Poster No:
999
Submission Type:
Abstract Submission
Authors:
Analia Marzoratti1, Daniel Lipscomb1, Benjamin Newman1, Jim Soland1, John Van Horn1, Tanya Evans1
Institutions:
1University of Virginia, Charlottesville, VA
First Author:
Co-Author(s):
Introduction:
Children's development is influenced by their socioeconomic status (SES), which affects access to physical and social resources (Evans et al., 2012). SES is linked to outcomes like academic performance (particularly math; James-Brabnam et al., 2023), cognition (e.g., executive functioning; Leonard et al., 2015), and neural structure/function (Hanson et al., 2023). SES is often measured by household income or parent education (Duncan & Magnuson, 2012), shaping the home environment (Paulus et al., 2021), which impacts academic achievement (Bradley et al., 2001) and neurodevelopment (Tooley et al., 2021). Further research is needed to clarify the interactions between SES, home factors, and brain and behavior, which could inform equity-focused interventions (Dietrichson et al., 2017).
This study examines how typical SES indicators and home context characteristics influence brain structure and math fluency, focusing on the intraparietal sulcus (IPS), which is crucial for numerical processing and relevant to domain-general skills like working memory (Killebrew et al., 2015).
Methods:
We analyzed a subsample (N= 897) from the third wave of the adolescent brain cognitive development (ABCD) study (2019-2021, ages 12-13; M= 12.54 years, SD = 0.60). Demographic measures were derived from parent surveys. Income-to-needs ratios (INR) were calculated using household income bins and poverty thresholds. Caregiver education level was treated as a continuous variable. Child-reported home conflict, household learning attitudes, and home organization were derived from the PhenX Family Environment Scale (Hamilton, 2011; α = 0.71), and factor scores estimated using confirmatory factor analysis. Child sleep data was also collected.
Structural measures of the IPS included bilateral cortical area, thickness, volume, and sulcal depth (Destrieux et al., 2010). Cognitive function was assessed using age-corrected standard scores from the NIH Toolbox (Akshoomoff et al., 2013), and math fluency was measured by the Stanford Mental Arithmetic Response Time Evaluation (SMARTE). General linear models controlled for child age and sex, reporting standardized beta coefficients and adjusted p-values.
We modeled math fluency (controlling for crystallized intelligence) and eight neural measures with SES and home context predictors, as well as their interactions. A second model examined the effects of IPS structural measures on math fluency. Random forest regression (RFR) was used as a data-driven robustness check of our results' uniqueness to the IPS versus 76 other cortical regions, using mean decrease in impurity (MDI) as a metric for feature importance (Yuan et al., 2022).
Results:
Controlling for both SES indicators, home conflict negatively predicted left IPS cortical volume (B= -0.07, p= .04) and area (B= -0.06, p= .04), with higher parent education mitigating these effects (B= 0.08, p= .04). Controlling for both SES indicators, home learning attitudes positively predicted IPS cortical thickness (B= 0.08, p= .02). Home organization also interacted with parent education to enhance its positive effect on left cortical thickness (B= 0.10, p< .01).
Math fluency was positively predicted by left IPS cortical area and volume (B= 0.07, p< .001), controlling for intelligence, age, and sex. RFR showed that INR (MDI= 132.7) and sleep (MDI= 129.4) were strong predictors of math fluency, while IPS measures had moderate predictive value (MDI= 31.2), with parietal-occipital regions (MDI= 42.2) and age (MDI= 43.4) being more predictive.
Conclusions:
Our findings show that, even controlling for SES indicators, modifiable aspects of early experiences predict both neural measures of math performance and math fluency. The supplemental machine learning analysis support these effects in the IPS, and extends them to other cortical regions. This work highlights the importance of incorporating home context in SES-related cognitive and neural research to enhance its empirical and practical value
Lifespan Development:
Lifespan Development Other 1
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Cognition
Data analysis
Learning
Machine Learning
Modeling
Statistical Methods
Structures
1|2Indicates the priority used for review
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Structural MRI
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Provide references using APA citation style.
Akshoomoff, N., Beaumont, J. L., Bauer, P. J., Dikmen, S., Gershon, R., Mungas, D. M., Slotkin, J., Tulsky, D. S., Weintraub, S., Zelazo, P. D., & Heaton, R. K. (2013). VIII. NIHToolbox Cognition Battery (CB): Composite Scores Of Crystallized, Fluid, And Overall Cognition. Monographs of the Society for Research in Child Development, 78(4), 119–132.
Bradley, R. H., Corwyn, R. F., Burchinal, M., McAdoo, H. P., & Coll, C. G. (2001). The Home Environments of Children in the United States Part II: Relations with Behavioral Development through Age Thirteen. Child Development, 72(6), 1868–1886.
Destrieux, C., Fischl, B., Dale, A. M., & Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage, 53(1), 1–15.
Dietrichson, J., Bøg, M., Filges, T., & Jørgensen, A. K. (2017). Academic Interventions for Elementary and Middle School Students With Low Socioeconomic Status: A Systematic Review and Meta-Analysis. Review of Educational Research, 87(2), 243–282.
Duncan, G. J., & Magnuson, K. (2012). Socioeconomic status and cognitive functioning: Moving from correlation to causation. WIREs Cognitive Science, 3(3), 377–386.
Evans, G. W., & Kim, P. (2012). Childhood poverty and young adults’ allostatic load. Psychological Science, 23(9), 979–983.
Hamilton, et al. (2011). The PhenX Toolkit: Get the Most From Your Measures. American Journal of Epidemiology, 174(3), 253–260.
Hanson, J. L., Adkins, D. J., Nacewicz, B. M., & Barry, K. (2023). Impact of socioeconomic status on amygdala and hippocampus subdivisions in children and adolescents. bioRxiv (Cold Spring Harbor Laboratory).
James‐Brabham, E., Loveridge, T., Sella, F., Wakeling, P., Carroll, D. J., & Blakey, E. (2023). How do socioeconomic attainment gaps in early mathematical ability arise? Child Development, 94(6), 1550–1565.
Yuan, D., Hahn, S., Allgaier, N., Owens, M. M., Chaarani, B., Potter, A., & Garavan, H. (2022). Machine learning approaches linking brain function to behavior in the ABCD STOP task. Human Brain Mapping, 44(4), 1751–1766.
No