Childhood socio-economic status (SES), a measure of the availability of material and social resources, is one of the strongest predictors of lifelong well-being. Here we review evidence that experiences associated with childhood SES affect not only the outcome but also the pace of brain development. We argue that higher childhood SES is associated with protracted structural brain development and a prolonged trajectory of functional network segregation, ultimately leading to more efficient cortical networks in adulthood. We hypothesize that greater exposure to chronic stress accelerates brain maturation, whereas greater access to novel positive experiences decelerates maturation. We discuss the impact of variation in the pace of brain development on plasticity and learning. We provide a generative theoretical framework to catalyse future basic science and translational research on environmental influences on brain development.

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Recent evidence has suggested that sleep may facilitate language learning. The current study examined variation in language ability in 29 toddlers with Down syndrome (DS) in relation to levels of sleep disruption. Toddlers with DS and poor sleep (66%, n = 19) showed greater deficits on parent-reported and objective measures of language, including vocabulary and syntax. Correlations between sleep and language were found in groups with equivalent medical and social backgrounds and after control for relevant behavioral co-morbidities, including autism symptoms. These results emphasize the important role of quality sleep in all children’s expressive language development, and may help increase our understanding of the etiology of language deficits in developmental disorders, potentially leading to new treatment approaches.

Higher socioeconomic status (SES) in childhood is associated with increased cognitive abilities, higher academic achievement, and decreased incidence of mental illness later in development. Accumulating evidence suggests that these effects may be due to changes in brain development induced by environmental factors. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8-22 years, n = 1012) to examine developmental changes in functional brain network topology as estimated from resting state functional magnetic resonance imaging data. We quantitatively characterize this topology using a local measure of network segregation known as the clustering coefficient, and find that it accounts for a greater degree of SES-associated variance than meso-scale segregation captured by modularity. While whole-brain clustering increased with age, high-SES youth displayed faster increases in clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The effect of SES on developmental increases in clustering was strongest for connections of intermediate physical length, consistent with faster decreases in local connectivity in these regions in low-SES youth, and tracked changes in BOLD signal complexity in the form of regional homogeneity. Our findings suggest that neighborhood SES may fundamentally alter intrinsic patterns of inter-regional interactions in the human brain in a manner that is consistent with greater segregation of information processing in late childhood and adolescence. arXiv:1807.07687v2 [q-bio.NC]

Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as “glass-box” methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

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Exposure to adversity can accelerate biological aging. However, existing biomarkers of early aging are either costly and difficult to collect, like epigenetic signatures, or cannot be detected until late childhood, like pubertal onset. We evaluated the hypothesis that early adversity is associated with earlier molar eruption, an easily assessed measure that has been used to track the length of childhood across primates. In a preregistered analysis (n = 117, ages 4 to 7 y), we demonstrate that lower family income and exposure to adverse childhood experiences (ACEs) are significantly associated with earlier eruption of the first permanent molars, as rated in T2-weighted magnetic resonance images (MRI). We replicate relationships between income and molar eruption in a population-representative dataset (National Health and Nutrition Examination Survey; n = 1,973). These findings suggest that the impact of stress on the pace of biological development is evident in early childhood, and detectable in the timing of molar eruption.

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