Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology

Abstract

Childhood socioeconomic status (SES) impacts cognitive development and mental health, but its association with structural brain development is not yet well-characterized. Here, we analyzed 1243 longitudinally-acquired structural MRI scans from 623 youth to investigate the relation between SES and cortical and subcortical morphology between ages 5 and 25 years. We found positive associations between SES and total volumes of the brain, cortical sheet, and four separate subcortical structures. These associations were developmentally fixed rather than age-dependent.Surface-based shape analysis revealed that higher SES is associated with areal expansion of (i) lateral prefrontal, anterior cingulate, lateral temporal, and superior parietal cortices and (ii) ventrolateral thalamic, and medial amygdalo-hippocampal sub-regions. Meta-analyses of functional imaging data indicate that cortical correlates of SES are centered on brain systems subserving sensorimotor functions, language, memory, and emotional processing. We further show that anatomical variation within a subset of these cortical regions partially mediates the positive association between SES and IQ. Finally, we identify neuroanatomical correlates of SES that exist above and beyond accompanying variation in IQ. Our findings clarify the spatiotemporal patterning of SES-related neuroanatomical variation and inform ongoing efforts to dissect the causal pathways underpinning observed associations between childhood SES and regional brain anatomy.

Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment

“…The strong relationship observed between SES and global surface area at age 14 is consistent with prior findings 4,8 . There are multiple potential mechanisms mediating an effect of SES on brain development, including stress and glucocorticoids during pregnancy, toxins, premature delivery, maternal care, lack of cognitive stimulation and chronic stress during childhood and adolescence 6,21 .…”

“…Socioeconomic status (SES) inequalities have been associated with differences in executive function, memory, emotional regulation and educational attainment [1][2][3] . SES is also associated with functional and structural neural differences in a wide range of cortical areas, including those underlying higher cognitive functions [4][5][6][7][8] . Although SES is commonly assumed to represent a purely environmental factor, large portions of variability in SES can be explained by additive genetic factors 9 .…”

“…The distinction is important because it tells us about the potential mechanisms in play, has functional consequences for the individual and could have implications for remedial interventions. Therefore, we tested for distinct global and regional associations -a practice still ignored in the literature 4,5,8 , with the exception of only one known study to date 16 . The latter, however, did not control for any genetic component of SES.…”

Lean mass index is positively associated with white matter volumes in several brain regions in children with overweight/obesity

“…They observed a negative association between FFMI (independent of percentage body fat) and grey matter at the right temporal pole and the bilateral ventromedial prefrontal cortex (particularly the subgenual portion of the anterior cingulate). Discrepancies among studies might be due to participants' differences in terms of age, BMI distribution and the inclusion of different covariates, such as socioeconomic status (eg, parental education) in the present study, which has been strongly associated with brain volumes 34 and body composition. 35 To the best of our knowledge, no studies have been published on the association between lean mass, fat mass and grey matter volumes.…”

Hippocampal alterations and functional correlates in adolescents and young adults with congenital heart disease

“…Shape analysis has the potential to further identify structural differences that cannot be captured by gross volume measurement and has been shown to be an index that is sensitive to neurodevelopmental abnormalities (Achterberg et al, 2014;M. M. Chakravarty et al, 2015;McDermott et al, 2019;Schroeder et al, 2017).…”