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

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.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Resistance to Sunk Cost Propensity Moderate Relationship between Negative Life Event and Hopelessness

Previous research has found that a negative life event is a main risk factor for hopelessness, which in turn is considered to be a proximal cause of major depression disorder and a suicide risk factor. Unfortunately, very little attention has been paid to the role of decision-making constructs between negative life events and hopelessness. To fill this gap, the present study aims to test the moderation role of sunk cost propensity in this relationship, which is an over-generalized tendency to persist, based on past investment. A total of 495 university students completed assessment of their resistance to sunk cost propensity, whereas the negative life events, hopelessness, mental health state (depression, anxiety) and big-five personality traits were measured by various questionnaires. Participants' tendency to resist sunk cost propensity moderated the relationship between negative life events and hopelessness. Individuals who tended to resist sunk cost bias are more vulnerable to the adverse effects of negative life events. This effect is still significant, even after controlling for individuals' psychological well-being (depression, anxiety) and big-five personality traits. The current findings provide preliminary evidence that resistance to sunk cost propensity may be an important characteristic associated with an individual's hopelessness when exposed to a negative life event.

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.