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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Children’s early experiences are associated with important later-life outcomes, including their earnings1, educational attainment2, physical well-being3 and mental health4. How are children’s experiences embedded in their developing brains to broaden, or constrain, their opportunities to live happy and healthy lives? Much of what we know about links between early experiences and adult outcomes has come from research on socio-economic status (SES). A multidimensional construct, SES is typically measured at the household level (for example, parental income, education or occupation) or the neighbourhood level (for instance, neighbourhood crime rate, poverty levels or median income). Higher SES is associated with lower exposure to stress, and with greater access to cognitive enrichment, such as high-quality education, child-directed language, books and toys. Variation in childhood SES has been associated with variation in measures of brain structure and function5,6,7,8. However, surprisingly little is known about whether and how experiences associated with childhood SES affect the trajectory of brain maturation.

Here, we synthesize evidence that experiences associated with childhood SES affect not only the outcome, but also the pace of brain development, and consider the implications of early brain development for plasticity in childhood. We focus on whole-brain cortical measures of structure and function because, as a broad and multidimensional construct, SES probably exerts effects on a complex constellation of brain regions and their connections. We highlight the few longitudinal studies on SES and brain development but, because these studies are rare, we also draw on cross-sectional studies of relationships between SES and brain structure and function across development9. We consider how experiences, including stress, cognitive enrichment and environmental variability, influence brain maturation and plasticity. We close by outlining promising future directions for research on how children’s early experiences lead to disparities in later-life outcomes.

Cortical thickness

Cortical thickness increases in the prenatal and immediate postnatal period, driven by dendritic and axonal growth as well as synaptogenesis10. Peak synaptic density and peak cortical thickness are reached at different times across the brain, with sensory regions showing faster development and earlier peaks, and association regions showing slower developmental trajectories11,12 (Fig. 1). The cortex thickens before 2 years of age, before undergoing widespread thinning across a protracted period starting between 2 and 5 years of age, and continuing through adolescence and early adulthood. Thinning is attributed to both regressive (synaptic pruning) and progressive (myelination) processes13,14. In adulthood, a thicker cortex is associated with larger, more complex pyramidal neurons15. Cortical surface area increases during childhood and into early adolescence, with the greatest increases occurring first in sensory areas, and latest in association areas16,17.


Trajectories shown in light and dark blue are conceptual, based on findings interpolated across multiple studies. Horizontal grey lines represent the age ranges of individual studies, as shown on the horizontal axis. Brain regions shown in blue indicate negative relationships between socio-economic status (SES) and cortical thickness (ref.21 corresponds to grey line 1). Brain regions shown in red indicate positive relationships between SES and cortical thickness (grey line 2, ref.19; grey line 3, ref.8; grey line 4, ref.22; grey line 5, ref.18; grey line 6, ref.25; grey line 7, ref.107; grey line 8, ref.36; grey line 9, ref.20; grey line 10, ref.24; grey line 11, ref.26). These curves are consistent with more modest main effects of SES on cortical thickness when averaging is done across large age ranges than when small age ranges are focused upon. The inset shows a schematic of potential cellular underpinnings of cortical thickness as measured by MRI: glial number and size, neuron number and size, synaptic complexity and myelination14,15,16. Cells are enlarged relative to cortical thickness to show detail. Brain image corresponding to grey line 1 adapted with permission from ref.21, OUP. Brain image corresponding to grey line 2 adapted with permission from ref.19, CC BY 4.0 ( Brain image corresponding to grey line 3 adapted with permission from ref.8, Sage Publishing. Brain image corresponding to grey line 4 adapted with permission from ref.22, CC BY 4.0 (

Children and adolescents from higher-SES environments generally have thicker cortex than those from lower-SES environments8,18,19,20, but there is evidence that relationships between SES and cortical thickness vary with age (Fig. 1). In the first postnatal year, when the cortex rapidly thickens, higher paternal education is associated with thinner cortex, particularly in the frontal lobes21. This pattern is suggestive of more prolonged maturational processes in infants from higher-SES backgrounds. Later in development, in youth aged 3–20 years, SES moderates the negative relationship between age and cortical thickness such that youth from lower-SES backgrounds show a steeper curvilinear decrease in cortical thickness at a younger age than do youth from higher-SES backgrounds22,23. Adolescents aged 12–18 years in low-income households show a steeper curvilinear relationship between age and cortical thickness than do adolescents in high-income households24. For females, but not males, in low-income households, living in high-inequality neighbourhoods is again associated with a steeper negative relationship between age and cortical thickness24. This evidence is consistent with the hypothesis that lower SES is associated with accelerated cortical thinning throughout childhood and adolescence. However, not all findings align with this hypothesis. Two recent studies examined youth aged 5–25 years25 and 14–19 years26 and did not find that SES moderated relationships between age and cortical thickness, although the former study reported positive correlations between SES and cortical thickness. However, examining a large age range such as 5–25 years might obscure interaction effects that vary over the course of development, and SES-related variability in the rate of cortical thinning during late adolescence when thinning has slowed may be minimal (Fig. 1). In addition, neither study examined non-linear relationships between age and cortical thickness moderated by SES.

Surface area

Fewer studies have examined associations between SES and cortical surface area development. In infancy, surface area is not related to parental education or income21. In late childhood and adolescence, however, higher SES is associated with greater surface area25,26,27. In an analysis of the Pediatric Imaging, Neurocognition, and Genetics (PING) dataset, researchers applied sample weights to structural brain imaging data collected from children aged 3–18 years to create a ‘weighted sample’ approximating the distribution of SES, race/ethnicity and sex in the US population. When the researchers used the weighted sample to examine associations between surface area and age, the surface area peak shifted earlier as compared with the unweighted sample, consistent with an interpretation of earlier or faster brain maturation in children from lower-SES backgrounds, who were under-represented in the original sample28. In a recent longitudinal study of adolescents, higher SES was associated with a smaller decline in total surface area between 14 and 19 years of age26.

Cellular underpinnings

The cellular processes that underlie cortical thickness and surface area measures obtained with MRI are still under active investigation. As noted already, cortical thickness is positively associated with synaptic density, and is negatively associated with myelination14,15. One possibility is that experiences associated with low SES drive earlier curtailment of synaptic proliferation and a subsequently decreased range for optimal synaptic pruning and wiring of functional networks. Computational models of synaptic proliferation suggest that synaptic overgrowth and then pruning of weak synapses maximizes network performance, given the metabolic constraints of the brain29. In biologically motivated models of network development, delaying synaptogenesis in higher-order layers of a network leads to greater energy efficiency and faster learning after development30. Moreover, networks with more initial connections are better able to learn than networks with fewer initial connections31. Computational models of synaptic proliferation and subsequent pruning early in development have identified a trade-off between rapid development, which enables earlier independence and less parental input, and optimal adult neural performance32. SES-associated differences in early synaptic proliferation would affect the development of functional connectivity, which we examine in the following section.

A key goal of brain development is to establish efficient, specialized cortical systems. Functional activation of specific systems can be studied by imaging individuals performing well-designed tasks, but SES-associated differences in task accuracy and the interpretation of stimuli can affect conclusions about the underlying anatomy33. By contrast, data collected when participants relax inside the scanner — that is, resting-state functional MRI (rs-fMRI) data — can be used to study all systems simultaneously without task confounds34. Components of a functional system show statistically similar patterns of fluctuations in blood oxygenation, commonly referred to as functional connectivity35.

Resting-state analyses have generated conflicting answers to the question of whether higher SES is associated with faster functional maturation. One compelling study integrated grey and white matter structure with regional rs-fMRI measures to develop a model to classify individuals’ ages. It was found that individuals aged 8–22 years from lower-SES backgrounds were more likely to be classified as adults than their higher-SES counterparts36. Other rs-fMRI studies also suggest that lower SES is associated with faster functional development: in youth aged 6–17 years, lower SES was associated with weaker connectivity in corticostriatal connections that typically showed decreases in strength with age over development37,38. However, some studies have found the opposite pattern: higher SES has been associated with greater functional connectivity between limbic regions that typically show age-related increases in functional connectivity over development39,40,41. These studies largely examined patterns of regional metrics or connectivity between specific sets of regions rather than testing for broad effects of SES on the pace of network development throughout the brain. However, region-to-region connectivity can be strengthened by repeated co-activation, just as cells that fire together will wire together. Therefore, it is difficult to infer broad developmental processes from examining links between specific regions42.

Newer approaches to analysing rs-fMRI data are computationally better suited to test the hypothesis that higher childhood SES is associated with protracted development of functional networks across the entire cortex. A network science approach, in particular, represents the brain as a collection of nodes (regions) and edges (connections), enabling us to address the whole-brain pattern of connectivity43,44. The resulting network architecture can then be quantitatively characterized with use of tools from graph theory to identify key properties relevant to maturation45. Two such properties are segregation and integration, both of which change during development46. Segregation quantifies the presence of groups or subnetworks of densely interconnected nodes in a network, whereas integration assesses the extent to which information can be rapidly combined from distributed regions43. Integration has a distinct meaning when one is interpreting diffusion data compared with when one is interpreting functional data47 (Box 1). Together, integration and segregation constitute the unique property of small-worldness found in adult brain networks: the perhaps counterintuitive presence of high levels of both segregation and integration at many different scales (see ref.48 for a recent review). Given the associations between functional network segregation at rest and cognitive abilities35,49, and that most research on SES and functional network development has examined segregation rather than integration, we focus specifically here on measures of functional network segregation.

Segregation in brain networks changes markedly over development, and can be measured at several scales. One measure of segregation at the nodal level is the clustering coefficient, which quantifies the connectivity in a node’s immediate neighbourhood. At the mesoscale and global levels, modularity captures the extent to which a network can be divided into distinct subnetworks or modules, and system segregation captures the extent to which systems within a functional network are distinctly partitioned35. A coarse proxy for system segregation is within-system connectivity.

Studies of prenatal development show that a segregated network structure is present even in utero, with modular subnetworks that coarsely resemble those found in adults50,51. Inter-regional variation in the width of time windows of synaptogenesis during prenatal and early postnatal development (for example, as seen in ref.11) gives rise to the highly connected hub nodes and modular structure seen in adult brain networks52,53. Similarly to structural brain development11,12, functional subnetworks underlying sensory systems become established at an earlier age than do the subnetworks underlying association systems54,55. Mesoscale segregation increases with age later in childhood and adolescence, probably reflecting the refinement of network architecture; higher-order association systems in particular become more segregated with development49,56 (although some studies do not find positive associations between age and segregation during adolescence, perhaps owing to differences in age range and node or edge definitions; see ref.57). Maturation at the cellular level probably gives rise to these macroscale developmental changes. Inhibitory interneurons have a role in limiting resting-state functional connectivity and establishing the boundaries between brain regions that are necessary for network segregation58. In addition, connection strength is associated with microscale properties of connected brain regions, including the size and complexity of layer III pyramidal neurons59,60, cytoarchitectonic similarity61 and excitatory–inhibitory receptor balance62.

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Only a few studies have examined associations between SES and functional brain development using a network science approach (Fig. 2), and these studies have used different measures of segregation. Although the use of different measures of segregation at different scales makes an overarching pattern difficult to interpret, here we draw upon existing studies to sketch a theoretical model for future work to detail. One study63 of infants less than 1-year-old found marginally significant associations between higher SES and both similarity to adult systems and within-system connectivity, a proxy for system segregation. The study’s authors interpret these observations as indicative that greater maturation is associated with higher SES. However, the significant associations were found only at 6 months of age and not at the other time points examined (1, 3, 9 or 12 months). In another study, youth aged 8–22 years from high-SES neighbourhoods show a stronger association between age and local segregation — clustering — than did youth from low-SES neighbourhoods64. Although the study authors also examined a mesoscale measure of segregation, namely modularity, the moderating effect of SES on associations between age and modularity was accounted for by local segregation, suggesting that the fundamental driver was variation in local network topology. Specifically, during late childhood, youth from high-SES neighbourhoods showed lower local cortical functional segregation than did youth from low-SES neighbourhoods. However, youth from high-SES neighbourhoods showed a steeper positive relationship between segregation and age during adolescence, such that by their early 20s, they showed greater functional network segregation than youth from low-SES neighbourhoods. Another study of individuals in a similar age range (6–17 years) revealed an interaction between household and neighbourhood SES, such that among youth in low-SES neighbourhoods, higher household SES is associated with greater local functional network segregation (assessed by the clustering coefficient) in the prefrontal cortex65. The available evidence is consistent with the hypothesis that higher SES is associated with more protracted functional network development, with youth from high-SES backgrounds showing more widespread connectivity and thus lower segregation early in development, before the rapid development of a more segregated network architecture that continues into adulthood10,11.