Sleep duration, brain structure, and psychiatric and cognitive problems in children

Abstract

Low sleep duration in adults is correlated with psychiatric and cognitive problems. We performed for the first time a large-scale analysis of sleep duration in children, and how this relates to psychiatric problems including depression, to cognition, and to brain structure. Structural MRI was analyzed in relation to sleep duration, and psychiatric and cognitive measures in 11,067 9–11-year-old children from the Adolescent Brain Cognitive Development (ABCD) Study, using a linear mixed model, mediation analysis, and structural equation methods in a longitudinal analysis. Dimensional psychopathology (including depression, anxiety, impulsive behavior) in the children was negatively correlated with sleep duration. Dimensional psychopathology in the parents was also correlated with short sleep duration in their children. The brain areas in which higher volume was correlated with longer sleep duration included the orbitofrontal cortex, prefrontal and temporal cortex, precuneus, and supramarginal gyrus. Longitudinal data analysis showed that the psychiatric problems, especially the depressive problems, were significantly associated with short sleep duration 1 year later. Further, mediation analysis showed that depressive problems significantly mediate the effect of these brain regions on sleep. Higher cognitive scores were associated with higher volume of the prefrontal cortex, temporal cortex, and medial orbitofrontal cortex. Public health implications are that psychopathology in the parents should be considered in relation to sleep problems in children. Moreover, we show that brain structure is associated with sleep problems in children, and that this is related to whether or not the child has depressive problems.

Full text

Molecular Psychiatry
https://doi.org/10.1038/s41380-020-0663-2
ARTICLE
Sleep duration, brain structure, and psychiatric and cognitive
problems in children
Wei Cheng1,2 ●Edmund Rolls 1,3,4 ●Weikang Gong5●Jingnan Du1,2 ●Jie Zhang1,2 ●Xiao-Yong Zhang 1,2 ●Fei Li6●
Jianfeng Feng1,2,3
Received: 15 September 2019 / Revised: 11 December 2019 / Accepted: 23 January 2020
© The Author(s), under exclusive licence to Springer Nature Limited 2020
Abstract
Low sleep duration in adults is correlated with psychiatric and cognitive problems. We performed for the first time a large-scale
analysis of sleep duration in children, and how this relates to psychiatric problems including depression, to cognition, and to
brain structure. Structural MRI was analyzed in relation to sleep duration, and psychiatric and cognitive measures in 11,067
9–11-year-old children from the Adolescent Brain Cognitive Development (ABCD) Study, using a linear mixed model,
mediation analysis, and structural equation methods in a longitudinal analysis. Dimensional psychopathology (including
depression, anxiety, impulsive behavior) in the children was negatively correlated with sleep duration. Dimensional
psychopathology in the parents was also correlated with short sleep duration in their children. The brain areas in which higher
volume was correlated with longer sleep duration included the orbitofrontal cortex, prefrontal and temporal cortex, precuneus,
and supramarginal gyrus. Longitudinal data analysis showed that the psychiatric problems, especially the depressive problems,
were significantly associated with short sleep duration 1 year later. Further, mediation analysis showed that depressive problems
significantly mediate the effect of these brain regions on sleep. Higher cognitive scores were associated with higher volume of
the prefrontal cortex, temporal cortex, and medial orbitofrontal cortex. Public health implications are that psychopathology in
the parents should be considered in relation to sleep problems in children. Moreover, we show that brain structure is associated
with sleep problems in children, and that this is related to whether or not the child has depressive problems.
Introduction
Sleep is necessary for maintaining cognitive and emotional
abilities. Lack of sleep has been associated with health-
related and cognitive consequences [1–4]. Population-based
studies show that individuals with short sleep duration or
insomnia are at significantly greater risk for cerebrovascular
diseases [5], mental disorders [6,7], and metabolic disorders
[8,9]. A meta-analysis of 91 studies found that sleep
These authors contributed equally: Wei Cheng, Edmund Rolls,
Weikang Gong
*Wei Cheng
wcheng@fudan.edu.cn
*Edmund Rolls
Edmund.Rolls@oxcns.org
*Jianfeng Feng
jianfeng64@gmail.com
1Institute of Science and Technology for Brain-Inspired
Intelligence, Fudan University, Shanghai 200433, China
2Key Laboratory of Computational Neuroscience and Brain-
Inspired Intelligence, Fudan University, Ministry of Education,
Shanghai 200433, China
3Department of Computer Science, University of Warwick,
Coventry CV4 7AL, UK
4Oxford Centre for Computational Neuroscience, Oxford, UK
5Centre for Functional MRI of the Brain (FMRIB), Nuffield
Department of Clinical Neurosciences, Wellcome Centre for
Integrative Neuroimaging, University of Oxford, Oxford, UK
6Department of Developmental and Behavioral Pediatric and Child
Primary Care/MOE-Shanghai Key Laboratory of Children’s
Environmental Health, Xin Hua Hospital Affiliated to Shanghai
Jiao Tong University School of Medicine, Shanghai, China
Supplementary information The online version of this article (https://
doi.org/10.1038/s41380-020-0663-2) contains supplementary
material, which is available to authorized users.
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alterations, such as sleep depth and altered rapid eye
movement sleep, were present in many mental disorders
including schizophrenia, anxiety, affective, eating, and
autistic disorders [10]. A previous study in adults revealed a
high association between sleep quality and the depressive
problems score that was mediated by functional con-
nectivities involving the lateral orbitofrontal cortex, anterior
cingulate cortices, hippocampus, and precuneus [7]. More-
over, the impact of sleep and insomnia on brain structure and
function is striking. For example, the functional connectivity
in the resting state of the default mode, dorsal attention, and
hippocampal networks was different after sleep deprivation.
[11]. In adults aged ≥55 years, self-reported short sleep
duration was associated with faster expansion of the ven-
tricles and faster decline in cognitive performance in the
following 2 years [12]. However, most studies on the rela-
tion between sleep and brain structure and function have
used relatively small numbers of participants (e.g., 1972
participants in 24 studies in a recent meta-analysis [13]),
indicating the need for a robust large-scale study such as the
present one that included 11,067 participants.
Sleep states are active processes that may function to
support reorganization of brain circuitry [14], which makes
sleep especially important for children at this stage of brain
development. A recent meta-analysis showed a high corre-
lation between sleep and cognitive functioning and beha-
vioral problems in school-age children. Shorter sleep
duration was associated with poorer cognitive functioning
and worse school performance, and also was related to
behavioral problems [15]. Although many studies have
identified associations between sleep and cognitive perfor-
mance and adverse mental health outcomes in children, only
a few imaging studies have examined the association
between low sleep duration and brain structure [16] and
development [17,18], and the neural mechanisms under-
lying the association between sleep and cognition and
mental health in children are still unknown.
A large-scale multimodal neuroimaging study tracking
over 11,000 children in the Adolescent Brain Cognitive
Development (ABCD) cohort provides an unprecedented
opportunity to investigate the neural differences in children
with short sleep duration, and the possible neural mechan-
isms underlying the association between sleep and various
mental health disorders and cognitive performance [19].
The ABCD dataset has been shown useful in investigating
the relationship between children’s behavior and brain
structure [20,21]. In the present investigation, we analyzed
in the ABCD cohort the relation between brain structure and
sleep duration, cognitive performance and mental health by
measuring brain morphometric information of different
brain regions, and then using a mediation analysis to assess
the underlying mechanisms. The main hypothesis that we
investigate is that the relation between brain structure and
sleep is mediated by psychiatric problems related to brain
structure. The second hypothesis that we investigate in
another mediation analysis is the extent to which the rela-
tion between brain structure and cognition is mediated by
sleep duration. Strengths of the present investigation are the
longitudinal data with a large number of child participants
(baseline 11,067; 1-year follow-up 4696) leading to robust
findings; the mediation analysis; linking the findings to
recent advances in understanding brain mechanisms related
to mental disorders, cognition, and sleep; and the inherent
interest of the relation between sleep, cognition, and psy-
chiatric problems to a wide readership.
Materials and methods
Participants and data preprocessing
The dataset used for this investigation was selected from the
Annual Curated Data Release 2.01 from the ABCD con-
sortium (https://abcdstudy.org/index.html) which contains
over 11,000 children aged 9–11 years recruited from 21
centers throughout the United States with a diverse range of
geographic, socioeconomic, ethnic, and health backgrounds
[19,22]. The 21 centers obtained parents’full written
informed consent and the childrens' assent, and research
procedures and ethical guidelines were followed in accor-
dance with the Institutional Review Boards (IRB). Our
sample includes 11,067 subjects (ages 9–11 years, 5301
females) scanned with three 3 Tesla (T) scanner platforms:
Siemens Prisma, General Electric 750 and Phillips. More
details of the subjects, and the collection and preprocessing
parameters of the data are provided at the ABCD website
(https://abcdstudy.org/scientists/protocols/) and also are
described elsewhere [19,22].
We obtained preprocessed structural imaging data (T1 and
T2) using the ABCD pipeline, with all the data preprocessing
procedures performed by the ABCD team as described in
their image processing paper [22] and in the Supplementary
Material. Morphometric measures consisting of the cortical
area, volume, and thickness of 68 cortical regions [23]and
40 subcortical regions [24] from the ABCD preprocessing
were used in the following analyses. The imaging data were
only available for baseline (11,067 participants).
The quality control of the processed images was done by
the ABCD team and 481 participants who failed to pass the
quality control of the ABCD team were removed from
subsequent analysis. In addition, three participants who did
not contain the full information of both the sleep behavior
measures and the structural images were excluded. Finally,
11,067 participants were left for this study and the demo-
graphic characteristics of these participants are summarized
in Table 1.
W. Cheng et al.

Table 1 The demographic characteristics of the ABCD participants.
Basic information
Age (month) Gender (Male/Female) BMI Parents income Parents education Puberty Race (White/Black/Indian/Others)
119.0 ± 7.47 5766/5301 18.79 ± 4.17 7.56 ± 2.53 16.63 ± 2.75 1.61 ± 0.49 8279/2298/366/124
Cognitive measurement
nihtbx_picvocab nihtbx_flanker nihtbx_list nihtbx_cardsort nihtbx_pattern nihtbx_picture nihtbx_reading
84.55 ± 8.10 94.09 ± 9.05 96.80 ± 12.00 92.61 ± 9.45 88.18 ± 14.56 102.94 ± 12.08 90.91 ± 6.87
nihtbx_fluidcomp nihtbx_cryst nihtbx_totalcomp
91.70 ± 10.58 86.43 ± 7.04 86.36 ± 9.08
Psychiatric problem measurement
cbcl_scr_syn_anxdep cbcl_scr_syn_withdep cbcl_scr_syn_somatic cbcl_scr_syn_social cbcl_scr_syn_thought cbcl_scr_syn_attention cbcl_scr_syn_rulebreak
2.52 ± 3.08 1.03 ± 1.71 1.50 ± 1.96 1.61 ± 2.27 1.61 ± 2.19 2.94 ± 3.47 1.18 ± 1.84
cbcl_scr_syn_aggressive cbcl_scr_syn_internal cbcl_scr_syn_external cbcl_scr_syn_totprob cbcl_scr_dsm5_depress cbcl_scr_dsm5_anxdisord cbcl_scr_dsm5_somaticpr
3.25 ± 4.33 5.05 ± 5.54 4.43 ± 5.82 18.09 ± 17.89 1.26 ± 2.01 2.06 ± 2.44 1.09 ± 1.51
cbcl_scr_dsm5_adhd cbcl_scr_dsm5_opposit cbcl_scr_dsm5_conduct cbcl_scr_07_sct cbcl_scr_07_ocd cbcl_scr_07_stress
2.59 ± 2.95 1.76 ± 2.03 1.27 ± 2.34 0.52 ± 1.00 1.34 ± 1.82 2.89 ± 3.35
nihtbx_picvocab NIH Toolbox Picture Vocabulary Test Age 3+v2.0 Uncorrected Standard Score, nihtbx_flanker NIH Toolbox Flanker Inhibitory Control and Attention Test Ages 8–11 v2.0
Uncorrected Standard Score, nihtbx_list NIH Toolbox List Sorting Working Memory Test Age 7+v2.0 Uncorrected Standard Score, nihtbx_cardsort NIH Toolbox Dimensional Change Card
Sort Test Ages 8–11 v2.0 Uncorrected Standard Score, nihtbx_pattern NIH Toolbox Pattern Comparison Processing Speed Test Age 7+v2.0 Uncorrected Standard Score, nihtbx_picture NIH
Toolbox Picture Sequence Memory Test Age 8+Form A v2.0 Uncorrected Standard Score, nihtbx_reading NIH Toolbox Oral Reading Recognition Test Age 3+v2.0 Uncorrected Standard
Score, nihtbx_fluidcomp Cognition Fluid Composite Uncorrected Standard Score, nihtbx_cryst Crystallized Composite Uncorrected Standard Score, nihtbx_totalcomp Cognition Total Composite
Score Uncorrected Standard Score, cbcl_scr_syn_anxdep Anxious/Depressed CBCL Syndrome Scale, cbcl_scr_syn_withdep Withdrawn/Depressed CBCL Syndrome Scale, cbcl_scr_syn_so-
matic Somatic Complaints CBCL Syndrome Scale, cbcl_scr_syn_social Social Problems CBCL Syndrome Scale, cbcl_scr_syn_attention Attention Problems CBCL Syndrome Scale,
cbcl_scr_syn_rulebreak Rule-Breaking Behavior CBCL Syndrome Scale, cbcl_scr_syn_aggressive Aggressive Behavior CBCL Syndrome Scale, cbcl_scr_syn_internal Internalizing Problems
CBCL Syndrome Scale, cbcl_scr_syn_external Externalizing Problems CBCL Syndrome Scale, cbcl_scr_dsm5_depress Depressive Problems CBCL DSM-5 Scale, cbcl_scr_dsm5_anxdisord
Anxiety Problems CBCL DSM-5 Scale, cbcl_scr_dsm5_somaticpr Somatic Problems CBCL DSM-5 Scale, cbcl_scr_dsm5_adhd ADHD CBCL DSM-5 Scale, cbcl_scr_dsm5_opposit
Oppositional Defiant Problems CBCL DSM-5 Scale, cbcl_scr_dsm5_conduct Conduct Problems CBCL DSM-5 Scale, cbcl_scr_07_sct Sluggish Cognitive Tempo (SCT) CBCL Scale2007 Scale,
cbcl_scr_07_ocd Obsessive-Compulsive Problems (OCD) CBCL Scale2007 Scale, cbcl_scr_07_stress Stress Problems CBCL Scale2007 Scale.
Sleep duration, brain structure, and psychiatric and cognitive problems in children

Behavioral measures
Sleep assessments
The sleep disturbances scale for children is based on the
ABCD Parent Sleep Disturbance Scale for Children
(abcd_sds01) which is useful for assessing the sleep–wake
rhythm of a child and of any problems in his/her sleep
behavior [25]. In the current study, we focus on an impor-
tant measure of sleep, sleep duration, which is question 1 of
the above scale, namely "How many hours of sleep does
your child get on most nights" (with further details in the
Supplementary Material). We note that a high sleep dura-
tion score in this measurement system indicates short sleep
duration. In this paper, we use “sleep duration”as the
descriptor, where a high sleep duration indicates a large
number of hours of sleep. The data were available for
baseline (11,067 participants) and 1-year follow-up (4696
participants).
Cognitive assessments
To investigate the relationship between sleep duration and
cognitive performance, the NIH Cognition Battery Toolbox
(abcd_tbss01) was used to evaluate the cognitive perfor-
mance of children [26]. The summary score of the NIH
Cognition Battery Toolbox contains seven components:
language vocabulary knowledge, attention, cognitive con-
trol, working memory, executive function, episodic mem-
ory, and language. These data were only available for
baseline (11,067 participants).
Mental health assessments
We also investigated the relationship between sleep dura-
tion and psychiatric problems. The Parent Child Behavior
Checklist Scores (abcd_cbcls01) were used to assess the
dimensional psychopathology and adaptive functioning in
children [27]. It contains ten empirically-based syndrome
scales related to psychiatric problems: anxious/depressed,
withdrawn/depressed, somatic complaints, social problems,
thought problems, attention problems, rule-breaking beha-
vior, aggressive behavior, internalizing broad band score
and externalizing broad band score; and a psychiatric pro-
blems total score. Six DSM-Oriented Scales were also
calculated based on the questionnaire. A high score indi-
cates dimensional psychopathology. The data were avail-
able for baseline (11,067 participants) and 1-year follow-up
(4696 participants).
More details of these behavior assessments are provided
in the Supplementary Material (Table S1) and also can be
found at the ABCD website (https://abcdstudy.org/
scientists-protocol.html).
Statistical analysis
Association analysis
A linear mixed-effect model (LMM) was used to test the
associations of the sleep duration with the brain morpho-
metric measures and with the children’s cognitive scores
from the NIH Cognitive Toolbox and the psychiatric pro-
blems scores from the Child Behavior Checklist noted
above that are provided by ABCD. As recommended by the
ABCD and used in many studies [21,28], a LMM was used
to take account of the correlated observations within
families due to twins and siblings and at sites. In this way,
the LMM was specified to model family nested within site.
The LMM was implemented using the MATLAB function
fitlme. A morphometry measurement or behavioral score
was modeled as the dependent variable, and the sleep
duration and the nuisance covariates were modeled as fixed
effects, while the family structures nested within sites were
modeled as random effects. To ensure that the following
variables did not influence the results, they were used in the
LMM as covariates of no interest: children’s age, sex, body
mass index, puberty score, race (coded as 3-column dummy
variables), and parents’income and number of years of
education. A t-statistic and the effect size Cohen's dwere
obtained for each LMM model to reflect the association
between sleep duration and the dependent variable. Finally,
false discovery rate (FDR) and Bonferroni corrections to
correct the results for multiple comparisons were performed
[29]. All brain measurements and behavioral variables used
in the association analysis were collected at the ABCD
baseline time when the average age was 119 months.
Mediation analysis
A standard mediation analysis was performed using the
Mediation Toolbox developed by Tor Wager’s group
(https://github.com/canlab/MediationToolbox), which has
been widely used in neuroimaging studies [30,31]. A
standard three-variable path model was used here [32], with
the detailed methodology described in the supplementary
material of [30]. Briefly, mediation analysis tests whether
the covariance between two variables can be explained by a
third variable (the mediator). The two hypotheses investi-
gated the relation of cortical morphometry with psychiatric
and cognitive problems and sleep duration. For the first
hypothesis, the independent (predictor) variable was a
morphometry measure and the dependent (predicted) vari-
able was sleep duration. The proposed mediator (in the
indirect path) was the depressive score. (The proposed
mediator in another test in the Supplementary Material was
the psychiatric problems total score.) For the second
hypothesis, the independent (predictor) variable was a
W. Cheng et al.

morphometric measure and the dependent (predicted) vari-
able was the Cognition Total Composite Score. The pro-
posed mediator was sleep duration. Confounding variables
as in the association analysis were regressed out in the
mediation model. The significance of the mediation was
estimated by the bias-corrected bootstrap approach (with
10,000 random samplings). All brain measurements and
behavioral variables used in the mediation analysis were
collected at the ABCD baseline time. We note that sig-
nificant mediation effects show how much of the correlation
between independent and dependent variables is related to
another variable termed the mediator, and is strictly a
measure of association, which does not prove causality.
Longitudinal data analysis
For the above-mentioned measurements, it was possible to
perform a longitudinal analysis for approximately half the
participants (4696) using the sleep duration and psychiatric
problems scores which were obtained in the follow-up, 1 year
after the baseline time. The neuroimaging was available only
at the baseline time. A classic two-wave cross-lagged panel
model (CLPM, implemented by Mplus (version 7.4) [33])
based on structural equation modeling was implemented to
investigate the longitudinal associations between sleep dura-
tion and each of the psychiatric problems scores, using the
longitudinal panel data in ABCD [34,35]. Specifically, let xt
and ytbe the demeaned baseline sleep duration score and a
psychiatric problem score of a subject, and xt+1and yt+1be
their 1-year follow-up. The CLPM models them as
xtþ1¼αtxtþβtytþη1ztþε1
ytþ1¼δtytþγtxtþη2ztþε2;
where αt,βt,δt,γt,η1, and η2are the coefficients of the
model, ztis the confounding variable, and ε1and ε2are the
error term. The model was estimated by using maximum
likelihood estimation with robust standard errors that also
take clustering of cases into account. The standardized
regression coefficients and their standard errors of variables
of interest (namely αt,βt,δt,γt) are reported throughout.
Confounding variables, as in the association analysis, were
regressed out in the CLPM analysis.
Results
The correlation between sleep duration and other
measures in the ABCD dataset
The dimensional psychopathology measures (abcd_cbcl01,
abcd_ksad501, abcd_ksad01) were significantly negatively
correlated with the sleep duration (that is, pathology was
positively associated with short sleep durations) (Fig. 1and
Table 2). 82 out of 119 individual items (119 questions in
the CBCL questionnaire, abcd_cbcl01) were significant
after Bonferroni correction (p< 0.05). Indeed, all the psy-
chiatry problems scores (abcd_cbcls01) were negatively
correlated with the sleep duration with rvalues ranging
from −0.062 to −0.208 (Bonferroni corrected, p< 0.05,
Table 2and Fig. 1c). The depressive problems score was the
score most significantly negatively correlated with the sleep
duration (r=−0.208, Cohen's d=−0.425, p<1×10
−10).
Thus the children with shorter sleep duration tended to have
higher psychiatric problems scores. What is also interesting
is that many parent dimensional psychopathology measures
(including abcd_asrs01 and pasr01) were significantly
negatively correlated with the sleep duration of the child. As
shown in Fig. 1a, the screen time utilization (e.g., the use of
mobile phones, TV, internet, and video games, abcd_stq01)
was negatively correlated with sleep duration, which is
consistent with previous studies [36,37]. Interestingly, the
environmental risk (abcd_rhds01) related measures were
also correlated with sleep duration, suggesting that a sense
of safety (low risk) may be associated with children’s sleep
duration. These, and also the other sleep component scores
(abcd_sds01), were negatively correlated with sleep dura-
tion with rvalues ranging from −0.07 to −0.21 (Bonferroni
corrected, p< 0.05). In addition, subsyndromal mania
(abcd_pgbi01); school, family, and social relations (dibf01);
monitoring of children's functioning (abcd_ssbpmtf01); and
cultural values (macv01) were negatively correlated with
sleep duration in the children (Fig. 1a).
Eight out of ten of the cognitive summary scores
(abcd_tbss01) were positively correlated with the sleep
duration (Bonferroni corrected, p< 0.05, Fig. 1b). The
range of rvalues between the sleep duration and cognitive
measurements was between 0.032 and 0.069, and these
were highly significant (Table 2and Fig. 1b). Further, 15
out of 49 individual items within neurocognition
(abcd_tbss01) were positively correlated with long sleep
durations (Bonferroni corrected, p< 0.05, Fig. 1a and
Table 2). Thus children with long sleep duration have better
cognitive performance.
Short sleep duration, poor cognition, and
psychiatric problems are associated with low cortical
area and volume
Both cortical area and volume of the whole brain were
significantly positively correlated with sleep duration (r=
0.046, Cohen's d=0.093, p=1.0 × 10−6and r=0.047,
d=0.095, p=6.2 × 10−7, respectively). Specifically, in
children with short sleep, the cortical areas/volumes were
lower of the lateral and medial orbitofrontal cortex, superior
Sleep duration, brain structure, and psychiatric and cognitive problems in children

and middle frontal gyrus and medial superior frontal,
inferior and middle temporal gyrus, precuneus and posterior
cingulate cortex, ventromedial prefrontal cortex, supramar-
ginal gyrus, and some motor cortical areas (FDR corrected,
p< 0.005, Figs. 2a and S1A), and volumes were lower of
the thalamus, caudate, and the pallidum (FDR corrected,
p< 0.005, Table S2). (Cortical thickness was not associated
with sleep duration or with most of the other measures, and
for brevity is not described further.) Given the significant
association between sleep duration, and cognition and the
other sleep component scores, we also performed a com-
plementary analysis with these variables (cognitive total
score and all five sleep component scores) as covariates, and
found a high correlation between the associated brain pat-
terns with and without these variables regressed out in the
analysis of the association between cortical area (or volume)
Fig. 1 Sleep duration, and cognitive and psychiatric problems
scores. a The correlation between sleep duration and a wide variety of
measurements including physical and mental health, neurocognition,
substance use, culture, environment and mobile technology, etc. Here
we highlight the 17 measurements that are significantly correlated with
sleep duration. For example, the positive rvalue for the cognition
scores indicates that long sleep duration is positively correlated with
good cognition. The full names for these measurements are shown
below and the details for each item are provided in Table S1. bA
histogram showing the relation between the number of hours of sleep
and cognitive measures. The Yaxis is the cognitive score and the error
bar is the standard error of the mean (SEM). There was a significant
correlation between the cognitive score and the number of hours of
sleep (Bonferroni corrected, p< 0.05). The children with long sleep
duration tended to have good cognitive performance. cA histogram
showing the relation between the number of hours of sleep and the
psychiatric problems scores. The Yaxis is the psychiatric problems
score and the error bar is the SEM. There was a significant negative
correlation between the psychiatric problems scores and the number of
hours of sleep (Bonferroni corrected, p< 0.05). The children with short
sleep duration tended to have high psychiatric problems scores.
abcd_saiq02 ABCD Parent Sports and Activities Involvement Ques-
tionnaire (SAIQ), abcd_sds01 ABCD Parent Sleep Disturbance Scale
for Children, abcd_ssphp01 ABCD Sum Scores Physical Health Par-
ent, abcd_tbss01 ABCD Youth NIH TB Summary Scores, abc-
d_asrs01 Adult Self Report summary scores, abcd_cbcl01 ABCD
Parent Child Behavior Checklist Raw Scores Aseba (CBCL),
abcd_cbcls01 Child Behavior Check List summary scores, abcd_k-
sad01 ABCD Parent Diagnostic Interview for DSM−5 Full, abcd_k-
sad01 ABCD Parent Diagnostic Interview for DSM−5 Full (KSADS-
5), abcd_ksad501 ABCD Youth Diagnostic Interview for DSM-5
(KSADS-5), abcd_pgbi01 ABCD Parent Parent General Behavior
Inventory-Mania, abcd_ssbpmtf01 ABCD Summary Scores Brief
Problem Monitor-Teacher Form for Ages 6–18, dibf01 ABCD Parent
Diagnostic Interview for DSM-5 Background Items Full, pasr01
ABCD Parent Adult Self Report Raw Scores Aseba, abcd_rhds01
Residential History Derived Scores, fes02 ABCD Parent Family
Environment Scale-Family Conflict Subscale Modified from PhenX,
macv01 ABCD Parent Mexican American Cultural Values Scale
Modified, abcd_stq01 ABCD Youth Screen Time Survey.
W. Cheng et al.

and sleep duration (r> 0.95, p<1×10
−10). This shows that
the results are robust and consistent and do not depend on
these other variables.
Total cortical area and volume were significantly posi-
tively correlated with the cognition total composite score
(r=0.13, d=0.27, p<1×10
−10 and r=0.11, d=0.23,
p<1×10
−10, respectively). The brain areas with significant
correlations with the cognition score included the prefrontal
and anterior cingulate cortex, medial orbitofrontal cortex,
temporal cortex, insula, inferior parietal gyrus, thalamus,
caudate, and putamen (FDR corrected, p< 0.005, Figs. 2b,
S1B and Table S2).
Total cortical area and volume were significantly nega-
tively correlated with the psychiatric problems total score
(r=−0.07, d=−0.15, p=1.3 × 10−14 and r=−0.07,
d=−0.14, p=2.9 × 10−14, respectively). The significant
brain regions after FDR correction (p< 0.005) are shown in
Fig. S2. Given the high correlation between depression and
sleep problems, we next focus on the depressive problems
score. We found that both cortical area and volume were
significantly negatively correlated with the depressive pro-
blems score (r=−0.05, d=−0.09, p=8.1 × 10−7and
r=−0.05, d=−0.09, p=6.4 × 10−7, respectively). Fig. 2c
shows that the brain areas with lower area in participants
with high depressive problems’scores included the lateral
and medial orbital frontal cortex, temporal cortex,
precuneus, superior and middle frontal gyrus and superior
medial frontal cortex, angular and supramarginal gyrus,
hippocampus, thalamus, caudate, and motor areas (FDR
corrected, p< 0.005, Figs. 2c, S1C and Table S2).
The brain areas with correlations with both sleep dura-
tion and cognitive performance are shown in Figs. 2d and
S1D; and with both sleep duration and depressive problems
in Figs. 2e and S1E.
Next, we performed two complementary analyses to
determine whether the present brain structure findings were
regionally specific. Firstly, we included the intracranial
volume as a covariate, which is a useful normalization
procedure used in morphometric analyses [38]. As shown in
Fig. S3, the association patterns with brain regions were
similar between the cases with and without regression of the
intracranial volume for all behavioral measures including
sleep duration, depressive score, and cognitive score. The
regions associated with sleep duration included the orbito-
frontal cortex, superior, middle and medial superior frontal
gyrus, inferior and middle temporal gyrus, precuneus, and
posterior cingulate cortex, all of which remained significant
after regressing out the effect of the intracranial volume
(FDR p< 0.05). Secondly, we also performed the same
analysis using the total cortical volume or area as covari-
ates. Although no region was significant in this case, the
association patterns with and without regression of the total
Table 2 The correlation between the sleep duration and the cognitive and psychiatric problems scores.
Cognitive measurement rvalue Cohen's dpvalue Cognitive measurement rvalue Cohen's dpvalue
NIH Toolbox Cognition Battery Scores
nihtbx_picvocab 0.060 0.121 3.2 × 10−10 nihtbxeading 0.051 0.103 8.3 × 10−8
nihtbx_list 0.038 0.077 6.5 × 10−5nihtbx_fluidcomp 0.049 0.099 2.6 × 10−7
nihtbx_cardsort 0.039 0.077 5.8 × 10−5nihtbx_cryst 0.069 0.138 5.9 × 10−13
nihtbx_pattern 0.032 0.063 9.3 × 10−4nihtbx_totalcomp 0.069 0.139 5.8 × 10−13
Psychiatric measurement rvalue Cohen's dpvalue Psychiatric measurement rvalue Cohen's dpvalue
Parent Child Behavior Checklist Scores
cbcl_scr_syn_anxdep −0.100 −0.201 <1 × 10−10 cbcl_scr_dsm5_depress −0.208 −0.425 <1 × 10−10
cbcl_scr_syn_withdep −0.120 −0.242 <1 × 10−10 cbcl_scr_dsm5_anxdisord −0.114 −0.229 <1 × 10−10
cbcl_scr_syn_somatic −0.079 −0.159 <1 × 10−10 cbcl_scr_dsm5_somaticpr −0.062 −0.124 <1 × 10−10
cbcl_scr_syn_social −0.118 −0.237 <1 × 10−10 cbcl_scr_dsm5_adhd −0.128 −0.258 <1 × 10−10
cbcl_scr_syn_thought −0.187 −0.380 <1 × 10−10 cbcl_scr_dsm5_opposit −0.108 −0.217 <1 × 10−10
cbcl_scr_syn_attention −0.134 −0.270 <1 × 10−10 cbcl_scr_dsm5_conduct −0.113 −0.227 <1 × 10−10
cbcl_scr_synulebreak −0.118 −0.238 <1 × 10−10 cbcl_scr_07_sct −0.099 −0.200 <1 × 10−10
cbcl_scr_syn_aggressive −0.119 −0.240 <1 × 10−10 cbcl_scr_07_ocd −0.104 −0.209 <1 × 10−10
cbcl_scr_syn_internal −0.120 −0.242 <1 × 10−10 cbcl_scr_07_stress −0.146 −0.295 <1 × 10−10
cbcl_scr_syn_external −0.126 −0.254 <1 × 10−10 cbcl_scr_syn_totprob −0.156 −0.315 <1 × 10−10
Positive rvalues indicate positive correlations; negative rvalues indicate negative correlations.
A high cognitive score means better performance, and a high psychiatric score means a worse mental state. All cognitive measures were positively
correlated with sleep duration and all psychiatric problems scores were negatively correlated with sleep duration (Bonferroni corrected, p< 0.05),
indicating that the children with short sleep duration tend to have poor cognitive performance and psychiatric problems.
Sleep duration, brain structure, and psychiatric and cognitive problems in children

cortical volume or area were highly correlated (rranging
from 0.66 to 0.85, all p< 1.0 × 10−5).
The longitudinal association between sleep duration
and the psychiatric problems scores
The structural equation modeling path estimates were used to
analyze the changes that occurred between the baseline ages
of around 10 and 1 year later. These showed that psychiatric
problems at the baseline age including the depressive pro-
blems and total psychiatric problems’scores were
significantly associated with decreased sleep duration at the 1-
year follow-up (Figs. 3a and S4). The reverse was not found.
It should be noted that the depressive problems’score is the
top contribution from the different psychiatric measures to the
association with decreased sleep duration (β=−0.081, SE =
0.016, p<1×10
−4); and the reverse was not found (β=
−0.018, SE =0.013, p=0.17). The model accounted for
23.8% of the variance in sleep duration, and 40.1% of the
variance in the depressive problems’score in children aged
around 11, by taking into account the measures of sleep and
depressive problems 1 year earlier.
Fig. 2 Brain regions with their area significantly related to sleep
duration, cognitive scores, and depressive scores. a Brain regions
with their cortical area significantly associated with sleep duration
(FDR corrected, p< 0.005). The red color indicates brain regions with
high area positively associated with longer sleep duration. bBrain
regions with their area significantly associated with the cognitive total
score (FDR corrected, p< 0.005). The red color indicates brain regions
where high area is positively correlated with a higher cognitive score.
Here, we only show the regions with Cohen’sdlarger than 0.15.
cBrain regions with their area significantly associated with the
depressive problems’score (FDR corrected, p< 0.005). Blue indicates
brain regions with a negative correlation between area and the
depressive problems score (i.e., a low cortical area is associated with
depressive problems). The brain regions shown here were confirmed
using a nonparametric approach that utilized a permutation test (with
5000 random samplings). dBrain regions with their area associated
with both sleep duration and the cognitive total score. The regions
shown are the overlap of what is shown in aand b.eBrain regions
with their area associated with both sleep duration and the depressive
problems score. The regions shown are the overlap of what is shown in
aand c.
W. Cheng et al.

Mediation analysis
The two main hypotheses are defined above. First, the
depressive problems significantly mediated the relationship
between the mean cortical area of the significant brain regions
showninFig.2e and sleep duration (Fig. 3b, path AB, 25.9%
of the total effect size, β=0.015, p=2.5 × 10−10; 95% CI,
0.011–0.020, FDR corrected, p< 0.005). The brain regions
with areas related to both sleep and depressive problems,
and involved in the mediation analysis just described, are
shown in Fig. 2e and include the medial and lateral orbi-
tofrontal cortex, dorsolateral prefrontal cortex, precuneus,
and angular gyrus. The interpretation is that depressive
problems significantly mediate the effect of brain structure
on sleep. (Similar findings for brain volume are provided in
Fig. S5A.)
Similar results were also found for the total psychiatric
problems score (Fig. S5B and C). The interpretation is that
psychiatric problems taken together significantly mediate
the effect of brain structure on sleep.
The second hypothesis investigated is the extent to which
the relation between brain structure and cognition is medi-
ated by sleep duration. Fig. 3c shows that the relation
between brain structure (the mean cortical area of the sig-
nificant brain regions shown in Fig. 2d) and cognition was
significantly mediated by sleep duration (path AB, 2% of
the total effect size, β=0.032, p=1.5 × 10−4; 95% CI,
0.018–0.053, FDR corrected, p< 0.005). (The result for
brain volume was similar as shown in Fig. S5D.)
Third, in addition, we showed that brain structure
mediated some of the effects found. For example, the
mediation implemented by cortical area from depressive
problems on sleep duration was significant (β=−0.0009,
p=7.8 × 10−4;95%CI,−0.0014 to −0.0004, FDR cor-
rected, p< 0.005) (Fig. S6). In addition, the mediation
implemented by cortical area from sleep duration on
cognition was significant (β=0.065, p=1.5 × 10−6;95%
CI, 0.04–0.092, FDR corrected, p< 0.005) (Fig. S6).
These analyses provide evidence that the brain structure
differences described here that are associated with sleep
duration are closely related to the cognitive and depres-
sive problems present in the children. We note that these
analyses show partial mediation effects. Further, we note
that the brain is a significant mediator in all these models,
and that the brain in all these models also has a significant
effect as an independent variable.
Discussion
We found that dimensional psychopathology (including
depression, anxiety, impulsive behavior) in the children
Fig. 3 The relation between depressive problems scores and sleep
duration. a The longitudinal association between the depressive
problems score and the sleep duration revealed by structural equa-
tion modeling (using a two-wave cross-lagged panel model). The
depressive problems score was significantly associated with lower
sleepdurationmeasured1yearlater(β=−0.081, SE =0.016, p<
1×10
−4); and the reverse (dashed line) was not true (β=−0.018,
SE =0.013, p=0.174). bMediation analysis: the mediation
implemented by depressive problems from the cortical area on sleep
duration was significant (β=0.015, p=2.5 × 10−10). The indirect
path (A, AB, and B) shows that the depressive problems’score
mediates part of the effect of cortical area on sleep duration. Path A:
effect of the independent variable, the mean cortical area of the brain
regions shown in Fig. 2e which are associated with both sleep
duration and the depressive score, on the mediator, the depressive
problems’score; Path B: the effect of the mediator (depressive
problems score) on the outcome (sleep duration); Path C shows that
the regression coefficient (beta value) of the cortical area on the
sleep duration was high when the sleep duration was not taken into
account. The beta values show the regression coefficient of the effect
of the independent variable (cortical area) on the dependent variable
(sleep duration). Path Cʹindicates the direct effect of the cortical
area on the outcome (sleep duration) controlling for the mediator
(the depressive problems score). Path Cʹshows some reduction in
the regression coefficient when the effect of the depressive problems
score was taken into account. Path AB indicates the extent to which
taking the depressive problems score into account can explain the
25.9% effect of the cortical area on sleep duration, which is sig-
nificant as noted above at p=2.5 × 10−10.cMediation analysis: the
mediation implemented by sleep duration from the cortical area on
cognition was significant (β=0.032, p=1.5 × 10−4). SE
standard error.
Sleep duration, brain structure, and psychiatric and cognitive problems in children

aged 9–11 was negatively correlated with sleep duration
(Fig. 1and Table 2). Very interestingly, dimensional psy-
chopathology in the parents was also correlated with short
sleep duration in their children (Fig. 1). The brain areas in
which higher volume was correlated with longer sleep
duration included the superior and middle frontal gyri and
superior medial frontal areas, inferior and middle temporal
gyrus, precuneus, supramarginal gyrus, thalamus, caudate,
and the lateral and medial orbital frontal cortex. Long-
itudinal data analysis showed that the psychiatric problems,
especially the depressive problems, were associated with
lower sleep duration measured 1 year later. The reverse
association was not significant. Further, mediation analysis
showed that depressive problems mediate considerably the
effect of brain structure (Fig. 2e) on sleep. The areas we
suggest include the orbitofrontal cortex because of its
functions in depression and other psychiatric problems
[39,40], and the dorsolateral prefrontal cortex, because of
their importance in cognitive functions such as episodic
memory and working memory [41].
The mediation analyses showing that depressive pro-
blems mediate considerably the effect of low brain area or
volume on low sleep duration implicated the lateral orbito-
frontal cortex. Part of what was found was that low volume
in the orbitofrontal cortex was associated with a high
depressive problems’score (Fig. 2c). This is of interest, for
the lateral orbitofrontal cortex is implicated in depression
[39,42]. A possible hypothesis is that reduced volume of the
medial orbitofrontal cortex related to reward processing, and
of the lateral orbitofrontal cortex related to nonreward pro-
cessing, is related to depression [42]. Further, if the orbito-
frontal cortex is not yet well developed as reflected in its
volume, then problems related to depression and thereby
poor sleep may occur (as shown by the mediation analysis in
Fig. 3b). Many of the other brain areas with structure related
to sleep duration and psychiatric problems especially
depression including other frontal cortical areas, and the
precuneus, are also implicated by functional connectivity
analyses in depression and have different connectivity with
the orbitofrontal cortex [39,42–44]. There is also evidence
for a role of the thalamus, especially the “nonspecific”
intralaminar and midline thalamic nuclei, and the reticular
nuclei, in sleep [45,46].
The brain areas in which higher cognitive scores were
associated with higher volume included the prefrontal cor-
tex (involved in working memory), the temporal cortex
(involved in perception and semantic representation), the
medial orbitofrontal cortex, and the pregenual cingulate
cortex (involved in reward [42,47]) (Fig. 2b), and it was of
interest that high sleep duration was also associated with
high volume of similar areas (Fig. 2d). Sleep had a small
effect in mediating the relationship between brain structure
and cognition (Fig. 3c).
One of the interesting possibilities raised by this research
is that children who are developmentally advanced for their
age may have more brain volume in some of the areas
identified in this investigation including the orbitofrontal
cortex and precuneus, and may thereby be developing
cognitive and behavioral capacities that enable them to
perform better. Another point is the finding that dimensional
psychopathology in the parents was positively correlated
with short sleep durations in their children (and with
the children's brain volumes and problems). This could be
due to inherited characteristics, or could be due to the
influences of the environment produced by the parents, or
an interaction between the two. Further research will be of
interest to address these issues, including genetic mediation
analyses.
Research on the role of brain development in children as
a factor to help understand the links between sleep, psy-
chiatric problems, and brain structure has not provided clear
answers yet. One study showed that sleep disturbances in
the first 6 years are associated with smaller gray matter
volumes and thinner dorsolateral prefrontal cortex at age 7
[48]. A recent study also showed that sleep problems in
children at 1.5, 2, and 6 years of age were associated with
lower fractional anisotropy values at 10 years [49].
Although these studies with sleep measures at different
times in children potentially reflect the effect of sleep pro-
blems on neurodevelopment, the long-term effect of sleep
on brain development remains unknown, partly due to the
lack of longitudinal neuroimaging studies. A recent sys-
tematic review concluded that "Although the research pre-
sented supports and offers more insight into the importance
of sleep for the developing brain of children and adoles-
cents, no firm conclusions that apply broadly may be
drawn" [13]. The present study involved more than a
magnitude more participants than these previous studies,
and a much wider range of measures. It will be useful to
explore the causal relationship between sleep and brain
structure and function when the follow-up brain imaging
data are available in the ABCD dataset in the future.
Several strengths of the research described here are (1)
the very large sample size (11,067) with many behavioral
measures of children of almost the same age thereby con-
trolling for age effects; (2) the longitudinal design of the
study that enabled us to analyze the relation between psy-
chiatric problems, and sleep duration measured 1 year later;
(3) the mediation analysis providing evidence that the
psychiatric problems mediated the relationship between
brain structure and sleep.
In summary, we show that dimensional psychopathology
(including depression, anxiety, and impulsive behavior) is
negatively correlated with sleep duration in a large cohort of
11,067 children. Importantly, dimensional psychopathology
in the parents was also correlated with short sleep duration
W. Cheng et al.

in their children. Further, this is the first large-scale inves-
tigation of how brain regions contribute to sleep problems
in children. We show that psychiatric problems mediate
these effects to a considerable extent, via brain regions that
include the orbitofrontal cortex, frontal including medial
cortical areas, inferior and middle temporal gyrus, pre-
cuneus, and supramarginal gyrus (Fig. 2e). We also show
that sleep duration mediates a small part of the relation
between high brain volume and good cognition.
Acknowledgements Use of the ABCD (https://abcdstudy.org/) dataset
is acknowledged. A full list of supporters of ABCD project is available
at https://abcdstudy.org/nih-collaborators. JF is supported by the 111
Project (No. B18015), the key project of Shanghai Science and
Technology (No. 16JC1420402), National Key R&D Program of
China (No. 2018YFC1312900), National Natural Science Foundation
of China (NSFC 91630314), Shanghai Municipal Science and Tech-
nology Major Project (No. 2018SHZDZX01), and ZJLab. WC is
supported by grants from the National Natural Sciences Foundation of
China (No. 81701773, 11771010), sponsored by Shanghai Sailing
Program (No. 17YF1426200). WC is also sponsored by Natural Sci-
ence Foundation of Shanghai (No. 18ZR1404400). JZ is supported by
grants from the National Natural Science Foundation of China (No.
61573107), and also sponsored by Natural Science Foundation of
Shanghai (No. 17ZR1444200). XYZ is supported by grants from the
National Natural Science Foundation of China (No. 81873893). FL is
supported by funding from the National Natural Science Foundation
of China (No. 81571031, No. 81761128035, No. 81930095,
and No. 81701334), Shanghai Municipal Commission of Health and
Family Planning (No. 2017ZZ02026, No. 2018BR33, No.
2017EKHWYX−02, and No. GDEK201709), Shanghai Shenkang
Hospital Development Center (No. 16CR2025B), Shanghai Municipal
Education Commission (No. 20152234), Shanghai Committee of
Science and Technology (No. 17XD1403200, No. 19410713500, and
No. 18DZ2313505), Shanghai Municipal Science and Technology
Major Project (No. 2018SHZDZX01), Guangdong Key Project in
"Development of new tools for diagnosis and treatment of Autism"
(2018B030335001), Xinhua Hospital of Shanghai Jiao Tong Uni-
versity School of Medicine (2018YJRC03, Talent introduction−014,
Top talent−201603).
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Publisher’s note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
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