Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood

Abstract

Significance
Childhood poverty has been linked to emotion dysregulation, which is further associated with negative physical and psychological health in adulthood. The current study provides evidence of prospective associations between childhood poverty and adult neural activity during effortful attempts to regulate negative emotion. Adults with lower family income at age 9 exhibited reduced ventrolateral and dorsolateral prefrontal cortex activity and failure to suppress amygdala activation at age 24. Chronic stressor exposure across childhood mediated the relations between family income at age 9 and prefrontal cortex activity. The concurrent adult income, on the other hand, was not associated with neural activity. The information on the developmental timing of poverty effects and neural mechanisms may inform early interventions aimed at reducing health disparities.

Full text

Effects of childhood poverty and chronic stress on
emotion regulatory brain function in adulthood
Pilyoung Kim
a,1
, Gary W. Evans
b
, Michael Angstadt
c
, S. Shaun Ho
c
, Chandra S. Sripada
c
, James E. Swain
c,d
,
Israel Liberzon
c
, and K. Luan Phan
e,f
a
Department of Psychology, University of Denver, Denver, CO 80208;
b
Departments of Design and Environmental Analysis and Human Development,
Bronfenbrenner Center for Translational Research, Cornell University, Ithaca, NY 14853;
c
Department of Psychiatry, University of Michigan, Ann Arbor,
MI 48109;
d
Child Study Center, Yale University, New Haven, CT 06520-7900;
e
Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60608;
and
f
Mental Health Service Line, Jesse Brown Veterans Affairs Medical Center, Chicago, IL 60612
Edited* by Bruce S. McEwen, The Rockefeller University, New York, NY, and approved August 21, 2013 (received for review May 2, 2013)
Childhood poverty has pervasive negative physical and psycho-
logical health sequelae in adulthood. Exposure to chronic stressors
may be one underlying mechanism for childhood poverty−health
relations by influencing emotion regulatory systems. Animal work
and human cross-sectional studies both suggest that chronic
stressor exposure is associated with amygdala and prefrontal cor-
tex regions important for emotion regulation. In this longitudinal
functional magnetic resonance imaging study of 49 participants,
we examined associations between childhood poverty at age 9
and adult neural circuitry activation during emotion regulation
at age 24. To test developmental timing, concurrent, adult income
was included as a covariate. Adults with lower family income at
age 9 exhibited reduced ventrolateral and dorsolateral prefrontal
cortex activity and failure to suppress amygdala activation during
effortful regulation of negative emotion at age 24. In contrast to
childhood income, concurrent adult income was not associated
with neural activity during emotion regulation. Furthermore,
chronic stressor exposure across childhood (at age 9, 13, and 17)
mediated the relations between family income at age 9 and ven-
trolateral and dorsolateral prefrontal cortex activity at age 24. The
findings demonstrate the significance of childhood chronic stress
exposures in predicting neural outcomes during emotion regula-
tion in adults who grew up in poverty.
fMRI
|
childhood adversity
|
socioeconomic status
|
reappraisal
Childhood poverty is related to increased risk of psychopa-
thology (1–3) and physical illness in adulthood (4, 5). Fur-
thermore, childhood poverty predicts adult morbidity irrespective
of adult poverty (5–7). One possible mechanism to explain the
far-reaching effects of childhood poverty on health is chronic
stress (8). Chronic exposure to stressors associated with living in
low-income families has long-term negative effects on physio-
logical stress regulatory systems (9–12), eventually resulting
in pathology (13, 14). Growing evidence suggests exposure to
chronic stress and socioeconomic adversity produces lasting
neurobiological changes (15, 16). However, little is known about
whether childhood poverty is prospectively associated with cen-
tral nervous system mechanisms involved in emotion regulation.
Such knowledge may provide insights into identifying neural
patterns for emotion regulatory dysfunction among adults who
grew up in childhood poverty.
The amygdala and prefrontal cortex (PFC) play a critical role
for stress and emotion regulation. The amygdala detects and
responds to threats from the environment, activating physiological
stress responses (17). The PFC is widely considered as a top-down
region that regulates the amygdala (18, 19). More specifically, the
ventrolateral PFC (VLPFC), dorsolateral PFC (DLPFC), and
medial PFC (mPFC) implement cognitive strategies such as cog-
nitive reappraisal involved in emotion regulation (18–20). During
reappraisal of negative stimuli, increased activity in the VLPFC,
DLPFC, and mPFC regions is associated with diminished amyg-
dala reactivity to negative stimuli as well as decreased perceived
negative affect (21). Amygdala and PFC dysregulation has also
been observed in populations with mood dysregulation, including
depression (22), anxiety disorders (23, 24) including post-
traumatic stress disorder (25), impulsive aggression (26), and
substance abuse (27). Aberrant amygdala reactivity and in-
efficient or blunted PFC regulatory function are considered
a neurobiological mechanism involved in impaired emotion
regulation in these psychiatric disorders.
Amygdala and PFC functions have also been shown to be af-
fected by socioeconomic disparities (28, 29). In children, low
socioeconomic status (SES) has been related to greater amyg-
dala volume (30) and reduced PFC activity during cognitive tasks
(31). In adults, retrospective reports of childhood SES were as-
sociated with elevated amygdala activity while processing nega-
tive facial expressions independently of adult SES (32) and
reduced VLPFC activity while experiencing social exclusion (33).
However, whether the amygdala and PFC functions associated
with childhood poverty are directly related to effortful emotion
regulation has never been examined.
At present, little is known about underlying mechanisms that
account for the relation between childhood SES and neural
functioning. Chronic stress is one hypothetical mediator of the
negative link between childhood poverty and adult health out-
comes (8, 10). For example, children living in poverty are more
likely to be exposed to multiple chronic stressors including vio-
lence, family turmoil, separation from family members, and
substandard living environments (34, 35). In our previous stud-
ies, poverty exposure at age 9 prospectively predicted physio-
logical stress dysregulation (34) and emotion dysregulation
Significance
Childhood poverty has been linked to emotion dysregulation,
which is further associated with negative physical and psy-
chological health in adulthood. The current study provides
evidence of prospective associations between childhood pov-
erty and adult neural activity during effortful attempts to
regulate negative emotion. Adults with lower family income at
age 9 exhibited reduced ventrolateral and dorsolateral pre-
frontal cortex activity and failure to suppress amygdala acti-
vation at age 24. Chronic stressor exposure across childhood
mediated the relations between family income at age 9 and
prefrontal cortex activity. The concurrent adult income, on the
other hand, was not associated with neural activity. The in-
formation on the developmental timing of poverty effects and
neural mechanisms may inform early interventions aimed at
reducing health disparities.
Author contributions: P.K., G.W.E., S.S.H., J.E.S., I.L., and K.L.P. designed research; M.A.,
S.S.H., and J.E.S. performed research; P.K., G.W.E., M.A., C.S.S., I.L., and K.L.P. analyzed
data; and P.K., G.W.E., M.A., S.S.H., C.S.S., J.E.S., I.L., and K.L.P. wrote the paper.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
1
To whom correspondence should be addressed. E-mail: pilyoung.kim@du.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1308240110/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1308240110 PNAS Early Edition
|
1of6
PSYCHOLOGICAL AND
COGNITIVE SCIENCES

(36, 37) in adolescence when concurrent levels of poverty ex-
posure were controlled. In these studies, poverty exposure at age
9 was concurrently and prospectively associated with chronic
stress exposure at age 9, 13, and 17 (37–39), and elevated chronic
stress, in turn, mediated the association between childhood
poverty and later outcomes. Furthermore, animal studies and
recent human brain imaging studies demonstrate that repeated
exposure to chronic stress impacts amygdala and PFC devel-
opment, potentially leading to impaired emotion regulation
(40–43).
Therefore, in this longitudinal study, we investigated whether
childhood family income was associated prospectively with adult
neural activity in the amygdala and PFC during emotion regu-
lation. We also examined a stress pathway linking childhood
poverty and the subsequent neural functions for emotion regu-
lation. The current study used family income assessed at age 9 as
a direct measure of childhood poverty exposure. To investigate
the developmental timing of poverty and neural functioning, we
examined the link between childhood poverty and adult neural
functioning after controlling for adult income levels. We used
a well-established emotion regulation functional magnetic reso-
nance imaging (fMRI) paradigm (18, 44), in which participants
are instructed to experience the natural emotional state (Main-
tain) or to decrease the intensity of their negative affect by using
cognitive reappraisal (Reappraisal) while viewing negative images.
We hypothesized that, in the contrast of Reappraisal vs. Maintain
conditions, low family income at age 9 would be associated with
increased amygdala and decreased PFC activation. The amygdala
and PFC activation may also be associated with self-reports of
emotion regulation (Materials and Methods). Furthermore, we
assessed chronic stress by averaging exposure to multiple physical
(i.e., substandard housing, crowding, and noise) and social (i.e.,
family turmoil, violence, and child–family separation) risk factors
across ages 9–17. We hypothesized that the influence of childhood
income on amygdala and PFC activity would be mediated by
chronic stress exposure throughout childhood.
Results
Descriptive and Behavioral Data. Ratings of negative affective state
between two conditions, Reappraisal and Maintain, were signifi-
cantly different, t(48) =4.13, P<0.001. The average ratings
decreased from the Maintain (mean =2.93 ±0.81) to Reap-
praisal (mean =2.48 ±0.95). However, the success of regulating
negative emotions (calculated by subtracting Reappraisal ratings
from Maintain ratings) was not significantly associated with family
income at age 9, chronic stress exposure across ages 9–17, or
current adult income levels at age 24.
Family Income at Age 9 and Neural Emotion Regulation at Age 24.
Reappraisal of emotion (compared with maintaining one’s emo-
tional response) produced greater activation in bilateral inferior/
middle/superior frontal gyrus, precentral gyrus, striatum, insula,
parietal lobe, and temporal gyrus (P<0.05, corrected for multiple
comparisons; Materials and Methods). However, no amygdala
activation was detected in the contrast of Reappraisal vs. Main-
tain using a region of interest (ROI) approach, although bilateral
amygdala activation was detected on Reappraisal (vs. Baseline)
and Maintain (vs. Baseline) conditions (P<0.05, corrected).
Enhanced neural activation during Reappraisal was predicted
by family income at age 9. In particular, in the contrast of the
Reappraisal vs. Maintain, lower family income at age 9 predicted
reduced activation in the left DLPFC (Fig. 1A), VLPFC/insula/
temporopolar area (Fig. 1B), precentral gyrus, and inferior pa-
rietal lobe/superior temporal gyrus (Ps<0.05, corrected; Table 1;
all analyses controlled for current income). No cluster was iden-
tified with a significant positive association with the current, adult
income level.
Amygdala ROI analysis revealed that activation in the Reap-
praisal vs. Maintain conditions was negatively associated with
childhood income in the left amygdala [t(46) =2.48, x,y,z=–30,
–4, –22; 140 voxels] (P<0.05, uncorrected) (Fig. 1C), controlling
for adult income level. Besides the amygdala activity from ROI
analysis, no other cluster showed a negative association with
family income at age 9 or current adult income level.
Furthermore, we explored functional connectivity between
the left amygdala and VLPFC/DLPFC regions using the psy-
chophysiological interaction (PPI) analysis at P<0.001, un-
corrected, cluster size >10 voxels (SI Text). The analysis revealed
that amygdala activity was positively coupled with the left VLPFC
[x,y,z=–58, 18, 8; 58 voxels; t(46) =3.97; Fig. S1] during
Reappraisal among individuals with lower family income at age 9,
whereas amygdala activity was negatively coupled with the left
VLPFC during Reappraisal among individuals with higher family
income at age 9 (Fig. S2). Family income at age 9 was not as-
sociated with the amygdala−DLPFC connectivity.
Finally, we calculated the correlation between regulation suc-
cess scores and the amygdala, VLPFC, and DLPFC activity
during Reappraisal. The correlation analysis revealed that Re-
appraisal success was positively correlated with both DLPFC [r
(49) =0.31, P<0.05] and VLPFC [r(49) =0.27, P<0.05], but
not with amygdala activity. Thus, DLPFC and VLPFC activity
during Reappraisal was associated with greater success in down-
regulating negative emotions. However, the use of everyday re-
appraisal coping (Materials and Methods) was not associated with
the neural activity.
Fig. 1. The upper panels are regions showing a significant association with family income-to-needs ratio at age 9. (A) Dorsolateral prefrontal cortex (PFC)
(x,y,z=–40, 12, 28; 343 voxels; P<0.05, corrected). (B) Ventrolateral PFC, insula, temporopolar area (x,y,z=–46, 10, –8; 672 voxels; P<0.05, corrected). (C)
Amygdala (x,y,z=–30, –4, –22; 140 voxels; P<0.05, uncorrected). The lower panels depict partial regression plots describing the associations between family
income-to-needs ratio at age 9 and parameter estimates of a region in the contrast of Reappraisal vs. Maintain, controlling for adult income level.
2of6
|
www.pnas.org/cgi/doi/10.1073/pnas.1308240110 Kim et al.

Childhood Chronic Stressor Exposure as a Mediator. We next tested
whether exposure to chronic stressors during childhood (ages 9–
17) mediated the relations between family income at age 9 and
adult DLPFC and VLPFC activity during Reappraisal. Elevated
childhood chronic stress exposure mediated the associations
between family income at age 9 and increased adult left DLPFC
activity during Reappraisal, controlling for concurrent, adult
income (Fig. 2A). The addition of childhood chronic stress
shrank the beta weight for childhood income 75%, which was no
longer significant and suggested full mediation [indirect effect =
0.09, 95% confidence intervals (CIs) =0.02–0.18].
Because the size of the suprathreshold cluster including the
left VLPFC was large and contained other parietal and temporal
regions, an ROI approach was used to separate the estimated
activity of the left VLPFC from the estimated activity of other
regions. We placed an 8 mm radius sphere at the left VLPFC peak
(x,y,z=–50, 22, 6) from a meta-analysis of fMRI reappraisal
studies (19). We found elevated chronic stress exposure across
ages 9–17 mediated the relations between family income at age 9
and increased left VLPFC activity during Reappraisal, controlling
for concurrent adult income (Fig. 2B). The addition of childhood
chronic stress shrank the beta weight for childhood income 64%,
which was no longer significant and suggested full mediation (in-
direct effect =0.09, 95% CIs =0.02–0.16). An analysis performed
on amygdala activity did not find a mediation effect.
Discussion
We examined whether childhood poverty was prospectively
linked to adult neural activity in the PFC and amygdala, regions
centrally involved in emotional regulation. We found a significant
relation between childhood income and neural functions. During
emotion regulation with cognitive reappraisal, lower family in-
come at age 9 was associated with reduced activity in the adult
DLPFC and VLPFC but increased amygdala activity. In contrast
to childhood income level, current income level as an adult was
not linked to neural activity during emotion regulation. When
the individual’s stress history was incorporated into our model,
exposure to chronic stressors throughout childhood (i.e., ages 9–
17) mediated the links between family income at age 9 and re-
duced adult DLPFC and VLPFC activity. Reduced PFC and
increased amygdala activity among adults who grew up in poverty
provides evidence for neural embedding of childhood poverty.
Furthermore, the mediating role of chronic stressor exposure in
childhood may help account for the link between childhood
poverty and adult neural functions, which may contribute to
physiological and psychological stress regulation difficulties.
We found that lower family income at age 9 was associated
with reduced DLPFC and VLPFC activity in 24-y-olds during
emotion regulation using cognitive reappraisal. Both DLPFC
and VLPFC are involved in cognitive control and executive
functioning and facilitate goal-directed behaviors (45). Further-
more, increased DLPFC and VLPFC activity was associated with
greater success in down-regulating negative emotions, further
supporting the role of these cortical regions in emotion regula-
tion. In contrast to reduced DLPFC and VLPFC activity, family
income at age 9 was associated with increased adult amygdala
activity during emotion regulation. More specifically, the data in
Fig. 1Csuggest that in adults who had higher family income at
age 9, the negative values of neural activity suggest less amygdala
activity during Reappraisal relative to the Maintain condition.
However, in those with lower family income at age 9, the pos-
itive values of neural activity suggest greater amygdala activity
during Reappraisal than Maintain, indicating potential failure
of amygdala regulation using Reappraisal. The functional con-
nectivity findings further suggested altered relations between
the amygdala and VLPFC activity in the context of childhood
poverty exposure. A negative amygdala−
VLPFC coupling among
individuals with lower family income at age 9 suggests that higher
childhood family income is associated with greater VLPFC ac-
tivity to suppress amygdala activity during emotion regulation.
On the other hand, a positive amygdala−VLPFC coupling during
Reappraisal suggests that lower childhood income is associated
with ineffective amygdala activity suppression of the VLPFC
activity during emotion regulation. Such failure of amygdala
regulation, in part by the dampened VLPFC and DLPFC ac-
tivity, has been suggested as neural deficits in many psychiatric
illnesses associated with childhood exposure to chronic stress.
With regard to the amygdala findings, we found no main effect
of Reappraisal (vs. Maintain) on diminishing amygdala activity;
therefore, the potential interpretation of the reappraisal-related
Table 1. Brain areas with the positive associations between family income-to-needs ratio at
age 9 and neural activity in the Reappraise vs. Maintain contrast at age 24
MNI coordinates
Area of activation Brodmann area Side # voxels xyzt(1, 46)
Dorsolateral PFC 9, 46 L 343 −40 12 28 3.99
−46 0 48 3.92
−42 2 38 3.13
Ventrolateral PFC, 47, 13, 38 L 672 −46 10 −8 4.04
Insula, −28 14 −14 3.95
Temporopolar area −48 22 −2 3.30
Precentral gyrus 6, 8 R, L 602 0 12 68 4.26
8 26 64 4.03
−2 20 58 3.20
Superior temporal 22, 40 L 293 −64 −48 18 4.23
gyrus, Inferior −58 −48 32 3.60
parietal gyrus −56 −56 30 3.14
P<0.05, corrected. L, left; MNI, Montreal Neurological Institute; PFC, prefrontal frontal cortex; R, right.
Fig. 2. A path diagram showing a mediation model with the unstandardized
coefficients for each association. Chronic stress exposure across ages 9–17
mediated the relationship between family income-to-needs ratio and neural
activity during Reappraisal in (A) dorsolateral prefrontal cortex (DLPFC) and
(B) ventrolateral prefrontal cortex (VLPFC). ***P<0.001, *P<0.05.
Kim et al. PNAS Early Edition
|
3of6
PSYCHOLOGICAL AND
COGNITIVE SCIENCES

amygdala modulation is limited to the context of variability in
childhood family income at age 9. Reduction in amygdala acti-
vation during Reappraisal has been inconsistently reported across
previous studies (22, 44, 46). The reason for the inconsistent
findings may be associated with different types of regulatory
strategies used across different studies. All regulatory strategies
require emotional appraisal and attention to emotional stimuli—
processes strongly associated with amygdala activity (47). How-
ever, some strategies such as positive reinterpretation or dis-
tancing from the content of negative stimuli may recruit more
amygdala activity than distraction (directing attention away from
negative stimuli) (48). The current study focused on positive re-
interpretation and distancing, and this may have produced no
main effect on amygdala activity during Reappraisal vs. Maintain.
In addition to altered VLPFC and DLPFC functioning, child-
hood poverty predicted activity in several other frontal, parietal,
and temporal regions of the adult brain, including the precentral
gyrus, inferior parietal lobe, superior temporal gyrus, and tem-
poropolar gyrus, during cognitive reappraisal. In all of these
regions, low family income at age 9 predicted reduced activity
during cognitive reappraisal. Each of these regions has been
shown to be involved in emotion regulation via cognitive reap-
praisal (18, 19, 44). The precental gyrus contributes to the top-
down control of cognitive and emotional processes through
selective attention (49). Although we did not find a significant
association between childhood income and mPFC, a region in-
volved in reappraisal, the precentral gyrus is structurally inter-
connected and frequently activated with the mPFC as well as
lateral PFC during Reappraisal (50). The inferior parietal lobe is
a part of the attention system along with the DLPFC (51). Thus,
the activity in the precentral gyrus and inferior parietal lobe may
contribute to reappraisal by selectively monitoring information.
Previously, adult retrospective reports of childhood SES have
been associated with lower activity in the inferior partial lobe
during monetary reward processing (52). Temporopolar area and
superior temporal gyrus are related to the representation of per-
ceptual and semantic information that likely assists in the reap-
praisal process (18). Thus, the significant associations between
childhood family income and neural activity in these regions un-
covered herein may reflect the pervasive effects of childhood SES
disparities on neural functions across multiple regions involved in
emotional regulation.
We also tested the hypothesis that exposure to chronic stressors
across ages 9–17 would help explain the relations between child-
hood poverty and reduced DLPFC and VLPFC activity during
emotion regulation. Chronic stressor exposure may be particularly
significant for PFC plasticity because the PFC matures primarily
during adolescence (ages 9–17) (53). However, in the amygdala,
chronic stress exposure across ages 9–17 did not mediate the link
between family income at age 9 and neural activity. This could be
due to the weaker association of childhood income with amygdala
responses, compared with PFC responses, during Reappraisal.
Our finding that family income at age 9 predicts adult neural
function that is mediated by childhood chronic stressor exposure
is consistent with the hypothesis that early experiences of poverty
become embedded within the organism, setting individuals on
lifelong trajectories that portend morbidity (5, 54). Furthermore,
these trends hold independently of concurrent poverty during
adulthood. The latter added no additional explanatory power to
the prediction of adult neural functioning. Although in our study
the poverty exposure data are available at age 9, when children
were first recruited, children in poverty at age 9 are likely to have
been disadvantaged at an earlier age as well. This earlier expo-
sure to poverty may have impacted long-term neural functions.
Growing animal and human evidence suggests exposure to
chronic stress in early childhood produces lasting neurobiologi-
cal changes in the amygdala and PFC when neural regions are
immature and rapidly developing (41). For instance, institu-
tionalization in infancy was associated with increased amygdala
activity in response to negative expressions in children at age 10
(43). Exposure to cumulative risk including maternal depression
and financial stress in infancy was also associated with decreased
amygdala and PFC connectivity during rest in adolescent females
(55). Therefore, it is critical that future research more directly
investigates the developmental timing of poverty, chronic stressor
exposure, and neural functioning at shorter time intervals across
a wider range of maturation.
In addition to more in-depth assessments over time, the
present results should be considered in light of several limi-
tations. First, participants were Caucasian, had no psychiatric or
neurological disease, and grew up in rural areas. Thus, our
findings may not generalize to a more racially heterogeneous or
urban population. Second, the VLPFC and DLPFC play an
important role in cognitive control, including attention, executive
function, and working memory. Therefore, reduced activity in
the regions may be related to difficulties in cognitive processes,
not specifically to emotional processing per se. Indeed, child-
hood SES has also been related to impaired cognitive functions
such as lack of sustained attention and deficits in executive
functioning (56, 57). Thus, it is important to further investigate
common and unique patterns of neural activity during cognitive
and emotional task demands and their associations with child-
hood SES. Third, we found that the associations among reduced
negative affect, environmental, and neural factors were limited
to DLPFC and VLPFC activity. Childhood income, chronic
stress, and amygdala activity were not associated with regulation
success based on subjective ratings. It may be possible that
subjective ratings from the instructed emotion regulation were
not sensitive to individual differences in childhood adversity and
amygdala activity. Therefore, the interpretation of emotion
dysregulation should be restricted to DLPFC and VLPFC ac-
tivity. More studies using other methods such as self-report or
observed measures of emotion regulation are necessary to con-
firm whether there are associations among childhood adversity,
amygdala activity, and changes in negative affect. Fourth, the
current study did not detect evidence that neural activity was
associated with the use of reappraisal strategies in response to
everyday stress. Thus, effects of childhood SES on neural activity
may not generalize to everyday coping efforts using cognitive
reappraisal. However, because the Reappraisal scale included
only four items across any stressors in life, the scale may have
had a limited ability to detect individual differences. Studies
assessing coping strategies specifically in response to financial
hardships suggest that children and adults living in poverty rely
more on involuntary coping strategies in their daily lives, such as
avoidance, than on active coping strategies, such as cognitive
reappraisal (58). Furthermore, teaching coping skills including
cognitive reappraisal helped adult, low-income women decrease
their reliance on involuntary coping strategies and reduce their
depressive symptoms (58). Thus, future studies should investigate
whether childhood SES effects on the neural functioning during
cognitive reappraisal are linked to everyday reappraisal efforts for
particular stressors including financial hardships.
The current study revealed that childhood poverty is related to
reduced activity in the VLPFC and DLPFC and increased ac-
tivity in the amygdala during emotion regulation among young
adults. Furthermore, in both the DLPFC and VLPFC, childhood
chronic stressor exposure mediated the relations between
childhood poverty and decreased activity during emotion regu-
lation. The current study demonstrates the significance of
childhood family income and stress exposure in predicting neural
outcomes in young adults during emotion regulation. Greater
knowledge about the developmental timing of risk exposures and
brain development would be extremely valuable for informing
interventions. Thus, future longitudinal studies should examine
the timing of childhood poverty, stressor exposures, and brain
development, ideally from conception throughout childhood.
Materials and Methods
Participants. Participants were initially recruited for a longitudinal study on
rural poverty and child development (mean age, 9.2 y) in rural counties in the
Northeastern United States from public schools, the Cooperative Extension
4of6
|
www.pnas.org/cgi/doi/10.1073/pnas.1308240110 Kim et al.

System, Head Start, and other antipoverty programs. One child per family
participated, and low-income families were oversampled. The participants were
followed up during wave 2 (mean age, 13.4 y) and wave 3 (mean age, 17.3 y).
See Evans etal. (34, 35) for further details on subjectrecruitment and protocols.
Among individuals participating in these three waves of data collection, 54
participants were recruited for this study. This study was approved by the
University of Michigan and Cornell University Institutional Review Boards,
and all participants provided informed consent. All participants had no MRI
contraindications (e.g., metallic/ferrous materials in their body), no prior or
current treatment for any psychiatric disorder [clinician-conducted psychiatri c
evaluation based on the Structured Clinical Interview for Diagnostic and
Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV)], and no
current neurological condition. Approximately half of the participants were
from low-income backgrounds at age 9, and half were from families with
incomes two to four times above the poverty line. Of 54 participants, 49
participants completed the Emotion Regulation Task (ERT) task, and had a full
set of usable fMRI data. Two participants did not complete the task, one
participant had a severe artifact in fMRI data, and two participants had
excessive movement in fMRI data beyond our criteria (2 mm/2° in any
directions). The average age was 23.61 (SD =1.30, range, 20–27), and 55.1%
(27 out of 49) were males. The average income-to-need ratio at age 9 was
1.8 (SD =1.1) and at age 24 was 3.2 (SD =3.0).
Procedure. In the longitudinal study, a pair of trained researchers visited
children’s homes at each wave of data collection, independently interview-
ing the participant and his/her mother. Only one child per household was
eligible for the study. Demographic information, measures of mental health,
and chronic stress exposure were assessed during the home visits. In the fMRI
study, participants visited the University of Michigan’s neuroimaging center,
where trained researchers administered training and fMRI sessions.
Measures. Income-to-needs. The ratio of family income-to-needs was computed
by dividing total family income by the poverty threshold at each wave of data
collection. This ratio is an annually adjusted, per capita index of income that
the US Census Bureau calculates using a standardized formula. The income-to-
needs ratio at age 24 was calculated based on the participants’own income.
Chronic stress. Children’s exposure to chronic stress was assessed at wave 1
(age 9), wave 2 (age 13), and wave 3 (age 17). Chronic stressors included
three psychosocial risk factors (child–family separation, violence, and family
turmoil) and three physical risk factors (noise, crowding, and housing qual-
ity). Psychosocial risk factors were assessed by maternal reports at wave 1
and combined maternal and child reports at waves 2 and 3. Mothers com-
pleted the Life Events and Circumstances Checklist (59), with subscales on
child–family separation, violence, and family turmoil. Mothers answered
dichotomous items (yes/no) to indicate whether specific events or circum-
stances had happened to their child during the interval since the prior in-
terview. Children also completed a life event scale based on a modified
version of the Adolescent Perceived Events Scale (60), answering dichotomous
items (yes/no) to specific events. An event was counted a single time if it was
reported by the child, the mother, or both. As for physical risk factors,
housing quality was rated by trained observers on a standardized scale (61).
Noise was assessed by two, 2-h readings of average decibel levels (Leq) in the
primary social space of the home (typically the living room). Crowding was
defined as the ratio of occupants to number of rooms in the home.
For eachparticipant, each ofthe six risk factors werecoded dichotomously—1
if scores were in the upper quartile based on the data distribution of the
entire sample at each age point, and 0 otherwise. Chronic stressor exposure
at each wave of data collection was calculated by summing the dichotomous
scores of all risk factors (range, 0–6). Additive indices of cumulative stress
exposure are robust and consistently predict physical and mental health
outcomes better than indices of singular stressor exposure or alternative
multiple stressor exposure metrics (62). Chronic stress exposure scores were
then averaged across the three waves.
Everyday reappraisal coping. The use of reappraisal strategies in responses to
everyday stress was assessed by the COPE Inventory (63). The measure has
a 4-point scale ranging from “I don’tdothisatall”to “I do this a lot.”The
scale Positive Reinterpretation and Growth included four items (e.g., “Itry
to see it in a different light, to make it seem more positive”), and the
summary score of the four items was included as an indicator of everyday
use of reappraisal strategies.
fMRI paradigm. Neural activity of participants was recorded while they were
engaged in the ERT (44, 64). During the Look condition of the ERT, partic-
ipants were asked to simply look at pictures with emotionally neutral va-
lence. During the Maintain task, participants were instructed to attend to
and experience naturally (without trying to change or alter) the emotional
state elicited by the pictures. During the Reappraisal task, participants were
instructed to voluntarily decrease the intensity of their negative affect by
using the cognitive strategy of reappraisal. The participants were asked to
use one of two strategies for each picture: (i) transforming the depicted
scenario into less negative or positive terms (e.g., people crying outside the
church are leaving a wedding and the tears are joyful) and (ii) rationalizing
or objectifying the content of the pictures (e.g., an abused woman smoking
a cigarette is an actress in a movie between scenes). During the practice
session, participants were asked to go through the reappraisal process out
loud. They were assisted in reevaluating the images if their strategies were
judged as inappropriate by the experimenters. The fMRI sessions were con-
ducted only after all participants demonstrated full understanding of the task.
The fMRI task involved a block-related design in which participants viewed
20 s blocks of aversive or neutral pictures; each picture was presented for 5 s
consecutively without an interstimulus interval. Before each block of pictures,
the instruction to “look,”“maintain,”or “reappraise”appeared at the center
of a black screen for a duration of 5 s. Immediately following each Look,
Maintain, or Reappraisal block, a rating scale appeared on a screen for 5 s
asking participants to rate the intensity of their negative affect on a 5-point
scale (1, least negative/neutral; 5, extremely negative) via button response.
The look, maintain, and reappraise blocks were interspersed with 20 s
baseline blocks consisting of a fixation cross. During this period, participants
were asked to stop maintaining or reappraising their emotional experience
and to relax. The total task duration was 10 min spread across two runs.
fMRI data acquisition and preprocessing. Scanning took place in a 3.0 Tesla Philips
magnet scanner in the fMRI laboratory at Veterans Affairs Ann Arbor using
a standard eight-channel SENSE head coil. Functional data were acquired
(300 T2*-weighted echo-planar-imaging (EPI) volumes; TR =2,000 ms; TE =
30 ms; flip angle =90; field of view =220 mm; matrix size, 64 ×64; 42 axial
slices; voxels =3.44 ×3.44 ×2.80 mm). A high-resolution anatomical
T1-weighted image with a 3D gradient recalled echo was also acquired.
Functional imaging data were preprocessed and analyzed using Statistical
Parametric Mapping 8 (Wellcome Trust Center for Neuroimaging, University
College, London; www.fil.ion.ucl.ac.uk/spm). Five images at the beginning of
each fMRI run were discarded. Slice timing correction was performed using
a middle slice as a reference (slice 21), and then images within each run were
realigned to the first image of the first run to correct for movement. The
realigned functional images were spatially normalized to a functional tem-
plate, resampled to 2 ×2×2 mm voxels, and then spatially smoothed using
a Gaussian filter (full width at half maximum, 8 mm).
fMRI data analysis. At the individual subject level, response amplitudes were
estimated for each condition using the general linear model. A high-pass
filter of 0.0078 Hz was used. Conditions that were modeled included look,
maintain, and reappraise blocks as well as instruction and rating periods. The
current study was primarily interested in emotion regulation; thus, for in-
dividual subjects, we contrasted images of the blood-oxygen-level–dependent
(BOLD) signal changes associated with the Reappraisal vs. Maintain contrast,
which estimated the neural functions involved in explicit regulation of neg-
ative emotion. For the group-level analysis, contrast images for individual
subjects were entered into a random-effects analysis. To identify regions that
were more active on Reappraisal, we first performed contrast compared ac-
tivation in the Reappraisal vs. Maintain condition. To identify regions that
were associated with childhood family income, a multiple regression was
performed with the income-to-needs ratio at age 9 as an independent vari-
able and the current income–to-needs ratio as a covariate of no interest. An
initial voxel-wise threshold of P<0.005 and a minimum cluster size of 265
voxels for the Reappraisal vs. Maintain contrast gave a corrected P<0.05. This
threshold was determined by Monte-Carlo simulations using the 3dClustSim
program of the AFNI toolkit (3dClustSim –mask –both –prefix–fwhmxyz 10.31
10.82 10.02; http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dClustSim.
html). The amygdala was a region of a priori interest and has a small struc-
ture; thus, an ROI approach was used. A threshold of P<0.05, uncorrected,
was applied, and estimates of signal change for eachcontrast averaged across
the entire suprathreshold region were extracted for each participant using
MarsBaR (Marseille boîte à région d’intérêt) (65) and were then entered into
Statistical Package for the Social Sciences (SPSS, Inc.) for additional analyses.
In the additional analyses, we used neural activity during the Reappraisal
task alone (vs. Fixation), rather than contrasting Reappraisal against Main-
tain. We sought to isolate the effects of Reappraisal as Maintain because
the contrast confounds potential interactions between the two tasks, both
of which involve appraisal and implicit and explicit control, whereas the
fixation condition approximates noncognitive/nonemotional control “base-
line.”The same approach was used in previous studies (44,48, 64, 66).First, we
estimated regulation success of negative emotions by subtracting Reappraisal
ratings from Maintain ratings (21). Then, the regulation success as well as
Kim et al. PNAS Early Edition
|
5of6
PSYCHOLOGICAL AND
COGNITIVE SCIENCES

everyday reappraisal coping were correlated with amygdala and PFC activity.
Second, in the PFC and amygdala, the indirect effect of chronic stress exposure
was tested using 95% bias-corrected CIs with bootstrapping procedures(10,000
bootstrap resamples) (67). The 95% bias-corrected CIs without the inclusion of
0 indicates a statistically significant indirect relationship at P<0.05 (67).
ACKNOWLEDGMENTS. We thank Erika Blackburn, Sarah Garfinkel, and
Robert Varney for assistance with data collection. The current study was
supported by National Institutes of Health Grant RC2MD004767, the William
T. Grant Foundation, the John D. and Catherine T. MacArthur Foundation
Network on Socioeconomic Status and Health, and the Robert Wood
Johnson Foundation.
1. Grant KE, et al. (2003) Stressors and child and adolescent psychopathology: Moving
from markers to mechanisms of risk. Psychol Bull 129(3):447–466.
2. Adler NE, Rehkopf DH (2008) U.S. disparities in health: Descriptions, causes, and
mechanisms. Annu Rev Public Health 29:235–252.
3. Bradley RH, Corwyn RF (2002) Socioeconomic status and child development. Annu Rev
Psychol 53(1):371–399.
4. Chen E, Matthews KA, Boyce WT (2002) Socioeconomic differences in children’s health:
How and why do these relationships change with age? Psychol Bull 128(2):295–329.
5. Cohen S, Janicki-Deverts D, Chen E, Matthews KA (2010) Childhood socioeconomic
status and adult health. Ann N Y Acad Sci 1186:37–55.
6. Poulton R, et al. (2002) Association between children’s experience of socioeconomic
disadvantage and adult health: A life-course study. Lancet 360(9346):1640–1645.
7. Blair C, Raver CC (2012) Child development in the context of adversity: Experiential
canalization of brain and behavior. Am Psychol 67(4):309–318.
8. Evans GW (2004) The environment of childhood poverty. Am Psychol 59(2):77–92.
9. Lupien SJ, King S, Meaney MJ, McEwen BS (2000) Child’s stress hormone levels correlate
with mother’s socioeconomic status and depressive state. Biol Psychiatry 48(10):976–980.
10. Evans GW, Kim P (2010) Multiple risk exposure as a potential explanatory mechanism
for the socioeconomic status-health gradient. Ann N Y Acad Sci 1186:174–189.
11. Evans GW, Chen E, Miller GE, Seeman TE (2012) How Poverty Gets Under the Skin: A
Lifecourse Perspective . The Oxford Handbook of Poverty and Child Development, eds
Maholmes V, King R (Oxford Univ Press, New York), pp 13–36.
12. McEwen BS (1998) Protective and damaging effects of stress mediators. N Engl J Med
338(3):171–179.
13. Adler NE, Marmot M, McEwen BS, Stewart J eds (1999) Socioeconomic Status and
Health in Industrial Nations (New York Academy of Sciences, New York).
14. Kessler RC, Price RH, Wortman CB (1985) Social factors in psychopathology: stress,
social support, and coping processes. Annu Rev Psychol 36:531–572.
15. Boyce WT, Sokolowski MB, Robinson GE (2012) Toward a new biology of social ad-
versity. Proc Natl Acad Sci USA 109(Suppl 2):17143–17148.
16. McEwen BS (2012) Brain on stress: How the social environment gets under the skin.
Proc Natl Acad Sci USA 109(Suppl 2):17180–17185.
17. Rodrigues SM, LeDoux JE, Sapolsky RM (2009) The influence of stress hormones on
fear circuitry. Annu Rev Neurosci 32:289–313.
18. Ochsner KN, Silvers JA, Buhle JT (2012) Functional imaging studies of emotion reg-
ulation: A synthetic review and evolving model of the cognitive control of emotion.
Ann N Y Acad Sci 1251(1):E1–E24.
19. Kalisch R (2009) The functional neuroanatomy of reappraisal: Time matters. Neurosci
Biobehav Rev 33(8):1215–1226.
20. Kalisch R, Wiech K, Critchley HD, DolanRJ (2006) Levels of appraisal: A medialprefrontal
role in high-level appraisal of emotional material. Neuroimage 30(4):1458–1466.
21. Wager TD, Davidson ML, Hughes BL, Lindquist MA, Ochsner KN (2008) Prefrontal-
subcortical pathways mediating successful emotion regulation. Neuron 59(6):1037–1050.
22. Johnstone T, van Reekum CM, Urry HL, Kalin NH, Davidson RJ (2007) Failure to reg-
ulate: Counterproductive recruitment of top-down prefrontal-subcortical circuitry in
major depression. J Neurosci 27(33):8877–8884.
23. Ball TM, Ramsawh HJ, Campbell-Sills L, Paulus MP, Stein MB (2013) Prefrontal dys-
function during emotion regulation in generalized anxiety and panic disorders. Psy-
chol Med 43(7):1475–1486.
24. Taylor SF, Liberzon I (2007) Neural correlates of emotion regulation in psychopa-
thology. Trends Cogn Sci 11(10):413–418.
25. New AS, et al. (2009) A functional magnetic resonance imaging study of deliberate
emotion regulation in resilience and posttraumatic stress disorder. Biol Psychiatry
66(7):656–664.
26. Coccaro EF, McCloskey MS, Fitzgerald DA, Phan KL (2007) Amygdala and orbitofrontal
reactivity to social threat in individuals with impulsive aggression. Biol Psychiatry
62(2):168–178.
27. O’Daly OG, et al. (2012) Withdrawal-associated increases and decreases in functional
neural connectivity associated with altered emotional regulation in alcoholism.
Neuropsychopharmacology 37(10):2267–2276.
28. Hackman DA, Farah MJ, Meaney MJ (2010) Socioeconomic status and the brain:
Mechanistic insights from human and animal research. Nat Rev Neurosci 11(9):
651–659.
29. McEwen BS, Gianaros PJ (2010) Central role of the brain in stress and adaptation:
Links to socioeconomic status, health, and disease. Ann N Y Acad Sci 1186:190–222.
30. Noble KG, Houston SM, Kan E, Sowell ER (2012) Neural correlates of socioeconomic
status in the developing human brain. Dev Sci 15(4):516–527.
31. Sheridan MA, Sarsour K, Jutte D, D’Esposito M, Boyce WT (2012) The impact of social
disparity on prefrontal function in childhood. PLoS ONE 7(4):e35744.
32. Gianaros PJ, et al. (2008) Potential neural embedding of parental social standing. Soc
Cogn Affect Neurosci 3(2):91–96.
33. Yanagisawa K, et al. (2013) Family socioeconomic status modulates the coping-
related neural response of offspring. Soc Cogn Affect Neurosci 8(6):617–622.
34. Evans GW, Kim P (2012) Early childhood poverty and adult chronic physiological
stress: The mediating role of childhood cumulative risk exposure. Psychol Sci 23(9):
979–983.
35. Evans GW (2003) A multimethodological analysis of cumulative risk and allostatic load
among rural children. Dev Psychol 39(5):924–933.
36. Evans GW, English K (2002) The environment of poverty: Multiple stressor exposure,
psychophysiological stress, and socioemotional adjustment. Child Dev 73(4):1238–1248.
37. Doan SN, Fuller-Rowell TE, Evans GW (2012) Cumulative risk and adolescent’sin-
ternalizing and externalizing problems: The mediating roles of maternal responsiveness
and self-regulation. Dev Psychol 48(6):1529–1539.
38. Wells NM, Evans GW, Beavis A, Ong AD (2010) Early childhood poverty, cumulative
risk exposure, and body mass index trajectories through young adulthood. Am J
Public Health 100(12):2507–2512.
39. Evans GW, Kim P (2007) Childhood poverty and health: Cumulative risk exposure and
stress dysregulation. Psychol Sci 18(11):953–957.
40. Davidson RJ, McEwen BS (2012) Social influences on neuroplasticity: Stress and in-
terventions to promote well-being. Nat Neurosci 15(5):689–695.
41. Lupien SJ, McEwen BS, Gunnar MR, Heim C (2009) Effects of stress throughout the
lifespan on the brain, behaviour and cognition. Nat Rev Neurosci 10(6):434–445.
42. Taylor SE, Eisenberger NI, Saxbe D, Lehman BJ, Lieberman MD (2006) Neural re-
sponses to emotional stimuli are associated with childhood family stress. Biol Psy-
chiatry 60(3):296–301.
43. Tottenham N, et al. (2011) Elevated amygdala response to faces following early
deprivation. Dev Sci 14(2):190–204.
44. Phan KL, et al. (2005) Neural substrates for voluntary suppression of negative affect:
A functional magnetic resonance imaging study. Biol Psychiatry 57(3):210–219.
45. Wager TD, et al. (2005) Common and unique components of response inhibition re-
vealed by fMRI. Neuroimage 27(2):323–340.
46. Urry HL, et al. (2006) Amygdala and ventromedial prefrontal cortex are inversely
coupled during regulation of negative affect and predict the diurnal pattern of
cortisol secretion among older adults. J Neurosci 26(16):4415–4425.
47. Phan KL, Sripada CS (2013) Emotion Regulation. The Cambridge Handbook of Human
Affective Neuroscience, eds Armony J, Vuilleumier P (Cambridge Univ Press, Cam-
bidge, UK), pp 375–400.
48. McRae K, et al. (2010) The neural bases of distraction and reappraisal. J Cogn Neurosci
22(2):248–262.
49. Sheth SA, et al. (2012) Human dorsal anterior cingulate cortex neurons mediate on-
going behavioural adaptation. Nature 488(7410):218–221.
50. Etkin A, Egner T, Kalisch R (2011) Emotional processing in anterior cingulate and
medial prefrontal cortex. Trends Cogn Sci 15(2):85–93.
51. Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network:
Anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38.
52. Gianaros PJ, et al. (2011) Parental education predicts corticostriatal functionality in
adulthood. Cereb Cortex 21(4):896–910.
53. Dahl RE (2004) Adolescent brain development: A period of vulnerabilities and op-
portunities. Keynote address. Ann N Y Acad Sci 1021:1–22.
54. Shonkoff JP, Boyce WT, McEwen BS (2009) Neuroscience, molecular biology, and the
childhood roots of health disparities: Building a new framework for health promotion
and disease prevention. JAMA 301(21):2252–2259.
55. Burghy CA, et al. (2012) Developmental pathways to amygdala-prefrontal function
and internalizing symptoms in adolescence. Nat Neurosci 15(12):1736–1741.
56. Evans GW, Schamberg MA (2009) Childhood poverty, chronic stress, and adult
working memory. Proc Natl Acad Sci USA 106(16):6545–6549.
57. Noble KG, McCandliss BD, Farah MJ (2007) Socioeconomic gradients predict individual
differences in neurocognitive abilities. Dev Sci 10(4):464–480.
58. Wadsworth ME, Santiago CD, Einhorn L, Etter EM, Rienks S, Markman H (2011) Pre-
liminary efficacy of an intervention to reduce psychosocial stress and improve coping
in low-income families. Am J Community Psychol 48:257–271.
59. Wyman PA, Cowen EL, Work WC, Parker GR (1991) Developmental and family milieu
correlates of resilience in urban children who have experienced major life stress. Am J
Community Psychol 19(3):405–426.
60. Compas BE (1997) Responses to Stress Questionnaire (Univ of Vermont,Burlington, VT).
61. Evans GW, Wells NM, Chan HY, Saltzman H (2000) Housing quality and mental health.
J Consult Clin Psychol 68(3):526–530.
62. Evans GW, Li D, Sepanski Whipple S (2013) Cumulative risk and child development.
Psychol Bull, in press.
63. Carver CS, Scheier MF, Weintraub JK (1989) Assessing coping strategies: A theoreti-
cally based approach. J Pers Soc Psychol 56(2):267–283.
64. Banks SJ, Eddy KT, Angstadt M, Nathan PJ, Phan KL (2007) Amygdala-frontal con-
nectivity during emotion regulation. Soc Cogn Affect Neurosci 2(4):303–312.
65. Brett M, Anton J, Valabregue R, Poline J (2002) Region of interest analysis using an
SPM toolbox. NeuroImage 16(2):CD-ROM, abstr 497.
66. Ochsner KN, Bunge SA, Gross JJ, Gabrieli JD (2002) Rethinking feelings: An FMRI study
of the cognitive regulation of emotion. J Cogn Neurosci 14(8):1215–1229.
67. Preacher KJ, Hayes AF (2008) Asymptotic and resampling strategies for assessing and
comparing indirect effects in multiple mediator models. Behav Res Methods 40(3):
879–891.
6of6
|
www.pnas.org/cgi/doi/10.1073/pnas.1308240110 Kim et al.