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Low socioeconomic status (SES) is associated with a higher probability of multiple exposures (e.g., neighborhood violence, poor nutrition, housing instability, air pollution, and insensitive caregiving) known to affect structural development of subcortical brain regions that subserve threat and reward processing, however, few studies have examined the relationship between SES and such subcortical structures in adolescents. We examined SES variations in volume and surface morphometry of subcortical regions. The sample comprised 256 youth in eighth grade (mean age = 13.9 years), in whom high dimensional deformation mapping of structural 3T magnetic resonance imaging scans was performed. Vertex-wise linear regression analyses examined associations between income to poverty ratio and surfaces of the hippocampus, amygdala, thalamus, caudate, putamen, nucleus accumbens and pallidum, with the covariates age, pubertal status, and intracranial volume. Given sex differences in pubertal development and subcortical maturation at this age, the analyses were stratified by sex. Among males, who at this age average an earlier pubertal stage than females, the relationship between SES and local shape variation in subcortical regions was almost entirely positive. For females, the relationship between SES and local shape variation was negative. Racial identity was associated with SES in our sample, however supplementary analyses indicated that most of the associations between SES and subcortical structure were independent of it. Although these cross-sectional results are not definitive, they are consistent with a scenario where low SES delays structural maturation of subcortical regions involved with threat and reward processing. Future longitudinal studies are needed to test this hypothesis.
Pubmed ID: 31571360
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Open source software suite for processing and analyzing human brain MRI images. Used for reconstruction of brain cortical surface from structural MRI data, and overlay of functional MRI data onto reconstructed surface. Contains automatic structural imaging stream for processing cross sectional and longitudinal data. Provides anatomical analysis tools, including: representation of cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from rest of brain, skull stripping, B1 bias field correction, nonlinear registration of cortical surface of individual with stereotaxic atlas, labeling of regions of cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures.Operating System: Linux, macOS.
View all literature mentionsA Matlab toolbox for the statistical analysis of univariate and multivariate surface data using linear mixed effects models and random field theory.
View all literature mentionsSoftware application which aims to assign metric distances on the space of anatomical images in Computational Anatomy thereby allowing for the direct comparison and quantization of morphometric changes in shapes. As part of these efforts the Center for Imaging Science at Johns Hopkins University developed techniques to not only compare images, but also to visualize the changes and differences. For additional information please refer to: Faisal Beg, Michael Miller, Alain Trouve, and Laurent Younes. Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision, Volume 61, Issue 2; February 2005. M.I. Miller and A. Trouve and L. Younes, On the Metrics and Euler-Lagrange Equations of Computational Anatomy, Annual Review of biomedical Engineering, 4:375-405, 2002. Software developed with support from National Institutes of Health NCRR grant P41 RR15241.
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