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

Do people know how suicidal they will be? Understanding suicidal prospection

INTRODUCTION

Little research has been done on how people mentally simulate future suicidal thoughts and urges, a process we term suicidal prospection.

METHODS

Participants were 94 adults with recent suicidal thoughts. Participants completed a 42-day real-time monitoring study and then a follow-up survey 28 days later. Each night, participants provided predictions for the severity of their suicidal thoughts the next day and ratings of the severity of suicidal thoughts over the past day. We measured three aspects of suicidal prospection: predicted levels of desire to kill self, urge to kill self, and intent to kill self. We generated prediction errors by subtracting participants' predictions of the severity of their suicidal thoughts from their experienced severity.

RESULTS

Participants tended to overestimate (although the average magnitude was small and the modal error was zero) the severity of their future suicidal thoughts. The best fitting models suggested that participants used both their current suicidal thinking and previous predictions of their suicidal thinking to generate predictions of their future suicidal thinking. Finally, the average severity of predicted future suicidal thoughts predicted the number of days participants thought about suicide during the follow-up period.

CONCLUSIONS

This study highlights prospection as a psychological process to better understand suicidal thoughts and behaviors.

Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space

Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human frontoparietal dorsal stream regions, but not in MT+, tracks the number of competing options, as preferred options are selectively maintained on the map, while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior β1/α oscillations desynchronize within 0.4 to 0.7 s, consistent with option encoding by competing β1-stabilized subpopulations. Together, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.

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.

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.

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.