Poster #10 - Educational Attainment Polygenic Scores, Socioeconomic Factors, and Resting-State Functional Connectivity in Children and Adolescents

• In Event: Poster Session 12

Abstract

Executive function refers to a set of cognitive skills necessary for flexible, goal-directed behavior. Socioeconomic factors and genome-wide polygenic scores for educational attainment (PGS-EA) have been positively associated with cortical structure in regions associated with executive function (Farah, 2017; Judd et al., 2020; Merz et al., 2022; Mitchell et al., 2020). In addition, socioeconomic factors have been positively associated with resting-state functional connectivity (rsFC) in networks responsible for executive function, such as the central executive network (CEN), ventral and dorsal attention networks (VAN, DAN), and cingulo-opercular network (CON) (Brody et al., 2019; Gur et al., 2019; Rakesh et al., 2021). Yet, little is known about how PGS-EA and socioeconomic factors (parental education, family income) may individual and jointly predict brain function. As such, the goal of this study is to examine the associations among socioeconomic factors, PGS-EA, and rsFC in neural networks associated with executive function.

Participants are typically developing individuals between the ages of 3 and 21 years (N=245, 51% female) from the Pediatric Imaging, Neurocognition, and Genetics (PING) study (Jernigan et al., 2016). PGS-EA based on the EA3 genome-wide association study (Lee et al., 2018) were calculated for participants with genomic data in the PING study (Merz et al., 2022). Within-network resting-state connectivity will be calculated for the CEN, VAN, DAN, and CON. For each node in each network, a region-of-interest (ROI) seed mask will be calculated as the average percentage signal change for all voxels for that region at each time point. The average percent signal change of each pair of nodes within each network will then be averaged to reflect the connectivity of all possible nodes within the network. Analyses will examine the independent main effects of socioeconomic factors and PGS-EA on the connectivity of the CEN, VAN, DAN, and CON. Additionally, socioeconomic-factor-by-PGS-EA interactions will be examined for within-network rsFC in each network.

We hypothesize that PGS-EA and socioeconomic factors (family income, parental education) will make unique contributions to rsFC in the CEN, VAN, DAN, and CON. Based on previous research expect these associations to be in the positive direction. Little is known about the interaction effects of PGS-EA and SES at the neural level, particularly with regard to rsFC. Thus, this interaction will be explored in this study. The findings from this study will elucidate the independent and potentially interacting genetic and environmental contributions to alterations in intrinsic functional neural networks underlying executive function. These results hope to contribute to a growing body of research aimed at incorporating genetic information, along with socioeconomic factors, to build policies and social interventions that create more social equality.

Authors

• Melissa Hansen, Colorado State University
  Presenting Author

• Jordan Strack, Colorado State University

• Michael Thomas

• Uku Vainik

• Emily Merz