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

Proinflammatory cytokine levels, cognitive function, and suicidal symptoms of adolescents and young adults with major depressive disorder

Whether proinflammatory cytokine dysregulation and cognitive dysfunction are associated with suicidal symptoms in adolescents and young adults with major depressive disorder (MDD) remains uncertain. We assessed the cognitive function and proinflammatory cytokine levels of 43 and 51 patients aged 15-29 years with MDD and severe and mild suicidal symptoms, respectively, as well as those of 85 age- and sex-matched healthy controls. Specifically, we measured serum levels of C-reactive protein, tumor necrosis factor-α (TNF-α), interleukin-2, and interleukin-6 and assessed cognitive function by using working memory and go/no-go tasks. The severity of the patients' suicidal symptoms was based on Item 10 of the Montgomery-Åsberg Depression Rating Scale; scores of ≤ 2 and ≥ 4 indicated mild and severe symptoms, respectively. The patients with MDD and severe suicidal symptoms had higher levels of C-reactive protein (p = .019) and TNF-α (p = .002) than did the patients with mild symptoms or the healthy controls. The number of errors committed on the go/no-go by patients with MDD and severe suicidal symptoms (p = .001) was significantly higher than those by patients with MDD and mild symptoms or by controls. After adjusting for nonsuicidal depressive symptoms, we observed suicidal symptoms to be positively associated with TNF-α levels (p = .050) and errors on the go/no-go task (p = .021). Compared with mild suicidal symptoms, severe symptoms are associated with greater serum levels of proinflammatory cytokines and inferior cognitive function in adolescents and young adults with MDD.

Distinct Effects of Major Affective Disorder Diagnoses and Suicidal Symptom Severity on Inhibitory Control Function and Proinflammatory Cytokines: Single-Site Analysis of 800 Adolescents and Adults

BACKGROUND

Inhibitory control function and proinflammatory cytokines play a role in the pathomechanisms underlying major affective disorders and suicidal behavior. However, the distinct or interactive effects of major affective disorders and suicidal symptom severity on inhibitory control function and proinflammatory cytokines remain unclear.

METHODS

This study included 287 patients with bipolar disorder, 344 with major depressive disorder, and 169 healthy controls. We categorized the participants into 3 groups based on Montgomery-Åsberg Depression Rating Scale (MADRS) item 10 (suicidal symptoms) score: 0, 2 or 3, and ≥4. The participants completed the go/no-go task and the measurements for C-reactive protein (CRP) and tumor necrosis factor-α (TNF-α) levels.

RESULTS

Errors in the go/no-go task were associated with suicidality (P = .040), regardless of the severity of suicidal symptoms and diagnosis. An elevated CRP level was especially associated with a Montgomery-Åsberg Depression Rating Scale item 10 score ≥4 (P = .001). An increased TNF-α level could distinguish bipolar disorder from major depressive disorder (P < .001).

DISCUSSION

Our study indicated the distinct effects of major affective disorder diagnosis and suicide symptom severity on inhibitory control function and CRP and TNF-α levels. Importantly, individuals with the poorest inhibitory control function and highest CRP levels had more severe suicidal symptoms.

Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention

OBJECTIVE

Develop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU).

METHODS

Analyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI).

RESULTS

A slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment.

CONCLUSIONS

PAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.

Impairment in recognition memory may be associated with near-term risk for suicide attempt in a high-risk sample

INTRODUCTION

Prior work has implicated several neurocognitive domains, including memory, in patients with a history of prior suicide attempt. The current study evaluated whether a delayed recognition test could enhance prospective prediction of near-term suicide outcomes in a sample of patients at high-risk for suicide.

METHODS

132 Veterans at high-risk for suicide completed a computer-based recognition memory test including semantically-related and -unrelated words. Outcomes were coded as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as aborted/interrupted attempt or preparatory behavior, or neither (noSE), within 90 days after testing.

RESULTS

Reduced performance was a significant predictor of upcoming ASA, but not OtherSE, after controlling for standard clinical variables such as current suicidal ideation and history of prior suicide attempt. However, compared to the noSE reference group, the OtherSE group showed a reduction in the expected benefit of semantic relatedness in recognizing familiar words. A computational model, the drift diffusion model (DDM), to explore latent cognitive processes, revealed the OtherSE group had decreased decisional efficiency for semantically-related compared to semantically-unrelated familiar words.

LIMITATIONS

This study was a secondary analysis of an existing dataset, involving participants in a treatment trial, and requires replication; ~10 % of the sample was excluded from analysis due to failure to master the practice tasks and/or apparent noncompliance.

CONCLUSION

Impairments in recognition memory may be associated with near-term risk for suicide attempt, and may provide a tool to improve prediction of when at-risk individuals may be transitioning into a period of heightened risk for suicide attempt.

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.

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers' ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data - without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work.