Graph theory analysis of whole brain functional connectivity to assess disturbances associated with suicide attempts in bipolar disorder

Brain targets to lower the high risk of suicide in Bipolar Disorder (BD) are needed. Neuroimaging studies employing analyses dependent on regional assumptions could miss hubs of dysfunction critical to the pathophysiology of suicide behaviors and their prevention. This study applied intrinsic connectivity distribution (ICD), a whole brain graph‐theoretical approach, to identify hubs of functional connectivity (FC) disturbances associated with suicide attempts in BD. ICD, from functional magnetic resonance imaging data acquired while performing a task involving implicit emotion regulation processes important in BD and suicide behaviors, was compared across 40 adults with BD with prior suicide attempts (SAs), 49 with BD with no prior attempts (NSAs) and 51 healthy volunteers (HVs). Areas of significant group differences were used as seeds to identify regional FC differences and explore associations with suicide risk-related measures. ICD was significantly lower in SAs than in NSAs and HVs in bilateral ventromedial prefrontal cortex (vmPFC) and right anterior insula (RaIns). Seed connectivity revealed altered FC from vmPFC to bilateral anteromedial orbitofrontal cortex, left ventrolateral PFC (vlPFC) and cerebellum, and from RaIns to right vlPFC and temporopolar cortices. VmPFC and RaIns ICD were negatively associated with suicidal ideation severity, and vmPFC ICD with hopelessness and attempt lethality severity. The findings suggest that SAs with BD have vmPFC and RaIns hubs of dysfunction associated with altered FC to other ventral frontal, temporopolar and cerebellar cortices, and with suicidal ideation, hopelessness, and attempt lethality. These hubs may be targets for novel therapeutics to reduce suicide risk in BD.

Age Differences in the Association Between Anxiety Symptoms and Suicide Risk

Suicide rates increase over the life-span, necessitating concern in older adults. Recent studies suggest that anxiety disorders are associated with suicidal thoughts and behavior. The present study examined the association between anxiety symptoms (General Anxiety Disorder-7) and suicide risk (Suicide Behaviors Questionnaire-Revised), testing whether the association differs between younger and older adults. Depression symptoms (Patient Health Questionnaire-8) were controlled for in the analyses. In a sample of 944 participants (46% 60+ years), anxiety symptoms, depression symptoms, and suicide risk were lower among older adults (60+ years) than younger adults (all p < .01). Age moderated the significant association between anxiety symptoms and suicide risk (ΔR2 = .008, p < .01). Results indicate that an increase in anxiety is associated with a smaller increase in suicide risk for older adults than younger adults. The need for suicide risk screening among individuals with elevated anxiety symptoms is critical, especially for younger adults.

Development and Validation of a Questionnaire to Measure Association Factors with Suicide

Objective: Our goal is to develop an online questionnaire to survey the prevalence of suicidal behavior. Methods: We developed a questionnaire with 51 variables and proceeded with validations. Validations were performed using face validity, content validity, and construct validity. Reliability was performed by test-rest. Results: The face validity was 1.0 and the content validity was 0.91. The exploratory factor analysis got KMO = 0.86 and extracted one principal factor. The confirmatory factor analysis demonstrates RMSEA= 0.000 and CFI=1.000. The test-retest had an intraclass correlated coefficient of 0.98. Conclusion: The adequate development questionnaire was validated, and we have an instrument to survey suicide behaviors in the pandemic time.

Suicide Related Phenotypes in a Bipolar Sample: Genetic Underpinnings

Suicide in Bipolar Disorder (BD) is a relevant clinical concern. Genetics may shape the individual risk for suicide behavior in BD, together with known clinical factors. The lack of consistent replication in BD may be associated with its multigenetic component. In the present contribution we analyzed a sample of BD individuals (from STEP-BD database) to identify the genetic variants potentially associated with three different suicide-related phenotypes: 1) a feeling that the life was not worth living; 2) fantasies about committing a violent suicide; 3) previous attempted suicide. The sample under analysis included 1115 BD individuals. None of the SNPs reached genome-wide significance. However, a trend of association was evidenced for rs2767403, an intron variant of AOPEP gene, in association with phenotype #1 (p = 5.977 × 10−6). The molecular pathway analysis showed a significant enrichment in all the investigated phenotypes on pathways related to post synaptic signaling, neurotransmission and neurodevelopment. Further, NOTCH signaling or the γ-aminobutyric acid (GABA) -ergic signaling were found to be associated with specific suicide-related phenotypes. The present investigation contributes to the hypothesis that the genetic architecture of suicide behaviors in BD is related to alteration of entire pathways rather than single genes. In particular, our molecular pathway analysis points on some specific molecular events that could be the focus of further research in this field.

Difficulties in Emotional Regulation, Suicidal Behaviors and Social Media Posts: A Mixed Method Study Among Young Adults Using Semiotic Analysis

The study is an explanatory sequential mixed method design aimed at understanding the relationship between difficulties in regulating emotions and suicidal behaviors. Furthermore, we explored their pattern of social media engagement to identify cues or indications, if any. The quantitative component consists of 100 young adults (18-30 years) sampled using convenience and snowball sampling techniques. The Difficulty in Emotion Regulation Scale (DERS) and Suicide Behaviors Questionnaire (SBQ) were administered. Spearman rank-order correlation analysis establishes a moderate positive correlation (rs (100) = .400, p < .0005) between difficulties in regulating emotions and suicidal behaviors. Furthermore, for the qualitative component, 15 participants were chosen, with five in each category of 1) difficulty regulating emotions and suicidal behaviors, 2) difficulty regulating emotions but no suicidal behaviors and 3) no difficulty regulating emotions but suicidal behaviors. Their active social media account was accessed with consent, and semiotic analysis of their posts from June 2020 to February 2021 was performed, as data collection spanned the timespan. Saussure and Pierce’s concept of semiotic analysis is adopted as the framework for the analysis. The results reveal some common and specific categories of signified in the domain of interpersonal relationships, intrapersonal relationships, coping activities, social implications, thought process/cognitions and attitudinal modifications. The connotations and myths associated with the same are also explored within the cultural framework. The results are then triangulated with the quantitative component to provide a broader understanding of the intricate links between the variables.

Addictive-Like Behaviors Increase The Risk of Suicide Behaviors in Chinese Middle School Students: A Latent Class Analysis

Background In adolescents, multiple addictive-like behaviors (ALBs) occur frequently together which are likely to be associated with suicide behaviors (SBs), increasing the risk of suicide. This study aimed to clarify the potential subgroups of ALBs in Chinese adolescents, and examine the associations between different patterns of ALBs and SBs. Methods A total of 22,628 middle school students were enrolled in this study. Self-reported ALBs and SBs were investigated by questionnaires. Latent class analysis (LCA) was performed based on five ALBs [smoking, alcohol use (AU), diet pills use (DPU), screen time (ST), problematic mobile phone use (PMPU)]. Multivariate logistic regressions were used to examine the associations between the different patterns of ALBs and SBs. Results Four subgroups of ALBs were identified by LCA, including high-risk class (smoking/AU/DPU/PMPU/ST), moderate-risk class 1 (DPU/PMPU), moderate risk class 2 (smoking/AU/ST), and low-risk class. Compared with the low-risk class, all of the moderate-risk class 1, moderate-risk class 2 and high-risk class had higher risks of suicide ideation, suicide plan, and suicide attempt. Conclusions These findings suggested that the patterns of ALBs were related to SBs in Chinese adolescents. Accordingly, considerations of different classes of ALBs may be essential for developing effective preventive programs.

The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention

Suicide, a leading cause of death, is a complex and a hard-to-predict human tragedy. In this article, we introduce a comprehensive outlook on the emerging movement to integrate computational linguistics (CL) in suicide prevention research and practice. Focusing mainly on the state-of-the-art deep neural network models, in this “travel guide” article, we describe, in a relatively plain language, how CL methodologies could facilitate early detection of suicide risk. Major potential contributions of CL methodologies (e.g., word embeddings, interpretational frameworks) for deepening that theoretical understanding of suicide behaviors and promoting the personalized approach in psychological assessment are presented as well. We also discuss principal ethical and methodological obstacles in CL suicide prevention, such as the difficulty to maintain people’s privacy/safety or interpret the “black box” of prediction algorithms. Ethical guidelines and practical methodological recommendations addressing these obstacles are provided for future researchers and clinicians.

The Hitchhiker's Guide to Computational Linguistics in Suicide Prevention

Suicide, a leading cause of death, is a complex and a hard-to-predict human tragedy. This article introduces a comprehensive outlook on the emerging movement to integrate Computational Linguistics (CL) in suicide prevention research and practice. Focusing mainly on the state-of-the-art Deep Neural Network models, this "travel guide" article describes, in a relatively plain language, how CL methodologies could facilitate early detection of suicide risk (section 1). Major potential contributions of CL methodologies (e.g., word embeddings, interpretational frameworks) for deepening our theoretical understanding of suicide behaviors (section 2) and promoting the personalized approach in psychological assessment (section 3), are presented as well. Importantly, the article also discusses principal ethical (section 4) and methodological (section 5) obstacles in CL-suicide prevention, such as the difficulty to maintain peoples' privacy/safety or interpret the "black box" of prediction algorithms. Ethical guidelines and practical methodological recommendations addressing these obstacles, are provided for future researchers and clinicians.