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. 2023 Jul 29;33(18):10087–10097. doi: 10.1093/cercor/bhad267

Associations between body mass index, sleep-disordered breathing, brain structure, and behavior in healthy children

Jianqi Cui 1,2, Guanya Li 3,4, Minmin Zhang 5,6, Jiayu Xu 7,8, Haowen Qi 9,10, Weibin Ji 11,12, Feifei Wu 13,14, Yaqi Zhang 15,16, Fukun Jiang 17,18, Yang Hu 19,20, Wenchao Zhang 21,22, Xiaorong Wei 23, Peter Manza 24, Nora D Volkow 25, Xinbo Gao 26,27,✉, Gene-Jack Wang 28,✉, Yi Zhang 29,30,✉
PMCID: PMC10656948  PMID: 37522299

Abstract

Pediatric overweight/obesity can lead to sleep-disordered breathing (SDB), abnormal neurological and cognitive development, and psychiatric problems, but the associations and interactions between these factors have not been fully explored. Therefore, we investigated the associations between body mass index (BMI), SDB, psychiatric and cognitive measures, and brain morphometry in 8484 children 9–11 years old using the Adolescent Brain Cognitive Development dataset. BMI was positively associated with SDB, and both were negatively correlated with cortical thickness in lingual gyrus and lateral orbitofrontal cortex, and cortical volumes in postcentral gyrus, precentral gyrus, precuneus, superior parietal lobule, and insula. Mediation analysis showed that SDB partially mediated the effect of overweight/obesity on these brain regions. Dimensional psychopathology (including aggressive behavior and externalizing problem) and cognitive function were correlated with BMI and SDB. SDB and cortical volumes in precentral gyrus and insula mediated the correlations between BMI and externalizing problem and matrix reasoning ability. Comparisons by sex showed that obesity and SDB had a greater impact on brain measures, cognitive function, and mental health in girls than in boys. These findings suggest that preventing childhood obesity will help decrease SDB symptom burden, abnormal neurological and cognitive development, and psychiatric problems.

Keywords: ABCD, Brain morphometry, Behavior, Childhood obesity, Sleep disordered breathing

Introduction

Pediatric overweight/obesity is a common health problem. According to the World Health Organization, over 340 million children and adolescents aged 5–19 years were overweight or obese in 2016 (WHO Obesity and Overweight 2021, https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight). One of the major consequences with escalating levels of body mass index (BMI) is an increased likelihood of respiratory tract dysfunctions during sleep (Redline et al. 2007; Kohler and van den Heuvel 2008), which is a risk factor leading to the development of sleep-disordered breathing (SDB) including habitual snoring, obstructive sleep apnea syndrome (OSA), and desaturations preceded by central apneas (Kohler and van den Heuvel 2008). Higher BMI is a significant predictor of SDB severity in children (Verhulst et al. 2008; Spruyt and Gozal 2012). Further, epidemiological studies showed that SDB affects approximately 4–11% of children overall, but is more prevalent in children with obesity (20.8%) than normal-weight peers (6.3%) (Lumeng and Chervin 2008; Abazi et al. 2020).

Neuroimaging studies showed that both BMI and SDB symptom burden were associated with brain structural changes in children (Kheirandish-Gozal et al. 2018; Musso et al. 2020; Ronan et al. 2020; Isaiah et al. 2021; Jiang et al. 2022). Higher BMI was associated with thinner cortices in widespread parts of the frontal cortex involved with executive function and the temporal cortex involved with sensory integration and mental activities (Gluck et al. 2017; Laurent et al. 2020; Ronan et al. 2020). The orbitofrontal cortex (OFC) has been linked to higher BMI, and its activity was involved with inhibition, reward processing, and emotional modulation (Rudebeck and Rich 2018). A longitudinal study showed that children with obesity compared with normal weight peers had greater reductions in gray matter volume in the prefrontal cortex (PFC), precentral gyrus (PreCG), and caudate over the 2-year period (Jiang et al. 2022). Isaiah et al. (2021) reported that SDB symptom burden was associated with thinner cortical thickness within several regions including OFC, PreCG, and insula (INS), which are regions involved with attention/working memory, sensorimotor, behavioral inhibition, and interoception (Anderson et al. 1999; Cooke and Graziano 2004; Craig 2011). Children with OSA had lower gray matter volumes in regions involved with emotional processing and cognitive control (e.g. frontal and prefrontal cortices, superior and lateral parietal cortices, and superior temporal lobe) (Philby et al. 2017; Musso et al. 2020). However, previous studies mainly investigated the impact of higher BMI and SDB on brain structure and function, respectively, in children (Laurent et al. 2020; Ronan et al. 2020; Isaiah et al. 2021). It remains unknown whether SDB, as a common complication in patients with overweight/obesity, partially mediates the impact of higher BMI on regional brain structural changes.

A number of studies reported that childhood overweight/obesity and SDB were independently associated with cognitive dysfunction, attention problems, and impulsive behavior (Pan et al. 2018; Laurent et al. 2020; Isaiah et al. 2021; Menzies et al. 2022). Children with higher BMI had poorer cognitive and mental performance than their lean counterparts, which could exacerbate poor decision-making with regards to diet and exacerbate negative emotions thus contributing to negative health outcomes (Smith et al. 2011; Pan et al. 2018). Children with SDB are at heightened risk of behavioral problems such as inattention, hyperactivity, and aggression as well as cognitive deficits (Mitchell and Kelly 2006; Xanthopoulos et al. 2015; Menzies et al. 2022). Previous studies have examined the relationship between BMI, SDB, executive function, attention, and externalizing behaviors (Spruyt and Gozal 2012; Xanthopoulos et al. 2015). However, the relatively small sample sizes in these studies calls for replication, and it remains unclear how interactions between higher BMI and SDB symptom burden influence cognitive performance and mental health, as well as brain structures in children and whether there are sex differences in these effects.

To provide a more comprehensive investigation in a larger sample size, this study employed structural magnetic resonance imaging (sMRI) and behavior measurements to identify associations among BMI, SDB, brain morphometry and psychiatric symptoms, and cognitive performance in 8484 children aged 9–11 years old obtained from the Adolescent Brain Cognitive Development (ABCD) dataset. We hypothesized that (1) SDB symptom burden would partially mediate the associations between BMI and abnormal brain structures; (2) higher BMI- and SDB-related brain alterations are associated with cognitive performance and psychiatric symptoms in children; and (3) higher BMI and SDB in girls because of their faster brain developmental trajectories (Tomasi and Volkow 2023) would have a great negative impact than in boys.

Materials and methods

Participants

This cross-sectional study used prospective cohort data from the ABCD Study (release 4.0; https://abcdstudy.org/). The ABCD study is a prospective longitudinal cohort study aiming to examine the development and health of brain and behavior in children aged 9–11 years old through young adulthood (Volkow et al. 2018). The ABCD study was designed to recruit over 11,000 participants from 21 sites throughout the United States with diverse range of ethnic/geographical backgrounds, socioeconomic status, and health experiences (Casey et al. 2018). Baseline dataset including 11,876 children from the ABCD study (version 4.0) was used for cross-sectional analysis. Children with missing values on covariates (including age, gender, race/ethnicity, household income, parental education, BMI, intracranial volume (ICV), and site as well as behavior and image data) were excluded from this study, and we also excluded subjects with any neurologic condition (e.g. brain injury, cerebral palsy, and/or seizures), type 1 or type 2 diabetes, lead exposure, muscular dystrophy, gestational age younger than 28 weeks, and underweight (according to the CDC age-sex-height-weight-specific cutoffs (Kuczmarski et al. 2002)) (please see Appendix S1.1 for more details). A total of 8484 children with complete data were included in this study.

Body mass index

Participant BMI was calculated as weight in kilograms divided by height in meters squared and converted into BMI z-scores (BMIZ) in accordance with the World Health Organization Child Growth Standards (https://www.who.int/publications/i/item/924154693X).

Structural brain imaging

The ABCD dataset obtained T1-weighted sMRI images using 3.0 Tesla (T) scanner platforms (either including Siemens Prisma, General Electric 750 and Phillips) at 21 sites in the United States (Casey et al. 2018). All sMRI data were processed by the ABCD Study team using FreeSurfer version 7.1.1, including skull-stripping, white matter segmentation, initial mesh creation, correction of topological defects, and nonlinear registration to a spherical surface-based atlas, as well as estimation of morphometric measures of average cortical measures of surface area, volume, and thickness from the Desikan–Killany–Tourville atlas (Fischl et al. 2002; Hagler et al. 2019). Quality control of the processed images was performed by the ABCD study team, including review of the accuracy of cortical reconstruction by trained MRI analysts (Hagler et al. 2019) (please see Appendix S1.2 for more details). Children who failed to pass FreeSurfer quality control criteria were excluded from this study. Morphometric measures consisting of the cortical thickness, areas, and volumes were used in the current analyses.

Sleep assessments

The sleep disturbance scale for children (SDSC) is a 26-item clinically validated scale, which was designed to assess the presence of difficulties in children’s sleep behavior (Bruni et al. 1996). It was collected based on parent-reported symptoms during the last 6 months. These 26 items are rated on a Likert-type scale with range of values from 1 (never) to 5 (daily). The SDSC is divided into 6 sleep disorder domains, including disorders of initiating and maintaining sleep (DIMS), sleep hyperhidrosis (SHY), SDB, sleep–wake transition disorders (SWTD), disorders of arousal (DA), and disorders of excessive daytime somnolence (DOES). This study mainly focused on the SDB score that comprises 3 key symptoms (including snoring, breathing difficulty during sleep, and gasping or respiratory pauses) with range of values from 3 to 15. The higher scores indicate greater SDB symptoms burden. According to the SDSC validation criteria, SDB scores ≥7 was considered to be pathological (Zambrelli et al. 2020).

This study also attempted to focus on sleep duration, which is questioned “How many hours of sleep does your child get on most nights” in the SDSC. We note that a high score in this measurement system indicates short sleep duration. This study used “sleep duration” as the descriptor, where a high sleep duration indicates a large number of hours of sleep (please see Appendix S1.3 for more details).

Cognitive and mental health assessments

Cognitive function was measured using The NIH Cognition Battery Toolbox (NIHTB) and Matrix Reasoning Task (MRT). The NIHTB is divided into 7 cognitive tasks covering 6 cognitive domains, including executive function, working memory, episodic memory, attention, processing speed, and language abilities. The pattern comparison processing speed, list-sorting working memory, picture sequence memory, Flanker, and dimensional change card sort tests were used to generate fluid composite scores, the oral reading recognition and picture vocabulary tests were used to generate crystallized composite scores, and total cognition scores were calculated from fluid and crystallized composite scores within the NIHTB (Luciana et al. 2018). The age-corrected standard scores were used in this study. The MRT is a subtest of the Wechsler Intelligence Scale for Children-V (Na and Burns 2016), which can be used to evaluate the fluid intelligence and cognitive performance of children in ABCD.

Dimensional psychopathology and adaptive functioning were assessed with the parents-reported Child Behavior Checklist Scores (CBCL), which used to record the behavioral problems and competencies of children aged 4–18 (Cheng et al. 2021). It includes 8 syndrome scales (including anxious/depressed, withdrawn, somatic complaints, social, thought, attention problems, rule-breaking behavior, and aggressive behavior), 2 composite scores (including internalizing and externalizing broad band score), and a total summative score (Cheng et al. 2021). The raw scores were corrected using age and gender to yield a final t-score.

Statistical analysis

Linear mixed-effects modeling

Linear mixed-effect (LME) model was used to assess the associations among BMI or SDB, cortical structures, cognitive ratings from the NIHTB and MRT, and psychiatric problems scores from CBCL. LME model was implemented using function lme4 of R (version 4.1.3). Children’s age, gender, race/ethnicity, parental education, and household income were modeled as fixed-effect covariates. As recommended by the ABCD website and used in many studies (e.g. Paulus et al. 2019; Cheng et al. 2021; Saragosa et al. 2022), the family structure nested within sites was modeled as random effects in the LME models to take into account the potential impact of twins and siblings within families and the sites. We controlled ICV as a fixed-effect covariate in models involving brain morphometry. The effect size (Cohen’s d) was calculated for each LME model to reflect the associations among BMIZ, SDB, and other variables. All statistical results were corrected for multiple comparisons using false discovery rate (FDR) correction.

Mediation analysis

PROCESS, a macro developed for SPSS (Release 25.0, IBM), was used to perform all the mediation analyses. A mediation analysis was performed to assess whether SDB mediates the associations between BMIZ and regional cortical morphometry.

Serial two-mediator analyses were performed to assess whether SDB and regional cortical structures mediated the associations between BMI and behavior measurements, such that BMI was the independent variable, psychiatric symptoms/cognitive function were the dependent variable, SDB was the first mediator, and regional cortical structures were the second mediator. Serial mediation models should be implemented when the first mediator may causally influence the second mediator. The same confounding variables as in the LME models were regressed out in the mediation model. FDR correction was applied for multiple comparisons of P-values for direct effects. The 95% confidence interval (CI) was calculated using the bootstrap method (5,000 bootstrap samples) to evaluate the significance of indirect effects. CIs that do not contain 0 indicate a significant indirect finding.

We also considered the fact that SDB could cause obesity in an alternative model. Another mediation analysis was performed to assess whether BMIZ mediates the associations between SDB and regional cortical morphometry. Serial two-mediator analyses were performed to assess whether BMIZ and regional cortical structures mediate the association between SDB and behavior measurements.

Then, we attempted to replace SDB with sleep duration in SDSC to evaluate whether the associations among SDB and BMI, cortical structures were simply affected by sleep duration. We repeat the above identical analyses in boys and girls respectively to assess whether there are any differences in the effect sizes of BMI and SDB in brain and cognition and mental health between boys and girls. Furthermore, we also attempted to evaluate the impact of BMIZ and SDB symptom burden at baseline on psychiatric symptoms/cognitive function score at 2-year follow-up (please see Appendix S1.5 for more details).

Results

Demographic characteristics

A total of 8484 children (mean [SD] age, 9.91 [0.62] years; 4037 females [47.6%]; 1302 children with overweight [15.3%]; 1423 children with obesity [16.8%]; and 349 children with pathological SDB [4.11%]) were included in this study (Table 1). Prevalence of SDB in children with overweight (5.07%) and obesity (9.07%) was higher than that in children with normal-weight (2.67%) (see Supplementary Table S1). Prevalence of obesity and SDB in boys (17.4% obesity and 4.36% SDB) was higher than that in girls (16.1% obesity and 3.84 SDB) (Table 1, see Supplementary Table S1).

Table 1.

Demographic characteristics of participants.

Participants of Baseline Sex subsample
Characteristic Normal weight
(n = 5759)
(Mean ± SE)		 Overweight
(n = 1302)
(Mean ± SE)		 Obese
(n = 1423)
(Mean ± SE)		 Girls
(n = 4037)
(Mean ± SE)		 Boys
(n = 4447)
(Mean ± SE)
Age (mouths) 118.96 ± 0.099 119.14 ± 0.208 118.62 ± 0.198 118.74 ± 0.118 119.10 ± 0.113
Gender 2767 F/2992 M 621 F/681 M 649 F/774 M N/A N/A
BMI 16.73 ± 0.019 20.83 ± 0.029 26.13 ± 0.095 19.01 ± 0.644 18.86 ± 0.597
ICV (cm  3) 1496.07 ± 1.828 1504.37 ± 4.205 1490.63 ± 4.071 1429.74 ± 1.921 1556.97 ± 2.012
Race/Ethnicity (%)
Hispanic 862 (15.0) 318 (24.4) 421 (29.6) 763 (18.9) 838 (18.8)
Asian 342 (5.9) 61 (4.7) 48 (3.4) 205 (5.1) 246 (5.5)
White 3676 (63.8) 626 (48.1) 491 (34.5) 2241 (55.5) 2552 (57.4)
Black 601 (10.4) 215 (16.5) 357 (25.1) 587 (14.5) 586 (13.2)
Other 278 (4.8) 82 (6.3) 106 (7.4) 241 (6.0) 225 (5.1)
Household income，$ (%)
< 50 K 1349 (23.4) 463 (35.6) 675 (47.4) 1204 (29.8) 1283 (28.9)
50 K—100 K 3596 (62.4) 720 (55.3) 680 (47.8) 2347 (58.1) 2649 (59.6)
> 100 K 814 (14.1) 119 (9.1) 68 (4.8) 486 (12.0) 515 (11.6)
Parental education (%)
< High school 194 (3.4) 99 (7.6) 154 (10.8) 225 (5.6) 222 (5.0)
High school diploma or GED 447 (7.8) 135 (10.4) 254 (17.8) 381 (9.4) 455 (10.2)
Some college or associate degree 1548 (26.9) 442 (33.9) 511 (35.9) 1173 (29.1) 1328 (29.9)
Bachelor’s degree 1847 (32.1) 335 (25.7) 292 (20.5) 1184 (29.3) 1290 (29.0)
Postgraduate degree 1723 (29.9) 291 (22.4) 212 (14.9) 1074 (26.6) 1152 (25.9)
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Abbreviation: BMI, Body mass index; F, female; M, male; ICV, Intracranial volume; GED, General educational development.

Associations among BMIZ, SDB, and regional cortical structures

BMIZ was positively correlated with SDB (Cohen’s d = 0.286, P < 2 × 10−16, uncorrected). Higher BMIZ was correlated with lower cortical thickness, areas, and volumes in most frontal, temporal, and parietal cortices (Cohen’s d ranging from −0.249 to −0.048, PFDR < 0.05; Fig. 1A, see Supplementary Table S2–S4).

Fig. 1.

Fig. 1

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Associations between BMIZ, SDB, and regional cortical thickness area and volume. (A) The correlations between BMIZ and regional cortical structures; (B) the correlations between SDB and regional cortical structures; (C) brain regions associated with both BMIZ and SDB, i.e. the overlap of A and B. Cortical area was not significantly associated with SDB, and thus it was not discussed further. Abbreviation: BMIZ, BMI z-scores.

Significant negative correlations were observed between SDB and regional cortical thickness and volumes. Specifically, children with SDB had lower cortical thickness in left lingual gyrus (LING_L), PreCG_L, INS_L/R, right lateral orbitofrontal cortex (lOFC_R), and smaller cortical volumes in left postcentral gyrus (PoCG_L), PreCG_L/R, left precuneus (PCUN_L), left superior parietal lobule (SPL_L), and INS_R (Cohen’s d ranging from −0.083 to −0.063, PFDR < 0.05; Fig. 1B). Cortical area was not significantly associated with SDB, and thus it was not discussed further.

Brain structures associated with both BMIZ and SDB included cortical thickness in LING_L and lOFC_R, and cortical volumes in PoCG_L, PreCG_L, PCUN_L, SPL_L, and INS_R (Fig. 1C).

Mediation analysis of BMIZ, SDB, and regional cortical structures

Mediation analysis showed that SDB partially mediated the associations between BMIZ and cortical thickness in the LING_L (c’ = −0.022 ± 0.0061, PFDR < 0.001; a × b: 10.2% of the total effect size, 95% CI [−0.0043, −0.0007]; Fig. 2A) and lOFC_R (c’ = −0.0392  ± 0.0058, PFDR < 0.001; a × b: 4.4% of the total effect size, 95% CI [−0.0036, −0.0002]; Fig. 2A), and cortical volumes in the PoCG_L (c’ = −0.0077 ± 0.0036, PFDR = 0.033; a × b: 15.4% of the total effect size, 95% CI [−0.0025, −0.0004]; Fig. 2B), PreCG_L (c’ = −0.0104 ± 0.0029, PFDR < 0.001; a × b: 11.1% of the total effect size, 95% CI [−0.0023, −0.0005]; Fig. 2B), PCUN_L (c’ = −0.0212  ± 0.0050, PFDR < 0.001; a × b: 8.2% of the total effect size, 95% CI [−0.0035, −0.0005]; Fig. 2B), SPL_L (c’ = −0.0229 ± 0.0052, PFDR < 0.001; a × b: 6.9% of the total effect size, 95% CI [−0.0033, −0.0002]; Fig. 2B) and INS_R (c’ = −0.0114 ± 0.0033, PFDR = 0.004; a × b: 12.4% of the total effect size, 95% CI [−0.0028, −0.0005]; Fig. 2B). These models showed good fit to the data (Lt and Bentler 1999) (please see Appendix S1.4 and Table S5 for more details). Furthermore, BMIZ also partially mediated the association between SDB and these brain structures in children (please see Appendix S2.1 and Fig. S2 for more details).

Fig. 2.

Fig. 2

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Mediation analysis of BMIZ, SDB, and regional cortical thickness/volume. Abbreviation: BMIZ, BMI z-scores. Note: Significant effects: *P < 0.05, **P < 0.01, ***P < 0.001.

Associations among BMIZ, SDB, and mental/cognitive behaviors

BMIZ was negatively correlated with task scores in the List Sorting Working Memory, Dimensional Change Card Sort, Picture Sequence Memory, Fluid and Totalcomp composite scores, and MRT score (Cohen’s d ranging from −0.117 to −0.057, PFDR < 0.05; see Supplementary Table S7). Score in psychiatric problems for withdrawn, somatic complaints, social problems, aggressive behaviors, internalizing, externalizing, and total CBCL score were positively associated with BMIZ (Cohen’s d ranging from 0.064 to 0.111, PFDR < 0.05; see Supplementary Table S7).

SDB was negatively correlated with scores in the Pattern Comparison Processing Speed task, Fluid and Totalcomp composite scores, and MRT score (Cohen’s d ranging from −0.071 to −0.060, PFDR < 0.05; see Supplementary Table S8). Score for all CBCL subscales had significantly positive associations with SDB (Cohen’s d ranging from 0.214 to 0.378, PFDR < 0.001; see Supplementary Table S8).

Associations between regional cortical structures and mental/cognitive behaviors

Correlation analyses between BMIZ- and SDB-related childhood behaviors and brain measures showed that regional cortical volumes were negatively associated with psychiatric symptoms, and positively associated with cognitive function (see Supplementary Table S11–S14). For example, the total problem CBCL score was negatively correlated with cortical volumes in PreCG_L, SPL_L and INS_R (Cohen’s d ranging from −0.087 to −0.063, PFDR < 0.05; see Supplementary Table S11–S14); total cognitive score in NIHTB was positively correlated with cortical volumes in PreCG_L (Cohen’s d = 0.062, PFDR = 0.008; see Supplementary Table S12) and INS_R (Cohen’s d = 0.051, PFDR = 0.022; see Supplementary Table S14); the MRT score was positively correlated with cortical volumes in PreCG_L (Cohen’s d = 0.095, PFDR < 0.001; see Supplementary Table S12) and INS_R (Cohen’s d = 0.110, PFDR < 0.001; see Supplementary Table S14).

The serial two-mediator analysis of BMIZ, SDB, regional cortical structures, and mental/cognitive behaviors

The relationship between BMIZ and externalizing problems was mediated by the cortical volume in the PreCG_L via SDB indirect path (Fig. 3, Table 2). Cortical volume in the INS_R via SDB indirect path mediated the effects of BMIZ on externalizing problems and the CBCL total score and MRT score (Fig. 3, Table 2). The SDB indirect path, and cortical volumes in PreCG_L and INS_R paths all mediated the relationship between BMIZ and externalizing problems and total CBCL score and MRT score (Table 2).

Fig. 3.

Fig. 3

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Mediation analysis of BMIZ, SDB, cortical volumes, psychiatric and cognitive measures. (A) Serial two-mediation model demonstrating associations among BMIZ, SDB, cortical volumes in left precentral gyrus, and mental problems in CBCL; (B) serial two-mediation model demonstrating associations among BMIZ, SDB, cortical volumes in right insula, and MRT score; (C) serial two-mediation model demonstrating associations among BMIZ, SDB, cortical volumes in right insula, and mental problems in CBCL. Abbreviation: BMIZ, BMI z-scores. Note: Significant effects: *P < 0.05, **P < 0.01, ***P < 0.001.

Table 2.

Serial two-mediation model demonstrating associations among BMIZ, SDB, cortical volumes in INS_R/PreCG_L, mental problems in CBCL, and matrix reasoning score.

Effect BootSE BootLLCI BootLLCI
INS_R Externalizing Problem in CBCL
Total effect 0.0337*** 0.0101 0.0139 0.0534
Direct effect 0.0133 0.0101 −0.0065 0.0331
Indirect effect
Total effect 0.0204 0.0023 0.0163 0.0253
Ind1 0.0190 0.0022 0.0150 0.0237
Ind2 0.0002 0.0001 0.0001 0.0004
Ind3 0.0013 0.0005 0.0005 0.0026
Total Problem in CBCL
Total effect 0.0404*** 0.0097 0.0214 0.0593
Direct effect 0.0146 0.0096 −0.0042 0.0334
Indirect effect
Total effect 0.0258 0.0025 0.0213 0.0313
Ind1 0.0244 0.0025 0.0200 0.0298
Ind2 0.0002 0.0001 0.0001 0.0004
Ind3 0.0012 0.0005 0.0004 0.0025
Matrix Reasoning Task Score
Total effect −0.0470*** 0.0077 −0.0622 −0.0318
Direct effect −0.0432*** 0.0078 −0.0585 −0.0279
Indirect effect
Total effect −0.0038 0.0012 −0.0063 −0.0015
Ind1 −0.0025 0.0012 −0.0048 −0.0002
Ind2 −0.0002 0.0001 −0.0003 −0.0001
Ind3 −0.0012 0.0005 −0.0024 −0.0004
PreCG_L Externalizing Problem in CBCL
Total effect 0.0337*** 0.0101 0.0139 0.0534
Direct effect 0.0132 0.0101 −0.0066 0.0330
Indirect effect
Total effect 0.0205 0.0023 0.0163 0.0252
Ind1 0.0190 0.0022 0.0150 0.0237
Ind2 0.0002 0.0001 0.0001 0.0004
Ind3 0.0013 0.0006 0.0005 0.0027
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Abbreviation: BMIZ, BMI z-scores; SDB, Sleep disordered breathing; INS_R, Right insula; PreCG_L, Left precentral gyrus; CBCL, The child behavior checklist scores.

Note: Significant effects are indicated in bold; *P < 0.05, **P < 0.01, ***P < 0.001.

BootSE = Bootstrapped standard error.

BootLLCI = Bootstrapped lower limit confidence interval.

BootULCI = Bootstrapped upper limit confidence interval.

Indirect Effect 1 = BMIZ → SDB → Mental problems/cognitive performance.

Indirect Effect 2 = BMIZ → SDB → INS_R/PreCG_L → Mental problems/cognitive performance.

Indirect Effect 3 = BMIZ → INS_R/PreCG_L → Mental problems/cognitive performance.

In addition, serial two-mediator analyses also showed that the cortical volume in the PreCG_L via BMIZ indirect path mediated the correlation between SDB and externalizing and total CBCL score as well as MRT score (please see Appendix S2.2, Fig. S3 and Table S15 for more details). Cortical volume in the INS_R via BMIZ indirect path mediated the correlation between SDB and Fluid composite scores and MRT score, as well as social problem, internalizing, externalizing, and total CBCL score (please see Appendix S2.2, Fig. S3 and Table S16 for more details).

The identical analyses of sleep duration replacing SDB

Sleep duration was positively associated with cortical volumes in left banks of superior temporal sulcus (STS_L) and left supramarginal gyrus (SMG_L). Sleep duration mediated the correlation between BMIZ and cortical volume in SMG_L. However, none of the serial two-mediator analyses for sleep duration survived significance (please see Appendix S2.3, Fig. S4 and Table S17-S19 for more details).

Sex differences in BMI and SDB effects in brain measures, behaviors, and cognition

BMIZ and SDB in girls were negatively correlated with more regional brain thickness, area and volumes and psychiatric symptoms (e.g. withdrawn and externalizing problem, etc.) than for boys (please see Appendix S2.4 and Table S20-S27 for more details). The correlation of SDB in boys with poor cognitive function was not significant. In contrast, poorer cognitive performance (including episodic memory, fluid cognition, and matrix reasoning ability) was associated with higher BMI and greater SDB symptom burden in girls (please see Appendix S2.4 and Table S26-S27 for more details).

Association between BMIZ, SDB at baseline, and mental/cognitive behaviors at 2-year follow-up

Higher BMIZ at baseline was negatively correlated with Flanker Inhibitory-control and Attention test score and Picture Sequence Memory test score at 2-year follow-up, and was positively correlated with score in psychiatric problems for withdrawn, somatic complaints, social problems, internalizing, externalizing, and total CBCL score at 2-year follow-up (please see Appendix S2.5 and Table S31 for more details). Baseline SDB was negatively associated with Oral Reading Recognition test score and Crystallized Composite score at 2-year follow-up, and was significantly positively associated with score for all CBCL subscales at 2-year follow-up (please see Appendix S2.5 and Table S32 for more details).

Discussion

Here, we evaluated the associations among BMI and SDB, brain morphometry, and mental/cognitive behaviors in children. Results showed that BMI was positively associated with SDB, and both were negatively associated with cortical thickness in LING_L, lOFC_R, and cortical volumes in PoCG_L, PreCG_L, PCUN_L, SPL_L, and INS_R. SDB partially mediated the impact of overweight/obesity on these brain structures. BMI and SDB were correlated with abnormal neurodevelopment and behaviors in children (i.e. higher risk of psychiatric symptoms and cognitive deficits) with a greater impact on girls than boys. The associations between BMI, externalizing symptoms, and cognitive function were mediated by SDB and cortical volumes in the PreCG_L and INS_R. These findings highlight the associations between abnormal brain development and SDB symptom burden as well as childhood obesity, which may be a potential risk factor affecting mental health and cognitive function in children.

Epidemiological studies reported the correlation between obesity and SDB (Verhulst et al. 2008; Mitchell et al. 2015). Previous studies have shown that obesity is generally accepted to be an important risk factor in the development of SDB, in children as well as adults (Kohler and van den Heuvel 2008; Verhulst et al. 2008). Obesity promotes SDB in multiple ways. Obesity leads to fat deposition in the pharyngeal wall and may increase the external compression of the superficial fat mass, which in turn narrows the cross-sectional area of the upper airway (Shelton et al. 1993). Deposition of fat in the pharyngeal wall may alter the shape of the pharyngeal airway, which in turn increases extra-luminal tissue pressure, and this combined with anatomical changes can promote collapse of the upper airway (Schwartz et al. 2010; Ryan et al. 2014). Unhealthy dietary behaviors (i.e. consumption of high fats and carbohydrates) and increased visceral adipose tissue may promote secretion of inflammatory cytokines, which in turn might alter sleep–wake rhythms (Muscogiuri et al. 2019). In addition, obesity through increased levels of abdominal fat is also associated with reduced lung volumes, particularly functional residual capacity, which leads to decreased longitudinal traction of the pharynx and can indirectly cause upper airway instability by disrupting reflex mechanisms of respiratory control (Gifford et al. 2010).

Neuroimaging studies reported that overweight/obesity and SDB could adversely affect brain structure development throughout childhood and adolescence (Horne et al. 2018; Macey et al. 2018; Laurent et al. 2020; Musso et al. 2020; Isaiah et al. 2021; Jiang et al. 2022). Interestingly, our data revealed that while thinner cortex in widespread parts of the brain was associated with higher BMI, relatively few regions was significantly correlated with SDB symptom burden. The structure-BMI associations are stronger and more widespread than the structure-SDB associations. However, nearly all of the significant SDB-structure associations were in regions that overlapped with BMI-structure associations. Measures of brain structure in these regions involved with cognitive decision-making, visual–spatial performance, and somatosensory movement may be jointly affected by obesity and SDB in children. Specifically, OFC plays a role in emotional decision-making and the learning of cue-outcome associations (Rudebeck and Rich 2018), and maladaptive valuation processes related to detrimental structural changes in OFC may lead to poor decision-making with regard to diet and thus contribute to weight gain (Laurent et al. 2020; Jiang et al. 2022). A prefrontal model for OSA showed that sleep disruption and blood oxygen abnormalities related to OSA could cause dysfunction in PFC by disrupting cellular/chemical homeostasis and preventing restorative processes during sleep, thus leading to impairment of executive function (Beebe and Gozal 2002). LING plays a role in certain cognitive functions, such as visual recognition (Yang et al. 2015) and situational memory consolidation (Kukolja et al. 2016). A meta-analysis reported that participants with obesity activated LING in response to viewing high-calorie food cues, and that higher reward salience of high-calorie food images modulated neural activity in visual areas (Yang et al. 2021). Ji et al. (2021) reported that the functional abnormality of the LING elicited by OSA may be correlated with cognitive impairments in children. The SPL and PCUN are important for visual–spatial functioning and may be involved in working memory (Koenigs et al. 2009; Jahn et al. 2012) and its structural changes may be associated with poor visual–spatial and working memory performance in children with obesity or SDB (Biggs et al. 2011; Liang et al. 2014; Zhang et al. 2022). Gray matter volume loss in subjects with OSA encompassed the posterior parietal association areas, and deficits in these regions impaired the ability to consciously perceive sensory stimuli from the upper airway and also interfere with sensory–motor integration, suggesting that structural changes in the parietal cortex may affect the integration of sensory and motor function, particularly during arousal from OSA (Macey et al. 2002). The PreCG is part of the classic network important for sensory and complex motor task processing (Cooke and Graziano 2004). Studies showed that the structural alterations in PreCG may be associated with the differences in gross and fine motor skills in children with obesity (Ou et al. 2015). One functional MRI study demonstrated greater PoCG response to visual food stimuli in adolescents with obesity, indicating elevated somatosensory region responsivity to food (Gearhardt et al. 2014). Children with OSA had abnormal structural and functional connectivity involving sensorimotor areas. Lee et al. (2022) showed that children with OSA have reduced structural interaction between brain regions including PreCG, which may be induced by hypoxia-related disruption of structural pathways. Consistent with our findings, Isaiah et al. (2021) reported a correlation between higher SDB and lower cortical volume in the PoCG (). INS is a key brain structure related to somatic sensation and emotional awareness (Craig 2011), and overweight/obesity in adolescents was associated with disrupted tuning of the INS system toward interoceptive input, which may interfere with the normal perception of interoceptive cues, thereby affecting normal eating behavior (Mata et al. 2015). Schon et al. (2008) suggested that the INS played an important role in the perception of dyspnea, and lesions of the INS cortex reduced the sensitivity to dyspnea and pain perception. INS may be an important region for the development of OSA (Li et al. 2015). Furthermore, our data revealed that SDB partially mediated the impact of BMI on measures of these brain structures. We speculate that the abnormal development of brain structures involved with cognitive control, somatic sensation, and visual–spatial functioning as well as movement in children with overweight/obesity may be partly attributed to increases of SDB symptom burden.

Serial two-mediator analyses showed that SDB and cortical volume in PreCG_L and INS_R mediated the correlation between BMI and externalization and MRT score. Previous studies have reported negative associations between childhood obesity and matrix reasoning cognition and behavioral problems, which may exacerbate problematic eating and thus contribute to increased BMI (Pan et al. 2018; Laurent et al. 2020). Sleep disruption, fragmentation, and hypercapnia/hypoxemia associated with SDB have been associated with abnormal behaviors, especially inattention, hyperactivity, and other “externalizing” behaviors (e.g. oppositionality, conduct problems) (Mitchell and Kelly 2006), and executive function (Menzies et al. 2022). In addition, overweight and obesity comorbid with SDB increased the risk of externalizing behaviors such as aggression in children (Biggs et al. 2017). Compared with adolescents with obesity and normal peers, adolescents with obesity and comorbid OSA showed more serious cognitive problems (Xanthopoulos et al. 2015). Attention should be paid to the impact of SDB on the psychological symptoms and cognitive function in children with obesity. Changes in structure and function in PreCG have been related to psychopathy and violent behavior (Narayan et al. 2007) and impulsive behavior in children and adolescents (Shannon et al. 2011). Meanwhile, structural damage in INS was closely related to neuropsychological and psychiatric disorders (internalizing problems (depression and anxiety), externalizing problems (substance use disorders)) (Goodkind et al. 2015; Wu et al. 2019).

While the underlying mechanisms are still unknown, there seems to be increasing evidence suggesting a causal relationship between the pathogenesis of obesity and SDB. Besides the well-defined impact of obesity on the pathogenesis of SDB, there are several potential mechanisms that support the reverse relationship, the adverse effects of SDB on obesity. OSA has a potential role in the development and intensification of obesity by altering sleep duration arising from fragmented sleep, eating habits, and the neurohormonal mechanisms that control satiety and hunger and energy expenditure during sleep and wakefulness (Ong et al. 2013; Ryan et al. 2014). Therefore, we also showed the partially mediation role of higher BMI on the correlation between SDB and regional brain structures as a supplement. Serial two-mediator analyses showed that BMI and cortical volumes in PreCG and INS mediated the associated between SDB and externalizing and total CBCL score as well as MRT score. In summary, obesity and SDB may lead to structural changes in PreCG and INS, which may further affect externalizing problems and cognitive performance in children.

Previous studies reported associations between the reduction in sleep duration and obesity as well as abnormal brain development in children (Bonuck et al. 2015; Cheng et al. 2021). In this study, we found that higher BMI and greater SDB symptom burden were associated with shorter sleep duration. When we repeated the identical analyses using sleep duration replacing SDB, results showed that the associations with regional cortical structures were inconsistent with the pattern seen with SDB. Our data revealed associations between longer sleep duration and larger cortical volumes in STS_L and SMG_L. STS, as part of the “social brain,” integrates sensory and emotional inputs (Frith 2007), while SMG has been associated with speech output and short-term memory (Binder 2017), and structural changes in these regions have been associated with sleep duration in children (Cheng et al. 2021). However, we did not find similar association between SDB and cortical morphometry in these brain regions. We therefore speculate that the associations between BMI, SDB, and brain morphometry are not solely the consequence of reduced sleep duration.

Then, we showed that BMI and SDB in girls were associated with more regional structural changes (lower cortical thickness, area, and volume) than for boys. Although we did not find any association between SDB in boys and poor cognitive function, in girls we showed that both BMI and SDB were negatively associated with poorer cognitive performance. Though the mechanisms underlying these sex differences are unclear, we speculate that the greater impact of obesity and SDB on brain development, cognitive function, and mental health in girls than boys might reflect a greater vulnerability due to their faster neurodevelopmental trajectories when compared with boys (Tomasi and Volkow 2023).

We also showed the correlation between BMI and SDB symptom burden at baseline and cognitive and psychiatric symptoms in children at 2-year follow-up. The higher BMI and SDB symptoms burden at baseline were associated with somatic complaints, social problems, internalizing, and externalizing problems, as well as cognitive performance at 2-year follow-up. Studies have reported an increased risk of adolescents with overweight or obesity becoming adults with overweight or obesity, and mental and physical health problems associated with childhood obesity continue into adulthood, increasing the risk of ill health throughout the life span (Singh et al. 2008; Jiang et al. 2022). Obesity and weight gain in children are associated with persistent SDB (Frye et al. 2019). Accompanied by escalating levels of BMI, SDB symptoms burden may also have a sustained impact on internalization and externalization problems, as well as cognitive function in children during the transition from childhood to early adolescence (Lo Bue et al. 2020; Nosetti et al. 2022). In summary, these findings suggest that preventing obesity in the early stages of childhood development may help decrease SDB symptom burden in children, while also avoiding the long-term impact of higher BMI and SDB symptom burden on abnormal neurological and cognitive development as well as psychiatric problems.

Limitations

There are some limitations of this study. Notably, this study focused on cross-sectional data, although the results demonstrated the association between BMI and SDB at baseline and cognitive performance and psychiatric symptoms at 2-year follow-up in children. As follow-up data become available, future studies could explore the causal effects between overweight/obesity and SDB, as well as the long-term effects on brain morphometry and behavior measurements in children. In addition, our findings only focused on sMRI studies, and multi-modal MRI is warranted to more deeply characterize impact of obesity and SDB on brain development in children.

Conclusion

This study aimed to investigate the impact of BMI and SDB on brain morphometry, mental health, and cognitive function as well as their associations. These findings revealed that SDB mediated the impact of overweight/obesity on cortical thickness in lOFC_R and LING_L and cortical volumes in PoCG_L, PreCG_L, PCUN_L, SPL_L, and INS_R. SDB and cortical volumes in PreCG_L and INS_R were related to externalizing symptoms and cognitive performance. These findings provide evidence that SDB contributes to the adverse mental health and cognitive outcomes in children with overweight/obesity and highlights the importance of therapeutic interventions that target sleep impairments related to obesity.

Supplementary Material

Supplemental_Material_bhad267
Click here for additional data file. (2.7MB, zip)

Acknowledgments

Data used in preparing this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), which was a multisite, longitudinal study aiming to recruit more than 10,000 children aged 9–10 years and track them over 10 years. The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. The full list of supporters is available at https://abcdstudy.org/nih-collaborators, and the list of participating sites as well as the complete list of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from NIMH Data Archive Digital Object Identifier (http://dx.doi.org/10.15154/1519007).

Contributor Information

Jianqi Cui, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Guanya Li, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Minmin Zhang, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Jiayu Xu, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Haowen Qi, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Weibin Ji, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Feifei Wu, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Yaqi Zhang, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Fukun Jiang, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Yang Hu, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Wenchao Zhang, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Xiaorong Wei, Kindergarten, Air Force Medical University, Xi'an, Shaanxi 710032, China.

Peter Manza, Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Nora D Volkow, Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Xinbo Gao, Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Institute for Brain, Guangyang Bay Laboratory, Chongqing 400064, China.

Gene-Jack Wang, Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Yi Zhang, Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education， Xi'an, Shaanxi 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Author contributions

Conceptualization, Gene-Jack Wang, Yi Zhang; Acquisition, analysis, and interpretation of data, Jianqi Cui, Guanya Li, Weibin Ji, Fukun Jiang, Yaqi Zhang, Feifei Wu, Wenchao Zhang, Yang Hu, Minmin Zhang, Jiayu Xu, Haowen Qi; Writing-Original Draft, Jianqi Cui, Yi Zhang, Gene-Jack Wang; Writing-Review and Editing, Yi Zhang, Xinbo Gao, Gene-Jack Wang, Nora D. Volkow, and Peter Manza. All authors critically reviewed the content and approved the final version for publication.

Funding

This work was supported by the National Natural Science Foundation of China (grant number 82172023, 82202252); National Key R&D Program of China (No.2022YFC3500603); Natural Science Basic Research Program of Shaanxi (grant number 2022JC-44, 2022JQ-622, 2023-JC-QN-0922, 2023-ZDLSF-07); the Fundamental Research Funds for the Central Universities (grant number ZYTS23188; XJSJ23190; XJS221201; QTZX23093) and support in part from the Intramural Research Program of the National Institute on Alcoholism and Alcohol Abuse (grant number Y1AA3009) to P.M., N.D.V., G.J.W.

Conflict of interest statement: The authors declare no biomedical financial interests or potential conflicts of interest.

CRediT author statement

Jianqi Cui (Data curation, Formal analysis, Writing—original draft, Writing—review & editing), Guanya Li (Data curation, Formal analysis), Minmin Zhang (Data curation, Formal analysis), Jiayu Xu (Data curation, Formal analysis), Haowen Qi (Data curation, Formal analysis), Weibin Ji (Data curation, Formal analysis), Feifei Wu (Data curation, Formal analysis), Yaqi Zhang (Data curation, Formal analysis), Fukun Jiang (Data curation, Formal analysis), Yang Hu (Data curation, Formal analysis), Wenchao Zhang (Data curation, Formal analysis), Xiaorong Wei, (Data curation, Formal analysis), Peter Manza, (Funding acquisition, Writing—review & editing), Nora Volkow, (Funding acquisition, Writing—review & editing), Xinbo Gao, Writing—review & editing, Gene-Jack Wang, (Conceptualization, Funding acquisition, Writing—original draft, Writing—review & editing), Yi Zhang (Conceptualization, Funding acquisition, Writing—original draft, Writing—review & editing).

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