Associations of serotonin transporter gene promoter polymorphisms and monoamine oxidase A gene polymorphisms with oppositional defiant disorder in a Chinese Han population

The role of monoamine oxidase A in the neurobiology of aggressive, antisocial, and violent behavior: A tale of mice and men

Over the past two decades, research has revealed that genetic factors shape the propensity for aggressive, antisocial, and violent behavior. The best-documented gene implicated in aggression is MAOA (Monoamine oxidase A), which encodes the key enzyme for the degradation of serotonin and catecholamines. Congenital MAOA deficiency, as well as low-activity MAOA variants, has been associated with a higher risk for antisocial behavior (ASB) and violence, particularly in males with a history of child maltreatment. Indeed, the interplay between low MAOA genetic variants and early-life adversity is the best-documented gene × environment (G × E) interaction in the pathophysiology of aggression and ASB. Additional evidence indicates that low MAOA activity in the brain is strongly associated with a higher propensity for aggression; furthermore, MAOA inhibition may be one of the primary mechanisms whereby prenatal smoke exposure increases the risk of ASB. Complementary to these lines of evidence, mouse models of Maoa deficiency and G × E interactions exhibit striking similarities with clinical phenotypes, proving to be valuable tools to investigate the neurobiological mechanisms underlying antisocial and aggressive behavior. Here, we provide a comprehensive overview of the current state of the knowledge on the involvement of MAOA in aggression, as defined by preclinical and clinical evidence. In particular, we show how the convergence of human and animal research is proving helpful to our understanding of how MAOA influences antisocial and violent behavior and how it may assist in the development of preventative and therapeutic strategies for aggressive manifestations.

Machine learning-based prediction of persistent oppositional defiant behavior for 5 years

BACKGROUND

Early detection of oppositional defiant behavior is warranted for timely intervention in children at risk. This study aimed to build a predictive model of persistent oppositional defiant behavior based on a machine learning algorithm.

METHODS

With nationwide cohort data collected from 2012 to 2017, a tree-based ensemble model, random forest, was exploited to build a predictive model for persistent oppositional defiant behavior. The persistent oppositional defiant behavior was defined by the presence of oppositional defiant behavior for all the five years. The area under the receiver operating characteristic curve (AUC), overall accuracy, sensitivity, specificity, and Matthew's correlation coefficients (MCC) were measured.

RESULTS

Data of 1,323 children were used for building the machine learning-based predictive model. The baseline mean ± standard deviation month-age of the participants was 51.0 ± 1.2 months. The proportion of persistent oppositional defiant behavior was 0.98% (13/1323). In the hold-out test set, the overall accuracy, AUC, sensitivity, specificity, and MCC were 0.955, 0.982, 1.000, 0.954, and 0.417, respectively.

CONCLUSION

Our study demonstrated that the machine learning-based approach is useful for predicting persistent oppositional defiant behavior in preschool-aged children.