Socioeconomic status and structural brain development

Abstract

Recent advances in neuroimaging methods have made accessible new ways of disentangling the complex interplay between genetic and environmental factors that influence structural brain development. In recent years, research investigating associations between socioeconomic status (SES) and brain development have found significant links between SES and changes in brain structure, especially in areas related to memory, executive control, and emotion. This review focuses on studies examining links between structural brain development and SES disparities of the magnitude typically found in developing countries. We highlight how highly correlated measures of SES are differentially related to structural changes within the brain.

Full text

FOCUSED REVIEW
published: 04 September 2014
doi: 10.3389/fnins.2014.00276
Socioeconomic status and structural brain
development
Natalie H. Brito*and Kimberly G. Noble*
Department of Pediatrics, Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA
Edited by:
Hauke R. Heekeren, Freie Universität
Berlin, Germany
Revi ewed by:
Sebastian J. Lipina, Unidad de
Neurobiología Aplicada (UNA,
CEMIC-CONICET), Argentina
Rajeev Krishnadas, University of
Glasgow, UK
Martha Farah, University of
Pennsylvania, USA
*Correspondence:
Natalie H. Brito is a Robert Wood
Johnson Health and Society Scholar at
Columbia University. She received her
PhD in Psychology with a concentration
in Human Development and Public
Policy from Georgetown University. Dr.
Brito’s research focuses on how early
environmental variations shape the
trajectory of cognitive development. She
has published work examining multiple
language exposure and memory
development. Currently, she is connecting
her previous work in bilingualism with
research into socioeconomic disparities.
nhb2111@columbia.edu
Recent advances in neuroimaging methods have made accessible new ways of
disentangling the complex interplay between genetic and environmental factors
that influence structural brain development. In recent years, research investigating
associations between socioeconomic status (SES) and brain development have found
significant links between SES and changes in brain structure, especially in areas related
to memory, executive control, and emotion. This review focuses on studies examining
links between structural brain development and SES disparities of the magnitude typically
found in developing countries. We highlight how highly correlated measures of SES are
differentially related to structural changes within the brain.
Keywords: socioeconomic status, brain development, structural imaging, environmental variation
INTRODUCTION
Human development does not occur within a vacuum. The environmental contexts and social
connections a person experiences throughout his or her lifetime significantly impact the devel-
opment of both cognitive and social skills. The incorporation of neuroscience into topics more
commonly associated with the social sciences, such as culture or socioeconomic status (SES),has
led to an increased understanding of the mechanisms that underlie development across the lifespan.
However, more research is necessary to disentangle the complexities surrounding early environ-
mental variation and neural development. This review highlights studies examining links between
structural brain development and SES disparities of the magnitude typically found in developing
countries. We do not include studies examining children who have experienced extreme forms of
early adversity, such as institutionalization or severe abuse. We also limit this review to findings
concerning socioeconomic disparities in brain structure, as opposed to brain function.
SES is a multidimensional construct, combining objective factors such as an individual’s (or
parent’s) education, occupation, and income (McLoyd, 1998).NeighborhoodSESisalsooftencon-
sidered (Leventhal and Brooks-Gunn, 2000), as are subjective measures of social status (Adler et al.,
2000). In 2012, 46.5 million people in the United States (15%) lived below the official poverty line
(United States Census Bureau, 2012) and numerous studies have reported socioeconomic dispari-
ties profoundly affecting physical health, mental well-being, and cognitive development (Anderson
and Armstead, 1995; Brooks-Gunn and Duncan, 1997; McLoyd, 1998; Evans, 2006). In turn, SES
accounts for approximately 20% of the variance in childhood IQ (Gottfried et al., 2003)andit
has been estimated that by age five, chronic poverty is associated with a 6- to 13-point IQ reduc-
tion (Brooks-Gunn and Duncan, 1997; Smith et al., 1997). Disparities in cognitive development
outweigh disparities in physical health, possibly contributing to the propagation of poverty across
generations (Duncan et al., 1998).
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Brito and Noble SES and structural brain development
KEY CONCEPT 1 | Socioeconomic status (SES)
Refers to an individual’s access to economic and social resources, as well as the benefits and social standing that
come from these resources. Most often measured by educational attainment, income, or occupation.
KEY CONCEPT 2 | Poverty
Comparison of a household’s income with a threshold level of income that varies with family size and inflation.
Households below the poverty threshold are considered “poor.” Households above this threshold are considered
“not poor” even if the amount of money between “poor” and “not poor” is diminutive. Poverty guideline for a family
of four in 2014 is $23,850.
*Correspondence:
Kimberly G. Noble is a developmental
cognitive neuroscientist and pediatrician
in the Department of Pediatrics and the
G.H. Sergievsky Center at Columbia
University. She received her
undergraduate, graduate, and medical
degrees from the University of
Pennsylvania, and completed
post-doctoral training at the Sackler
Institute for Developmental
Psychobiology at Weill Cornell Medical
College. Dr. Noble’s research focuses on
socioeconomic disparities in child
neurocognitive development. She is
interested in understanding the time
course with which socioeconomic
disparities in brain development emerge,
the mechanisms via which exposures and
experiences contribute to specific
neurocognitive outcomes, and in
applying this knowledge to the
development of public health-focused
interventional strategies.
kgn2106@columbia.edu
Evidence suggests multiple possible, and non-mutually-exclusive, explanations for these find-
ings. Socioeconomically disadvantaged children tend to experience less linguistic, social, and
cognitive stimulation from their caregivers and home environments than children from higher SES
homes (Hart and Risley, 1995; Bradley et al., 2001; Bradley and Corwyn, 2002; Rowe and Goldin-
Meadow, 2009). Additionally, individuals from lower SES homes report more stressful events during
their lifetime, and the biological response to stressors has been hypothesized as one of the under-
lying mechanisms for health and cognitive disparities in relation to SES (Anderson and Armstead,
1995; Hackman and Farah, 2009; Noble et al., 2012a).
In turn, these experiential differences are likely to have relatively specific downstream effects on
particular brain structures (see Figure 1 for one theoretical model). For example, disparities in the
quantity and quality of linguistic stimulation in the home have been associated with developmental
differences in language-supporting cortical regions in the left hemisphere (Kuhl et al., 2003; Conboy
and Kuhl, 2007; Kuhl, 2007). In contrast, the experience of stress has important negative effects
on the hippocampus (Buss et al., 2007; McEwen and Gianaros, 2010; Tottenham and Sheridan,
2010), the amygdala (McEwen and Gianaros, 2010; Tottenham and Sheridan, 2010), and areas of
the prefrontal cortex (Liston et al., 2009; McEwen and Gianaros, 2010)—structures which are linked
together anatomically and functionally (McEwen and Gianaros, 2010). As discussed below, different
components of SES may differentially relate to these varying experiences, and thus may have varying
associations with particular structures across the brain.
Measures of parental SES are often used as indicators of children’s family or home conditions,
but these distal measures may not fully account for children’s experiences. For example, while a
parent may be highly educated, unforeseen circumstances, such as a recession, may cause short-
or long-term unemployment and inadequate income, leading to reduced resources and increased
family stress experienced by the child. Studies examining an individual’s own SES may more accu-
rately represent the individual’s current experience during adulthood, but may possibly discount the
environmental experiences that shaped neural development as a child. Some studies have included
measures of both childhood and adult SES (see Table 1), attempting to obtain a complete mea-
sure of SES development, but retrospective SES relies on the individual’s memory of past events,
and therefore may be biased. Overall, accurate and complete measures of SES are often difficult to
obtain and these complications render it difficult to disentangle precise associations between spe-
cific socioeconomic indicators and outcomes of interest. Despite this, even approximate assessments
of SES have, across multiple independent laboratories, been shown to predict clinically and statis-
tically significant differences in brain structure and function, signifying the prominent association
between environmental factors and brain development.
SES VARIABLES REPORTED IN STRUCTURAL IMAGING STUDIES
Although many studies have reported a high degree of correlation between various components
of SES, different socioeconomic factors reflect different aspects of experience and should not be
used interchangeably (Duncan and Magnuson, 2012). For example, families with greater economic
resources may be better able to purchase more nutritious foods, provide more enriched home learn-
ing environments, or afford higher-quality child care settings or safer neighborhoods. In contrast,
parental education may influence children’s development by shaping the quality of parent–child
interactions (Duncan and Magnuson, 2012). The notion that these SES components might differ-
entially influence development is supported by the neuroscience literature, in which whole-brain
structural analyses (Lange et al., 2010; Jednoróg et al., 2012) and studies with a priori testing of
regions of interest (Hanson et al., 2011; Noble et al., 2012a; Luby et al., 2013) have indicated that
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Brito and Noble SES and structural brain development
FIGURE 1 | Hypothesized mechanisms by which SES operates to
influence structural and functional brain development.
different SES components may be associated with different brain
structural attributes. Additionally, SES disparities tend not to be
global, but rather, are disproportionately associated with differ-
ences in the structures of the hippocampus, amygdala, and the
prefrontal cortex (see Tab l e 1 ).
INCOME
Household or family income is usually calculated as the sum of
total income, typically measured monthly or annually. Although
income can be considered a continuous variable, many studies
ask participants to select what category of income they fall into.
For example, a participant may indicate that they earn between
$30,000 and $60,000 dollars per year, and researchers often
take the midpoint of the participant’s estimate (i.e., $45,000),
thereby reducing variability between participants. Income is one
of the more volatile of the SES markers, as family circumstances
frequently fluctuate across time, resulting in varying levels of
income throughout childhood and adolescence (Duncan, 1988;
Duncan and Magnuson, 2012). Income-to-Needs (ITN) is a sim-
ilar marker of SES, in which total family income is divided by the
official poverty threshold for a family of that size. Hanson et al.
(2011); Noble et al. (2012a) and Luby et al. (2013) all find signifi-
cant positive correlations between income/ITN and hippocampal
size, with children and adolescents from lower SES families having
smaller hippocampal volumes. Examining income-related differ-
ences in amygdala volumes, we find some discrepancies across
studies. While both Hanson et al. (2011) and Noble et al. (2012a)
find no association between income/ITN and amygdala volume,
Luby et al. (2013) report a significant positive correlation, where
children from lower income homes also have smaller amygdala
volumes. The families in the latter study reported lower family
income than the families in the other two studies; thus it may be
possible that, unlike the hippocampus, substantial income insuf-
ficiency is necessary to observe structural differences in amygdala
volumes.
KEY CONCEPT 3 | Income-to-Needs
The ratio of total family income divided by the federal poverty level for a
family of that size, in the year data were collected. A family living at the
poverty line would have an income-to-needs of ratio of 1. In 2012, 20.4 million
people reported an income below 50% of their poverty threshold, including
7.1 million children under the age of 18.
EDUCATION
Parental education or educational attainment is usually measured
by participants reporting their highest level (or their parents’
highest levels) of education (e.g., college degree). While fam-
ily income has been associated with resources available to the
family and levels of environmental stress (Evans and English,
2002), parental education has been more closely linked to cogni-
tive stimulation in the home (Hoff-Ginsberg and Tardif, 1995).
Compared to parents with lower levels of education, parents
with higher levels of education tend to spend more time with
their children (Guryan et al., 2008),usemorevariedandcom-
plex language (Hart and Risley, 1995; Hoff, 2003), and engage
in parenting practices that promote socioemotional develop-
ment (Duncan et al., 1994; McLoyd, 1997; Bradley and Corwyn,
2002). Again, like income/ITN, we find some inconsistencies
across studies when examining links between parental educa-
tion and children’s brain structure. Luby et al. (2013) and Noble
et al. (2012a) find no significant correlations between parental
education (measured as the average or highest level of edu-
cation of any parents or guardians living in the home) and
hippocampal volumes. Hanson et al. (2011) report a significant
association between right hippocampal volumes and paternal,
but not maternal, education levels. There are differences across
studies in reported amygdala volumes as well. Whereas Noble
et al. (2012a) find a negative correlation between parental edu-
cation and amygdala volumes, Luby et al. (2013) and Hanson
et al. (2011) find no association. These differences may be due in
part to how parental education was measured (average parental
education vs. separate indicators for mothers and fathers)
and/or how parental education was coded (continuously vs.
categorically).
Examining the relation between brain structure and one’s own
educational attainment in adulthood (as opposed to parental
education), both Gianaros et al. (2012) and Piras et al. (2011)
found positive associations between educational attainment and
increases in white matter integrity using diffusion tensor imag-
ing (indexed by increases in fractional anisotropy and decreases in
mean diffusivity, respectively). Whereas Gianaros and colleagues
found widespread associations, Piras and colleagues found that,
once controlling for age, only microstructural changes in the
hippocampi significantly correlated with educational attainment.
Noble et al. (2012b) also found no simple correlation between
reported educational attainment and either hippocampal or
amygdala volumes in adulthood. Educational attainment did,
however, moderate the association between age and hippocam-
pal volume. Specifically, as has been reported previously, age
was quadratically related to hippocampal volume, with the vol-
ume of this structure tending to increase until approximately
the age of 30, at which point volume starts to decline (Grieve
et al., 2011). Although this quadratic relation between hip-
pocampal volume and age was present across the entire sample,
the volumetric reduction seen at older ages was more pro-
nounced among less educated individuals, and was buffered
among more highly educated individuals. Differences in hip-
pocampal structure between higher and lower educated individ-
uals may therefore be most apparent in the later stages of the
lifespan.
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Brito and Noble SES and structural brain development
Table 1 | Studies reporting on associations between SES and structural brain development.
Study Participants SES measures Areas of the brain Morphometry analysis Main findings
Children Eckert et al.,
2001
10–12 years old
M=11.4 years
N=39
Participation in a federally
subsidized school lunch
program.
Low-income families had annual
incomes less than $14,597
Whole-brain and ROIsain
planum temporale and
central sulcus
MRI: cerebral volume
(PV-wave and manual tracing);
surface areas of ROIs (manual
tracing)
Children who participated and children
who did not participate in a federally
subsidized school lunch program
showed similar correlations between
planum temporale asymmetry and
phonological skill, although
phonological skill was lower in the
lower-SES group.
Hanson et al.,
2013
0–5 years old
M=13.5 months
(first scan)
N=77
Family income
Mode =$50k to $75k
Range =$0 to >$100k
SES categories
Low-SES (=or <200% of FPL),
Moderate-SES (between 200 and
400% of FPL), High-SES (greater
than 400% of FPL)
Whole-brain and ROIs in
frontal, parietal, temporal,
and occipital lobes
MRI: cerebral volume; gray
and white matter volumes in
ROIs
(Expectation–Maximization
algorithm)
Children from lower income families
had lower total gray matter volumes,
and frontal and parietal volumes. No
differences were found for total
cerebral volume or parietal and
temporal lobes. Children from lower
income families showed reduced total
gray matter trajectory.
Jednoróg et al.,
2012
8–10 years old
M=9.6 years
N=23
Hollingshead 2-factor index
(maternal education and maternal
occupation)
Mean =44, SD =28
Range =84–11
(Low- to high-SES families)
Whole-brain and ROIs in
hippocampi, middle
temporal gyri, left
fusiform, right and inferior
occipito-temporal gyri.
Overall white matter
microstructure
MRI: VBMb—total brain
volume and gray matter
volumes in ROIs (SPM8);
SBMc—intracranial volume,
hemispheric thickness, total
surface area, and gray matter
surface area, thickness, and
volumes in ROIs (FreeSurfer)
DTId: Fractional anisotropy
(BrainVISA and FSL)
Hollingshead Index positively
correlated with gray matter volumes in
hippocampi, parahippocampal, gyri,
middle temporal gyri, insula, left
fusiform gyrus, right inferior
occipito-temporal region, and left
superior/middle frontal gyrus.
Hollingshead values not significantly
correlated with white matter
microstructure.
Luby et al.,
2013
6–12 years old
M=9.8 years
N=145
Family Income-to-Needs (ITN)e
Mean =2.14, SD =1.27
Range =0–4.74
Parental education
Mode =Some college (38%)
Range =Less than HS to
graduate degree
Whole-brain and ROIs in
hippocampus and
amygdala
MRI: cerebral volume; gray
and white matter volumes in
ROIs (FreeSurfer)
Family ITN positively correlated with
total white and gray matter volumes as
well as hippocampal and amygdala
volumes. Effects of poverty on
hippocampal volume were mediated
by caregiving and stressful life events,
but not parental education.
Raizada et al.,
2008
5-year-olds
M=5.3 years
N=14
Hollingshead 4-factor index
(Marital status, employment,
educational attainment, and
occupational prestige)
Range =31.5–66
(Middle- to high-SES families)
Left inferior frontal gyrus MRI: gray and white matter
volume in ROI (SPM5)
Hollingshead Index was marginally
positively correlated with both gray
and white matter volumes in the left
inferior frontal gyrus.
(Continued)
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Brito and Noble SES and structural brain development
Table 1 | Continued
Study Participants SES measures Areas of the brain Morphometry analysis Main findings
Children
and
Adolescents
Hanson et al.,
2011
4–18 years old
M=11.2 years
N=317
Family income
Range =Less than $5k—more
than $100k
Mode =$75k–$100k (28%)
Parental education
Range =Less than HS to
graduate degree
Mode =College (27%)
Whole-brain and ROIs in
hippocampi and
amygdalae
MRI: VBM- total brain volume
and gray matter volumes in
ROIs (DARTEL and SPM8)
Family income was positively correlated with
hippocampal volume. No association between
income and amygdala volumes. Positive
correlation between paternal ED, but not
maternal ED, and total and right hippocampal
volumes. No relationship between income and
cerebral volume.
Lange et al.,
2010
4–18 years old
M=10.9 years
N=285
Family income
Mean =73, 047, SD =1816
Less than $50K (27%),
$50k–$100k (50%), greater than
$100k (23%)
Parental education
Range =HS to graduate degree
Mean =73, 047, SD =1816
Modes =College (31%) and
Graduate School (31%)
Whole-brain and ROIs in
intracranial cavity,
cerebellum, brainstem,
thalamus, caudate
nucleus, putamen, globus
pallidus, and frontal,
temporal, parietal, and
occipital lobes
MRI: total brain volume (sum
of gray and white matter
volumesinROIsplus
cerebrospinal fluid); gray and
white matter volumes in ROIs
(automated tissue
segmentation algorithm)
Parental education levels were not correlated
with brain volumes. Both family income and
parental education were related to full scale IQ.
Positive correlation between full scale IQ and
cerebral volume. Total or regional brain
volumes do not mediate association between
parental education and IQ in children.
Brain
Development
Cooperative
Group, 2012
4–18 years old
M=10.9 years
N=325
Family income
Mean =72, 458, SD =31,695
Parental education
Modes =College (31%) and
graduate school (31%)
Range =HS to graduate degree
Whole-brain and ROIs in
intracranial cavity,
cerebellum, brainstem,
thalamus, caudate
nucleus, putamen, globus
pallidus, and frontal,
temporal, parietal, and
occipital lobes
MRI: total brain volume (sum
of gray and white matter
volumesinROIsplus
cerebrospinal fluid); gray and
white matter volumes in ROIs
(“mni_autoreg” software
package and automatic
nonlinear image matching and
anatomical labeling)
Family income and parental education levels
were not associated with any regional brain
volume.
Lawson et al.,
2013
4–18 years old
M=11.5 years
N=283
Family income
Mode =$75k–$100k (27%)
Range =Less than $5k–$150k
Parental education
Mean =7.53, SD =2.31
Range =2–12
Frontal gyri (superior,
middle and inferior),
anterior cingulate gyri,
and orbitofrontal gyri
MRI: cortical thickness (ANTS
and DiReCT)
Parental education, but not family income,
predicted increased cortical thickness in the left
superior frontal gyrus and right anterior
cingulate gyrus. No parental education by age
interactions.
Noble et al.,
2012a
5–17 years old
M=11.4 years
N=60
Income-to-Needs (ITN)
Mean =3.3, SD =1.9
Range =0.23–6.7
Parental education
Mean =15.1 , SD =2.7
Range =8–21 years
Left temporal gyrus
(superior, middle, and
inferior), left fusiform
gyrus, hippocampi,
amygdalae, and anterior
cingulate cortex
MRI: gray and white matter
volumes in ROIs (FreeSurfer)
Parental education was negatively correlated
with amygdala volume. No correlation between
ITN and amygdala volume. ITN was positively
correlated with hippocampal volume, but no
correlation between parental education and
hippocampal volume. Education by age
interaction observed in left superior temporal
gyrus and left inferior frontal gyrus.
(Continued)
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Brito and Noble SES and structural brain development
Table 1 | Continued
Study Participants SES measures Areas of the brain Morphometry analysis Main findings
Adults Butterworth
et al., 2012
44–48 years old
M=46.7 years
N=403
Experience of financial hardship
over past year
4 dichotomous variables: pawned
or sold something, went without
meals, unable to heat home, or
asked for help from
welfare/community organizations
Childhood poverty (y/n)
Amygdala and
hippocampus
MRI: gray and white matter
volumes in ROIs (FreeSurfer)
Experience of current financial hardship was
correlated with smaller hippocampus and
amygdala. Childhood poverty was not
associated with either brain structure.
Cavanagh
et al., 2013
36–65 years old
M=50.94 years
N=42
Early life SES (ESES)
(Number of siblings, people per
room, paternal social class,
parental housing tenure, and use
of a car by family)
Current SES (CSES)
(Current income, current social
class, current housing tenure)
Cerebellum MRI: cerebellar gray matter
volume (FreeSurfer)
Both early life and current SES predicted
cerebellar gray matter volume. Current SES
explained significant additional variance to early
life SES, but not vice-versa. Lower SES was
associated with smaller cerebellar gray matter
volumes.
Chiang et al.,
2011
18–29 years old
M=23.7 years
N=499
Adult occupation (Australian
socioeconomic index: SEI)
Median =67.5
25th Percentile =39.7
75th Percentile =83.8
Overall white matter
microstructure
DTI: fractional anisotropy
(FSL)
No main effect of SEI on white matter
microstructure, but interaction between SEI
and genetic components that affect white
matter integrity. Higher SEI participants had
higher heritability in the thalamus, left middle
temporal gyrus, and callosal splenium. Lower
SEI participants had higher heritability in the
anterior corona radiate.
Gianaros et al.,
2007
31–54 years old
M=44.7 years
N=100
Subjective social status (SSS)
Education
Mode: College (47%)
Range =Less than HS to PhD
Income
Mode: $50–65k (25%) and greater
than $80k (25%)
Personal SES =composite of
education and income
Community SES =zip code
Anterior cingulate cortex,
amygdala and
hippocampus
MRI: VBM—total brain
volume and gray matter
volumesinROIs(SPM2and
Matlab)
Lower subjective social status was associated
with reduced gray matter volume in the
perigenual area of the anterior cingulate cortex,
but not anterior cingulate cortex, hippocampus,
or amygdala. No associations between brain
structures and educational attainment, income,
personal, or community SES measures.
Gianaros et al.,
2012
30–50 years old
M=40.7 years
N=155
Educational attainment
M=17.17 , SD =3.2
Range =11–24 years
Income Community SES
Overall white matter
microstructure
DTI: fractional anisotropy
(FSL)
Individuals higher in education, earning higher
incomes, and living in more advantaged
communities demonstrated increases in white
matter integrity and decreases in radial
diffusivity.
(Continued)
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Brito and Noble SES and structural brain development
Table 1 | Continued
Study Participants SES measures Areas of the brain Morphometry analysis Main findings
Krishnadas
et al., 2013
35–64 years old
M=51 years
N=42
Neighborhood SES
Scottish index of multiple
deprivation (SIMD)
Overall brain network
structure and cortical
thickness
MRI: cortical thickness
(FreeSurfer)
Controlling for age and alcohol use, compared
to the least deprived (LD) group the most
deprived (MD) had significant cortical thinning
in bilateral perisylvian cortices.
Liu et al., 2012 67–79 years old
M=73 years
N=113
Education
M=11 years, SD =2.5
Range =6–16 years
Temporal pole, transverse
temporal gyrus, and
isthmus of cingulate
cortex
MRI: volumes in 15 ROIs and
cortical thickness in 33 ROIs
(FreeSurfer)
Participants with higher levels of education had
significantly larger temporal pole, transverse
temporal gyrus, and isthmus of cingulate
cortex.
Noble et al.,
2012b
17–87 years old
M=39.7 years
N=275
Educational attainment
High school or less (32%)
Some college (30%)
College and graduate degree
(38%)
Amygdala and
hippocampus
MRI: amygdala and
hippocampal volumes
(FreeSurfer)
Education by age interaction found in the
hippocampus, such that the volumetric
reduction seen at older ages was more
pronounced among less educated individuals,
and was buffered among more highly educated
individuals. No main effects of education or age
by education interactions found for amygdala
volumes.
Noble et al.,
2013
17–23 years old
M=20.1 years
N=47
Educational attainment
Mean =14.1 , SD =1.8
Range =11–18 years
White matter
microstructure (ROIs:
superior longitudinal
fasciculus, cingulum
bundle, anterior coronal
radiata)
DTI: fractional anisotropy
(fMRIB Diffusion Toolbox and
FNIRT)
Educational attainment significantly correlated
with white matter microstructure in the
superior longitudinal fasciculus and cingulum
bundle (controlling for age).
Piras et al.,
2011
18–65 years old
M=40.35 years
N=150
Educational attainment
M=14.5, SD =3.3
Range =5–21 years
Thalamus, caudate
nucleus, putamen, globus
palidus, hippocampus,
and amygdala
MRI: Gray and white matter
volumes in ROIs
DTI: fractional anisotropy and
mean diffusivity (FSL)
Educational attainment negatively correlated
with microstructural changes in both left and
right hippocampi (controlling for age).
Staffetal.,
2012
Older adults
M=68.7 years
N=235
Educational attainment
Paternal occupation
Retrospective at age 11
Self-occupation
Range =1–9
Current neighborhood
environment
Zip code
Childhood home environment
Number of public rooms in home
and number of people expected to
share sanitation facility
Hippocampus MRI: hippocampal volume
(FreeSurfer)
Childhood SES (latent factor including paternal
occupation and childhood home environment)
positively correlated with hippocampal volume
after adjusting for mental ability (at age 11),
adult SES (self-occupation and current
neighborhood environment) and educational
attainment.
aROI, region of interest.
bVBM, voxel-based morphometry.
cSBM, surface-based morphometry.
dDTI: diffusion tensor imaging.
eIncome to Needs (ITN), total family income divided by the federal poverty level for a family of that size, in the year data was collected.
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Brito and Noble SES and structural brain development
OCCUPATION
Occupations generally reflect education, earnings, and prestige
(Jencks et al., 1988), and have been extensively studied as an
important aspect of SES as they are directly related to both edu-
cation and income. Chiang et al. (2011) found that occupational
status, measured using the Australian Socioeconomic Index (SEI),
a 0–100 scale based on an individual’s occupational category, was
not related to white matter integrity. However, the authors did
find an interaction between occupational status and white mat-
ter integrity, controlling for subjects’ age and sex. Specifically,
higher SEI was associated with higher heritability white matter
integrity in the thalamus, left middle temporal gyrus, and callosal
splenium.
SES COMPOSITE MEASURES
Some studies have combined different SES markers to create aver-
age or composite measures. Cavanagh et al. (2013) used indicators
of early life SES (number of siblings, number of people per room,
paternal social class, parental housing tenure, and use of car by
family) and current SES (current income, current social class, and
current housing tenure) to predict cerebellar gray matter volume.
Both composite measures positively predicted cerebellar struc-
ture, where current SES explained significant additional variance
to early life SES, but not vice-versa. Staff et al. (2012) also mea-
sured both childhood SES (indexed by paternal education and
childhood home conditions) as well as adult SES (indexed by
the individual’s educational attainment, occupational status, and
neighborhood deprivation). These authors reported a significant
association between hippocampal volume and childhood SES,
after adjusting for the individual’s SES as an adult more than 50
years later. These results may suggest that early life conditions may
have an effect on structural brain development over and above
conditions later in life.
The Hollingshead scale (Hollingshead, 1975) is a commonly
used measure of SES, which combines occupation and education
(Two-Factor Index) or occupation, education, marital status, and
employment status (Four-Factor Index). Duncan and Magnuson
(2003) have argued that aggregating these SES measures is faulty
as fluctuations within each measure of SES differentially affect
parenting and child developmental outcomes. Imaging studies
using these composite measures of SES have found significant
correlations between composite scores and regions in the medial
temporal lobe and frontal lobe (Raizada et al., 2008; Jednoróg
et al., 2012), but without knowing associations to specific SES
markers, it is difficult to compare these studies with other
structural imaging studies.
NEIGHBORHOOD SES
Of note, SES can describe a single participant, the participant’s
family or even the participant’s neighborhood. The neighborhood
context is associated with various health outcomes (Pickett and
Pearl, 2001) as it is another source of potential exposure to stres-
sors (e.g., violence) or protection from them (e.g., community
resources, social support). Some studies have found correlations
between neighborhood disadvantage and cognitive outcomes
independent of individual level SES (Wight et al., 2006; Sampson
et al., 2008), whereas others have not (Hackman et al., 2014).
Studies examining neighborhood SES and brain structure have
also had mixed findings. Gianaros et al. (2007, 2012) have used
census tract level data (median household income, percentage
of adults with college degrees or higher, proportion of house-
holds below federal poverty line, and single mother households)
to create composite indicators of community SES. Although com-
munity SES was not associated with total brain volume or gray
matter volumes in regions of interest (Gianaros et al., 2007), com-
munity SES was positively associated with white matter integrity
independent of self-reported levels of stress and depressive symp-
toms (Gianaros et al., 2012). Similarly, Krishnadas et al. (2013)
found that neighborhood SES, indexed using the Scottish Index
of Multiple Deprivation, was related to cortical thickness,with
men living in more disadvantaged areas demonstrating more cor-
tical thinning in areas that support language function (bilateral
perisylvian cortices) than men living in more advantaged areas.
KEY CONCEPT 4 | Cortical thickness
Defined in neuroimaging studies as the shortest distance between the white
matter surface and pial gray matter surface.
SUBJECTIVE SOCIAL STATUS
Finally, subjective social status is another marker of SES used in
some research. In these studies, participants are typically asked to
indicate on a drawing of a ladder where they believe they rank in
terms of social standing among a particular group. In past studies,
lower social ladder standings have been correlated with negative
physical and mental health outcomes (Adler et al., 2000; Kopp
et al., 2004; Hu et al., 2005), even after accounting for objective
measures of education, income, and potential reporting biases
(Adler et al., 1994). Gianaros et al. (2007) found that subjective
social status was not correlated with hippocampal or amygdala
volumes, but was significantly associated with reduced gray mat-
ter volume in the perigenual area of the anterior cingulate cortex
(pACC). This finding may be understood by recognizing that the
pACC is a region in the brain involved in experiencing emotions
and regulating behavioral and physiological reactivity to stress.
Measures of subjective social status may not take into account
objective measures of SES, but relate more to the individual’s
experience of disadvantage.
WORDS OF CAUTION IN SELECTING SES VARIABLES
Collecting and utilizing multiple independent measures of SES is
necessary to accurately assess structural brain changes through-
out development. SES is too complex to be captured by a single
indicator or even a composite measure. Each measure of SES is
its own distinct construct with varying associations with experi-
ence and cognitive development. However, while SES variables are
not interchangeable, they are nonetheless highly correlated. It is
therefore essential to avoid model multicollinearity in statistical
analyses. This may be accomplished by first carefully consider-
ing which variables are most appropriate for testing particular
hypotheses, and then confirming low variance inflation factors
(VIF) within the model. Increasing sample size, centering vari-
ables, and utilizing residuals are additional methods to avoid
inappropriate analysis and interpretation.
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Brito and Noble SES and structural brain development
As a final word of caution, many of the SES indicators refer-
enced above are based on studies completed inWestern countries.
Further work will be necessary to explore the generalizability of
findings across different countries and cultures (Minujin et al.,
2006; Lipina et al., 2011).
COVARIATES, MEDIATORS, AND MODERATORS
When examining SES disparities in brain structural develop-
ment, additional demographic factors must be considered as well.
First and foremost, the age of the participant must be taken
into account, as brain structural volumes change significantly
across childhood and adolescence (Paus et al., 1999; Lenroot
and Giedd, 2006). Further, the timing of volumetric growth
and reductions vary across different brain structures (Grieve
et al., 2011). Inconsistencies in results across studies highlighted
above may therefore be due to variability in the age ranges
of the samples studied. Caution is advised when generalizing
results reported within a narrow-age-range sample, as SES dis-
parities in brain structure may vary substantially as a function
of age.
Several studies include relatively wide age ranges, recruiting,
for example, both children and adolescents in their imaging sam-
ples (Lange et al., 2010; Hanson et al., 2011; Noble et al., 2012a;
Lawson et al., 2013). Two additional studies have taken a lifes-
pan approach to examining SES and structural brain development
(Piras et al., 2011; Noble et al., 2012b). Incorporating wide age
ranges into a study allows researchers to consider whether results
vary as a function of participant age. For example, both Noble
et al. (2012b) and Piras et al. (2011) examine associations between
subcortical structures and educational attainment in a wide age
range of participants. Piras et al. (2011) found that microstruc-
tural changes in the hippocampus, but not changes in gross
volume in this structure, were significantly predicted by education
levels. However, due to a large negative correlation between edu-
cation and age, the decreases in microstructure may have been
more closely related to older age than greater education. As dis-
cussed above, Noble et al. (2012b) reported that higher levels of
educational attainment buffered against age-related reductions
in hippocampal volume, signifying that the association between
age and hippocampal volume is not constant across all levels
of education. Of course, distinctions between development and
decline are, in some respects, arbitrary, and may be more appro-
priately classified according to functional rather than structural
measures.
Sex is another important demographic characteristic to con-
sider. Volumetric variation in brain structures increase within
and between males and females during puberty (Sowell et al.,
2003). Sex differences have been reported for cortical thickness.
Using a longitudinal sample of participants ages 9–22 years,
Raznahan et al. (2010) observed differences in cortical matura-
tion, with males demonstrating a thicker cortex in frontopolar
regions at younger ages and subsequent greater cortical thinning
than females during adolescence. It has also been reported that
females demonstrate more rapid cortical thinning than males in
specific cortical areas (right temporal, left temporoparietal junc-
tion, and left orbitofrontal cortex) corresponding to the “social
brain” (Mutlu et al., 2013). It will be important in future work to
better understand how the links between SES variables and struc-
tural brain development may vary by sex, and/or a combination
of sex and age.
In addition, studies have reported that families living in
chronic poverty have differential outcomes based on when and
for how long poverty was experienced (National Institute of Child
Health and Human Development Early Child Care Research
Network, 2005). While the brain is most malleable in early child-
hood, it nonetheless retains a substantial degree of plasticity
throughout the lifespan, and the extent to which the timing and
duration of socioeconomic disadvantage are associated with brain
structural differences is virtually unexplored in the neuroscience
literature to date.
Finally, it is important to consider environmental exposures
and experiences that may account for links between distal socioe-
conomic factors and brain structural differences. For example,
Luby et al. (2013) recently reported that links between income
and hippocampal volume were mediated by caregiving sup-
port/hostility and stressful life events. Of course, there are many
potential experiential correlates of SES that have not been well
studied in the context of SES disparities in brain development,
including nutrition, exposure to environmental toxins, safety of
the play environment, or quality of the child’s linguistic environ-
ment. In order to develop interventions that effectively target the
SES gap in achievement, it will be essential to try to understand
the particular component(s) of the environment that are most
influential in explaining disparities.
VOLUME vs. CORTICAL THICKNESS/SURFACE AREA
Differences in findings across studies may also be accounted for
by the techniques used to measure morphometry. Most stud-
ies examining SES differences in brain structure have reported
cortical volumes as their outcome of interest (but see Jednoróg
et al., 2012; Liu et al., 2012; Krishnadas et al., 2013; Lawson
et al., 2013). However, cortical volume is a composite measure
that is determined by the product of surface area and corti-
cal thickness, two genetically and phenotypically independent
structures (Panizzon et al., 2009; Raznahan et al., 2011). Though
the cellular mechanisms are not fully understood, it has been
hypothesized that symmetrical cell division in the neural stem cell
pool contribute to exponential increase in the number of radial
columns that result in surface area, without changes to corti-
cal thickness. In contrast, asymmetrical cell division in founder
cells is independently responsible for a linear increase in the
number of neurons in the radial column, leading to changes in
KEY CONCEPT 5 | Cortical volumes
The most commonly used outcome in studies of socioeconomic dispari-
ties in brain structure. Cortical volume is actually a composite of cortical
thickness and surface area, two genetically and phenotypically distinct
morphometric properties of the brain.
KEY CONCEPT 6 | Surface area
The area of exposed cortical surface or convex hull area (CHA) and the area
of cortex hidden in sulci.
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Brito and Noble SES and structural brain development
cortical thickness but not surface area (Rakic, 2009). As such,
these two properties of the cortical sheet develop differentially;
cortical surface area tends to expand through childhood and
early adolescence and decrease in adulthood, whereas cortical
thickness tends to decrease rapidly in childhood and early ado-
lescence, followed by a more gradual thinning and ultimately
plateauing (Schnack et al., 2014). Cortical thinning is related to
both synaptic pruning and increases in white matter myelina-
tion, resulting in a reduction of gray matter as measured on MRI
(Sowell et al., 2003). These maturational changes occur concur-
rently and together contribute to the development of the mature
human brain.
Thus, studies in which the dependent measure is cortical
volume may not adequately reflect the complexities of morpho-
metric brain development. Indeed, cross-sectional comparisons
of cortical volume are poor indicators of brain maturation (Giedd
and Rapoport, 2010), whereas cortical thickness has been shown
to be a more meaningful index of brain development (Sowell
et al., 2004; Paus, 2005) and has been associated with both cog-
nitive ability (Porter et al., 2011)andbehavior(Shaw et al.,
2011). For example, IQ has been correlated with the trajectory
of cortical thickness, such that, during childhood, more intel-
ligent children have thinner cortices than children with lower
IQ, with this association strengthening through adolescence.
In contrast, by middle adulthood, a thicker cortex is related
to higher IQ (Schnack et al., 2014). Importantly, IQ has also
been independently correlated with the trajectory of surface area
development, such that more intelligent children exhibit greater
surface area during childhood, though surface area expansion
is completed earlier and then decreases more quickly in more
intelligent adults (Schnack et al., 2014). Together, these findings
suggest that both surface area and cortical thickness may be crit-
ical in accounting for individual differences in cognitive abilities,
and that these factors must be considered independently rather
than lumping them into a single composite measure of cortical
volume.
In summary, when considering associations between expe-
rience and brain morphometry, cortical thickness and surface
area should be assessed separately, rather than reporting on
the composite metric of cortical volume (Winkler et al., 2010;
Raznahan et al., 2011). Research investigating cortical complex-
ity and its association with SES variables will be vital to further
understanding how environmental influences over the life course
influence structural brain development.
CONCLUSIONS
Children living in socioeconomic disadvantage are more likely
to experience cognitive delays and emotional problems (Brooks-
Gunn and Duncan, 1997), but the underlying causal pathways
between disadvantage and developmental outcomes are not clear.
The nascent field of socioeconomic disparities in brain structure
is an exciting one, which holds promise in helping to under-
stand this question. However, while progress has been made in
understanding how socioeconomic disparities may affect brain
development, there are many avenues for further research. Careful
social science approaches to assessing individual socioeconomic
factors must be combined with cutting-edge neuroscientific
approaches to measuring precise aspects of brain morphometry.
Consideration of how results interact with demographic fac-
tors such as age and sex are critical. Differences in exposures
and experiences that may mediate socioeconomic disparities in
brain development must be rigorously assessed to help identify or
confirm underlying mechanisms.
Although this review has focused on SES disparities in brain
structure as opposed to function, it is readily acknowledged
that the two approaches are complementary. While a structural
approach lends itself to greater spatial resolution as well as,
arguably, more precision in understanding proximal experience-
dependent mechanisms, it is limited in terms of functional
interpretations. Ultimately, linking both structural and func-
tional imaging to cognitive outcomes is essential for examining
associations between anatomy, physiology, and behavior. Brain
structural measures can be viewed as mediators between SES and
cognition, or as outcome variables in their own right; having clear
theoretical pathways ensures accurate interpretation of results and
implications, and will help inform the design of effective policies,
emphasizing early and targeted interventions.
ACKNOWLEDGMENT
The authors are grateful for funding from the Robert Wood
Johnson Foundation Health and Society Scholars program and
the GH Sergievsky Center.
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Conflict of Interest Statement: The authors declare that the research was con-
ducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 02 June 2014; accepted: 17 August 2014; published online: 04 September
2014.
Citation: Brito NH and Noble KG (2014) Socioeconomic status and structural brain
development. Front. Neurosci. 8:276. doi: 10.3389/fnins.2014.00276
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