Computational markers of risky decision-making predict for relapse to alcohol.

BACKGROUND: Relapse remains the major challenge in treatment of alcohol use disorder (AUD). Aberrant decision-making has been found as important cognitive mechanism underlying relapse, but factors associated with relapse vulnerability are unclear. Here, we aim to identify potential computational markers of relapse vulnerability by investigating risky decision-making in individuals with AUD. METHODS: Forty-six healthy controls and fifty-two individuals with AUD were recruited for this study. The risk-taking propensity of these subjects was investigated using the balloon analog risk task (BART). After completion of clinical treatment, all individuals with AUD were followed up and divided into a non-relapse AUD group and a relapse AUD group according to their drinking status. RESULTS: The risk-taking propensity differed significantly among healthy controls, the non-relapse AUD group, and the relapse AUD group, and was negatively associated with the duration of abstinence in individuals with AUD. Logistic regression models showed that risk-taking propensity, as measured by the computational model, was a valid predictor of alcohol relapse, and higher risk-taking propensity was associated with greater risk of relapse to drink. CONCLUSION: Our study presents new insights into risk-taking measurement and identifies computational markers that provide prospective information for relapse to drink in individuals with AUD.

Does early adversity predict aggression among Chinese male violent juvenile offenders? The mediating role of life history strategy and the moderating role of meaning in life.

BACKGROUND: Adolescent aggression has long been of interest to researchers. However, few studies have examined the influencing factors and mechanisms of aggression among violent juvenile offenders. This study tests a moderated mediation model with Chinese male violent juvenile offenders as subjects. Specifically, it explores the relationship between early adversity and aggression, as well as the mechanisms of life history strategy and meaning in life in this relationship. METHODS: A total of 537 Chinese male violent juvenile offenders completed the Childhood Environment Scale, the Life History Strategy Short Form Scale, the Aggression Questionnaire, and the Meaning in Life Questionnaire. After controlling for socioeconomic status (SES), the current cross-sectional study used structural equation modeling (SEM) to examine a moderated mediation model. RESULTS: The results showed that life history strategy mediated the relationship between early adversity and aggression, and early adversity affected individuals' aggression by accelerating their life history strategies. The results also showed that meaning in life moderated the relationship between early adversity and life history strategy. For individuals with high meaning in life scores, the negative predictive effect of early adversity on life history strategy was stronger than that for individuals with low meaning in life scores. CONCLUSION: The results of this study can advance the understanding of how early adversity affects aggression among violent juvenile offenders and provide theoretical support for prison staff to develop educational strategies and subsequent interventions.

Post-error slowing predicts for relapse in individuals with alcohol use disorder.

BACKGROUND: Relapse characterizes addiction. The cognitive phenotype underlying relapse in individuals with alcohol use disorder (AUD) remain unelucidated. Here we aimed to investigate the potential changes in behavioral adjustment in AUD, and the association with relapse. METHOD: Forty-seven subjects with AUD at Shandong Mental Health Center completed the stop-signal task, the PACS, the Beck Depression Inventory and the State-Trait anxiety questionnaires. Thirty age-matched male healthy subjects served as the control group (HC). In the follow-up, twenty-one subjects remained abstinent, while twenty-six subjects relapsed. Independent sample t-test was applied to measure differences between two groups and logistic regression analysis was conducted to explore the potential predictors on relapse. RESULTS: The results showed that there were significant differences in stop signal reaction time (SSRT) and trigger failure between the AUD and HC groups. Relapsed group showed longer post-error slowing (PES) when compared to the non-relapsed group. The PES could predict relapse in alcohol use disorder. CONCLUSION: Individuals with AUD showed impaired inhibitory control, which may predict relapse.

Peripheral non-enzymatic antioxidants as biomarkers for mood disorders: Evidence from a machine learning prediction model.

Background: Oxidative stress is related to the pathogenesis of mood disorders, and the level of oxidative stress may differ between bipolar disorder (BD) and major depressive disorder (MDD). This study aimed to detect the differences in non-enzymatic antioxidant levels between BD and MDD and assess the predictive values of non-enzymatic antioxidants in mood disorders by applying a machine learning model. Methods: Peripheral uric acid (UA), albumin (ALB), and total bilirubin (TBIL) were measured in 1,188 participants (discover cohort: 157 with BD and 544 with MDD; validation cohort: 119 with BD and 95 with MDD; 273 healthy controls). An extreme gradient boosting (XGBoost) model and a logistic regression model were used to assess the predictive effect. Results: All three indices differed between patients with mood disorders and healthy controls; in addition, the levels of UA in patients with BD were higher than those of patients with MDD. After treatment, UA levels increased in the MDD group, while they decreased in the BD group. Finally, we entered age, sex, UA, ALB, and TBIL into the XGBoost model. The area under the curve (AUC) of the XGBoost model for distinguishing between BD and MDD reached 0.849 (accuracy = 0.808, 95% CI = 0.719-0.878) and for distinguishing between BD with depression episode (BD-D) and MDD was 0.899 (accuracy = 0.891, 95% CI = 0.856-0.919). The models were validated in the validation cohort. The most important feature distinguishing between BD and MDD was UA. Conclusion: Peripheral non-enzymatic antioxidants, especially the UA, might be a potential biomarker capable of distinguishing between BD and MDD.

Microglia Involves in the Immune Inflammatory Response of Poststroke Depression: A Review of Evidence.

Poststroke depression (PSD) does not exist before and occurs after the stroke. PSD can appear shortly after the onset of stroke or be observed in the weeks and months after the acute or subacute phase of stroke. The pathogenesis of PSD is unclear, resulting in poor treatment effects. With research advancement, immunoactive cells in the central nervous system, particularly microglia, play a role in the occurrence and development of PSD. Microglia affects the homeostasis of the central nervous system through various factors, leading to the occurrence of depression. The research progress of microglia in PSD has been summarized to review the evidence regarding the pathogenesis and treatment target of PSD in the future.