ROC analysis of three‐dimensional psychological pain in suicide ideation and suicide attempt among patients with major depressive disorder

Psychological pain and sociodemographic factors classified suicide attempt and non-suicidal self-injury in adolescents

This study aimed to utilize machine learning to explore the psychological similarities and differences between suicide attempt (SA) and non-suicidal self-injury (NSSI), with a particular focus on the role of psychological pain. A total of 2385 middle school students were recruited using cluster sampling. The random forest algorithm was used with 25 predictors to develop classification models of SA and NSSI, respectively, and to estimate the importance scores of each predictor. Based on these scores and related theories, shared risk factors (control feature set) and distinct risk factors (distinction feature set) were selected and tested to distinguish between NSSI and SA. The machine learning algorithm exhibited fair to good performance in classifying SA history [Area Under Curves (AUCs): 0.65-0.87] and poor performance in classifying NSSI history (AUC: 0.61-0.68). The distinction feature set comprised pain avoidance, family togetherness, and deviant peer affiliation, while the control feature set included pain arousal, painful feelings, and crisis events. The distinction feature set slightly but stably outperformed the control feature set in classifying SA from NSSI. The three-dimensional psychological pain model, especially pain avoidance, might play a dominant role in understanding the similarities and differences between SA and NSSI.

Identifying suicide attempter in major depressive disorder through machine learning: the importance of pain avoidance, event-related potential features of affective processing

How to achieve a high-precision suicide attempt classifier based on the three-dimensional psychological pain model is a valuable issue in suicide research. The aim of the present study is to explore the importance of pain avoidance and its related neural features in suicide attempt classification models among patients with major depressive disorder. By recursive feature elimination with cross-validation and support-vector-machine algorithms, scores from the measurements and the task-based EEG signals were chosen to achieve a suicide attempt classification model. In the multimodal suicide attempt classifier with an accuracy of 83.91% and an area under the curve of 0.90, pain avoidance ranked as the top one in the optimal feature set. Theta (reward positive feedback minus neutral positive feedback) was the shared neural representation ranking as the top one of event-related potential features in pain avoidance and suicide attempt classifiers. In conclusion, the suicide attempt classifier based on pain avoidance and its related affective processing neural features has excellent accuracy among patients with major depressive disorder. Pain avoidance is a stable and strong indicator for identifying suicide risks in both traditional analyses and machine-learning approaches. A novel methodology is needed to clarify the relationship between cognitive and affective processing evoked by punishment stimuli and pain avoidance.

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.

An investigation of the relationships between suicidal ideation, psychache, and meaning in life using network analysis

BACKGROUND

Previous studies have investigated the relationships between psychache or meaning in life and suicidal ideation based on sum score of corresponding scale. However, this practice has hampered the fine-grained understanding of their relationships. This network analysis study aimed to conduct a dimension-level analysis of these constructs and the relationships among them in a joint framework, and identify potential intervention targets to address suicidal ideation.

METHODS

Suicidal ideation, psychache, and meaning in life were measured using self-rating scales among 738 adults. A network of suicidal ideation, psychache, and meaning in life was constructed to investigate the connections between dimensions and calculate the expected influence and bridge expected influence of each node.

RESULTS

"Psychache" was positively linked to "sleep" and "despair", while "presence of meaning in life" had negative associations with "psychache", "despair", and "pessimism". The most important central nodes were "sleep" and "despair", and the critical bridge nodes were "presence of meaning in life" and "psychache".

CONCLUSION

These preliminary findings uncover the pathological pathways underlying the relationships between psychache, meaning in life, and suicidal ideation. The central nodes and bridge nodes identified may be potential targets for effectively preventing and intervening against the development and maintenance of suicidal ideation.

Translation, cross-cultural adaptation, and psychometric investigation of Chinese version of the Orbach & Mikulincer Mental Pain Scale in patients with depressive disorder

PURPOSE

This study aimed to translate, cross-culturally adapt, and preliminarily test the psychometric properties of the Chinese version of the Orbach & Mikulincer Mental Pain Scale (OMMP).

METHODS

Psychometric investigation was performed on 240 depressed patients. The reliability of the Chinese version of the OMMP scale was expressed by internal consistency reliability and test-retest reliability (2-week interval), and confirmatory factor analysis (CFA) on the 8-factor, 31-item OMMP was conducted to examine the construct validity.

RESULTS

The CFA showed that the modified model with 31 items had good reliability (Cronbach's α range = 0.691-0.871; ICC = 0.818). Criterion-related validity was also supported by significant and positive correlations between the eight factors and worst-ever suicidal ideation as well as depressive symptoms.

CONCLUSION

The results indicated the usefulness of the OMMP-31 for Chinese depressed patients. It is necessary to estimate psychological pain to improve suicide management in the clinical setting.

Fusion of pain avoidance and the contingent negative variation induced by punitive condition predict suicide ideation in a college population

This study examined behavioral and ERP features involved in pain processing as predictors of suicide ideation. Twenty-seven depressed undergraduates with high suicide ideation (HSI), 23 depressed undergraduates with low suicide ideation (LSI), and 32 healthy controls (HCs) completed the clinical Scales. The amplitudes of LPP, P2, P3, CNV, FRN, power in the beta, theta, and delta bands in the SAID task were multimodal EEG features. A machine learning algorithm known as support vector machine was used to select optimal feature sets for predicting pain avoidance, depression, and suicide ideation. The accuracy of suicide ideation classification was significantly higher for multimodal features (78.16%) which pain avoidance ranked the first and the CNV ranked the fifth than a single ERP feature model (66.62%). Pain avoidance emerged as the most optimal feature of suicide ideation classification than depression. And the CNV elicited by punitive cues may be a biomarker in suicide ideation. Pain avoidance and its related EEG components may improve the efficacy of suicide ideation classification as compared to depression.

The characterization of static and dynamic brain functional networks in suicide attempters with major depressive disorder and its relation to psychological pain

Psychological pain is a robust predictor of suicide attempts in patients with major depressive disorder (MDD). However, whether suicide and psychological pain share a common neural basis remains unclear. Patients with MDD (n = 64) and healthy controls (HC) (n = 35) were recruited and patients were allocated to two groups: those with a history of suicide attempts (MDD-SA) (n = 25) and those without such a history (MDD-NSA) (n = 39). All participants completed the measurements and underwent functional magnetic resonance imaging to investigate the resting-state static and dynamic brain functional networks in default mode (DMN), central executive (CEN), salience (SN), and basal ganglia (BGN) networks. The MDD-SA group scored higher in pain avoidance than the MDD-NSA and HC groups. Functional connectivity within the dorsal DMN in the MDD-SA group was greater than that in the MDD-NSA and HC groups, and was significantly correlated with pain avoidance and suicide attempts. Dynamic functional connectivity analysis revealed that the proportion of State I in the fraction windows in the MDD-SA group was higher than those in the MDD-NSA and HC groups. Therefore, the increased functional connectivity within the dorsal DMN and segregation of networks may represent potential biological markers for suicide attempts related to psychological pain processing.

Patterns of psychological pain and self-harm behaviors in adolescents

BACKGROUND

The study examined the role of campus stressors and psychological pain on non-suicidal self-injury (NSSI) and suicide attempt (SA).

METHODS

Specific patterns of co-occurring psychological pain, campus stressors, and self-harm behaviors were identified by latent profile analysis, and their odds ratios (ORs) on NSSI and SA were analyzed in cross-sectional study and 2-year follow-up study. Structural Equation Model was used to explore indirect effect of campus stressors on SA and NSSI via different components of psychological pain.

RESULTS

Three classes were identified as low-risk class (68.58%) with low endorsements on the three measures, moderate-risk class (26.52%) with the elevated academic stressors, high levels of painful feelings, and high probabilities on NSSI; and high-risk class (4.90%) with the elevated combined stressors, high levels of pain avoidance, and high probabilities on SA. Compared to the moderate-risk class, adolescents in the high-risk class had a 4.97 OR of reporting NSSI, 17.98 OR of reporting SA. Pain avoidance class at baseline reported a higher probability in SA class (OR = 224.00) in a 2-year study.

CONCLUSIONS

Painful feelings might be shared psychosocial correlates for NSSI and SA. However, pain avoidance may play a role in distinguishing SA from NSSI, which shed light on the intervention of adolescents who engage in self-harm behaviors.

Predictive Modeling for Suicide-Related Outcomes and Risk Factors among Patients with Pain Conditions: A Systematic Review

Suicide is a leading cause of death in the US. Patients with pain conditions have higher suicidal risks. In a systematic review searching observational studies from multiple sources (e.g., MEDLINE) from 1 January 2000-12 September 2020, we evaluated existing suicide prediction models' (SPMs) performance and identified risk factors and their derived data sources among patients with pain conditions. The suicide-related outcomes included suicidal ideation, suicide attempts, suicide deaths, and suicide behaviors. Among the 87 studies included (with 8 SPM studies), 107 suicide risk factors (grouped into 27 categories) were identified. The most frequently occurring risk factor category was depression and their severity (33%). Approximately 20% of the risk factor categories would require identification from data sources beyond structured data (e.g., clinical notes). For 8 SPM studies (only 2 performing validation), the reported prediction metrics/performance varied: C-statistics (n = 3 studies) ranged 0.67-0.84, overall accuracy(n = 5): 0.78-0.96, sensitivity(n = 2): 0.65-0.91, and positive predictive values(n = 3): 0.01-0.43. Using the modified Quality in Prognosis Studies tool to assess the risk of biases, four SPM studies had moderate-to-high risk of biases. This systematic review identified a comprehensive list of risk factors that may improve predicting suicidal risks for patients with pain conditions. Future studies need to examine reasons for performance variations and SPM's clinical utility.

Pain avoidance and functional connectivity between insula and amygdala identifies suicidal attempters in patients with major depressive disorder using machine learning

Pain avoidance can effectively classify suicide attempters from non-attempters among patients with major depressive disorder (MDD). However, the neural circuits underlying pain processing in suicide attempters have not been described comprehensively. In Study 1, we recruited MDD patients with a history of suicide attempts (MDD-SA), and those without (MDD-NSA) to examine the patterns of psychological pain using the latent profile analysis. Further, in Study 2, participants including the MDD-SA, MDD-NSA, and healthy controls underwent resting-state functional magnetic resonance imaging. We used machine learning that included features of gray matter volume (GMV), the functional connectivity (FC) brain patterns of the region of interest, and behavioral data to identify suicide attempters. The results identified three latent classes of psychological pain in MDD patients: the low pain class (18.9%), the painful feeling class (37.2%), and the pain avoidance class (43.9%). Furthermore, the proportion of suicide attempters with high pain avoidance was the highest. The accuracy of multimodality classifiers (63%-92%) was significantly higher than that of brain-only classifiers (56%-85%) and behavior-only classifiers (64%-73%). Pain avoidance ranked first in the optimal feature set of the suicide attempt classification model. The crucial brain imaging features were FC between the left amygdala and right insula, right orbitofrontal and left thalamus, left anterior cingulate cortex and left insula, right orbitofrontal, amygdala, and the GMV of right thalamus. Additionally, the optimal feature set, including pain avoidance and crucial brain patterns of psychological pain neural circuits, was provided for the identification of suicide attempters.

Structural Model of Suicidal Ideation and Behavior: Mediating Effect of Impulsivity

The present study aimed to establish the associations between hopelessness, depression and impulsivity with respect to suicidal ideation and behavior, and to explore the role that impulsivity plays in the mechanism that operates between depression and hopelessness. Through an empirical observational study, with an analytical scope based on a cross-sectional design for a sample of 228 university students and using The Inventory of Suicide Orientation (ISO-30); Beck Depression Inventory (BDI); Beck Hopelessness Scale (BHS); and Barratt Impulsiveness Scale (BIS-11). The results indicated a significant positives correlations between BDI, BHS, BIS and ISO-30. Regression analysis showed that depression, impulsivity and hopelessness explain between 57% and 67% of the variance in the risk of suicidal ideation and behavior. Through the analysis of structural equation modeling, three models were established showing that impulsivity mediates the relationship between depressive symptomatology and suicidal ideation and behavior. This study has implications for mental health intervention and research, in that it emphasizes the importance of impulsivity traits as factors that act as triggers in the association between the presence of depressive symptoms and suicidal behavior.

Psychometric properties and diagnostic capacity of the scale of resilience to suicide attempts-18

OBJECTIVE

Early detection of suicide attempts remains a handicap for suicide prevention. Most studies have focused on risk factors, but few have assessed protective factors that promote resilient outcomes, especially in subpopulations vulnerable to suicide re-attempts. This study aims to create and adapt a new Scale of Resilience to Suicide Attempts (SRSA), and to analyse its predictive validity and diagnostic capacity for the detection of suicide re-attempts at six months in people who have made a previous attempt.Design and main outcome measures: The psychometric properties and diagnostic capacity of the resulting SRSA-18 scale were assessed in 229 persons (where 133-58.1% were women, aged 18- to 76-year old) who had made a previous suicide attempt.

RESULTS

Factor analyses (AFE and AFC) yielded a three-dimensional structure with excellent goodness-of-fit indices RMSEA, high levels of reliability and adequate convergent validity with the Suicide Resilience Inventory-25 (SRI-25) scale. Additionally, the SRSA-18 has significant diagnostic power on suicide re-attempts across months of follow-up.

CONCLUSION

Reliable and valid protective factor-based instruments for the detection of future suicide re-attempts may help in the prevention of suicide-associated mortality in specific clinical subpopulations.

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.

Psychological pain and risk of suicide in adolescence

Psychological pain or psychache is intolerable and unacceptable mental pain. This construct plays a key role in the risk of suicide in adolescence. This is a frequent condition in adolescents with depression; a variety of studies propose a bidirectional relationship between these two circumstances. Adolescents with psychological pain are exposed to the risk of suicidal ideation, and behavior. This review aimed to explore psychological pain in adolescents, and the correlation to the risk of suicide. We have conducted, following PRISMA guidelines, a systematic review for the literature on PubMed. Search terms were "Suicide risk in adolescents" AND "Mental Pain in adolescence" OR "Psychological Pain in adolescence". An initial search identified publications 1189. A total of 15 studies met the predefined inclusion criteria and were analyzed. Mental pain is a stronger factor of vulnerability of suicidal ideation and behavior. Intense negative emotions, such as guilt, shame, hopelessness, may become a psychological pain in adolescence, and many studies showed a relation between psychological pain and capability for suicide.