Integrating Psychometric and Neurocognitive Biomarkers in Computational Models to Predict Cognitive Behavioral Therapy Outcomes in Adolescents with Anxiety and Depression

Abstract

This study investigated the predictive relationships between psychometric indicators and neurocognitive biomarkers in determining Cognitive Behavioral Therapy (CBT) outcomes among adolescents diagnosed with anxiety and depressive disorders. A quantitative prospective longitudinal research design was employed to examine how psychological and cognitive factors contributed to variability in treatment response. The study sample consisted of 120 adolescents aged between 12 and 18 years who were receiving structured CBT interventions in outpatient clinical settings. Data were collected at pretreatment, midpoint, and posttreatment stages using standardized psychometric scales and neurocognitive assessment tasks. Psychometric variables included anxiety severity, depressive symptoms, cognitive distortions, behavioral avoidance, emotional regulation difficulties, and resilience, while neurocognitive variables included attention bias, executive control, working memory performance, cognitive flexibility, emotional reactivity, and reward sensitivity. Descriptive analysis indicated substantial reductions in symptom severity over the course of treatment, with mean anxiety scores decreasing from 31.42 (SD = 6.85) at pretreatment to 17.63 (SD = 5.27) at posttreatment, while depressive symptoms declined from 28.73 (SD = 7.11) to 16.84 (SD = 5.89). Approximately 65% of participants were classified as treatment responders, demonstrating clinically significant improvement following CBT. Correlation analysis revealed significant relationships between psychological variables and treatment outcomes. Behavioral avoidance (r = -0.37, p < 0.01) and emotional regulation difficulties (r = -0.33, p < 0.01) were negatively associated with treatment improvement, whereas resilience demonstrated a positive correlation with treatment outcomes (r = 0.36, p < 0.01). Hierarchical regression analysis indicated that psychometric variables explained 39% of the variance in CBT outcomes (R² = 0.39, p < 0.001). When neurocognitive predictors were incorporated, the explanatory power of the model increased to 52% of the variance (R² = 0.52, p < 0.001). Executive control (β = 0.34, p < 0.01) and cognitive flexibility (β = 0.28, p < 0.05) emerged as significant positive predictors of treatment improvement, while emotional reactivity (β = -0.22, p < 0.05) was negatively associated with therapy outcomes. These findings demonstrated that integrating psychometric and neurocognitive indicators improved the prediction of CBT effectiveness. The study highlighted the importance of considering cognitive functioning, emotional regulation capacity, and behavioral coping mechanisms when evaluating treatment outcomes in adolescent psychotherapy.