O R I G I N A L R E S E A R C H A R T I C L E

Prenatal and Childhood Adverse Events

and Child Brain Morphology: A Population-Based

Study

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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ABSTRACT

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

INTRODUCTION

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

exposures.

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).

Measures

Adversities

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.

MATERIALS AND METHODS

Participants

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

Information).

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).

Covariates

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).

RESULTS

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.

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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.

DISCUSSION

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

Adversities

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.

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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

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: 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).

ACKNOWLEDGMENTS

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.

CONFLICTS OF INTEREST

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.

REFERENCES

1. McLaughlin KA, Weissman D, Bitrán D. Childhood Adversity and Neural

Development: A Systematic Review. Annual Review of Developmental

Psychology. 2019;1(1):277–312.

2. Jones SL, Dufoix R, Laplante DP, Elgbeili G, Patel R, Chakravarty MM,

et al. Larger Amygdala Volume Mediates the Association Between

Prenatal Maternal Stress and Higher Levels of Externalizing Behaviors: Sex

Speci c Effects in Project Ice Storm. Frontiers in Human Neuroscience.

2019;13(144).

3. Nelson III CA, Zeanah CH, Fox NA, Marshall PJ, Smyke AT, Guthrie D.

Cognitive Recovery in Socially Deprived Young Children: The Bucharest

Early Intervention Project. Science. 2007;318(5858):1937–40.

4. Davis EP, Narayan AJ. Pregnancy as a Period of Risk, Adaptation, and

Resilience for Mothers and Infants. Development and Psychopathology.

2020;32(5):1625–39.

5. Stiles J, Jernigan TL. The Basics of Brain Development. Neuropsychology

Review. 2010;20(4):327–48.

6. Nelson CA, III. The Implications of Early Adversity Even Before Birth. JAMA

Network Open. 2020;3(1):e1920030-e.

7. Franke K, Van den Bergh BRH, de Rooij SR, Kroegel N, Nathanielsz PW,

Rakers F, etal. Effects of Maternal Stress and Nutrient Restriction during

Gestation on Offspring Neuroanatomy in Humans. Neuroscience &

Biobehavioral Reviews. 2020;117:5–25.

8. Obel C, Hedegaard M, Henriksen TB, Secher NJ, Olsen J. Stressful Life

Events in Pregnancy and Head Circumference at Birth. Developmental

Medicine & Child Neurology. 2003;45(12):802–6.

9. Tegethoff M, Greene N, Olsen J, Meyer AH, Meinlschmidt G. Maternal

Psychosocial Adversity During Pregnancy Is Associated With Length of

Gestation and Offspring Size at Birth: Evidence from a Population-Based

Cohort Study. Psychosomatic Medicine. 2010;72(4):419–26.

10. Bick J, Nelson CA. Early Adverse Experiences and the Developing Brain.

Neuropsychopharmacology. 2016;41(1):177–96.

11. Roth MC, Humphreys KL, King LS, Gotlib IH. Self-reported Neglect,

Amygdala Volume, and Symptoms of Anxiety in Adolescent Boys. Child

Abuse & Neglect. 2018;80:80–89.

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-

tion collected when mothers were 20–25 weeks preg-

nant about adverse events that occurred in the preced-

ing 12 months. By including events that occurred before

pregnancy, we could have misclassi ed some women

who were not experiencing prenatal stress as exposed.

However, cumulative preconception adversities have also

been shown to predict poor offspring outcomes (56).

Additionally, events occurring after the 20- to 25-week

assessment (in the third trimester of pregnancy) were not

included, thus leading to a potential under-inclusion of

late prenatal adversities.

CONCLUSION

In conclusion, we found that the number of adversities

experienced by the mother during pregnancy was not

related to brain morphological differences in children

from the general population. Childhood adversities were

consistently associated with smaller brain volumes, with

alterations in both gray and white matter volumes. The

association between childhood adversities and the glob-

al brain volume was neither modi ed by maternal psy-

chopathology nor by the number of prenatal adversities.

Our results support a cumulative association between

childhood adversities and brain morphology, previously

described in small high-risk samples. If the adversity and

brain morphology relation is replicated in large samples

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.

ETHICS STATEMENT

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.

DATA AVAILABILITY

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

O R I G I N A L R E S E A R C H A R T I C L E

: 2022, Volume 2 - 10 -

12. Tupler LA, De Bellis MD. Segmented Hippocampal Volume in Children

and Adolescents with Posttraumatic Stress Disorder. Biological Psychiatry.

2006;59(6):523–9.

13. Hanson JL, Nacewicz BM, Sutterer MJ, Cayo AA, Schaefer SM, Rudolph

KD, etal. Behavioral Problems After Early Life Stress: Contributions of the

Hippocampus and Amygdala. Biological Psychiatry. 2015;77(4):314–23.

14. Walsh ND, Dalgleish T, Lombardo MV, Dunn VJ, Van Harmelen A-L, Ban

M, etal. General and Speci c Effects of Early-Life Psychosocial Adversities

on Adolescent Grey Matter Volume. NeuroImage: Clinical. 2014;4:308–18.

15. Luby JL, Barch D, Whalen D, Tillman R, Belden A. Association Between

Early Life Adversity and Risk for Poor Emotional and Physical Health in

Adolescence: A Putative Mechanistic Neurodevelopmental Pathway.

JAMA Pediatrics. 2017;171(12):1168–75.

16. Luby JL, Tillman R, Barch DM. Association of Timing of Adverse Childhood

Experiences and Caregiver Support With Regionally Speci c Brain

Development in Adolescents. JAMA Network Open. 2019;2(9):e1911426-e.

17. Bergink V, Larsen JT, Hillegers MHJ, Dahl SK, Stevens H, Mortensen PB,

etal. Childhood Adverse Life Events and Parental Psychopathology as Risk

Factors for Bipolar Disorder. Translational Psychiatry. 2016;6(10):e929-e.

18. Olson L, Chen B, Fishman I. Neural Correlates of Socioeconomic

Status in Early Childhood: A Systematic Review of the Literature. Child

Neuropsychology. 2021;27(3):390–423.

19. Amso D, Lynn A. Distinctive Mechanisms of Adversity and Socioeconomic

Inequality in Child Development: A Review and Recommendations for

Evidence-Based Policy. Policy Insights from the Behavioral and Brain

Sciences. 2017;4(2):139–46.

20. Mick E, Biederman J, Faraone SV, Sayer J, Kleinman S. Case-Control Study

of Attention-De cit Hyperactivity Disorder and Maternal Smoking, Alcohol

Use, and Drug Use During Pregnancy. Journal of the American Academy of

Child & Adolescent Psychiatry. 2002;41(4):378–85.

21. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, etal.

Relationship of Childhood Abuse and Household Dysfunction to Many of

the Leading Causes of Death in Adults: The Adverse Childhood Experiences

(ACE) Study. American Journal of Preventive Medicine. 1998;14(4):245–58.

22. Smith KE, Pollak SD. Rethinking Concepts and Categories for

Understanding the Neurodevelopmental Effects of Childhood Adversity.

Perspectives on Psychological Science. 2021;16(1):67–93.

23. McLaughlin KA, Sheridan MA. Beyond Cumulative Risk: A Dimensional

Approach to Childhood Adversity. Current Directions in Psychological

Science. 2016;25(4):239–45.

24. Sheridan MA, Fox NA, Zeanah CH, McLaughlin KA, Nelson CA. Variation

in Neural Development as a Result of Exposure to Institutionalization

Early in Childhood. Proceedings of the National Academy of Sciences.

2012;109(32):12927–32.

25. White TJH. Brain Development and Stochastic Processes During Prenatal

and Early Life: You Can’t Lose It if You’ve Never Had It; But It’s Better

to Have It and Lose It, Than Never to Have Had It at All. Journal of the

American Academy of Child & Adolescent Psychiatry. 2019;58(11):1042–50.

26. Edmiston EE, Wang F, Mazure CM, Guiney J, Sinha R, Mayes LC, etal.

Corticostriatal-Limbic Gray Matter Morphology in Adolescents with Self-

reported Exposure to Childhood Maltreatment. Archives of Pediatrics &

Adolescent Medicine. 2011;165(12):1069–77.

27. McLaughlin KA, Sheridan MA, Winter W, Fox NA, Zeanah CH, Nelson

CA. Widespread Reductions in Cortical Thickness Following Severe Early-

Life Deprivation: A Neurodevelopmental Pathway to Attention-De cit

/Hyperactivity Disorder. Biological Psychiatry. 2014;76(8):629–38.

28. Jaddoe VWV, van Duijn CM, Franco OH, van der Heijden AJ, van IJzendoorn

MH, de Jongste JC, et al. The Generation R Study: Design and Cohort

Update 2012. European Journal of Epidemiology. 2012;27(9):739–56.

29. White TJH, Muetzel RL, El Marroun H, Blanken LME, Jansen P, Bolhuis K,

etal. Paediatric Population Neuroimaging and the Generation R Study: the

Second Wave. European Journal of Epidemiology. 2018;33(1):99–125.

30. Miller MA, Rahe RH. Life Changes Scaling for the 1990s. Journal of

Psychosomatic Research. 1997;43(3):279–92.

31. Molenaar NM, Tiemeier H, van Rossum EFC, Hillegers MHJ, Bockting CLH,

Hoogendijk WJG, et al. Prenatal Maternal Psychopathology and Stress

and Offspring HPA Axis Function at 6 Years. Psychoneuroendocrinology.

2019;99:120–27.

32. Dunn EC, Nishimi K, Neumann A, Renaud A, Cecil CAM, Susser ES, etal.

Time-dependent Effects of Exposure to Physical and Sexual Violence on

Psychopathology Symptoms in Late Childhood: In Search of Sensitive

Periods in Development. Journal of the American Academy of Child &

Adolescent Psychiatry. 2020;59(2):283–95.

33. Amone-P’Olak K, Ormel J, Huisman M, Verhulst FC, Oldehinkel AJ, Burger

H. Life Stressors as Mediators of the Relation Between Socioeconomic

Position and Mental Health Problems in Early Adolescence: The TRAILS

Study. Journal of the American Academy of Child & Adolescent Psychiatry.

2009;48(10):1031–38.

34. Brown GW, Harris T. Social Origins of Depression: A Study of Psychiatric

Disorder in Women. London: Tavistock; 1978.

35. Fischl B. FreeSurfer. NeuroImage. 2012;62(2):774–81.

36. Muetzel RL, Blanken LME, Ende JVD, Marroun HE, Shaw P, Sudre G, etal.

Tracking Brain Development and Dimensional Psychiatric Symptoms in

Children: A Longitudinal Population-Based Neuroimaging Study. American

Journal of Psychiatry. 2018;175(1):54–62.

37. Henrichs J, Schenk JJ, Roza SJ, van den Berg MP, Schmidt HG, Steegers

EAP, etal. Maternal Psychological Distress and Fetal Growth Trajectories:

The Generation R Study. Psychological Medicine. 2010;40(4):633–43.

38. Jaddoe VWV, Verburg BO, de Ridder MAJ, Hofman A, Mackenbach JP,

Moll HA, et al. Maternal Smoking and Fetal Growth Characteristics in

Different Periods of Pregnancy: The Generation R Study. American Journal

of Epidemiology. 2007;165(10):1207–15.

39. Verburg BO, Steegers EAP, De Ridder M, Snijders RJM, Smith E, Hofman

A, etal. New Charts for Ultrasound Dating of Pregnancy and Assessment

of Fetal Growth: Longitudinal Data from a Population-Based Cohort Study.

Ultrasound in Obstetrics & Gynecology. 2008;31(4):388–96.

40. Silva LM, Jansen PW, Steegers EAP, Jaddoe VWV, Arends LR, TiemeierH,

etal. Mother’s Educational Level and Fetal Growth: The Genesis of Health

Inequalities. International Journal of Epidemiology. 2010;39(5):1250–61.

41. Verburg BO, Mulder PGH, Hofman A, Jaddoe VWV, Witteman JCM,

Steegers EAP. Intra- and Interobserver Reproducibility Study of Early Fetal

Growth Parameters. Prenatal Diagnosis. 2008;28(4):323–31.

42. Statistical Yearbook of the Netherlands 2004. Voorburg, the Netherlands:

Statistics Netherlands; 2004.

43. Troe E, Raat H, Jaddoe VWV, Hofman A, Looman CWN, Moll HA, et al.

Explaining Differences in Birthweight between Ethnic Populations. The

Generation R Study. BJOG: An International Journal of Obstetrics &

Gynaecology. 2007;114(12):1557–65.

44. Derogatis LR. Brief Symptom Inventory (BSI): Administration, Scoring and

Procedures Manual. 3rd ed. Minneapolis, MN: National Computer Systems;

1993.

45. Lenroot RK, Giedd JN. Brain Development in Children and Adolescents:

Insights from Anatomical Magnetic Resonance Imaging. Neuroscience &

Biobehavioral Reviews. 2006;30(6):718–29.

46. Cheng YKY, Leung TY, Lao TTH, Chan YM, Sahota DS. Impact of Replacing

Chinese Ethnicity-Speci c Fetal Biometry Charts with the INTERGROWTH-

21st Standard. BJOG: An International Journal of Obstetrics & Gynaecology.

2016;123(S3):48–55.

47. R Core Team. R: A language and Environment for Statistical Computing.

Vienna, Austria: R Foundation for Statistical Computing; 2020.

48. van Buuren S, Groothuis-Oudshoorn K. Mice: Multivariate Imputation by

Chained Equations in R. Journal of Statistical Software. 2011;45(3):67.

49. Hamaker EL, Mulder JD, van IJzendoorn MH. Description, Prediction and

Causation: Methodological Challenges of Studying Child and Adolescent

Development. Developmental Cognitive Neuroscience. 2020;46:100867.

50. Pittner K, van IJzendoorn MH, Alink LRA, Buisman RSM, Compier-de

Block LHCGC, van den Berg LJM, etal. The Genetic and Environmental

Etiology of Child Maltreatment in a Parent-Based Extended Family Design.

Development and Psychopathology. 2019;31(1):157–72.

51. Uematsu A, Matsui M, Tanaka C, Takahashi T, Noguchi K, Suzuki M, etal.

Developmental Trajectories of Amygdala and Hippocampus from Infancy

to Early Adulthood in Healthy Individuals. PLOS ONE. 2012;7(10):e46970.

52. Jhaveri DJ, Tedoldi A, Hunt S, Sullivan R, Watts NR, Power JM, et al.

Evidence for Newly Generated Interneurons in the Basolateral Amygdala

of Adult Mice. Molecular Psychiatry. 2018;23(3):521–32.

53. Imayoshi I, Sakamoto M, Ohtsuka T, Takao K, Miyakawa T, Yamaguchi

M, et al. Roles of Continuous Neurogenesis in the Structural and

Functional Integrity of the Adult Forebrain. Nature Neuroscience. 2008;

11(10):1153–61.

54. Jensen SKG, Pangelinan M, Björnholm L, Klasnja A, Leemans A, Drakesmith

M, etal. Associations between Prenatal, Childhood, and Adolescent Stress

and Variations in White-Matter Properties in Young Men. NeuroImage.

2018;182:389–97.

55. Dong M, Anda RF, Felitti VJ, Dube SR, Williamson DF, Thompson TJ, etal.

The Interrelatedness of Multiple Forms of Childhood Abuse, Neglect,

and Household Dysfunction. Child Abuse & Neglect. 2004;28(7):771–84.

56. Witt WP, Cheng ER, Wisk LE, Litzelman K, Chatterjee D, Mandell K, etal.

Maternal Stressful Life Events Prior to Conception and the Impact on Infant

Birth Weight in the United States. American Journal of Public Health.

2014;104(S1):S81–S9.

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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.

Covariates

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.

Additional References

1. Muetzel RL, Blanken LME, Ende Jvd, Marroun HE, Shaw P, Sudre G, et al. Tracking Brain Development and Dimensional Psychiatric Symptoms in Children:

ALongitudinal Population-Based Neuroimaging Study. American Journal of Psychiatry. 2018;175(1):54–62.

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

in Psychiatry. 2019;10(696).

3. Weeland CJ, White T, Vriend C, Muetzel RL, Starreveld J, Hillegers MHJ, etal. Brain Morphology Associated With Obsessive-Compulsive Symptoms in 2,551

Children from the General Population. Journal of the American Academy of Child & Adolescent Psychiatry. 2021;60(4):470–78.

4. Statistics Netherlands. Standaard Onderwijsindeling 2003 Voorburg/Heerlen, the Netherlands: Statistics Netherlands (Centraal Bureau voor de Statistiek);

2005 [Available from: https://www.cbs.nl/nl-nl/onze-diensten/methoden/classi caties/onderwijs-en-beroepen/standaard-onderwijsindeling--soi--/standaard

-onderwijsindeling-2003].

O R I G I N A L R E S E A R C H A R T I C L E

: 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

N=2242

Children with available data for MRI and

childhood adverse events

N=2923

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

interviewN=3117

Excludedchildren:

-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

N=3072

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

N=2357

Children with available data for MRI and

prenatal and/or childhood adverse events

N=3146

(with data on both exposures: N=2283)

Children with available data for MRI and

prenatal and/or childhood adverse events

N=2993

(with data on both exposures: N=2172)

Children with incomplete data for the

prenatal adverse events

N=110

Children with no data for the prenatal

adverse events

N=719

Children with data available for the

prenatal adverse events

N=2467

Prenatal adversities Childhood adversities

Fig. S1. Flowchart of sample selection.