Cross-sectional and longitudinal associations of family income-to-needs ratio with cortical and subcortical brain volume in adolescent boys and girls

Abstract

Highlights• Sex-specific associations of SES with neurodevelopment may emerge in adolescence.• We used a whole-brain approach to examine gray and white matter volume.• Sex interacted with SES to explain variation in volume across adolescence.• Sex is an important variable to consider in analyses of SES and brain volume. AbstractDeviations in neurodevelopment may underlie the association between lower childhood socioeconomic status and difficulties in cognitive and socioemotional domains. Most previous investigations of the association between childhood socioeconomic status and brain morphology have used cross-sectional designs with samples that span wide age ranges, occluding effects specific to adolescence. Sex differences in the association between childhood socioeconomic status and neurodevelopment may emerge or intensify during adolescence. We used tensorbased morphometry, a whole brain approach, to examine sex differences in the cross-sectional association between normative variation in family income-to-needs ratio (INR) and cortical and subcortical gray and white matter volume during early adolescence (ages 9-13 years, N=147), as well as in the longitudinal association between in INR and change in volume from early to later adolescence (ages 11-16 years, N=109). Biological sex interacted with INR to explain variation in volume in several areas cross-sectionally and longitudinally. Effects were primarily in cortical gray matter areas, including regions of the association cortex and sensorimotor processing areas. Effect sizes tended to be larger in boys than in girls. Biological sex may be an important variable to consider in analyses of the effects of family income on structural neurodevelopment during adolescence.

Effects of the COVID-19 Pandemic on Mental Health and Brain Maturation in Adolescents: Implications for Analyzing Longitudinal Data

“…Participants’ parents reported on their annual household income, from which we computed an income-to-needs ratio by dividing the midpoint of their reported income bin by the low-income value for Santa Clara County. Importantly, this calculation considers the number of people in the home and the time period in which the study occurred ( https://www.huduser.gov/portal/datasets/il/il2017/2017summary.odn ; ( 32 ). Attesting to the success of our careful matching procedure, there were no significant group differences in participant characteristics between the pre-COVID and peri-COVID subgroups for either the “mental health” or the “brain” samples (all individual p s>.06).…”