Youths' socioeconomic status (SES) correlates with academic, cognitive, and neural outcomes, partly driven by influences on the developmental environment not captured by caregiver income and education alone. Leveraging Adolescent Brain Cognitive Development (ABCD) study data (M = 11.92 years; total n = 8,764), this study took an exploratory comparative approach, examining independent and interacting associations of SES indicators and home context qualities with neural structure and cognitive skills. Theory-driven analyses focused on the intraparietal sulcus (IPS), a key region for numerical and domain-general processing. Random forest regression (RFR) was used to evaluate results' brain-wide generalizability across 74 other cortical areas. Caregiver attentiveness and nightly sleep showed consistent positive associations with cognitive skills and IPS structure, respectively, controlling for SES and other home context factors. Interactions indicated that home learning emphasis and organization moderated SES associations with IPS morphology and cognitive skills. Data-driven analyses indicated that most associations identified with IPS morphology were moderate in strength rather than region-specific, with frontal and temporal cortical measures showing stronger links with SES. This work illustrates how data-driven approaches can complement theory-driven analyses by contextualizing the scope of theorized neural effects, an important check when modeling inherently complex developmental phenomena. Moreover, by directly comparing specific, potentially modifiable home context factors rather than focusing on SES indicators alone, such comparative approaches help clarify which factors may be most promising to prioritize in future intervention research. SUMMARY: Exploratory comparison of contextual factors as independent predictors and moderators of socioeconomic status (SES) associations with neural structure and cognition (ABCD; n = 8,764). Caregiver attentiveness and sleep quality were consistently associated with cognitive skills and intraparietal sulcus (IPS) morphology, respectively. Home organization and learning attitudes moderated SES associations. Machine learning sensitivity analyses suggest observed associations extend beyond the IPS to multiple cortical regions, supporting the relevance of SES-context analyses for understanding brain-wide effects. Integrating theory-driven regression with data-driven analyses reveals limits of region-specific models and underscores the value of distributed, computational approaches for more fully capturing developmental patterns.
Marzoratti et al. (Wed,) studied this question.