Participants
The ABCD study is an ongoing project in 21 centers across the United States, following a cohort of 11,878 children aged 9–10 years [27]. Details of its recruitment and ethics have been published [28, 29]. Parents of all participants provided their written consent after the procedures were fully explained to them by the investigators; additionally, the children provided their assent before participating in the ABCD study [30]. We obtained data on the participants’ brain structure and behavior, as well as their demographic background from the National Institute of Mental Health (NIMH) Data Archive ABCD Data Release 5.0. The University of Fukui’s Research Ethics Committee approved the data analysis (Assurance no. FU-20210067). Participants’ demographic data and covariates are summarized in Table 1. For the analyses, we included data of 10,116 and 7,880 children at baseline and during the 2-year follow-up, respectively. To maximize the sample size, all models were performed with all the available participants.
Table 1
Demographic data and covariates of participants |
| Baseline (n = 10122) | 2-year follow up (n = 7880) |
Age (month) | 119.00 ± 7.50 | 143.69 ± 7.85 |
Sex Male Female | 5276 4846 | 3740 4139 |
Parents’ income < 49,999 50,000–74,999 75,000–99,999 100,000–199,999 ≥ 200,000 | 2928 1394 1469 3135 1196 | 2090 1109 1207 2530 944 |
Parents’ education (years) | 15.34 ± 2.53 | 15.46 ± 2.47 |
Race White Black Hispanic Asian Other | 5606 1327 2971 180 1038 | 4104 756 1322 116 690 |
Pubertal status (scores) | 1.63 ± 0.42 | 2.15 ± 0.65 |
Sleep duration (hours) | 9.80 ± 1.26 | 9.32 ± 1.28 |
Physical activity (times per week) | 3.54 ± 2.30 | 3.77 ± 2.16 |
Handedness-score rating | | |
Right-handed | 8132 | 6239 |
Left-handed | 730 | 558 |
Mixed-handed | 1361 | 1083 |
Total intracranial volume (cm3) | 1496.70 ± 143.12 | 1528.54 ± 146.85 |
Screen time (baseline) | 3.70 ± 3.00 | 3.60 ± 2.86 |
ADHD T-score | 53.08 ± 5.48 | 52.93 ± 5.13 |
Note: ADHD, attention-deficit/hyperactivity disorder |
Screen time
We assessed screen time using a self-reported questionnaire, and computed it as the total amount of time spent using various devices, including playing video games and watching television. The ABCD study provided the total screen time for typical weekdays and weekends, and we calculated a weighted-sum score to represent a daily screen time of 5/7× hours of screen time (weekday) + 2/7× hours of screen time (weekend) [31]. The data were available for the baseline (11,067 samples).
ADHD symptoms
To evaluate the level of the children’s ADHD symptoms, we used the ADHD-related DSM-5-oriented syndrome scales of the Child Behavior Checklist (CBCL) [32]. The data used in our study at baseline and during the 2-year follow-up comprised 10,116 and 6,986 samples, respectively. We recorded all the scores as T-scores, with higher scores representing greater behavioral problems.
Brain structure
Details of magnetic resonance imaging acquisition and data preprocessing in the ABCD study were published by Casey et al. and Hagler et al. [33, 34]. The ABCD study used three Tesla magnetic resonance scanners (Siemens, General Electric 750, and Philips) to obtain high-resolution T1-weighted three-dimensional structural images (1 mm isotropic) and acquisition parameters, as previously described [33]. The structural data were processed using FreeSurfer (version 5.3.0) with a standardized processing pipeline [34]. In our study, we used structural data with the Desikan-Killiany atlas-based classification for cortical regions, and atlas-based segmentation for subcortical regions.
We included data from participants whose data satisfied the FreeSurfer quality control for structural imaging data for analysis. We included 34 cortical regions and seven subcortical regions for one hemisphere (68 and 14 regions in total) for volume, and 34 cortical regions in each hemisphere (68 regions in total) for thickness. Additionally, as previous studies had found ADHD to be associated with cortical gray matter volume [26, 35, 36] and mean thickness of the cerebral cortex [25], we included total cortical volume and mean cortical thickness, directly measured by FreeSurfer in the analyses.
Demographic variables and covariates
Based on previous ABCD-based studies [37–40], we included the variables listed in Table 1 as covariates. We coded sex and race/ethnicity (White, Black, Hispanic, Asian, or others) as dummy variables, and treated parental income as a five-level categorical variable, as in previous studies [38, 39]. We included age, parental education levels, pubertal status, total intracranial volume, daily sleep duration, and physical activity as continuous variables, and recorded parental education levels by school year, as in previous studies [38, 39]. We assessed pubertal status using the Pubertal Development scale [41]. Additionally, according to previous studies, as sleep and physical activities are typically included in an analysis of the relationship between screen-related activities and ADHD [3, 4, 42], we included sleep duration and frequency of physical activities as covariates.
Statistical analysis
In this study, we used R (version 4.3.1; The R Foundation for Statistical Computing, Vienna, Austria) to perform statistical analysis. We created scatter plot figures using R-package “ggplot” and brain mapping figures using Python version 3.6.8 (The Python Software Foundation, USA) with python-package “Pysurfer” to display brain-related statistics.
First, we examined the association between screen time and ADHD symptoms. We analyzed data from 10,116 samples at baseline, and 6,986 samples after a 2-year follow-up. We winsorized data (outliers of screen time, brain structure, ADHD symptoms, and all continuous covariates) at three standard deviations from the mean. First, we adapted a linear mixed-effects model to examine cross-sectional relationships between screen time and ADHD symptoms using baseline data. We analyzed this model using R-package “lmertest.” Based on previous studies [38, 39], we modeled family ID (used to denote sibling status), multiple data collection, and twin or triplet status, as random effects. For the mixed-effects model with ADHD symptoms as the dependent variable, we considered children’s age, sex, race, pubertal status, household income, parental education, sleep duration, and physical activity as covariates. Second, we conducted a residualized change regression model [43] of a 2-year change in ADHD symptoms to examine the effect of screen time on the development of ADHD symptoms. Specifically, 2-year follow-up of ADHD symptoms were regressed on baseline screen time, controlling for baseline ADHD symptoms. In this model, we also adopted family ID, multiple data collection, and twin or triplet status as random effects and the abovementioned variables as covariates.
Further, we examined the association between screen time and brain structure. We analyzed the data of 9,713 and 6,426 samples at baseline and during the 2-year follow-up, respectively. First, we used linear mixed-effects models to examine the effect of screen time on brain structure. For the mixed-effects model, using brain structure as the dependent variable, we considered multiple data collections and twin or triplet status modeled as random effects. In addition to the covariates of the mixed-effects model of ADHD symptoms, we included handedness and total intracranial volume as covariates for the brain structure, including volume and thickness. The p-values were false discovery rate (FDR)-corrected for multiple testing per structure [44]. Second, we performed the same residualized change regression for each brain structure measurement. We regressed the brain structures at 2-year follow-up on baseline screen time, controlling for baseline brain structures. In this model, we also adapted family ID, multiple data collection, and twin or triplet status as random effects, and the above-mentioned variables as covariates.
Finally, we examined the mediating effect of brain structure on the association of screen time with ADHD symptoms at baseline and the development of ADHD symptoms. We analyzed data from 9,663 samples at baseline, and 5,472 samples after a 2-year follow-up. First, we performed a mediation analysis of the structures that were significantly associated with screen time in the relationship between screen time and ADHD symptoms at baseline. We residualized brain structure measures and ADHD symptoms for study site variables by the linear mixed-effects model, and then converted these measures to z-scores. We used the R-package “lanvnn” to perform a standard three-variable mediation analysis to estimate the significance of the mediating effect by using the bias-corrected bootstrap approach (with 10,000 random samplings). Second, we performed a mediation analysis of the development of structures that were significantly associated with screen time in residualized change regressions on the relationship between screen time and the development of ADHD symptoms. We residualized brain structure measures (2-year follow-up) for the above-mentioned variables and baseline brain structures, using the linear mixed-effects model, and then converted these measures to z-scores. We residualized ADHD symptoms (2-year follow-up) for the above-mentioned variables and baseline ADHD symptoms, using the linear mixed-effects model, and then converted these measures to z-scores. We performed a standard three-variable mediation analysis to estimate the significance of the mediating effect using a bias-corrected bootstrap approach (with 10,000 random samplings).