement learning) AND (computational OR model) AND (depression OR anxiety OR mood).

STUDY SELECTION

Studies were included if they fit reinforcement-learning models to human choice data from a cognitive task with rewards or punishments, had a case-control design including participants with mood and/or anxiety disorders and healthy control individuals, and included sufficient information about all parameters in the models.

DATA EXTRACTION AND SYNTHESIS

Articles were assessed for inclusion according to MOOSE guidelines. Participant-level parameters were extracted from included articles, and a conventional meta-analysis was performed using a random-effects model. Subsequently, these parameters were used to simulate choice performance for each participant on benchmarking tasks in a simulation meta-analysis. Models were fitted, parameters were extracted using bayesian model averaging, and differences between patients and control individuals were examined. Overall effect sizes across analytic strategies were inspected.

MAIN OUTCOMES AND MEASURES

The primary outcomes were estimated reinforcement-learning parameters (learning rate, inverse temperature, reward learning rate, and punishment learning rate).

RESULTS

A total of 27 articles were included (3085 participants, 1242 of whom had depression and/or anxiety). In the conventional meta-analysis, patients showed lower inverse temperature than control individuals (standardized mean difference [SMD], -0.215; 95% CI, -0.354 to -0.077), although no parameters were common across all studies, limiting the ability to infer differences. In the simulation meta-analysis, patients showed greater punishment learning rates (SMD, 0.107; 95% CI, 0.107 to 0.108) and slightly lower reward learning rates (SMD, -0.021; 95% CI, -0.022 to -0.020) relative to control individuals. The simulation meta-analysis showed no meaningful difference in inverse temperature between patients and control individuals (SMD, 0.003; 95% CI, 0.002 to 0.004).

CONCLUSIONS AND RELEVANCE

The simulation meta-analytic approach introduced in this article for inferring meta-group differences from heterogeneous computational psychiatry studies indicated elevated punishment learning rates in patients compared with control individuals. This difference may promote and uphold negative affective bias symptoms and hence constitute a potential mechanistic treatment target for mood and anxiety disorders.

A Computational Model of Hopelessness and Active-Escape Bias in Suicidality

Currently, psychiatric practice lacks reliable predictive tools and a sufficiently detailed mechanistic understanding of suicidal thoughts and behaviors (STB) to provide timely and personalized interventions. Developing computational models of STB that integrate across behavioral, cognitive and neural levels of analysis could help better understand STB vulnerabilities and guide personalized interventions. To that end, we present a computational model based on the active inference framework. With this model, we show that several STB risk markers - hopelessness, Pavlovian bias and active-escape bias - are interrelated via the drive to maximize one's model evidence. We propose four ways in which these effects can arise: (1) increased learning from aversive outcomes, (2) reduced belief decay in response to unexpected outcomes, (3) increased stress sensitivity and (4) reduced sense of stressor controllability. These proposals stem from considering the neurocircuits implicated in STB: how the locus coeruleus - norepinephrine (LC-NE) system together with the amygdala (Amy), the dorsal prefrontal cortex (dPFC) and the anterior cingulate cortex (ACC) mediate learning in response to acute stress and volatility as well as how the dorsal raphe nucleus - serotonin (DRN-5-HT) system together with the ventromedial prefrontal cortex (vmPFC) mediate stress reactivity based on perceived stressor controllability. We validate the model by simulating performance in an Avoid/Escape Go/No-Go task replicating recent behavioral findings. This serves as a proof of concept and provides a computational hypothesis space that can be tested empirically and be used to distinguish planful versus impulsive STB subtypes. We discuss the relevance of the proposed model for treatment response prediction, including pharmacotherapy and psychotherapy, as well as sex differences as it relates to stress reactivity and suicide risk.

Search for solutions, learning, simulation, and choice processes in suicidal behavior

Suicide may be viewed as an unfortunate outcome of failures in decision processes. Such failures occur when the demands of a crisis exceed a person's capacity to (i) search for options, (ii) learn and simulate possible futures, and (iii) make advantageous value-based choices. Can individual-level decision deficits and biases drive the progression of the suicidal crisis? Our overview of the evidence on this question is informed by clinical theory and grounded in reinforcement learning and behavioral economics. Cohort and case-control studies provide strong evidence that limited cognitive capacity and particularly impaired cognitive control are associated with suicidal behavior, imposing cognitive constraints on decision-making. We conceptualize suicidal ideation as an element of impoverished consideration sets resulting from a search for solutions under cognitive constraints and mood-congruent Pavlovian influences, a view supported by mostly indirect evidence. More compelling is the evidence of impaired learning in people with a history of suicidal behavior. We speculate that an inability to simulate alternative futures using one's model of the world may undermine alternative solutions in a suicidal crisis. The hypothesis supported by the strongest evidence is that the selection of suicide over alternatives is facilitated by a choice process undermined by randomness. Case-control studies using gambling tasks, armed bandits, and delay discounting support this claim. Future experimental studies will need to uncover real-time dynamics of choice processes in suicidal people. In summary, the decision process framework sheds light on neurocognitive mechanisms that facilitate the progression of the suicidal crisis. This article is categorized under: Economics > Individual Decision-Making Psychology > Emotion and Motivation Psychology > Learning Neuroscience > Behavior.

Delay discounting in suicidal behavior: Myopic preference or inconsistent valuation?

Prior studies sought to explain the predisposition to suicidal behavior in terms of myopic preference for immediate versus delayed reward, generating mixed evidence. Data from gambling and bandit tasks, however, suggest that suboptimal decisions in suicidal individuals are explained by inconsistent valuation rather than myopic preferences. We tested these two alternative hypotheses using a delay discounting task in 622 adults (suicide attempters with depression, suicide ideators with depression, nonsuicidal participants with depression, and healthy controls) recruited across three sites through inpatient psychiatric units, mood disorders clinics, primary care, and advertisements. Multilevel models revealed group differences in valuation consistencies in all three samples, with high-lethality suicide attempters exhibiting less consistent valuation than all other groups in Samples 1 and 3 and less consistent valuation than the healthy controls or participants with depression in Sample 2. In contrast, group differences in preference for immediate versus delayed reward were observed only in Sample 1 and were due to the high-lethality suicide attempters displaying a weaker preference for immediate reward than low-lethality suicide attempters. The findings were robust to confounds such as cognitive functioning and comorbidities. Seemingly impulsive choices in suicidal behavior are explained by inconsistent reward valuation rather than a true preference for immediate reward. In a suicidal crisis, this inconsistency may result in a misestimation of the value of suicide relative to constructive alternatives and deterrents. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Psilocybin, a Naturally Occurring Indoleamine Compound, Could Be Useful to Prevent Suicidal Behaviors

The available interventions for people who are at risk of suicide have limited efficacy. Recently, research on new mental health treatments has started to consider psychedelic compounds, particularly psilocybin, a molecule with a few thousand years of history of use in human societies. The possible effects of psilocybin on suicidal ideation and behaviors have not been specifically studied yet; however, the current knowledge on the suicidal process and the available data on es/ketamine suggest that psylocibin could be used to modulate the thoughts and behavioral patterns in individuals who are at risk of suicidal behaviors. Here, we summarize the available evidence on the possible mechanisms underlying psilocybin positive effects on suicide risk. Major pathways related to suicidal behaviors that might be modulated by psylocibin include serotonin receptors. Specifically, psylocibin directly stimulates the serotonin 2A receptor (5HT_2A), targeting the inflammatory and oxidative stress pathways and leading to a rapid increase in brain plasticity and inflammation suppression and increases in cognitive flexibility, spirituality, and empathy. We also present preliminary epidemiological data and provide a rationale for studying psilocybin in individuals with suicidal ideation or who are at risk of suicidal behaviors. This review presents a framework to understand the basis for psilocybin use in individuals who are at risk of suicidal behaviors and calls for clinical studies.

Neurocomputational mechanism of controllability inference under a multi-agent setting

Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one’s influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one’s own action and outcome if there are no other outcome-relevant agents in an environment. However, if there are multiple agents who can influence the outcome, estimation of one’s genuine controllability requires exclusion of other agents’ possible influence. Here, we first investigated a computational and neural mechanism of controllability inference in a multi-agent setting. Our novel multi-agent Bayesian controllability inference model showed that other people’s action-outcome contingency information is integrated with one’s own action-outcome contingency to infer controllability, which can be explained as a Bayesian inference. Model-based functional MRI analyses showed that multi-agent Bayesian controllability inference recruits the temporoparietal junction (TPJ) and striatum. Then, this inferred controllability information was leveraged to increase motivated behavior in the vmPFC. These results generalize the previously known role of the striatum and vmPFC in single-agent controllability to multi-agent controllability, and this generalized role requires the TPJ in addition to the striatum of single-agent controllability to integrate both self- and other-related information. Finally, we identified an innate positive bias toward the self during the multi-agent controllability inference, which facilitated behavioral adaptation under volatile controllability. Furthermore, low positive bias and high negative bias were associated with increased daily feelings of guilt. Our results provide a mechanism of how our sense of controllability fluctuates due to other people in our lives, which might be related to social learned helplessness and depression.

How we perceive controllability over an outcome if there are multiple other agents who can simultaneously influence that outcome? Previous ‘single-agent’ studies showed that an agents’ inferred controllability depends on contingency between its own action and following outcome and this inference involves striatum. Here, we show that in a multi-agent setting, other people’s action-outcome contingency information is integrated with one’s own action-outcome contingency to infer controllability, which was explained as a biased Bayesian inference. Notably, bias in inference played an adaptive role under volatile controllability and was associated with a perception of guilt. Striatum and temporoparietal junction (TPJ) were involved in this multi-agent Bayesian controllability inference and this controllability information was leveraged to increase motivated behavior in the vmPFC. Our results first provide a neurocomputational mechanism of multi-agent controllability inference.

Decision-making in suicidal behavior: A systematic review and meta-analysis

Impaired decision-making (DM) is well-known in suicidal behavior (SB). We aimed to review the evidence on DM and its mediating factors in SB and perform a meta-analysis on DM assessed using the Iowa Gambling Task (IGT). We conducted a search on databases of papers published on DM and SB up to 2020: 46 studies were included in the systematic review, and 18 in the meta-analysis. For meta-analysis, we compared DM performance between suicide attempters (SAs) and patients (PCs) or healthy controls (HCs). The systematic review showed that SAs have greater difficulties in all DM domains. The meta-analysis found worse IGT performance among SAs in comparison with PCs and HCs. A meta-regression did not find differences for age, gender, psychiatric disorder, and clinical status. Our findings indicate that SAs exhibited deficits in DM under conditions of risk though not ambiguity. Worse DM was independent of age, gender, psychiatric disorder, and suggested that DM impairment could be considered a cognitive trait of suicidal vulnerability, a risk factor and an attribute of SAs.

Highlighting psychological pain avoidance and decision-making bias as key predictors of suicide attempt in major depressive disorder-A novel investigative approach using machine learning

OBJECTIVE

Predicting suicide is notoriously difficult and complex, but a serious public health issue. An innovative approach utilizing machine learning (ML) that incorporates features of psychological mechanisms and decision-making characteristics related to suicidality could create an improved model for identifying suicide risk in patients with major depressive disorder (MDD).

METHOD

Forty-four patients with MDD and past suicide attempts (MDD_SA, N = 44); 48 patients with MDD but without past suicide attempts (MDD_NS, N = 48-42 of whom with suicide ideation [MDD_SI, N = 42]), and healthy controls (HCs, N = 51) completed seven psychometric assessments including the Three-dimensional Psychological Pain Scale (TDPPS), and one behavioral assessment, the Balloon Analogue Risk Task (BART). Descriptive statistics, group comparisons, logistic regressions, and ML were used to explore and compare the groups and generate predictors of suicidal acts.

RESULTS

MDD_SA and MDD_NS differed in TDPPS total score, pain arousal and avoidance subscale scores, suicidal ideation scores, and relevant decision-making indicators in BART. Logistic regression tests linked suicide attempts to psychological pain avoidance and a risk decision-making indicator. The resultant key ML model distinguished MDD_SA/MDD_NS with 88.2% accuracy. The model could also distinguish MDD_SA/MDD_SI with 81.25% accuracy. The ML model using hopelessness could classify MDD_SI/HC with 94.4% accuracy.

CONCLUSION

ML analyses showed that motivation to avoid intolerable psychological pain, coupled with impaired decision-making bias toward under-valuing life's worth are highly predictive of suicide attempts. Analyses also demonstrated that suicidal ideation and attempts differed in potential mechanisms, as suicidal ideation was more related to hopelessness. ML algorithms show useful promises as a predictive instrument.

Social Disconnection in Late Life Suicide: An NIMH Workshop on State of the Research in Identifying Mechanisms, Treatment Targets, and Interventions

Both social disconnection and suicide are significant public health concerns among older adults, and social disconnection is associated with greater risk for suicide-related thoughts and behaviors in late life. We present a synthesis of research discussed during a workshop hosted by the National Institute of Mental Health on social disconnection and late-life suicide. Social disconnection is related to suicide risk in late life via a variety of mechanisms, including biological, behavioral, and psychological correlates. Researchers in several scientific fields have begun to establish these connections and identify targets for interventions to reduce risk in late life. While research has demonstrated that social connection is amenable to change, there is little research to date on the most evidence-based interventions to mitigate social disconnection or the related risks. However, there are several promising biological, behavioral, and psychological interventions that may target various mechanisms, as well as social disconnection itself. With a relative paucity of research in this area, these lines of study are ripe for innovative investigation. In order to most effectively advance the field, we must establish more consistent definitions of social connection and disconnection; more accurately measure and assess older adults' social needs; examine the most effective approaches and modalities for assessment and intervention; take into account important contextual factors; and apply a translational, convergent scientific approach.

Brain-based mechanisms of late-life depression: Implications for novel interventions

Late-life depression (LLD) is a particularly debilitating illness. Older adults suffering from depression commonly experience poor outcomes in response to antidepressant treatments, medical comorbidities, and declines in daily functioning. This review aims to further our understanding of the brain network dysfunctions underlying LLD that contribute to disrupted cognitive and affective processes and corresponding clinical manifestations. We provide an overview of a network model of LLD that integrates the salience network, the default mode network (DMN) and the executive control network (ECN). We discuss the brain-based structural and functional mechanisms of LLD with an emphasis on their link to clinical subtypes that often fail to respond to available treatments. Understanding the brain networks that underlie these disrupted processes can inform the development of targeted interventions for LLD. We propose behavioral, cognitive, or computational approaches to identifying novel, personalized interventions that may more effectively target the key cognitive and affective symptoms of LLD.

Executive dysfunction and risk of suicide in older adults: a population-based prospective cohort study

OBJECTIVE

It is uncertain what factors increases the risk of suicide in older adults without depression, and it is unknown whether executive dysfunction (ED) is one of those factors. We aimed to examine the effect of ED on the risk of suicide in non-demented older adults without depression.

METHODS

In an ongoing population-based prospective cohort of Korean older adults, we identified suicide using the National Mortality Database and suicidal ideation or attempt (SIA) based on the Korean version of the Mini International Neuropsychiatric Interview. We defined ED as performing below -1.5 SD of age-adjusted, gender-adjusted and education-adjusted norms in any of following tests: Frontal Assessment Battery, Trail Making Test A, Digit Span Test or Verbal Fluency Test.

RESULTS

The mean age of the 4791 participants at baseline was 69.7 (SD 6.4) years, and 57.1% of them were women (mean follow-up duration=4.9 years). ED at baseline increased the risk of suicide by about seven times (HR 7.20, 95% CI 1.84 to 28.12, p=0.005) but did not change the risk of SIA. However, cognitive impairment without ED did not change the risks of suicide and SIA. In participants with ED, being aged 75 years or above, living alone, and having a low socioeconomic status were associated with the risk of suicide.

CONCLUSION

ED is a strong risk factor of late life suicide independent from depression, particularly in very old adults living in disadvantaged environments.

Atrophy of right inferior frontal orbital gyrus and frontoparietal functional connectivity abnormality in depressed suicide attempters

Although structural and functional brain abnormalities have been observed in depressed suicide attempters (DS), structural deficits and functional impairments together with their relationship in DS remain unclear. To clarify this issue, we aimed to examine the differences in gray matter (GM) alteration, corresponding functional connectivity (FC) change, and their relationship between DS and depressed non-suicide attempters (NDS). Sixty-eight DS, 119 NDS and 103 healthy controls were enrolled and subjected to magnetic resonance imaging scans. The patients were evaluated using the 17-item Hamilton Rating Scale for Depression (HRSD) and Nurses' Global Assessment of Suicide Risk (NGASR) scale. Both voxel-based morphometry and resting-state FC analyses were performed based on functional and structural imaging data. Compared with NDS, the DS group showed reduced GM volume in the right inferior frontal orbital gyrus (IFOG) and left caudate (CAU) but increased GM volume in the left calcarine fissure, weaker negative right IFOG-left rectus gyrus (REG) FC, and weaker positive right IFOG-left inferior parietal lobule (IPL) FC. In DS, the GM volume of the right IFOG and left CAU was negatively correlated with NGASR and HRSD scores, respectively; the right IFOG-left IPL FC was negatively correlated with cognitive factor scores; and the GM volume of the right IFOG was positively correlated with IFOG-REG and IFOG-IPL FC. Our findings indicate that structural deficit with its related functional alterations in brain circuits converged in right IFOG centralized pathways and may play a central role in suicidal behaviors in depression.

A lifetime of challenges: real-life decision outcomes in early- and late-onset suicide attempters

BACKGROUND

People who have attempted suicide display suboptimal decision-making in the lab. Yet, it remains unclear whether these difficulties tie in with other detrimental outcomes in their lives besides suicidal behavior. We hypothesize that this is more likely the case for individuals who first attempted suicide earlier than later in life.

METHODS

A cross-sectional case-control study of 310 adults aged ≥ 50 years (mean: 63.9), compared early- and late-onset attempters (first attempt < 55 vs. ≥ 55 years of age) to suicide ideators, non-suicidal depressed controls and non-psychiatric healthy controls. Participants reported potentially avoidable negative decision outcomes across their lifetime, using the Decision Outcome Inventory (DOI). We employed multi-level modeling to examine group differences overall, and in three factor-analytically derived domains labeled Acting Out, Lack of Future Planning, and Hassles.

RESULTS

Psychopathology predicted worse decision outcomes overall, and in the more serious Acting Out and Lack of Future Planning domains, but not in Hassles. Early-onset attempters experienced more negative outcomes than other groups overall, in Lack of Future Planning, and particularly in Acting Out. Late-onset attempters were similar to depressed controls and experienced fewer Acting out outcomes than ideators.

LIMITATIONS

The cross-sectional design precluded prospective prediction of attempts. The assessment of negative outcomes may have lacked precision due to recall bias.

CONCLUSIONS

Whereas early-onset suicidal behavior is likely the manifestation of long-lasting decision-making deficits in several serious aspects of life, late-onset cases appear to function similarly to non-suicidal depressed adults, suggesting that their attempt originates from a more isolated crisis.

Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry

Computational modeling has been applied for data analysis in psychology, neuroscience, and psychiatry. One of its important uses is to infer the latent variables underlying behavior by which researchers can evaluate corresponding neural, physiological, or behavioral measures. This feature is especially crucial for computational psychiatry, in which altered computational processes underlying mental disorders are of interest. For instance, several studies employing model-based fMRI-a method for identifying brain regions correlated with latent variables-have shown that patients with mental disorders (e.g., depression) exhibit diminished neural responses to reward prediction errors (RPEs), which are the differences between experienced and predicted rewards. Such model-based analysis has the drawback that the parameter estimates and inference of latent variables are not necessarily correct-rather, they usually contain some errors. A previous study theoretically and empirically showed that the error in model-fitting does not necessarily cause a serious error in model-based fMRI. However, the study did not deal with certain situations relevant to psychiatry, such as group comparisons between patients and healthy controls. We developed a theoretical framework to explore such situations. We demonstrate that the parameter-misspecification can critically affect the results of group comparison. We demonstrate that even if the RPE response in patients is completely intact, a spurious difference to healthy controls is observable. Such a situation occurs when the ground-truth learning rate differs between groups but a common learning rate is used, as per previous studies. Furthermore, even if the parameters are appropriately fitted to individual participants, spurious group differences in RPE responses are observable when the model lacks a component that differs between groups. These results highlight the importance of appropriate model-fitting and the need for caution when interpreting the results of model-based fMRI.

Risky decision-making in suicide attempters, and the choice of a violent suicidal means: an updated meta-analysis

BACKGROUND

Studies showed disadvantageous decision-making in suicide attempters. The present meta-analysis aims to examine the stability of these findings and related questions.

METHODS

EMBASE and Pubmed databases were searched for studies published between 01/01/2000 and 01/01/2020 with an additional search through bibliographical references. English or French articles published in peer-reviewed journals, reporting quantitative task-based measures of decision-making in suicide attempters were included: 3,582 records were identified, 33 full-text articles screened, and 21 articles finally included.

RESULTS

All studies were conducted in mood disorders; 18 used the Iowa Gambling Task (IGT) and 3 the Cambridge Gamble Task (CGT). With the IGT, suicide attempters showed riskier choices than patient controls (Hedges' g=-0.28 95%CI (-0.44 - -0.12)) and healthy controls (g=-0.54 (-0.83 - -0.25)) with no significant difference between control groups. The difference between suicide attempters and patient controls was not related to age group, mood disorder type, author, or research center while an effect of time of publication was found (p=0.006). Poorer performance was also found in suicide attempters compared to patient controls when using the CGT (g=-0.57 95%CI (-0.82 - -0.31)). Suicide attempters who used a violent means showed poorer IGT performance than those who used a non-violent means (3 studies).

LIMITATION

Limited number of studies outside mood disorders. No data to calculate a gender effect.

CONCLUSION

The present meta-analysis confirmed riskier decision-making in suicide attempters. Although group differences appear to be of modest effect size in general, they were particularly marked in the subgroup of those who used a violent suicidal means.

Predictors of serious suicidal behavior in late-life depression

The present study aimed to identify pathways to suicidal behavior in late life that can guide identification of those most at risk and improve treatment. In a longitudinal study of late-life depression, we prospectively assessed risk factors specifically associated with fatal and near-fatal as compared to less lethal suicidal behavior. We enrolled 401 participants (age 66+9.9): 311 with unipolar non-psychotic depression and 90 non-psychiatric controls. The median follow-up was 5.4 years. Results indicated that history of suicide attempt predicted a two-fold increase in the risk of dying from natural causes. In univariate models, male gender, higher income, current depression and current and worst lifetime suicidal ideation severity, cognitive control deficits, and low levels of non-planning impulsivity predicted fatal and near-fatal suicidal behavior. In contrast, incident less lethal suicidal behavior was mostly associated with maladaptive personality traits, impulsivity, and severity of psychiatric illness in univariate models. In multipredictor models, male gender, worst lifetime suicidal ideation, and deficits in cognitive control independently predicted fatal/near-fatal suicidal behavior, while introversion, history of suicide attempt, and earlier age of onset of depression predicted less lethal suicidal behavior. While clinicians may be familiar with suicide risk factors identified in younger samples such as dysfunctional personality, impulsivity, and co-morbid substance use, in late life these characteristics only pertain to lower-lethality suicidal behavior. Cognitive control deficits, which likely play a greater role in old age, predict serious suicidal behavior.

"Nothing Hurts Less Than Being Dead": Psychological Pain in Case Descriptions of Psychiatric Euthanasia and Assisted Suicide from the Netherlands: « Rien ne fait moins mal qu'être mort »: La douleur psychologique dans les descriptions de cas d'euthanasie et de suicide assisté psychiatrique aux Pays-Bas

OBJECTIVES

Euthanasia and assisted suicide (EAS) of individuals with mental disorders is a growing practice in several countries, including the Netherlands. Here, we aimed to identify the most frequent dimensions of and associated factors to psychological pain, which has been associated with suicidality, in individuals undergoing psychiatric EAS.

METHODS

An exploratory retrospective content analysis of the English translation of 66 digital case records of individuals who died by EAS in the Netherlands between 2011 and 2014 was performed. Nine standard psychological pain dimensions (irreversibility, loss of control, emptiness, emotional flooding, freezing, social distancing, narcissistic wounds, confusion, and self-estrangement), illness, and sociodemographic variables were evaluated by 2 independent raters using a premade data abstraction form (Kohen κ > 0.8 in all cases).

RESULTS

The mean number of dimensions was 4.64 ± 1.20 (median = 5), out of 9. The most frequent dimensions were irreversibility, loss of control, emptiness, and emotional flooding, in decreasing order. Past treatment refusal and the mention of social connections in case descriptions were related to the higher number of psychological pain dimensions (4.89 ± 1.24 vs. 4.31 ± 1.07, P = 0.03 and 5.05 ± 1.17 vs. 4.43 ± 1.17, P = 0.03, respectively). Emotional flooding was the only dimension specifically associated with specific psychiatric conditions, namely posttraumatic phenomena and personality disorders.

CONCLUSIONS

Numerous psychological pain dimensions were detected in case descriptions of individuals who underwent EAS before the procedure. Subjective nature of the study precludes definite conclusions but suggest that future studies should explore psychological pain and the role of interventions targeting it in patients requesting EAS.

Ventromedial prefrontal value signals and functional connectivity during decision-making in suicidal behavior and impulsivity

Suicide is linked to impaired value-based decision-making and impulsivity, but whether these risk factors share neural underpinnings is unclear. Disrupted ventromedial prefrontal cortex (vmPFC) value signals may underlie this behavioral phenotype. We investigated vmPFC value signals, vmPFC-frontoparietal connectivity, and the impact of impulsivity during decision-making in depressed individuals with and without suicidal behavior. Middle-aged and older adults (n = 116; 35 with a history of suicide attempts, 25 with ideation only, 25 depressed controls with no ideation, and 31 nonpsychiatric controls) completed a decision-making task with drifting reward probabilities during fMRI. Values of choices, estimated by a reinforcement learning model, were regressed against BOLD signal. VmPFC value activation was compared between groups. Moderating effects of impulsivity on vmPFC-frontoparietal connectivity were assessed in nonpsychiatric controls and compared among patient groups. VmPFC value responses in participants with a history of suicide attempts were reduced relative to nonpsychiatric controls (p < 0.05). In nonpsychiatric controls, vmPFC-frontoparietal connectivity was negatively moderated by impulsivity (pFWE corrected < 0.05). This effect was preserved in comparison patient groups but abolished in suicide attempters (p < 0.001). This change in neural connectivity patterns also affected behavior: people with a history of suicide attempts showed a disrupted effect of vmPFC-frontoparietal connectivity, impulsivity, and reinforcement on choice quality (p < 0.001). These effects were specific to vmPFC and not to striatum. In summary, findings from this study largely support disrupted vmPFC value signals in suicidal behavior. In addition, it uncovers an altered pattern of vmPFC-frontoparietal connectivity in impulsive people with suicidal behavior, which may underlie disrupted choice processes in a suicidal crisis.

Multiple Facets of Value-Based Decision Making in Major Depressive Disorder

Depression is clinically characterized by obvious changes in decision making that cause distress and impairment. Though several studies suggest impairments in depressed individuals in single tasks, there has been no systematic investigation of decision making in depression across tasks. We compare participants diagnosed with Major Depressive Disorder (MDD) (n = 64) to healthy controls (n = 64) using a comprehensive battery of nine value-based decision-making tasks which yield ten distinct measures. MDD participants performed worse on punishment (d = −0.54) and reward learning tasks (d = 0.38), expressed more pessimistic predictions regarding winning money in the study (d = −0.47) and were less willing to wait in a persistence task (d = −0.39). Performance on learning, expectation, and persistence tasks each loaded on unique dimensions in a factor analysis and punishment learning and future expectations each accounted for unique variance in predicting depressed status. Decision-making performance alone could predict depressed status out-of-sample with 72% accuracy. The findings are limited to MDD patients ranging between moderate to severe depression and the effects of medication could not be accounted for due to the cross sectional nature of the study design. These results confirm hints from single task studies that depression has the strongest effects on reinforcement learning and expectations about the future. Our results highlight the decision processes that are impacted in major depression, and whose further study could lead to a more detailed computational understanding of distinct facets of this heterogeneous disorder.

Biological bases of suicidal behaviours: A narrative review

Suicidal behaviour is a multifaceted phenomenon that concerns all human populations. It has been suggested that a complex interaction between the individual genetic profile and environmental factors throughout life underlies the pathophysiology of suicidal behaviour. Although epidemiological and genetic studies suggest the existence of a genetic component, exposure to biological and psychosocial adversities, especially during critical developmental periods, also contributes to altering the biological responses to threat and pleasure. This results in amplified maladaptive cognitive and behavioural traits and states associated with suicidal behaviours. Alterations in the cognitive inhibition and decision-making capacity have been implicated in suicidal behaviours. Structural and functional changes in key brain regions and networks, such as prefrontal cortex, insula and default mode network, may underlie this relationship. Furthermore, the shift from health to suicidal behaviour incorporates complex and dynamic changes in the immune and stress responses, monoaminergic system, gonadal system and neuroplasticity. In this review, we describe the major findings of epidemiological, genetic, neuroanatomical, neuropsychological, immunological and neuroendocrinological studies on suicide behaviours to provide a solid background for future research in this field. This broad overview of the biological bases of suicide should promote neuroscience research on suicidal behaviours. This might lead to improved biological models and to the identification of evidence-based biomarkers, treatment options and preventive strategies.