Depression is a heterogeneous disease, with individual symptoms uniquely associated with negative cognitive processing bias and self-control. However, studies on the relationships among them from a fine-grained level are lacking. The present study employed network analysis to explore the specific connections among the three constructs based on the dual-process model. Recruiting 1168 Chinese university students, the study estimated a regularized partial correlation network. Depression, negative cognitive processing bias, and self-control were assessed with the nine-item Patient Health Questionnaire (PHQ-9), the Negative Cognitive Processing Bias Questionnaire (NCPBQ), and the Brief Self-Control Scale (BSCS), respectively. Depression nodes fatigue, sad mood, and guilt were the most central symptoms. Negative memory bias, negative attention bias, and guilt were the bridge nodes. Network revealed distinct relations between different negative cognitive processing bias dimensions and depression symptoms, self-control and depression symptoms, and direct antagonistic effects between negative cognitive processing bias and self-control. The current study showed specific pathways between the three communities, and highlighted the role of dual-process model variables in depression development. Focusing on the identified critical depression nodes and related pathways could be effective for depression prevention and intervention.
Objective: To identify risk indicators at ages 6–18 years that are associated with DSM-IV diagnoses in adolescents and young adults with intellectual disabilities five years later. To assess the potential health gain and efficiency of preventive interventions targeting these risk indicators. Method: Parents reported on potential child, parental, and environmental risk indicators. Five years later, parents were interviewed using a standardised psychiatric interview schedule (DISC-IV) to assess DSM-IV diagnoses in children with ID (N = 614) at the age of 11 to 24 years. Logistic regression and linear probability models were used to test the contribution of risk indicators to the prediction of DSM-IV diagnoses. Results: Deviant levels of internalising and externalising problems, inadequate adaptive behaviour, and parental psychopathology predicted psychiatric disorder. Children/adolescents exposed to multiple risk indicators were at greater risk of developing DSM-IV disorders. Conclusions: Strategies aiming for the risk reduction of psychiatric disorders in children/adolescents with ID should focus on intervening at an early age, improving psychopathology and adaptive behaviour skills of the children/adolescents, and supporting their parents.
