Challenges and opportunities of gap score methods for studying psychopathology resilience and vulnerability
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
The widespread prevalence of psychopathology, which affects approximately 50% of the global population, often manifests during adolescence. Understanding why some individuals remain resilient while others experience mental health challenges despite similar environmental risks is essential for developing early interventions. However, past efforts have faced challenges with the retrospective definition of resilience. Here, we aim to address these challenges by quantifying resilience to psychopathology at the individual level.
Methods
In the Adolescent Brain and Cognitive Development (ABCD) Study® (N = 11,868), we utilized gradient-boosted tree regression to predict 2-year follow-up psychopathology from 208 Social Determinants of Health features. We used the “gap score” method—the difference between model-predicted and reported psychopathology—to quantify individual differences in psychopathology resilience and susceptibility, defined as the Resilience-Susceptibility Gap (RS-Gap). We validated the RS-Gap against independent 3-year follow-up clinical and quality-of-life outcomes.
Results
Collinearity between gap scores and reported symptoms was high (r=-0.84), requiring further correction. Four bias-correction techniques were implemented and compared. After appropriate bias-correction, greater RS-Gap scores were associated with a higher likelihood of poor academic and social outcomes one year later, suggesting that early adaptation to adversity may carry a latent long-term cost.
Conclusions
Dependency between RS-Gap and psychopathology scores is a statistical challenge for gap score resilience methods. Our comparisons demonstrate that correction is mandatory to separate resilience signal from shared variance with psychopathology scores. Findings converged across different bias correction methods, providing a validated framework for using gap scores to identify high-risk developmental trajectories in youth.
