Prenatal and Childhood Adverse Events
and Child Brain Morphology: A Population-Based
Andrea P. Cortes Hidalgo,a Scott W. Delaney,b Stavroula A. Kourtalidi,c Alexander Neumann,a
Runyu Zou,a Ryan L. Muetzel,a Marian J. Bakermans-Kranenburg,d
Marinus H. van IJzendoorn,c Henning Tiemeier,a,b and Tonya Whitea,e,*
a Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, Rotterdam, the Netherlands
b Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, United States
c Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
d Clinical Child & Family Studies, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
e Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
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: 2022, Volume 2 - 1 - CC-BY: © Cortes Hidalgo etal.
: 2022, Volume 2 - 1 -
Prenatal and childhood adverse events have been shown to be related to children’s cognitive and psychological development.
However, the in uence of early-life adversities on child brain morphology is not well understood, and most studies are based on
small samples and often examine only one adversity. Thus, the goal of our study is to examine the relationship between cumulative
exposures to prenatal and childhood adversities and brain morphology in a large population- based study. Participants included
2,993 children from the Generation R Study, a cohort of children growing up in Rotterdam, the Netherlands. Recruitment was ini-
tiated between 2002 and 2006, and the study is currently performing the 17- to 19-year follow-up wave. Prenatal adversities were
reported by mothers at 20–25 weeks of pregnancy, and the child’s lifetime exposure to adversities was reported by mothers when
the children were 10 years old. The total brain, gray and white matter volumes, and the volume of the cerebellum, amygdala, and
hippocampus were assessed with magnetic resonance imaging when children were 10 years old. In total, 36% of children had moth-
ers who were exposed to at least one adversity during pregnancy and 35% of children were exposed to adversities in childhood. In
our study sample, the cumulative number of prenatal adversities was not related to any brain outcome. In contrast, per each addi-
tional childhood adverse event, the total brain volume was 0.07 standard deviations smaller (SE = 0.02, p = 0.001), with differences
in both gray and white matter volumes. Childhood adversities were not related to the amygdala or hippocampal volumes. Addi-
tionally, the link between childhood events and the preadolescent brain was not modi ed by prenatal events and was not explained
by maternal psychopathology. Our results suggest that childhood adversities, but not prenatal adverse events, are associated with
smaller global brain volumes in preadolescence. Notably, this is the  rst large population-based study to prospectively assess the
association between the cumulative number of prenatal adversities and the preadolescent brain morphology. The study  ndings
extend the evidence from high- risk samples, providing support for a link between cumulative childhood adverse events and brain
morphology in children from the general population.
Keywords: Adversity; brain; childhood; magnetic resonance imaging; pregnancy; stress
Correspondence: Tonya White, MD, PhD, Child and Adolescent Psychiatry Department, Erasmus MC, Dr. Molewaterplein 60, Kp‐2869, 3000 CB Rotterdam,
the Netherlands. Email:t.white@erasmusmc.nl
Received: March 11, 2021
Accepted: October 18, 2021
DOI: 10.52294/0b464d35-41d5-406a-9f06-9b95875ccf9c
Children whose mothers experienced adversities during
pregnancy tend to have more behavioral problems (2),
and childhood adversities are associated with poorer
intellectual performance (3). Although studies in high-
risk samples have addressed the relationship between
early-life adversity and child brain morphology (1), the
Adversities, de ned as the negative experiences that
deviate from the expectable environment, need to be
chronic (e.g. parental loss) or suf ciently severe to re-
quire a considerable psychobiological adaptation (1).
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association of prenatal and childhood adversities with
child brain morphology is not well documented in the
general population.
Fetal life, when the brain undergoes its greatest rela-
tive growth, is a critical period for brain development (4).
Starting with differentiation of the ectoderm into neural
tissue, there is a complex cascade of events that involve
neurulation, neurogenesis and subsequent migration,
apoptosis, synaptogenesis, and dendritic arborization
(4, 5). This developmental period of incredible growth
and change is a sensitive window, in which environmental
factors that generate maternal toxic psychological stress
may have profound and lasting effects (6). However, few
studies have examined the relationship between prena-
tal adversities and offspring neurodevelopment. As re-
viewed by Franke, Van den Bergh (7), studies examining
head circumference (HC) at birth showed mixed results.
For example, prenatal adversities were not related to
HC at birth in a population-based sample (N = 4,211) (8),
whereas a small positive association was found in a larger
cohort (N = 78,017) (9). HC metrics are easily accessible
and a proxy for total brain volume. However, they might
not capture region- speci c differences (7). Only one study
assessed prenatal adversities and child brain morphology
using magnetic resonance imaging (MRI) and found that
girls whose mothers were exposed to an adverse event in
pregnancy had larger amygdala volumes (N = 68) (2). To
date, no large population-based study has examined the
relationship between cumulative prenatal adversities and
child brain morphology.
In contrast, there is substantial research on childhood
adversities and offspring neurodevelopment, includ-
ing case –control studies, where adversities are often
severe (e.g. institutionalization), and studies in children
exposed to a more graded scale of events. Severe ad-
versities have been related to smaller cerebellar (1) and
global brain volumes, with differences in multiple brain
regions (10). Evidence for differences in the amygdala
and hippocampus is mixed, with both larger (11, 12) and
smaller volumes (13) reported. Hanson, Nacewicz (13) ex-
amined three samples of children exposed to different
adversities (physical abuse, neglect, low socioeconomic
status (SES)) and found a smaller amygdala in relation to
all adversities.
Studies in children exposed to more common adver-
sities have reported differences in the cerebellum, cor-
tex, and limbic structures. Cumulative early-life adverse
experiences were associated with smaller gray matter
volumes of the cerebellum, the amygdala, and multiple
cortical regions in the frontal, parietal, and temporal
lobes in a sample of 58 adolescents (14) and with smaller
prefrontal cortex, amygdala, and hippocampal volumes
in a study oversampled for child depression (15, 16).
Importantly, the adversity de nition in the latter study
included parental psychopathology. Although having a
parent with psychopathology may represent an adver-
sity, shared genetic factors may underlie the association
(7), and parental psychopathology may additionally in-
teract with the adversities’ effect (17).
There are also other relevant factors that may in-
fluence the association between early adverse events
and downstream brain morphology. First, SES is relat-
ed to child brain morphology and function, possibly
through factors such as exposure to pollution, and the
availability of education, cognitive stimulation, and
healthcare (18). Importantly, while adversity occurs
more often in individuals experiencing poverty, stress
and the consequences thereof may also occur in other
socioeconomic strata. The effects of adversity are like-
ly explained by the biological stress response (19),
thus suggesting that adversity and SES could have in-
dependent pathways underlying their effects on brain
morphology. Determining whether early-life adversity
is associated with brain morphological differences in-
dependent of the already known effect of SES is im-
portant to obtain a more precise estimation of the
role of adversity on the brain (19). Second, account-
ing for the potential direct neurobiological effect of
maternal smoking and alcohol use during pregnancy
(20) can help to elucidate whether childhood adversity
is related to the child’s brain, independent of these
Evidence suggests a cumulative relationship between
childhood adversities and numerous health- related
outcomes, including health-risk behaviors and psychi-
atric disorders (21). To address a potential cumulative
adversity effect on brain morphology, two main ap-
proaches have been proposed. First, the “lumping”
approach focuses on the cumulative number of adverse
events, assuming that different stressful events have
similar effects on brain morphology (22). Second, the
“dimensional” approach, proposed by McLaughlin
and Sheridan (23), distinguishes between threatening
events such as community violence and physical abuse,
and deprivation-related events, or those related to
lack of cognitive and social stimulation such as neglect
and poverty. The dimensional approach hypothesizes
potentially different psychobiological effects and un-
derlying mechanisms between the two groups (23).
However, largely similar brain differences have been
described across the exposure to threatening and to
deprivation-related events (10, 13, 22), suggesting low
speci city across adversity types. We acknowledge that
both approaches could offer a complementary per-
spective on the mechanisms and public health impli-
cations of childhood adversity, and the debate on how
to assess adversity is still an open question. It is how-
ever clear that compared to examining single adversi-
ties, the cumulative adversity assessment offers a more
naturalistic view of the adversity exposure, because
adverse events are often related and tend to co-occur
(22). In this study, we assessed the association between
early-life adversities and brain morphology based on
the broader cumulative adversity approach.
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Erasmus Medical Center, and all parents gave written
informed consent.
T1- weighted MRI scans were acquired in 3,966 9-
to 11-year-old children (29), of which 3,186 had good
image quality data. Among these children, 3,146 had
complete information on prenatal and/or childhood
adversities. We randomly excluded one sibling (N =153)
to avoid nonindependent data. In total, 2,993 children
were included in the analyses (2,242 in prenatal adver-
sities analyses and 2,923 in childhood adversities analy-
ses; Figure S1).
Prenatal adversities. Adverse events occurring prena-
tally and shortly before pregnancy were assessed with
a Dutch- adapted version of the Social Readjustment
Rating Scale (SRRS) (30). At 20–25 weeks of pregnancy,
mothers reported the occurrence of ten stressful events
in the preceding 12 months (e.g. serious illnesses of fam-
ily members, partner’s death) (31). As part of the adver-
sity score, we included a measure of substantial  nancial
downturn to assess instability and drastic changes in the
preexisting social and economic resources that could
have led to a prolonged or severe biological stress re-
sponse. The occurrence of robbery, theft, physical abuse,
or rape was self-reported by the participant as a response
to a single question and was additionally included in the
prenatal adversities measure, given the relevance of
these adverse experiences. Moving to a new home, orig-
inally assessed by the SRRS, was excluded as it could also
re ect a positive situation. A prenatal adversities score
was computed as the cumulative number of occurrences
of ten adverse events (Table S1).
Childhood adversities. Occurrence of stressful life
events from birth to age 10 years was reported by moth-
ers during an interview when children were 10 years old
(32). This instrument was based on the TRAILS study
questionnaires (33) and the Life Events and Dif culty
Schedule (34) and comprised 24 events of varying
severity (e.g. high amount of school work, parental
con icts). To better measure severe adversities in this
population-based sample, speci c adverse events were
selected using as reference the Adverse Childhood
Experiences studies (e.g. Felitti, Anda (21)). A childhood
adversities score was computed as the cumulative oc-
currence of these adversities (Table S2).
The measures of prenatal and childhood adversities
were de ned assuming equal weights of the individual
events, following the “cumulative” mainstream approach
to adversity, as outlined by Smith and Pollak (22). This
approach provides a useful measure of adversity, which
is simple and can be replicated across studies indepen-
dent of sample-speci c differences that otherwise affect
data- driven approaches (e.g. latent constructs).
Notably, a randomized- controlled trial in institution-
alized children demonstrated that cognitive outcomes
improved when children were placed into foster care,
especially if this placement occurred at younger ages
(3). Sheridan, Fox (24) additionally described white
matter volume differences between the children who
remained in the institution and those never institution-
alized, but not when comparing the foster care group
with the never-institutionalized group. Thus, child neu-
rodevelopment can improve, within the available bio-
logical reserve, after adversity ceases (25). This has two
implications for our study. First, the timing of adversity
exposure may in uence the association with brain mor-
phology. Children with no childhood adversities, but
whose mothers experienced adversities during preg-
nancy, may show differences due to the pronounced
neurodevelopment that occurs during prenatal life (25).
Children with adversities in both the prenatal and child-
hood periods may have the largest brain differences.
Thus, we examined adversities in both periods in rela-
tion to child brain morphology. Second, when adversi-
ty occurs only prenatally, delays in brain development
could “catch up” postnatally, approaching the typical
growth curve (25). To examine whether postnatal brain
changes could have a role in our association of interest,
we included fetal HC measures in sensitivity analyses.
Overall, evidence suggests that childhood adversity
may be associated with the volume of the amygdala, the
hippocampus, and the cerebellum (1, 14, 26). Adversity
has also been found to be associated with widespread
cortical differences, including the frontal, parietal, tem-
poral, and occipital lobes (10, 14, 26, 27), likely indi-
cating a global cortical effect of adversity. Thus, in this
population-based study, we examined the relationship
between cumulative prenatal and childhood adversities
and preadolescent brain morphology, with a focus on
the hippocampus, amygdala, cerebellum, and global
brain volumes. We hypothesized that a greater number
of adversities would be related to smaller global brain,
amygdala, and hippocampal volumes. We additionally
hypothesized a stronger association between childhood
adversities and brain morphology in children whose
mothers were exposed to prenatal adversities.
This study is part of the Generation R Study, a
population-based prenatal birth cohort in Rotterdam,
the Netherlands (28). In total, 9,778 pregnant mothers
with a delivery date from April 2002 to January 2006 were
enrolled, and information was collected from children
and parents by questionnaires, interviews, and research
visits. Study protocols for each wave of data collection
were approved by the Medical Ethical Committee of the
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on the country of birth of this parent. We grouped the na-
tional origin minorities as non-Dutch Western (including
European, Indonesian, Japanese, Oceanian, and North
American) and non-Western (including other national ori-
gins, e.g. Surinamese and Moroccan) (42) (see also Troe,
Raat (43)). The highest household education and prenatal
alcohol consumption and smoking were reported through
questionnaires during pregnancy (see Supplemental
Maternal psychopathology in pregnancy was assessed
with the Brief Symptom Inventory, a validated and wide-
ly used questionnaire (44). We used the global severity
index score, a measure of the global severity of psycho-
pathology, in additional analyses.
Statistical Analyses
We examined the associations of prenatal and child-
hood adversities with brain outcomes using multiple
linear regression. We  rst  tted a minimally adjusted
model controlling for child sex and age at MRI scan,
total intracranial volume (in amygdala and hippocam-
pus analyses), and maternal national origin. Child sex
and age at MRI scan were included as precision vari-
ables to account for typical differences in brain mor-
phological characteristics (45). Child intracranial volume
was included in all analyses of the amygdala and hip-
pocampus to determine whether childhood adversi-
ty was associated with the volume of these regions of
interest independently of the adversity-related global
brain differences. Considering the multiethnic nature
of our study sample, the maternal national origin was
controlled for to account for differences in the adversity
exposure and possible anatomical brain variations across
national origins (46). In a second model, we adjusted
for the highest household education as an indicator of
SES. Although adversity occurs more frequently in fami-
lies experiencing poverty, it is argued that both factors
have an independent effect and potentially different
biological mechanisms (19). Therefore, we aimed to
determine the association between adversity and brain
morphology in children living in any SES. Finally, we
also controlled for prenatal alcohol use and smoking
in a third, fully adjusted model, since these factors may
have a direct neurobiological effect (20) and could be
also considered part of the pathway between prenatal
adversities and brain morphology.
We subsequently examined the interaction between
prenatal and childhood adversities in relation to brain
morphology. Additionally, for descriptive purposes, we
assessed the relationship between a categorical adver-
sity measure and the brain outcomes, using four groups:
children with one or more of the prenatal adversities that
we measured (N = 460), children with one or more of
the childhood adversities that we measured (N = 433),
children with adversities in both periods (N = 321), and
children with none of these adversities (N = 958).
Brain Imaging
Brain MRI data were obtained in 9- to 11-year-old chil-
dren using a 3 Tesla GE 750w Discovery platform (General
Electric, Milwaukee, WI) (29). T1-weighted images were
collected with a receive-only 8-channel head coil and an
inversion recovery fast spoil gradient recalled sequence
(TR = 8.77 ms, TE = 3.4 ms, TI = 600 ms,  ip angle = 10°,
eld of view = 220 × 220, acquisition matrix = 220 × 220,
slice thickness = 1 mm, number of slices = 230, ARC ac-
celeration factor = 2).
We processed and conducted the segmentation
and reconstruction of the neuroimaging data with the
FreeSurfer image analysis suite (v.6.0) (35). Reconstructed
images were inspected for quality, and poor-quality
reconstructions were excluded from further analyses
(Supplemental Information) (36). The total brain volume,
the cortical gray and cerebral white matter volumes, the
cerebellar volume, and the amygdala and hippocampal
volumes were included in the analyses.
Ultrasound Measures
Fetal ultrasound measures were collected at three time
points during pregnancy (37), at a median gestation-
al age of 13.1 weeks (95% range = 9.3, 17.5) for the  rst
assessment, 20.5 weeks (95% range = 18.4, 23.3) for the
midpregnancy assessment, and 30.4 weeks (95% range =
27.9, 33.0) for the last assessment (38). The HC data col-
lection was described in detail by Verburg, Steegers (39).
Brie y, sonographers established the gestational age
based on the  rst ultrasound assessment and measured
fetal HC based on the outline of the skull and to the near-
est millimeter using standardized techniques. The HC
measures collected during the third trimester of preg-
nancy were included in the sensitivity analyses. These HC
metrics have been shown to be predicted by maternal
smoking during pregnancy (38) and by maternal educa-
tion levels (40). Additionally, the HC metrics in our sample
had a correlation of 0.55 (p < 0.001) with the gestational
age at the ultrasound assessment and of 0.38 (p < 0.001)
with the total brain volume at age 10 years, supporting
the validity of our measures. There was high reliability for
the HC metrics in early pregnancy, with intra- and interob-
server intraclass correlation coef cient (ICC) of 0.995 and
0.988, respectively, and intra- and interobserver coef -
cient of variation (CV) of 2.2 and 3.8, respectively (41).
We included as covariates child sex and age at the MRI
scan, total intracranial volume, maternal national origin,
highest household education, and maternal prenatal alco-
hol use and smoking. Child sex was collected from birth
records. Maternal national origin was de ned based on
her parents’ birth country and was self-reported during
pregnancy. Maternal national origin was categorized as
Dutch, non-Dutch Western, and non-Western. Mothers
were considered of Dutch origin if both of her parents
were born in the Netherlands. When one of her parents
was born abroad, the maternal origin was de ned based
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to nonusable MRI data (N = 760) did not differ from chil-
dren included in the analyses (N = 2,993) in the exposure
to prenatal (p = 0.27) or childhood adversities (p = 0.31),
in maternal national origins (p = 0.09), or in maternal psy-
chiatric symptoms (p = 0.26). Excluded children more
often had mothers with lower education (54.0%) com-
pared to those in the analyses (47.3%; p = 0.01).
In our study sample, the child age at the MRI scan was
between 8.72 and 11.99 years (median: 9.93 years), with
90% of children below the age of 11.19 years. In total,
36% of children had mothers who were exposed to at
least one prenatal adversity and 35% of children were
exposed to adversities during childhood (Table 1).
Several sensitivity analyses were performed. We  rst
examined whether child sex modi ed the associations
between adversity and brain morphology. Second, we
analyzed the associations of adversity and brain mor-
phology in a more homogeneous group, children whose
mothers had a Dutch national origin, and we explored
the interaction between national origin and adversity on
the brain outcomes by adding an interaction term in a
model that included participants from all national origin
groups. Third, we explored whether associations be-
tween adversity and brain morphology were explained
by maternal psychopathology, and we examined the
interaction between maternal psychopathology and ad-
versity in relation to child brain morphology. Finally, we
explored whether postnatal brain growth and volumetric
changes in response to environmental factors (25) could
in uence the association of adversity and brain morphol-
ogy by assessing whether prenatal adversities were asso-
ciated with HC at the last pregnancy trimester, as HC is a
proxy for an early measure of total brain volume (analy-
ses adjusted for gestational age at ultrasound).
Analyses were performed in R v.3.6.1 (47). Outcomes
were standardized. Multiple imputations of missing val-
ues (maximum missingness: maternal psychopathology =
23.4%) were performed (“mice” package (48)), and re-
sults were pooled across 25 imputed datasets. We found
no signs of violation of the regression assumptions (i.e.
independence, normal distribution, homoscedasticity).
Additionally, the variance in ation factor was <2.5 for all
variables in analyses of the interaction between prenatal
and childhood adversity, suggesting no multicollinearity.
Adjustment for multiple testing was performed using the
Bonferroni approach in the analyses with prenatal adver-
sities, childhood adversities, and the interaction between
prenatal and childhood adversities (15 tests, including all
brain outcomes, except for total brain volume).
Nonresponse and MRI Exclusions Analyses
Children included in the analyses of prenatal adversities
and brain morphology (N = 2,242) were compared to
children with data on prenatal adversities but with no
neuroimaging data available (N = 3,552). Continuous
variables were compared with the Mann–Whitney U
test and categorical variables with chi-squared tests.
Mothers of children without imaging data were more
often exposed to prenatal adversities (one or more
events: 40.7%) than those of children in analyses (one or
more events: 36.1%) and were less often highly educat-
ed (22.1% vs 30.5%). Additionally, mothers of children
without imaging data were less often from Dutch origins
(no imaging data group: 50.6%; study sample: 61.1%)
and had more psychiatric symptoms (median (IQR) =
0.19 (0.1, 0.4)) than those in the analyses (median (IQR)
= 0.15 (0.1, 0.3)).
Children with prenatal and/or childhood adversity and
neuroimaging data available but who were excluded due
Table 1. Baseline characteristics
Mean (SD) or %* N
Adversity measures
Prenatal adversities (ten items), % (N = 2242)
0 63.9 1,432
1 20.6 461
2 10.5 236
3 3.8 85
4 or more 1.2 28
Childhood adversities (four items), %
(N = 2923)
0 64.9 1,897
1 27.2 795
2 6.3 185
3 1.4 41
4 0.2 5
Child characteristics
Sex, % girls 50.8 1,521
Age at MRI scan, years 10.1 (0.6) 2,993
Parental characteristics
Maternal national origin, % 2,993
Dutch 57.6 1,725
Non-Western 30.3 906
Other Western 12.1 362
Highest household education, % 2,993
Low education 41.0 1,227
Medium education 22.4 670
High education 36.6 1,096
Maternal prenatal alcohol use, % never
during pregnancy 41.0 1,226
Maternal prenatal smoking, % never during
pregnancy 76.9 2,303
Maternal psychiatric symptoms, median
(Q1, Q3) 0.15 (0.06, 0.32) 2,993
Characteristics of the sample with information for prenatal AND/OR childhood
adversities and brain structural MRI data (N = 2993). *Otherwise indicated. Based
on imputed datasets.
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prenatal alcohol use and smoking (total brain volume:
B = −0.07, SE = 0.02, p = 0.001) (Figure S3). Childhood
adversities were not related to the amygdala and hippo-
campus (Table 2). After adjustment for multiple testing,
the associations between childhood adversities and the
cortical gray (p-adjusted < 0.05), and cerebral white mat-
ter volumes (p-adjusted = 0.03) remained.
No interaction was observed between prenatal and
childhood adversities in relation to child brain morphol-
ogy (Table 3). Also, when using the categorical adversity
measure, the exposure to only prenatal adversities was
not related to the total brain volume, whereas the spe-
ci c exposure to childhood adversities was associated
with a 0.10 standard deviation smaller total brain volume
(p = 0.04). Additionally, children with adversities in both
periods had a 0.10 standard deviation smaller total brain
volume than those nonexposed to any of the adversities
measured (p = 0.06). Altogether, our results suggest that
only childhood events are related to brain morphology
and that this association is independent of the occur-
rence of prenatal adversities (Figure 1).
We further examined the speci city and robustness of
the association between childhood adversities and brain
morphology. No interaction was found between child sex
and childhood adversities for any brain outcome. When
including only children with Dutch mothers, childhood
adversities were related to the total brain, gray and white
matter, and cerebellar volumes (Table S4), and there was
no evidence of a signi cant moderating effect of national
origin on the association between adversities and brain
Children with mothers exposed to prenatal adversities
were more likely to experience adversities during child-
hood (41%) compared to those without prenatal adversi-
ties (31%). The most commonly reported prenatal event
was a substantial  nancial downturn (14.5%), followed by
a serious illness of a family member (11.6%) (Table S1).
In childhood, parental separation or divorce was the
most prevalent event (21.45%) (Table S2). Distributions
and Pearson correlations for all variables of interest are
presented in Figure S2 and Table S3, respectively. There
was a correlation of 0.13 (p < 0.001) between prenatal
and childhood adversities. Prenatal and childhood ad-
versities were more common in children of non-Western
mothers (any adversity = 51.4%, and 43.7%, respectively)
compared to children of Dutch mothers (any adversity =
30.2% and 31.1%, respectively). Prenatal adversities oc-
curred in 37.0% of boys and 35.0% of girls, and child-
hood adversities in 36.3% of boys and 33.3% of girls.
The cumulative number of prenatal adverse events was
not related to any brain outcome (Table 2). In contrast, a
consistent association was found between childhood ad-
versities and all global brain metrics (total brain, cortical
gray and white matter volumes, and total cerebellar vol-
umes). Children had, on average, a 0.11 standard devia-
tion smaller total brain volume (SE = 0.02, p < 0.001) per
each additional childhood adverse event, adjusting for
child sex, age at the MRI scan, and maternal national ori-
gin. The associations between childhood adversities and
the total brain, cortical gray and white matter volumes
remained after adjustment for parental education, and
Table 2. Associations between cumulative prenatal and childhood adversities and child brain morphology
Model 1 Model 2 Model 3
BSE p B SE p B SE p
Prenatal adversities
Global brain metrics
Total brain volume −0.03 0.02 0.14 −0.02 0.02 0.39 −0.01 0.02 0.52
Cortical grey matter volume −0.03 0.02 0.20 −0.01 0.02 0.57 −0.01 0.02 0.71
Cerebral white matter volume −0.02 0.02 0.23 −0.02 0.02 0.41 −0.01 0.02 0.56
Total cerebellar volume −0.03 0.02 0.10 −0.03 0.02 0.20 −0.02 0.02 0.26
Subcortical brain metrics
Amygdala, mean volume 0.02 0.02 0.40 0.01 0.02 0.41 0.01 0.02 0.52
Hippocampus, mean volume 0.01 0.02 0.42 0.01 0.02 0.42 0.01 0.02 0.50
Childhood adversities
Global brain metrics
Total brain volume −0.11 0.02 <0.001 −0.08 0.02 <0.001 −0.07 0.02 0.001
Cortical grey matter volume −0.11 0.02 <0.001 −0.08 0.02 0.001 −0.07 0.02 0.003*
Cerebral white matter volume −0.10 0.02 <0.001 −0.08 0.02 0.001 −0.07 0.02 0.002*
Total cerebellar volume −0.07 0.02 0.003 −0.05 0.02 0.03 −0.05 0.02 0.06
Subcortical brain metrics
Amygdala, mean volume 0 0.02 0.90 0 0.02 0.87 −0.01 0.02 0.70
Hippocampus, mean volume −0.01 0.02 0.58 −0.01 0.02 0.63 −0.01 0.02 0.59
Model 1 is adjusted for child age at MRI scan, child sex, total intracranial volume (in subcortical metrics), and maternal national origin. Model 2 is addition-
ally adjusted for the highest household education. Model 3 is additionally adjusted for maternal prenatal alcohol use and smoking. All outcomes are stan-
dardized. N = 2,242 in prenatal adversities analyses, N = 2,923 in childhood adversities analyses. *These p-values survived correction for multiple testing.
: 2022, Volume 2 - 7 - CC-BY: © Cortes Hidalgo et al.
: 2022, Volume 2 - 7 -
were related to global brain volume differences at age
10years. Our study provides two novel contributions to
the literature. This is the  rst study to examine the as-
sociation between cumulative prenatal adversities and
brain structure in children from the general population.
Contrary to our hypothesis, we found no relationship be-
tween cumulative prenatal adversities and preadolescent
brain morphology using a large population-based sam-
ple, an assessment of prenatal adversities while mothers
were pregnant, and neuroimaging data. Second, cumu-
lative childhood adversities were related to smaller total
brain volumes, and differences were observed across
gray and white matter volumes. These  ndings are con-
sistent with research in some small high-risk samples,
supporting a relationship between cumulative childhood
adversities and child neurodevelopment.
The absence of associations between prenatal adversi-
ties and child brain morphology is surprising, as the brain
undergoes dramatic developmental changes during
morphology. Also, the associations between childhood
adversities and brain morphology were not explained
nor modi ed by maternal prenatal psychopathology
(TableS5). Additionally, the cumulative number of prenatal
adversities was not related to variations in the fetal HC (B =
0.00, SE = 0.02, p = 0.82; N = 2,168). Finally, we performed
a post hoc analysis to assess whether the global brain
differences observed in relation to childhood adversities
were driven by a speci c adversity. We found that, except
for psychological abuse (B = 0.00, SE = 0.05, p = 0.94), all
childhood adversities were similarly related to total brain
volume (e.g. parental loss: B = −0.11, SE = 0.04, p = 0.004),
supporting the validity of our cumulative approach.
In this population-based study, childhood adversities, but
not prenatal adverse events experienced by the mother,
Table 3. Interaction between prenatal adversities and adversities in childhood in relation to brain morphology
Main Effect: Prenatal
Main Effect: Adversities in
Childhood Interaction Effect
BSE p B SE p B SE p
Global metrics
Total brain volume −0.02 0.02 0.33 −0.10 0.03 0.002 0.04 0.03 0.15
Cortical grey matter volume −0.02 0.02 0.33 −0.10 0.03 0.001 0.04 0.03 0.08
Cerebral white matter volume −0.02 0.03 0.55 −0.09 0.03 0.01 0.02 0.03 0.40
Total cerebellar volume −0.03 0.03 0.22 −0.06 0.03 0.09 0.03 0.03 0.35
Subcortical metrics
Amygdala, mean volume 0 0.02 0.96 −0.02 0.03 0.43 0.03 0.02 0.24
Hippocampus, mean volume 0.01 0.02 0.56 0.01 0.03 0.69 0 0.02 0.94
Model is adjusted for child age at MRI scan, child sex, total intracranial volume (in subcortical metrics), maternal national origin, the highest education in the household,
maternal prenatal alcohol use, and maternal prenatal smoking. All brain outcomes were standardized. Adversities measures represent the cumulative number of events.
N = 2,172.
Fig. 1. Associations between prenatal and childhood adversities with the total brain volume.
: 2022, Volume 2 - 8 - CC-BY: © Cortes Hidalgo et al.
: 2022, Volume 2 - 8 -
volumes have been reported (11–13). In addition to the
methodological differences across studies, various hy-
potheses could underlie these mixed  ndings. The volu-
metric growth of the amygdala and hippocampus peaks
at around age 10 years (51); thus, different  ndings could
be expected between studies assessing brain morpholo-
gy during childhood, preadolescence, and at later ages.
The adversity severity may also in uence the results, and
the impact of early adversity in some structures may only
become apparent later in development (10). Further,
the amygdala (52) and hippocampus (53) show contin-
ued neurogenesis after fetal life, suggesting that these
regions could undergo plastic changes in response to
adversity and other environmental factors.
Our adversity measures were selected with a focus
on concrete environmental events that could generate
stress in the pregnant mother or the child and require
a substantial psychobiological adaptation (1). The cu-
mulative prenatal adversity measure was based on a
major life events inventory (30), similar to those includ-
ed in other population-based studies (54). Similarly, our
childhood adversity measure included events assessed
by key childhood adversities studies (21, 55), previously
shown to be associated with greater child psychopa-
thology (32). Different items were used in the prenatal
and childhood adversity measures, to focus on maternal
stressful events in the prenatal measure and on child-
hood adverse events in the latter measure. Consistent
with previous studies (54), the cumulative exposure to
prenatal adversities was related to the number of child-
hood adversities. Our additive approach to adversity
was based on the well-established “lumping” adversi-
ty framework (22). Although multiple alternatives have
been proposed to assign weights to the speci c adverse
events, based on factors like the severity, intensity, and
the timing of occurrence (22), there is no current con-
sensus. Future studies should examine the role of these
factors, and especially focus on the variability among
individual perceptions of adversity, which likely has a
unique in uence in the determination of the adversity
effects (22).
Our study has some limitations. First, we did not ac-
count for the age of occurrence of childhood adversities.
Although events at speci c ages could have different
effects on brain morphology, it is dif cult to determine
the exact period of occurrence of adversities that are
often chronic and variable (2). Second, mothers report-
ed childhood adversities at age 10 years, and thus, these
reports could be affected by recall bias. Nonetheless,
other methods to collect information on childhood ad-
versity in the general population, such as adolescent re-
ports, are limited by the accuracy in reporting early-life
events (11). Third, mothers of children without imaging
data were more often exposed to prenatal adversities
and were less often highly educated than mothers in
our study. Fourth, we did not examine national origin in
detail given the limited sample size for speci c groups.
pregnancy (25). Our study may have lacked suf cient
power to observe subtle effects. However, we assessed
a considerably larger sample than previous studies (2).
The brain can adapt in response to environmental effects
(10), which raises the question of whether brain postna-
tal volumetric changes could have obscured an associa-
tion between prenatal adversities and brain morphology.
Given a rich and positive childhood environment, the
brain development of children whose mothers experi-
enced stress in pregnancy could catch up and return to
the normative trajectory (25). If this were the case, pre-
natal adversities would be related to brain differences
earlier in life. However, we found no association between
prenatal events and HC in the last pregnancy trimester,
arguing against the plasticity hypothesis (25) (see also a
study from this cohort examining family dysfunction and
fetal HC (37)). It is also possible that the adversity type
and severity in uence the relation with brain morpholo-
gy. Whereas Jones, Dufoix (2) found a relation between
the gestational exposure to a natural disaster and amyg-
dala volumes, the cumulative exposure to a range of
more normative adverse events was not associated with
the global brain volume nor the amygdala and hippo-
campus in our study.
Numerous studies have examined childhood adver-
sity and brain morphology, but results are dif cult to
compare due to differences in the events assessed, the
age of occurrence of adversities, and the age at the MRI
assessment (10). Overall, research suggests that chil-
dren exposed to early-life adversity have smaller total
brain, gray and white matter, and cerebellar volumes
(10). Consistently, we observed that childhood adversi-
ty was related to smaller total brain volumes, and this
nding was robust to the adjustment for confounders.
Analyses with the gray and white matter volumes fur-
ther supported this association. Additionally, maternal
psychopathology did not explain nor modify the rela-
tionship between childhood adversity and these brain
outcomes. Our results might be interpreted as re ecting
a causal effect of adversity on child brain morphology,
but our analyses are based on an observational study
sample and a single MRI assessment, thus precluding
the inference of causality (49). Other explanations for
our  ndings are also possible. Importantly, genetic and
biological characteristics, such as psychological traits,
and genetic in uences on hormonal and neural path-
ways may underlie our  ndings. These factors are partly
heritable and simultaneously related to the exposure to
adversity (e.g. emotional abuse (50)), which could ex-
plain a noncausal link between early-life adversity and
child brain morphology.
Contrary to what we expected, childhood adversities
were not related to the limbic volumes. The amygdala
and hippocampus are of particular interest because they
have a high density of cortisol receptors and cortisol
in uences the neuronal development (7). Interestingly,
both larger and smaller amygdala and hippocampal
: 2022, Volume 2 - 9 - CC-BY: © Cortes Hidalgo et al.
: 2022, Volume 2 - 9 -
data transfer agreements, the data can be made avail-
able upon request. Interested researchers can direct their
requests to Vincent Jaddoe (v.jaddoe@erasmusmc.nl).
This work was supported by the Netherlands Organization
for Health Research and Development (ZonMw TOP
91211021), Spinoza prize (to MHvIJ) the National Institutes
of Health (F31HD096820), the Harry Frank Guggenheim
Foundation, the Dutch Ministry of Education, Culture,
and Science (NWO 024.001.003 and 016.VICI.170.200),
the Canadian Institutes of Health Research, the European
Research Council (ERC-AdG.669249), the China Scholar-
ship Council (201606100056), the Sophia Foun dation
(S18-20), NWO Physical Sciences Division and SURFsara.
The Generation R Study is supported by Erasmus MC, the
Erasmus University Rotterdam, ZonMw, NWO, and the
Ministry of Health, Welfare and Sport.
Tonya White is current Editor-in-Chief of Aperture, and
thus, an external review is necessary. All other authors
declare no con icts of interest.
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Additionally, we only included maternal national origin,
as we expected a potentially differential exposure to
prenatal adversities by the national origin of the preg-
nant mother in contrast to the biological father. Finally,
the prenatal adversity measure was based on informa-
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Our results support a cumulative association between
childhood adversities and brain morphology, previously
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with repeated MRI and adversity assessments, priority
should be given to intervention studies that determine
whether providing additional support to children follow-
ing periods of adversities will prevent the emergence of
brain differences.
All study protocols and the measurements assessed
in each wave of data collection were approved by the
Medical Ethical Committee of the Erasmus MC, University
Medical Center Rotterdam.
The datasets analyzed in this study are currently not pub-
licly available due to legal and ethical restraints due to the
General Data Protection Regulations (GDPR). However,
the consent has been altered for the current wave of data
collection that will provide the participants the option to
determine the extent that they want their data shared. Via
: 2022, Volume 2 - 10 - CC-BY: © Cortes Hidalgo et al.
: 2022, Volume 2 - 10 -
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Supplemental Information for Prenatal and Childhood Adverse Events
and Child Brain Morphology: A Population-Based Study
Brain Imaging
Reconstructed FreeSurfer images were visually examined for accuracy as described previously (1, 2). Eight trained and
reliable raters compared the white and pial surfaces against the brain image at several slices and in sagittal, coronal,
and axial planes and visually inspected for artifacts in the three-dimensional in ated and pial surface representations.
All brain images were rated on a three-point scale, and images considered of “poor” quality were excluded from
analyses. To ensure inter-rater reliability, training was initially performed with a standardized MRI set, and raters were
considered reliable if they rated a training MRI set correctly. The amygdala and hippocampal segmentation was visu-
ally inspected by Weeland, White (3) in a subset of 2,551 MRI scans, with less than 1% of the images deemed as poor
quality, suggesting a low rate of problematic amygdala and hippocampal segmentations in the present cohort study.
Alcohol consumption during pregnancy included four categories: “never during pregnancy,” “until pregnancy was
known,” “continued drinking occasionally in pregnancy,” and “continued drinking frequently in pregnancy.” Maternal
prenatal smoking was categorized into the following: “never during pregnancy,” “until pregnancy was known,” and
“continued in pregnancy.” Information on maternal and paternal education was collected by self-report during preg-
nancy and was classi ed following the Dutch standard classi cation of education (4). The highest education in the
household was included in the analyses.
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2. Muetzel RL, Mulder RH, Lamballais S, Cortes Hidalgo AP, Jansen P, Gürog˘ lu B, etal. Frequent Bullying Involvement and Brain Morphology in Children. Frontiers
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: 2022, Volume 2 - 12 - CC-BY: © Cortes Hidalgo et al.
: 2022, Volume 2 - 12 -
Table S1. Prevalence of prenatal adverse events
Event Prevalence, % N Exposed
Have you been a victim of robbery, theft, physical abuse or rape? 3.88 87
Have you suffered a substantial downturn in your  nancial situation? 14.5 325
Have you become unemployed? 8.97 201
Has your partner or other member of your family become unemployed? 6.51 146
Has one or more of your children been seriously ill? 1.52 34
Has your partner, or other family member, or one of your parents (in-law) been seriously ill? 11.6 260
Has one of your children died? 0.71 16
Has your partner died? 0.04 1
Has your father or mother (in-law), a brother or sister, or good friend died? 7.09 159
Have you had a divorce or broken off the relationship with your partner? 3.57 80
Any category reported 36.13 810
N = 2,242.
Children with available data for MRI and
prenataladverse events
Children with available data for MRI and
childhood adverse events
Children with brainT1-weighted MRI
scans at 9-11 years N=3966
Children with usable brain T1-weighted
MRI scans at 9-11 years N=3186
Children with reliable data for the TLE
-Scans that use ASSET aceleration N=22
-Children with braces N=88
-Children with incidental findings N=24
-Non-usable structural data N=646
-Children with no Traumatic Life Events Interview
(TLE) data N= 4
-Children with no reliable data for the TLE
interview N=65
- Excluding all siblings, but one
per sibling pair N=153
Children with complete data for the
childhood adverse events
Children with:
-No data for the selected TLE items N=27
-Incomplete data for the selected TLE items N=18
Children with complete data for the
prenatal adverse events
Children with available data for MRI and
prenatal and/or childhood adverse events
(with data on both exposures: N=2283)
Children with available data for MRI and
prenatal and/or childhood adverse events
(with data on both exposures: N=2172)
Children with incomplete data for the
prenatal adverse events
Children with no data for the prenatal
adverse events
Children with data available for the
prenatal adverse events
Prenatal adversities Childhood adversities
Fig. S1. Flowchart of sample selection.