Socioeconomic Inequality and the Developing Brain: Spotlight on Language and Executive Function

Abstract

Robust evidence of the deleterious effects of poverty on children's academic achievement has generated considerable interest in the neural mechanisms underlying these associations. In studies of specific neu-rocognitive skills, researchers have found pronounced socioeconomic disparities in children's language and executive function (EF) skills. In this article, we review research linking socioeconomic factors (e.g., family income, parental education) with children's brain structure and function, focusing on the neural systems involved in language and EF. Then, we cover the potential mediators of these associations, developmental timing, and strategies for prevention and intervention. To complement research at the behavioral level, we conclude with recommendations for integrating measures of the developing brain into this ongoing work.

Full text

Socioeconomic Inequality and the Developing Brain:
Spotlight on Language and Executive Function
Emily C. Merz, Cynthia A. Wiltshire, and Kimberly G. Noble
Teachers College, Columbia University
ABSTRACT—Robust evidence of the deleterious effects of
poverty on children’s academic achievement has gener-
ated considerable interest in the neural mechanisms
underlying these associations. In studies of specific neu-
rocognitive skills, researchers have found pronounced
socioeconomic disparities in children’s language and
executive function (EF) skills. In this article, we review
research linking socioeconomic factors (e.g., family
income, parental education) with children’s brain struc-
ture and function, focusing on the neural systems involved
in language and EF. Then, we cover the potential media-
tors of these associations, developmental timing, and
strategies for prevention and intervention. To complement
research at the behavioral level, we conclude with recom-
mendations for integrating measures of the developing
brain into this ongoing work.
KEYWORDS—socioeconomic status; brain structure; brain
function; language; executive function
Nearly 16 million American children live below the poverty line
(Semega, Fontenot, & Kollar, 2017), and socioeconomic dispari-
ties in children’s educational outcomes have been well docu-
mented (Sirin, 2005). Differences in school-readiness skills
emerge in early childhood, preceding differences in academic
achievement that tend to widen over time. Empirical evidence
suggests that socioeconomic disadvantage may hinder cognitive
development and prevent children from reaching their educa-
tional potential (Duncan, Magnuson, & Votruba-Drzal, 2017).
Identifying ways to reduce this socioeconomic status (SES)-
achievement gap is crucial to improving the academic trajecto-
ries of many children in the United States and globally.
Recent research has shed light on correlations between SES
(family income, parental educational attainment, parental occupa-
tional prestige) and the developing brain, including the neural
mechanisms underlying disparities in academic achievement,
with the goal of identifying targets for effective prevention and
intervention strategies. Initial investigations examined associa-
tions between socioeconomic background and children’s perfor-
mance on batteries of neurocognitive tasks. These studies
revealed variation in the magnitude of associations across tasks,
with stronger associations for language and executive function
(EF) than for other neurocognitive skills (Noble, McCandliss, &
Farah, 2007; Noble, Norman, & Farah, 2005). In line with these
results, evidence points to large socioeconomic differences in lan-
guage comprehension and production (e.g., expressive and recep-
tive vocabulary, grammar, phonological awareness), along with
moderate to large differences in EF skills, including inhibitory
control, working memory, and cognitive flexibility (Lawson, Hook,
& Farah, 2017; Pace, Luo, Hirsh-Pasek, & Golinkoff, 2017).
In this article, we review studies examining associations
between family SES and children’s brain structure and function,
emphasizing the neural regions that support language and EF.
Then, we address the proximal factors through which SES may
affect language and EF development, the developmental timing
of socioeconomic disparities in language and EF, and programs
and policies that may mitigate the effects of socioeconomic dis-
advantage on language and EF. Because few studies have tack-
led these latter questions with regard to the brain, we
summarize evidence from behavioral studies of language and
EF, and use this research to suggest directions for ongoing
work.
Emily C. Merz, Cynthia A. Wiltshire, and Kimberly G. Noble,
Teachers College, Columbia University.
The research reported in this article was made possible by the
many families, over the many studies reviewed, who contributed
their time and effort to promote a deeper understanding of brain
development in the context of varying environmental experiences.
This work was also made possible by generous funding from Teach-
ers College, Columbia University and the Russell Sage Foundation.
Correspondence concerning this article should be addressed to
Kimberly G. Noble, Department of Biobehavioral Sciences, Teach-
ers College, Columbia University, 525 W. 120th Street, New York,
NY 10027; e-mail: kgn2106@tc.columbia.edu.
©2018 Society for Research in Child Development
DOI: 10.1111/cdep.12305
Volume 0, Number 0, 2018, Pages 1–6
CHILD DEVELOPMENT PERSPECTIVES

SOCIOECONOMIC INEQUALITY AND BRAIN
STRUCTURE IN YOUTH
Family SES has been associated repeatedly with differences in
children’s brain structure, particularly in regions responsible for
language and EF. Socioeconomic disadvantage has been associ-
ated with reduced cortical gray matter, as measured in terms of
volume (Hair, Hanson, Wolfe, & Pollak, 2015; Jednorog et al.,
2012), thickness (Lawson, Duda, Avants, Wu, & Farah, 2013;
Mackey et al., 2015; Romeo et al., 2017), and surface area
(Noble et al., 2015). For example, in a study of 3- to 20-year-
olds, higher family income and parental education were associ-
ated significantly with greater cortical surface area, independent
of age, sex, and genetic ancestry (Noble et al., 2015). The stron-
gest effects were seen in the left perisylvian cortical regions
underlying language processing, as well as in the regions of the
prefrontal cortex (PFC) underlying EF. These neuroanatomical
differences partially explained socioeconomic disparities in
vocabulary (Romeo et al., 2017), EF (Noble et al., 2015), and
standardized tests of academic achievement (Hair et al., 2015;
Mackey et al., 2015).
Studies examining these associations have also used diffusion
tensor imaging, a structural magnetic resonance imaging (MRI)
technique that measures the diffusion of water molecules in the
brain. In these studies, socioeconomic disadvantage was linked
with reduced integrity of white matter tracts, which may indicate
less efficient connections between brain regions (Ursache &
Noble, 2016). For example, in 8- to 10-year-olds, lower family
income was associated with lower integrity of white matter in
the left uncinate fasciculus, cingulum bundle, and superior lon-
gitudinal fasciculus (Dufford & Kim, 2017). Lower integrity of
the superior longitudinal fasciculus, which connects the frontal
lobe with parietotemporal regions, may relate to difficulties with
language processing and working memory in children from dis-
advantaged families. However, in another study, SES was not
associated with whole-brain white matter microstructure in 10-
year-olds, possibly because of the small sample size (Jednorog
et al., 2012). Thus, while growing evidence points to socioeco-
nomic differences in the structure of both gray and white matter
in areas of the brain responsible for language and EF, more
research is needed to delineate these differences and tie them to
academic achievement.
SOCIOECONOMIC INEQUALITY AND BRAIN FUNCTION
IN YOUTH
Socioeconomic differences in brain function in children and
adolescents have been observed using functional MRI (fMRI)
and electrophysiological methods. Disparities have been found
during language and reading tasks, including phonemic discrim-
ination and phonological processing (Farah, 2017). Regions that
have been implicated include the left perisylvian cortical
regions underlying language production and comprehension,
and temporal-occipital regions underlying reading skills (Con-
ant, Liebenthal, Desai, & Binder, 2017; Noble, Wolmetz, Ochs,
Farah, & McCandliss, 2006). For example, one fMRI study of 5-
year-olds examined associations between socioeconomic back-
ground and neural activation during a phonological awareness
(rhyming) task. Higher SES was associated with greater left lat-
eralization of inferior frontal activation during rhyming (Raizada,
Richards, Meltzoff, & Kuhl, 2008). Although another study did
not find socioeconomic differences in brain activation during
language perception (Monzalvo, Fluss, Billard, Dehaene, &
Dehaene-Lambertz, 2012), this could be a result of the proce-
dures used (measuring brain function during passive perception
of speech or printed words; Farah, 2017).
Researchers have also reported socioeconomic differences in
brain function during EF tasks. In some studies, socioeconomic
disadvantage has been linked with increased brain activation in
prefrontal regions in the context of similar task performance.
For example, youth from disadvantaged families performed less
successfully on EF tasks but showed greater recruitment of PFC
regions than youth from more advantaged families (Sheridan,
Peverill, Finn, & McLaughlin, 2017; Sheridan, Sarsour, Jutte,
D’Esposito, & Boyce, 2012; Spielberg et al., 2015). In another
study, lower family income tended to be associated with reduced
PFC activation as a function of higher working memory load, as
well as with reduced accuracy (though lower family income was
associated with greater PFC activation at lower working memory
loads; Finn et al., 2017). These differences in brain function
explained differences in mathematics achievement (Finn et al.,
2017). Taken together, this work could suggest that children
from higher and lower SES backgrounds rely on different pat-
terns of neural activation to perform EF tasks (Luna, Padmanab-
han, & O’Hearn, 2010).
Several studies have demonstrated socioeconomic differences
in event-related potential (ERP) activity during selective atten-
tion tasks involving EF (D’Angiulli, Herdman, Stapells, & Hertz-
man, 2008). Children from disadvantaged families have
demonstrated decreased neural activity during processing of rel-
evant information or decreased neural suppression of irrelevant
information in regions of the scalp consistent with PFC locations.
In some cases, this occurred even in the absence of socioeco-
nomic differences in task accuracy. For example, in a study of 7-
to 12-year-olds in which children from different socioeconomic
backgrounds performed equivalently on a target detection task,
ERPs nonetheless revealed that children from lower income
families showed an attenuated response to target stimuli
(Kishiyama, Boyce, Jimenez, Perry, & Knight, 2009). Another
study investigated socioeconomic disparities in neural indices of
auditory selective attention in 3- to 8-year-olds; although behav-
ioral performance was identical among children from different
socioeconomic backgrounds, children from more advantaged
backgrounds showed evidence of neural suppression of unat-
tended auditory stimuli compared with children from more dis-
advantaged backgrounds (Stevens, Lauinger, & Neville, 2009).
Child Development Perspectives, Volume 0, Number 0, 2018, Pages 1–6
2Emily C. Merz, Cynthia A. Wiltshire, and Kimberly G. Noble

Considering the functional neuroimaging and electrophysio-
logical results together, circumstances surrounding socioeco-
nomic disadvantage may favor the development of less efficient
EF, requiring greater recruitment of PFC regions to complete
tasks involving these skills. One explanation is that it may be
adaptive to maintain higher vigilance (and thus less selective
attention and less efficient EF) when threat is more likely (Ellis,
Bianchi, Griskevicius, & Frankenhuis, 2017). Such an interpre-
tation suggests that socioeconomic disparities in neural function
are not more or less optimal, but that neural function may be
optimized for the situation at hand.
MEDIATORS LINKING SOCIOECONOMIC
BACKGROUND WITH LANGUAGE AND EF
DEVELOPMENT
Socioeconomic factors are theorized to be distal factors that exert
their effects on brain development through more proximal factors
or mediating mechanisms (Bronfenbrenner & Morris, 1998). Lin-
guistic input in the home environment and family stress may be
important mediators of the effects of socioeconomic disadvantage
on the brain regions responsible for language and EF, respec-
tively (Noble, Houston, Kan, & Sowell, 2012).
Language
Striking socioeconomic disparities can be identified in the quan-
tity and quality of linguistic input that children receive (Pace
et al., 2017). For example, in a seminal study, Hart and Risley
(1995) observed large disparities in the number of words chil-
dren heard from their parents—more than three times as many
in higher income families as in lower income families. In follow-
up work, 3-year-olds from lower income families had less than
half the vocabulary of their counterparts from higher income
families. With these findings replicated in numerous studies,
converging evidence indicates that SES-based variability in lin-
guistic stimulation in the home (especially the quality of lan-
guage input) accounts partially for socioeconomic differences in
children’s language development (Pace et al., 2017). Echoing
these behavioral findings, in a recent study using fMRI, less
advantaged parents had fewer conversational exchanges with
their 4- to 6-year-olds than more advantaged parents. In turn,
children who experienced fewer conversational exchanges had
reduced activation in left inferior frontal regions during language
processing (Romeo et al., 2018).
Executive Function
Chronic stress is thought to be a key factor through which socioe-
conomic background influences the development of EF. Disad-
vantaged families tend to have many stressors in their lives (e.g.,
financial strain, neighborhood violence, crowding and noise, and
household chaos and unpredictability; Evans & Kim, 2013). At
the level of stress physiology, children from disadvantaged fami-
lies exhibit dysregulation of the hypothalamic-pituitary-adrenal
axis, as indicated by higher or lower levels of cortisol (Ursache,
Merz, Melvin, Meyer, & Noble, 2017). Chronic stress exerts pow-
erful effects in areas of the brain with high concentrations of glu-
cocorticoid receptors, such as the PFC (McEwen & Morrison,
2013). Empirical work suggests that chronic stress may mediate
the effects of socioeconomic disadvantage on the developing
PFC (Farah, 2017). For example, in a study using fMRI, chronic
exposure to stressors in childhood significantly mediated the
association between family income in childhood and PFC activity
in young adulthood (Kim et al., 2013).
Thus, linguistic input in the home and chronic stress may be
important mechanisms underlying socioeconomic disparities in
language and EF, respectively, and more work is needed to
examine the role of these mediators with regard to the underly-
ing neural circuitry. These mediators are not thought to be
mutually exclusive. Indeed, it is likely that to an extent, lan-
guage input also influences the development of EF and chronic
stress also affects language development. In particular, in addi-
tion to language outcomes, evidence suggests that children’s lan-
guage experiences influence their development of EF (Carlson,
Zelazo, & Faja, 2013).
DEVELOPMENTAL TIMING OF SOCIOECONOMIC
DISADVANTAGE
Exposure to socioeconomic disadvantage early in life may have
marked and enduring effects on brain development. Early child-
hood is a sensitive period when the brain may be particularly mal-
leable to environmental effects as a result of its rapid development
(Sheridan & McLaughlin, 2016). Empirical evidence supports the
notion that exposure to poverty during early childhood may be
especially detrimental to children’s brain development. Socioeco-
nomic differences in brain structure and function have been
observed from the first year of life (Betancourt et al., 2016; Han-
son et al., 2013; Tomalski et al., 2013) through adolescence, par-
alleling differences in cognitive performance that persist or widen
over time. Moreover, in research with adults, SES in childhood
was associated with brain structure and function even after
accounting for SES in adulthood (Farah, 2017; Staff et al., 2012).
Nonetheless, although neural plasticity may be diminished, it is
not absent later in childhood or adolescence. In particular, the
PFC develops in a protracted manner through adolescence, sug-
gesting a longer window of plasticity for EF. Thus, evidence points
to early childhood as a prime time for interventions to reduce
socioeconomic differences in language and EF, but suggests that
interventions at older ages may also be effective.
PROGRAMS AND POLICIES TO REDUCE
SOCIOECONOMIC DIFFERENCES IN BRAIN
DEVELOPMENT
Several interventions and policies have improved language and
EF in children from disadvantaged families. Interventions have
Child Development Perspectives, Volume 0, Number 0, 2018, Pages 1–6
Socioeconomic Inequality and the Developing Brain 3

taken various approaches, such as targeting the putative media-
tors of SES effects (e.g., linguistic stimulation in the home), pro-
viding enhanced curricula and early educational programs, or
changing SES directly by increasing family income. Early home
visiting programs, such as the Nurse–Family Partnership, have
yielded positive long-term outcomes for disadvantaged families
(Donelan-McCall, 2017). These programs aim to improve family
functioning or the home environment, with some interventions
narrower in scope and developmental mechanisms than others.
Pertinent to this article, some programs have enhanced the
quantity and quality of parents’ speech to children, which in
turn facilitated language development in children from disad-
vantaged families. For example, mothers who participated in the
Play and Learning Strategies intervention demonstrated greater
sensitivity and contingent responsiveness during interactions
with their infants than mothers who did not receive the interven-
tion; these increases in the quality of mother–child interactions
were linked with improved language skills in the children
(Landry, Smith, & Swank, 2006).
High-quality early care and education programs also support
language and EF development in children from disadvantaged
families. Intensive preschool interventions (e.g., the Perry Pre-
school and Abecedarian projects) have had positive effects into
adulthood (Ramey & Ramey, 2004), and in evaluations of pub-
licly funded prekindergarten programs, children had more opti-
mal language, literacy, and math outcomes (Weiland &
Yoshikawa, 2013) Although most of the research has focused on
early academic outcomes, especially language and literacy,
some studies have also demonstrated effects on EF (Weiland &
Yoshikawa, 2013). In addition, targeted preschool curricula
(e.g., classroom activities, approaches to teacher training) have
improved EF in preschoolers (Bierman, Nix, Greenberg, Blair,
& Domitrovich, 2008; Raver et al., 2011).
Programs and policies that provide income support to disad-
vantaged families have also improved children’s cognitive devel-
opment or academic outcomes, although they have not measured
language and EF specifically (Duncan et al., 2017). For exam-
ple, in the late 1960s and 1970s, policymakers considered a
negative income tax that would provide a guaranteed minimum
income to families with children. In studies of this approach,
elementary school children’s attendance and achievement rose
(Duncan et al., 2017).
Few intervention studies focused on socioeconomic disad-
vantage have included measures of brain structure or func-
tion. These interventions have sought to improve the
environments of children from disadvantaged families to
address some of the proximal causes of socioeconomic dispar-
ities in cognitive development. For example, in one study,
sessions to improve children’s attention coupled with sessions
to teach parents strategies to support children’s attention and
reduce family stress led to enhanced brain function (e.g.,
ERP correlates of selective attention) in disadvantaged
preschoolers (Neville et al., 2013). In another study, families
were randomly assigned to a multisession intervention focused
on parenting skills or a control group that received informa-
tion on children’s development, stress management, and exer-
cise (Brody et al., 2017). A longer duration of childhood
poverty was associated with smaller hippocampal and amyg-
dala volume in children in the control group when they were
young adults. For children whose parents participated in the
intervention, the duration of childhood poverty was not linked
to brain structure in these regions, suggesting that the inter-
vention mitigated the negative effects of childhood poverty on
these brain structures (Brody et al., 2017). Taken together,
these findings suggest that prevention and intervention pro-
grams may ameliorate the negative impact of socioeconomic
disadvantage on language and EF skills at the neural level.
CONCLUSIONS AND RECOMMENDATIONS
Recent research has yielded evidence of associations between
family SES and children’s brain structure and function.
Although these differences tend to be in widespread areas
across the brain, some of the largest and most consistent associ-
ations are in regions underlying language and EF. Socioeco-
nomic disadvantage has been linked with reduced gray matter
and integrity of white matter tracts in language and EF regions.
Functionally, socioeconomic disadvantage has been associated
with differences in the recruitment of the left perisylvian cortex
during language tasks, and in the recruitment of the PFC during
EF tasks. These brain differences partially account for the asso-
ciations between socioeconomic disadvantage and cognitive and
academic outcomes.
Most studies on this topic have been cross-sectional. Research
is needed that leverages prospective longitudinal designs to elu-
cidate how family SES influences developmental trajectories of
brain structure or function. In addition, longitudinal designs can
test the effects of the timing and duration of socioeconomic dis-
advantage on brain development. Research is also needed on
the mediators of SES effects on language and EF brain regions
(Noble et al., 2012). Because it is likely that many co-occurring
mediators link SES with these brain outcomes, it is important to
measure and analyze many mediators simultaneously to uncover
their relative contributions.
Although experimental and quasi-experimental research (e.g.,
natural experiments) has supported causal effects of family SES
on children’s cognitive and academic outcomes (Duncan et al.,
2017), few studies using these designs have included measures
of the brain. Thus, researchers should use these designs to make
inferences about the causal role of socioeconomic background
in children’s brain development. Additionally, such studies
make it possible to identify the neural mechanisms underlying
the effects of an intervention on children’s language and EF
skills. Pinpointing how an intervention works at the neural level
helps us understand what is needed to produce gains in chil-
dren’s language and EF.
Child Development Perspectives, Volume 0, Number 0, 2018, Pages 1–6
4Emily C. Merz, Cynthia A. Wiltshire, and Kimberly G. Noble

The families and children who participate in the interven-
tions we have described share the experience of socioeco-
nomic disadvantage but are otherwise a heterogeneous group,
varying in ways that could relate to whether and how much
they benefit from certain interventions. Researchers should
explore neurobiological factors as moderators of response to
interventions in this group. Studies that couple experimental
designs with measurement of potential biomarkers would pro-
vide information on the mechanisms underlying the effects of
socioeconomic disadvantage. Ultimately, this research could
be used to more precisely match children and families with
effective interventions.
Although socioeconomic disparities in children’s academic
outcomes represent a challenging public health problem, recent
research linking SES and children’s brain structure and function
has opened new avenues for addressing the problem. With this
work as a critical foundation, the field is now positioned to
understand in a more nuanced way how these brain differences
lead to differences in achievement and how proximal processes
contribute to these outcomes. Applying these findings to practice
and policy will help close the SES-achievement gap, and will
improve the educational outcomes and life chances of children
from disadvantaged families.
REFERENCES
Betancourt,L.M.,Avants,B.,Farah,M.J.,Brodsky,N.L.,Wu,J.,Ash-
tari, M., & Hurt, H. (2016). Effect of socioeconomic status (SES)
disparity on neural development in female African-American
infants at age 1 month. Developmental Science,19, 947–956.
https://doi.org/10.1111/desc.12344
Bierman,K.L.,Nix,R.L.,Greenberg,M.T.,Blair,C.,&Domitrovich,
C. E. (2008). Executive functions and school readiness interven-
tion: Impact, moderation, and mediation in the Head Start REDI
program. Development and Psychopathology,20,821–843. https://
doi.org/10.1017/S0954579408000394
Brody,G.H.,Gray,J.C.,Yu,T.,Barton,A.W.,Beach,S.R.H.,
Galvan, A., ... Sweet, L. H. (2017). Protective prevention effects
on the association of poverty with brain development. JAMA Pedi-
atrics,171,46–52. https://doi.org/10.1001/jamapediatrics.2016.
2988
Bronfenbrenner, U., & Morris, P. A. (1998). The ecology of developmen-
tal processes. In W. Damon & R. M. Lerner (Eds.), Handbook of
child psychology. Vol. 1: Theoretical models of human development
(5th ed., pp. 993–1023). New York, NY: Wiley.
Carlson, S. M., Zelazo, P. D., & Faja, S. (2013). Executive function. In
P. D. Zelazo (Ed.), The Oxford handbook of developmental psychol-
ogy (Vol. 1, pp. 706–743). Oxford, UK: Oxford University Press.
Conant, L. L., Liebenthal, E., Desai, A., & Binder, J. R. (2017). The
relationship between maternal education and the neural substrates
of phoneme perception in children: Interactions between socioeco-
nomic status and proficiency level. Brain and Language,171,14–
22. https://doi.org/10.1016/j.bandl.2017.03.010
D’Angiulli, A., Herdman, A., Stapells, D., & Hertzman, C. (2008). Chil-
dren’s event-related potentials of auditory selective attention vary
with their socioeconomic status. Neuropsychology,22,293–300.
https://doi.org/10.1037/0894-4105.22.3.293
Donelan-McCall, N. (2017). Parenting and home visiting interventions.
In E. Votruba-Drzal & E. Dearing (Eds.), The Wiley handbook of
early childhood development programs, practices, and policies (pp.
310–329). Hoboken, NJ: Wiley. https://doi.org/10.1002/97811189
37334.ch14
Dufford,A.J.,&Kim,P.(2017).Family income, cumulative risk exposure,
and white matter structure in middle childhood. Frontiers in Human
Neuroscience,11,1–13. https://doi.org/10.3389/fnhum.2017.00547
Duncan, G. J., Magnuson, K., & Votruba-Drzal, E. (2017). Moving
beyond correlations in assessing the consequences of poverty.
Annual Review of Psychology,68,413–434. https://doi.org/10.
1146/annurev-psych-010416-044224
Ellis, B. J., Bianchi, J., Griskevicius, V., & Frankenhuis, W. E. (2017).
Beyond risk and protective factors: An adaptation-based approach
to resilience. Perspectives on Psychological Science: A Journal of the
Association for Psychological Science,12,561–587. https://doi.org/
10.1177/1745691617693054
Evans, G. W., & Kim, P. (2013). Childhood poverty, chronic stress, self-
regulation, and coping. Child Development Perspectives,7,43–48.
https://doi.org/10.1111/cdep.12013
Farah, M. J. (2017). The neuroscience of socioeconomic status: Corre-
lates, causes, and consequences. Neuron,96,56–71. https://doi.
org/10.1016/j.neuron.2017.08.034
Finn, A. S., Minas, J. E., Leonard, J. A., Mackey, A. P., Salvatore, J.,
Goetz, C., ... Gabrieli, J. D. E. (2017). Functional brain organiza-
tion of working memory in adolescents varies in relation to family
income and academic achievement. Developmental Science,20,
https://doi.org/10.1111/desc.12450
Hair,N.L.,Hanson,J.L.,Wolfe,B.L.,&Pollak,S.D.(2015).Associa-
tion of child poverty, brain development, and academic achieve-
ment. JAMA Pediatrics,169,822–829. https://doi.org/10.1001/ja
mapediatrics.2015.1475
Hanson,J.L.,Hair,N.,Shen,D.G.,Shi,F.,Gilmore,J.H.,Wolfe,B.
L., & Pollak, S. D. (2013). Family poverty affects the rate of human
infant brain growth. PLoS ONE,8. https://doi.org/10.1371/journal.
pone.0080954
Hart, B., & Risley, T. R. (1995). Meaningful differences in the everyday
experience of young American children.Baltimore,MD:PaulH.
Brookes.
Jednorog, K., Altarelli, I., Monzalvo, K., Fluss, J., Dubois, J., Billard, C.,
... Ramus, F. (2012). The influence of socioeconomic status on
children’s brain structure. PLoS ONE,7. https://doi.org/10.1371/
journal.pone.0042486
Kim,P.,Evans,G.W.,Angstadt,M.,Ho,S.S.,Sripada,C.S.,Swain,J.E.,
... Phan,K.L.(2013).Effectsofchildhood poverty and chronic
stress on emotion regulatory brain function in adulthood. Proceedings
of the National Academy of Sciences of the United States of America,
110, 18442–18447. https://doi.org/10.1073/pnas.1308240110
Kishiyama, M. M., Boyce, W. T., Jimenez, A. M., Perry, L. M., & Knight,
R. T. (2009). Socioeconomic disparities affect prefrontal function
in children. Journal of Cognitive Neuroscience,21,1106–1115.
https://doi.org/10.1162/jocn.2009.21101
Landry,S.H.,Smith,K.E.,&Swank,P.R.(2006).Responsiveparent-
ing: Establishing early foundations for social, communication, and
independent problem-solving skills. Developmental Psychology,42,
627–642. https://doi.org/10.1037/0012-1649.42.4.627
Lawson,G.M.,Duda,J.T.,Avants,B.B.,Wu,J.,&Farah,M.J.
(2013). Associations between children’s socioeconomic status and
prefrontal cortical thickness. Developmental Science,16,641–652.
https://doi.org/10.1111/desc.12096
Child Development Perspectives, Volume 0, Number 0, 2018, Pages 1–6
Socioeconomic Inequality and the Developing Brain 5

Lawson, G. M., Hook, C. J., & Farah, M. J. (2017). A meta-analysis of
the relationship between socioeconomic status and executive func-
tion performance among children. Developmental Science,21,
e12529. https://doi.org/10.1111/desc.12529
Luna, B., Padmanabhan, A., & O’Hearn, K. (2010). What has fMRI told
us about the development of cognitive control through adoles-
cence? Brain and Cognition,72,101–113. https://doi.org/10.1016/
j.bandc.2009.08.005
Mackey,A.P.,Finn,A.S.,Leonard,J.A.,JacobySenghor,D.S.,West,
M. R., Gabrieli, C. F. O., & Gabrieli, J. D. E. (2015). Neuroanatom-
ical correlates of the income achievement gap. Psychological
Science,26,925–933. https://doi.org/10.1177/0956797615572233
McEwen, B. S., & Morrison, J. H. (2013). The brain on stress: Vulnera-
bility and plasticity of the prefrontal cortex over the life course.
Neuron,79,16–29. https://doi.org/10.1016/j.neuron.2013.06.028
Monzalvo, K., Fluss, J., Billard, C., Dehaene, S., & Dehaene-Lambertz, G.
(2012). Cortical networks for vision and language in dyslexic and
normal children of variable socio-economic status. NeuroImage,61,
258–274. https://doi.org/10.1016/j.neuroimage.2012.02.035
Neville,H.J.,Stevens,C.,Pakulak,E., Bell, T. A., Fanning, J., Klein,
S., & Isbell, E. (2013). Family-based training program improves
brain function, cognition, and behavior in lower socioeconomic sta-
tus preschoolers. Proceedings of the National Academy of Sciences
of the United States of America,110, 12138–12143. https://doi.org/
10.1073/pnas.1304437110
Noble,K.G.,Houston,S.M.,Brito,N.H.,Bartsch,H.,Kan,E.,Kuper-
man, J. M., ... Sowell, E. R. (2015). Family income, parental edu-
cation and brain structure in children and adolescents. Nature
Neuroscience,18,773–778. https://doi.org/10.1038/nn.3983
Noble,K.G.,Houston,S.M.,Kan,E.,&Sowell,E.R.(2012).Neural
correlates of socioeconomic status in the developing human brain.
Developmental Science,15,516–527. https://doi.org/10.1111/j.
1467-7687.2012.01147.x
Noble, K. G., McCandliss, B. D., & Farah, M. J. (2007). Socioeconomic
gradients predict individual differences in neurocognitive abilities.
Developmental Science,10,464–480. https://doi.org/10.1111/j.
1467-7687.2007.00600.x
Noble, K. G., Norman, M. F., & Farah, M. J. (2005). Neurocognitive cor-
relates of socioeconomic status in kindergarten children. Develop-
mental Science,8,74–
87. https://doi.org/10.1111/j.1467-7687.
2005.00394.x
Noble, K. G., Wolmetz, M. E., Ochs, L. G., Farah, M. J., & McCandliss,
B. D. (2006). Brain-behavior relationships in reading acquisition
are modulated by socioeconomic factors. Developmental Science,9,
642–654. https://doi.org/10.1111/j.1467-7687.2006.00542.x
Pace, A., Luo, R., Hirsh-Pasek, K., & Golinkoff, R. M. (2017). Identify-
ing pathways between socioeconomic status and language develop-
ment. Annual Review of Linguistics,3, 285–308. https://doi.org/10.
1146/annurev-linguistics-011516-034226
Raizada, R. D. S., Richards, T. L., Meltzoff, A., & Kuhl, P. K. (2008).
Socioeconomic status predicts hemispheric specialisation of the left
inferior frontal gyrus in young children. NeuroImage,40,1392–
1401. https://doi.org/10.1016/j.neuroimage.2008.01.021
Ramey, C. T., & Ramey, S. L. (2004). Early learning and school readi-
ness: Can early intervention make a difference? Merrill-Palmer
Quarterly,50, 471–491.
Raver, C. C., Jones, S. M., Li-Grining, C., Zhai, F., Bub, K., & Pressler, E.
(2011). CSRP’s impact on low-income preschoolers’ preacademic
skills: Self-regulation as a mediating mechanism. Child Development,
82,362–378. https://doi.org/10.1111/j.1467-8624.2010.01561.x
Romeo, R. R., Christodoulou, J. A., Halverson, K. K., Murtagh, J., Cyr,
A. B., Schimmel, C., ... Gabrieli, J. D. E. (2017). Socioeconomic
status and reading disability: Neuroanatomy and plasticity in
response to intervention. Cerebral Cortex,28,2297–2312; 1–16.
https://doi.org/10.1093/cercor/bhx131
Romeo, R. R., Leonard, J. A., Robinson, S. T., West, M. R., Mackey, A.
P., Rowe, M. L., & Gabrieli, J. D. E. (2018). Beyond the 30-mil-
lion-word gap: Children’s conversational exposure is associated
with language-related brain function. Psychological Science,29,
700–710. https://doi.org/10.1177/0956797617742725
Semega,J.L.,Fontenot,K.R.,&Kollar,M.A.(2017).Income and pov-
erty in the United States: 2016.Washington,DC:U.S.Census
Bureau Current Population Reports.
Sheridan, M. A., & McLaughlin, K. A. (2016). Neurobiological models of
the impact of adversity on education. Current Opinion in Behavioral
Sciences,10,108–113. https://doi.org/10.1016/j.cobeha.2016.05.
013
Sheridan,M.A.,Peverill,M.,Finn,A.S.,&McLaughlin,K.A.(2017).
Dimensions of childhood adversity have distinct associations with
neural systems underlying executive functioning. Development and
Psychopathology,29,1777–1794. https://doi.org/10.1017/S0954
579417001390
Sheridan,M.A.,Sarsour,K.,Jutte,D.,D’Esposito,M.,&Boyce,W.T.
(2012). The impact of social disparity on prefrontal function in
childhood. PLoS ONE,7, https://doi.org/10.1371/journal.pone.
0035744
Sirin, S. R. (2005). Socioeconomic status and academic achievement: A
meta-analytic review of research. Review of Educational Research,
75,417–453. https://doi.org/10.3102/00346543075003417
Spielberg, J. M., Galarce, E. M., Ladouceur, C. D., McMakin, D. L.,
Olino, T. M., Forbes, E. E., ... Dahl,R.E.(2015).Adolescent
development of inhibition as a function of SES and gender: Con-
verging evidence from behavior and fMRI. Human Brain Mapping,
36,3194–3203. https://doi.org/10.1002/hbm.22838
Staff,R.T.,Murray,A.D.,Ahearn,T.S.,Mustafa,N.,Fox,H.C.,&
Whalley, L. J. (2012). Childhood socioeconomic status and adult
brain size: Childhood socioeconomic status influences adult hip-
pocampal size. Annals of Neurology,71,653–660. https://doi.org/
10.1002/ana.22631
Stevens, C., Lauinger, B., & Neville, H. (2009). Differences in the neural
mechanisms of selective attention in children from different socioe-
conomic backgrounds: An event-related brain potential study.
Developmental Science,12,634–646. https://doi.org/10.1111/j.
1467-7687.2009.00807.x
Tomalski, P., Moore, D. G., Ribeiro, H., Axelsson, E. L., Murphy, E.,
Karmiloff-Smith, A., ... Kushnerenko, E. (2013). Socioeconomic
status and functional brain development—Associations in early
infancy. Developmental Science,16,676–687. https://doi.org/10.
1111/desc.12079
Ursache, A., Merz, E. C., Melvin, S., Meyer, J., & Noble, K. G. (2017).
Socioeconomic status, hair cortisol and internalizing symptoms in
parents and children. Psychoneuroendocrinology,78,142–150.
https://doi.org/10.1016/j.psyneuen.2017.01.020
Ursache, A., & Noble, K. G. (2016). Socioeconomic status, white matter,
and executive function in children. Brain and Behavior,6, e00531.
https://doi.org/10.1002/brb3.531
Weiland, C., & Yoshikawa, H. (2013). Impacts of a prekindergarten pro-
gram on children’s mathematics, language, literacy, executive func-
tion, and emotional skills. Child Development,84, 2112–2130.
https://doi.org/10.1111/cdev.12099
Child Development Perspectives, Volume 0, Number 0, 2018, Pages 1–6
6Emily C. Merz, Cynthia A. Wiltshire, and Kimberly G. Noble