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

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.

Machine learning and the prediction of suicide in psychiatric populations: a systematic review

Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.

Impact of Bullying Victimization on Chinese College Students' Suicidal Tendency: The Moderating Effect of Teachers' Emotional Support and Family Support

Objective

To explore the influence of bully victims on the suicidal tendencies of college students, and the moderating role of teacher emotional support and family support in the relationship between bully victims and college students' suicidal tendencies, in order to provide a reference for the effective intervention of college students' suicide behavior.

Methods

Based on a survey of 15,560 college students. Multiple stepwise regression and Interaction analysis explore the impact of the bully victimization on college students' suicidal tendencies and the moderating role of family support and teacher emotional support in the relationship between the bully victim and college students' suicidal tendencies.

Results

This study found that the Suicidal Tendencies score of college students was 19.79 points, indicating that some college students have a risk of suicidal tendencies; secondly, verbal bullying (β = 0.084, P <0.001), physical bullying (β = 0.222, P <0.001) and social relationship bullying (β = 0.122, P <0.001) have a positive and significant impact on the suicidal tendencies of college students; in addition, family support and teacher emotional support have a significant regulatory effect on the bully victim and college students Suicidal Tendencies and family support. The regulating effect was significantly higher than that of teacher emotional support.

Conclusion

Chinese college students have the risk of suicidal tendencies; peer bullying victimization is an important reason for affecting college students' suicidal tendencies, teacher emotional support is a protective factor for bully victims to affect college students' suicidal tendencies, and family support has a significant moderating effect on the bully victim and college students' suicidal tendencies. Therefore, it is necessary to actively adopt home-school linkage and home-school communication to reduce campus violence and increase the psychological resilience of college students.

Risky decision-making in major depressive disorder: A three-level meta-analysis

Background

Individuals with major depressive disorder (MDD) are usually observed making inappropriate risky decisions. However, whether and to what extent MDD is associated with impairments in risky decision-making remains unclear. We performed a three-level meta-analysis to explore the relationship between risky decision-making and MDD.

Method

We searched the Web of Science, PubMed, Scopus, and PsycINFO databases up to February 7, 2023, and calculated Hedges' g to demonstrate the difference in risky decision-making between MDD patients and healthy controls (HCs). The moderating effect of sample and task characteristics were also revealed.

Results

Across 73 effect sizes in 39 cross-sectional studies, MDD patients exhibited greater risk-seeking than HCs (Hedges' g = 0.187, p = .030). Furthermore, age (p = .068), region (p = .005), and task type (p < .001) were found to have moderating effects. Specifically, patients preferred risk-seeking over HCs as age increased. European patients showed significantly increased risk-seeking compared to American and Asian patients. Patients in the Iowa Gambling Task (IGT) exhibited a notable rise in risk-seeking compared to other tasks, along with an increased risk aversion in the Balloon Analogue Risk Task (BART). The multiple-moderator analysis showed that only task type had significant effects, which may be explained by a tentative framework of "operationalization-mechanism-measure" specificity.

Conclusions

MDD patients generally exhibit higher risk-seeking than HCs. It implies that impaired risky decision-making might be a noteworthy symptom of depression, which should be placed more emphasis for clinical management and psycho-education.

Potential neuropsychological mechanism involved in the transition from suicide ideation to action - a resting-state fMRI study implicating the insula

BACKGROUND

Understanding the neural mechanism underlying the transition from suicidal ideation to action is crucial but remains unclear. To explore this mechanism, we combined resting-state functional connectivity (rsFC) and computational modeling to investigate differences between those who attempted suicide(SA) and those who hold only high levels of suicidal ideation(HSI).

METHODS

A total of 120 MDD patients were categorized into SA group (n=47) and HSI group (n=73). All participants completed a resting-state functional MRI scan, with three subregions of the insula and the dorsal anterior cingulate cortex (dACC) being chosen as the region of interest (ROI) in seed-to-voxel analyses. Additionally, 86 participants completed the balloon analogue risk task (BART), and a five-parameter Bayesian modeling of BART was estimated.

RESULTS

In the SA group, the FC between the ventral anterior insula (vAI) and the superior/middle frontal gyrus (vAI-SFG, vAI-MFG), as well as the FC between posterior insula (pI) and MFG (pI-MFG), were lower than those in HSI group. The correlation analysis showed a negative correlation between the FC of vAI-SFG and psychological pain avoidance in SA group, whereas a positive correlation in HSI group. Furthermore, the FC of vAI-MFG displayed a negative correlation with loss aversion in SA group, while a positive correlation was found with psychological pain avoidance in HSI group.

CONCLUSION

In current study, two distinct neural mechanisms were identified in the insula which involving in the progression from suicidal ideation to action. Dysfunction in vAI FCs may gradually stabilize as individuals experience heightened psychological pain, and a shift from positive to negative correlation patterns of vAI-MFC may indicate a transition from state to trait impairment. Additionally, the dysfunction in PI FC may lead to a lowered threshold for suicide by blunting the perception of physical harm.

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.

Deep Learning-Based Text Emotion Analysis for Legal Anomie

Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences (such as adjectives, adverbs, nouns, etc.), which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory (ConvLSTM) network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis.