Heterogeneity in the Effects of Neighborhood Deprivation on Economic Decision Making and Mental Health in Children

Abstract

AbstractDelay discounting is linked to developmental trajectories of cognition and the brain, as well as psychopathology, such as psychosis. Although childhood socioeconomic deprivation is associated with both increased delay discounting and a higher incidence of psychotic disorders, the genetic and neural basis of these associations remains unclear. This study examined the causal relationships between neighborhood socioeconomic deprivation, delay discounting, and psychotic-like experiences (PLEs) in 2,135 preadolescent children using machine learning-based causal inference methods. We found that neighborhood deprivation, as measured by the Area Deprivation Index, had significant causal effects on delay discounting (β= −1.7297, p-FDR= 0.0258) and 1-year and 2-year follow-up PLEs (β= 1.3425∼1.8721, p-FDR≤ 0.0291). Furthermore, our analysis revealed significant heterogeneous causal effects of neighborhood deprivation on PLEs (p-FDR<0.005). The subgroups most vulnerable to these causal effects exhibited steeper discounting of future rewards, higher polygenic scores for educational attainment, reduced structural volume/area/white matter in the parahippocampal, right temporal pole, and right pars opercularis, and greater functional activation in the limbic system during Monetary Incentives Delay tasks. Our findings highlight the importance of a bioecological framework and the involvement of the mesocorticolimbic system in the causal relationship between socioeconomic deprivation and the risk of psychosis during childhood. Overall, our results support that enhancing the residential socioeconomic environment could positively influence child development.Significance StatementDiscerning the complex interplay of genetic, neural, and environmental factors within the relationship between childhood environment and psychopathology is essential for developing personalized health care. To provide optimal health care for each patient, identifying the biological and socioeconomic characteristics of the most vulnerable population is necessary. Through the application of state-of-the-art causal machine learning methods, this study shows that children with genetic and neural associates of impatient reward valuation are at a heightened risk of developing psychosis when exposed to neighborhood socioeconomic adversity. These findings underscore the significance of enhancing the childhood environment as a means to address social and health disparities.