The relationship between screen time and anxiety/depression symptoms in preschool children with autism spectrum disorder: the mediating role of cortical volume

Abstract

Background

Both screen time (ST) and the development of cortical volume have significant effects on the mental health of children. This study aims to examine the relationship between ST and mental health in preschool children with autism spectrum disorder (ASD) while investigating the potential mediating role of cortical volume. The findings may provide evidence to support clinical identification and intervention strategies.

Methods

A retrospective observational analysis was conducted on 149 children with ASD aged 18–60 months, who were categorized into two groups based on their daily ST: high-dose exposure (HDE) and low-dose exposure (LDE). The study compared the two groups in terms of demographic characteristics, ASD symptoms, neurodevelopmental level, mental health condition (assessed using the Child Behavior Checklist, CBCL), and cortical volume. Pearson correlation tests were used to determine the direct correlations between ST, cortical volume, and mental health. Multi-step linear regression analysis was used to investigate the mediating effect of cortical volume on the association between ST and mental health conditions.

Results

Children in the HDE group showed significantly higher anxiety/depression scores (60.02 ± 28.76 vs. 50.17 ± 23.10, p = 0.03) and reduced cortical volume in the left superior frontal area (25,232.13 ± 3069.41 vs. 26,441.19 ± 3032.22, p = 0.02) compared to the LDE group. ST was positively correlated with anxiety/depression symptoms (r = 0.20, p = 0.02) and negatively correlated with the left superior frontal cortical volume (r= -0.29, p < 0.001). Furthermore, mediation analysis revealed that the cortical volume of the left superior frontal area fully mediated the relationship between ST and anxiety/depression symptoms.

Conclusions

The findings suggest that ST adversely affects the mental health of children with ASD, with cortical volume in the left superior frontal area potentially serving as a key mediator in this relationship. These findings highlight the need for ST management in early ASD interventions.

Trial registration

Chinese Clinical Trial Registry (ChiCTR): ChiCTR2100051141. Registered on 14 September 2021. [URL: http://www.chictr.org.cn]

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-025-06203-5.

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by persistent deficits in social communication, as well as interaction and stereotyped or repetitive patterns of behavior, interests, or activities [1]. Recent statistics report that the prevalence of ASD is approximately 1 in 36 children aged 8 years [2], with this rate steadily increasing. In addition to its core characteristics, children with ASD experience comorbid mental health conditions, including anxiety, depression, aggression, and Attention-Deficit/Hyperactivity Disorder (ADHD) [3]. Research suggests that children with ASD face a higher risk of mental health challenges compared to children with typical development (TD) [4, 5]. Longitudinal studies further indicate that these poor mental health conditions tend to persist into adulthood for many individuals with ASD [6]. The presence of co-occurring mental health conditions in children with ASD can exacerbate long-term outcomes, placing substantial burdens on families and society [7]. Consequently, early prevention and intervention are critical for improving the well-being of these children.

The increasing prevalence of digital devices has made screen time (ST), whether active or passive, a common part of young children’s daily lives [8, 9]. The American Academy of Pediatrics (AAP) recommends that children under the age of two avoid digital devices, while children aged two to five should have no more than one hour of ST per day [10]. However, previous research suggested that excessive screen exposure is prevalent among children with ASD. A 2019 systematic literature review found that children and adolescents with ASD had longer ST than their typically developing peers [11]. In our previous clinical study, we reported that Chinese children under 6 years of age with ASD had an average ST of 3.34 h per day, significantly higher than the 0.91 h per day observed in TD children [12].

Excessive ST has been well documented as a risk factor for poor mental health conditions among children and adolescents. A 2022 meta-analysis of 87 studies suggested a weak but significant correlation between increased ST and both internalizing and externalizing mental health problems [13]. Additionally, a US cohort study revealed that adolescents who use social media for more than three hours daily are at greater risk for developing mental health issues [14]. Lissak (2018) reported that excessive ST is significantly associated with an increased risk of depression, suicidal behavior, and heightened severity of ADHD symptoms [15]. A Canadian study further identified ST as a potential risk factor or indicator of anxiety and depression among adolescents [16]. A study from Saudi Arabia also reported that early and prolonged exposure to electronic screens may be associated with increased dental anxiety in children aged 6–12 years [17]. Similarly, data from the 2016 National Survey of Children’s Health (NSCH) conducted by the U.S. Census Bureau indicated a correlation between ST and poorer mental health outcomes in children and adolescents [18].

On the other hand, excessive screen exposure in early childhood in children with ASD may be associated with alterations in brain development [19]. Early studies suggest that prolonged ST may contribute to alterations in both intra- and inter-network functional connectivity in children with ASD [20]. Given that the first few years of life are a critical period for rapid brain development [21], the developing brain is particularly sensitive to environmental stimuli, which can lead to significant alterations in brain structure and function [22]. Children with ASD often exhibit abnormal volumetric changes in brain structures and demonstrate impaired connectivity and integration of brain functions, which can render them more susceptible to environmental stressors [23–25]. Vaccarino et al. proposed that reductions in both white matter and gray matter volume might exhibit a correlative association with more severe ASD symptoms [26]. Previous studies have also indicated that excessive ST is associated with alterations in the microstructure of gray and white matter, manifested as atrophy of the frontal lobe gray matter and weakened interhemispheric connections [27], which is likely to further exacerbate the effects of ST on children with ASD during the crucial stage of brain development [28].

Furthermore, studies have shown that mental health issues in children and adolescents are closely linked to changes in brain structure. For instance, a 2018 longitudinal study found that incipient depression in adolescence is associated with thinning of the frontal cortex [29]. In contrast, a study comparing anxious children and adolescents with healthy controls observed increased cortical thickness in the ventromedial prefrontal cortex (vmPFC) among those with anxiety [30]. A large-sample study targeting children aged 9 to 10 demonstrated that the symptoms of ADHD and behavioral problems are inversely associated with global gray volume [31]. Moreover, research from the National Institutes of Health in the US indicated that thicknesses of the left orbitofrontal, right retrosplenial cingulate, and medial temporal cortex show a negative correlation with externalizing mental problems in children [32].

To date, no studies have explored the relationship between mental health issues, ST, and cortical development in preschool children with ASD, nor have the underlying mechanisms been explored. Given the complex interplay among these factors, we conducted this study. We proposed the following hypotheses: First, compared to the low-dose screen exposure group, children with ASD in the high-dose screen exposure group would show more significant developmental delays, more severe behavioral and mental health problems, and smaller cortical volumes. Second, screen time in children with ASD may serve as a predictor of anxiety/depression symptoms, with cortical volume changes potentially mediating this relationship. We conducted this study in order to investigate our hypothesis and enhance a comprehensive understanding of ASD in children.

Materials and methods

Participants

This observational study retrospectively analyzed patient files of 156 children with ASD who received initial diagnoses at Department of Developmental and Behavioral Pediatrics, First Hospital of Jilin University between August 2022 and March 2023. The inclusion criteria were as follows: (1) children aged 18–60 months; (2) meeting the diagnostic criteria for ASD in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), with no severity level restrictions; (3) neither the children nor their parents had previously participated in any systematic intervention or training programs. The exclusion criteria were: (1) incomplete or missing clinical data; (2) an inability to complete structural MRI or poor structural MRI quality. Ultimately, 149 subjects met the inclusion criteria. After assessing the average daily screen time for each participant, the median value was calculated. Based on this median, participants were divided into two groups: a low-dose exposure (LDE) group and a high-dose exposure (HDE) group.

Approval of standard protocols, registration, and patient consent

This study was carried out in strict accordance with ethical guidelines and received approval from the Ethics Committee of the First Hospital of Jilin University. It was registered with the Chinese Clinical Trial Registry (Registration Number: ChiCTR2100051141). Written informed consent was obtained from the legal guardians of all participating children, who were fully informed about the research objectives, procedures, and data usage.

Data collection

Baseline demographic and clinical assessments were conducted within 48 h following the children’s clinical visits. The evaluators gathered general demographic data, encompassing age, gender, premature birth status, birth weight, and annual family income. Screen time was measured as the total amount of time of exposure to various electronic screens (e.g., television viewing, gaming, and mobile phone using) during the six-month period prior to the survey. We did not differentiate between screen content types, parental controls, or co-viewing status. The collected screen time data included separate measurements for both weekdays (Monday to Friday) and weekends (Saturday and Sunday). The average daily ST of the children was computed and logged using the formula: Average daily ST (h) = [ST per weekday (h) × 5 + ST per weekend day (h) × 2]/7 [20]. The Childhood Autism Rating Scale (CARS) was employed to gauge the severity of ASD [33]. For evaluating the developmental level, the Griffiths Development Scales-Chinese Language Edition (GDS-C) was utilized. This scale categorizes children into five neurodevelopmental domains: locomotor, personal-social, hearing and language, eye-hand coordination, and performance. The developmental level of each domain is represented by a developmental quotient (DQ) [34].

Furthermore, the evaluators employed the Child Behavior Checklist (CBCL) to assess children’s behavior and mental health [35]. The revised version of CBCL for 1.5- to 5-year-olds is widely used for behavioral and mental screening, with good applicability for preschool children with ASD [36, 37]. This scale, comprising 99 items, has a total score that can be transformed into a T score. Generally, the higher the score, the more severe the problems are. The CBCL has seven clinical syndrome subscales: withdrawn, anxiety/depression, sleep problems, somatic complaints, aggressive behavior, attention problems, and emotionally reactive. Additionally, there are three summary scales: internalizing problems, externalizing problems, and total problems.

MRI acquisition

Within 72 h of collecting ST data, all participants underwent structural magnetic resonance imaging (MRI) examinations. The structural MRI data were acquired using a Philips Ingenia Elition 3.0 T magnetic resonance scanner (Amsterdam, The Netherlands) at the First Hospital of Jilin University. All subjects received 10% chloral hydrate (at a dose of 50 mg/kg) rectally one hour before MRI. This sedative has decades of clinical use with proven efficacy and safety, and is recommended by Japanese pediatric guidelines for non-invasive diagnostics [38, 39]. Rectal administration demonstrates fewer adverse effects than intravenous or oral administration, especially in uncooperative patients. Procedures strictly followed the Radiology Sedation Committee’s protocol, with post-scan recovery monitoring (heart rate, oxygen saturation, and respiratory rate) until meeting discharge criteria.

Structural MRI scans were obtained via a three-dimensional spoiled gradient echo (3D-SPGR) sequence. The imaging parameters were as follows: Field of view (FOV): 226 mm × 226 mm, Repetition time (TR): 6.6 ms, Echo time (TE): 3.0 ms, Flip angle (FA): 8°, Slice thickness: 1 mm, Number of slices: 150, Voxel size: 1.0 mm × 1.0 mm × 1.0 mm, Total scan time: 2 min 29 s.

Image preprocessing

We processed T1-weighted images with Freesurfer 6.0 (http://www.surfer.nmr.mgh.harvard.edu/), a commonly used tool for cortical reconstruction and subcortical volume segmentation. Here are the key preprocessing steps: Motion-corrected and averaged T1 images; non-brain tissue removal; automatic Talairach transformation; subcortical gray and white matter region segmentation; intensity normalization and skull stripping; pial surface generation and cortical reconstruction.

All reconstructed cortical and subcortical images were visually inspected by three trained researchers to identify errors in skull stripping, pial or white matter surface placement, and tissue segmentation. In cases where inaccuracies were observed (e.g., misclassification of gray/white matter boundaries or incomplete brain extraction), manual corrections were performed using FreeSurfer’s control points and editing tools in accordance with the software’s recommended procedures. Each corrected image was reprocessed and verified to ensure anatomical accuracy. Cortical volume measurements for 68 brain regions were then extracted according to the Desikan-Killiany atlas [40]. We obtained volumetric measurements of both whole and regional cortical volumes for each subject using a previously validated method. The previous description covered the technical details of this method [41].

Data analysis

Statistical analysis was performed using SPSS 27.0. The normality of continuous variables was assessed using the Kolmogorov-Smirnov test. Continuous variables with a normal distribution are expressed as mean ± standard deviation (SD), and categorical variables are expressed as frequency (percentages). Parametric data were analyzed using t-tests, and categorical variables were compared using a chi-squared or Fisher’s exact test. Pearson linear correlation analysis was used to analyze the values of cortical volumes and clinical data. Multiple linear regression analysis was conducted to assess the effect of screen time on anxiety/depression symptoms, with autism severity (CARS score), age at assessment, and annual family income included as covariates. Multistep linear regression analysis was used to explore the mediating role of cortical volumes in the relationship between ST and CBCL scores. A two-tailed p-value < 0.05 was considered statistically significant.

Results

Comparison of demographic and clinical characteristics

A comparison of the demographic characteristics between the two groups revealed several significant differences. The age at first visit in the HDE group was significantly younger than that in the LDE group, suggesting earlier symptom onset and diagnosis in the HDE group. (36.40 ± 9.72 vs. 40.06 ± 11.45, p = 0.04). The ASD severity levels in the HDE group were higher than those in the LDE group, but this difference did not reach statistical significance (35.75 ± 3.08 vs. 34.72 ± 4.00, p = 0.08). Furthermore, DQs for hearing and language were significantly lower in the HDE group than in the LDE group (35.54 ± 14.92 vs. 42.09 ± 19.64, p = 0.02) (Table 1).

Table 1.

Demographic and clinical characteristics of participants

Characteristics	LDE group (n = 81)	HDE group (n = 68)	t/x2	P
Age, months(mean ± SD)	40.06 ± 11.45	36.40 ± 9.72	2.07	0.04
Sex, male (%)	71.3	68.7	0.11	0.73
Preterm birth(%)	8.8	4.4	1.09	0.29
Annual income (10,000 yuan)(%)			5.04	0.08
0–5	20.0	35.3
5–10	58.8	42.6
> 10	21.3	22.1
Mean ST (min/day) (mean ± SD)	24.67 ± 17.41	181.93 ± 99.91	12.81	<0.001
Total CARS scores(mean ± SD)	34.72 ± 4.00	35.75 ± 3.08	1.71	0.08
Griffiths (DQ)
Locomotor(mean ± SD)	69.07 ± 14.69	70.80 ± 13.85	0.73	0.46
Personal − social(mean ± SD)	52.02 ± 16.05	49.75 ± 16.18	0.85	0.39
Hearing and language(mean ± SD)	42.09 ± 19.64	35.54 ± 14.92	2.31	0.02
Eye − hand coordination(mean ± SD)	54.49 ± 17.75	53.82 ± 16.30	0.23	0.81
Performance(mean ± SD)	57.96 ± 19.25	61.47 ± 21.33	1.05	0.29

LDE low-dose exposure, HDE high-dose exposure, ST screen time, CARS childhood autism rating scale, DQ developmental quotient

Comparison between the HDE and LDE groups of CBCL scores

In the comparison of behavior and mental health of the two groups of participants, we found that the CBCL scores of anxiety/depression (60.02 ± 28.76 vs. 50.17 ± 23.10, p = 0.03), emotionally reactive (56.98 ± 37.07 vs. 45.35 ± 27.85, p = 0.03) and internalizing problems (58.50 ± 19.52 vs. 52.00 ± 19.35, p = 0.04) in the HDE group were significantly higher than those of the LDE group (Fig. 1).

Fig. 1.

Comparison of CBCL scores between the HDE and LDE groups

Comparison between the HDE and LDE groups of cortical and subcortical volume

In the comparison of the cortical volume between the two participant groups, we found that the cortical volume of the left superior frontal area (25,232.13 ± 3069.41 vs. 26,441.19 ± 3032.22, p = 0.02) in the HDE group was significantly smaller than that of the LDE group (Fig. 2). In the comparison of subcortical volume between the two participant groups, we did not find statistically significant differences.

Fig. 2.

Comparison of cortical volume between the HDE and LDE groups

Correlations between ST and cortical volume and anxiety/depression symptoms

The results of Pearson correlation analysis showed that the CBCL scores of anxiety/depression were positively correlated with ST (r = 0.20, p = 0.02) (Fig. 3a). The cortical volume of the left superior frontal area was negatively correlated with ST (r = −0.29, p < 0.001) (Fig. 3b). The cortical volume of the left superior frontal area was negatively correlated with the CBCL scores of anxiety/depression (r = −0.22, p = 0.008) (Fig. 3c).

Fig. 3.

Pearson correlations among ST, cortical volume of the left superior frontal area, and anxiety/depression

Regression analysis suggested that ST is a predictor of anxiety/depression symptoms

The multiple linear regression analysis demonstrated that screen time was a significant predictor of CBCL anxiety/depression scores after controlling for age, CARS score, and annual family income. Longer screen time was associated with higher anxiety/depression scores (B = 0.048, SE = 0.02, β = 0.192, t = 2.401, p = 0.02) (Table 2).

Table 2.

Regression analysis suggested that ST is a predictor of anxiety/depression symptoms

	B(SE)	β	t	P	R 2
Total CARS scores	0.942(0.570)	0.131	1.653	0.10	0.144
Age, months	0.680(0.190)	0.281	3.583	< 0.001
Annual income	−5.193(2.945)	−0.138	−1.763	0.08
Mean ST, min	0.048(0.020)	0.192	2.401	0.02

ST screen time, CARS childhood autism rating scale

^* P

^*** P

The mediating effect of cortical volume on the relationship between ST and anxiety/depression symptoms

The multi-step linear regression model revealed a mediating role for the cortical volume of the left superior frontal area in the relationship between ST and CBCL scores for anxiety/depression. The mediation model showed that ST significantly negatively predicted cortical volume of the left superior frontal area (B = −8.733, SE = 2.344, β = −0.294, 95% CI[−13.3654, −4.1005], R² = 0.09, p < 0.001) (Fig. 4b). Additionally, both ST and cortical volume of the left superior frontal area significantly negatively predicted CBCL scores of anxiety/depression (B = −0.002, SE = 0.001, β = −0.173, 95% CI[−0.0029, −0.0001], R² = 0.07, p = 0.04) (Fig. 4b). In summary, cortical volume of the left superior frontal area completely mediated the relationship between ST and CBCL scores of anxiety/depression. The results for other indices were not statistically significant.

Fig. 4.

Mediation path model. Coefficients of determination in the relationship between ST and anxiety/depression symptoms, mediated by cortical volume of the left superior frontal area

Discussion

This study aimed to investigate the relationships among ST, cortical development, and mental health in young children with ASD. The primary findings of our research provide new insights into how ST may influence both cortical development and mental well-being in this vulnerable population. Notably, we found significant associations between daily ST, cortical volume in the left superior frontal area, and symptoms of anxiety/depression. These results suggest that the effects of ST on cortical development may play a key role in shaping mental health outcomes in children with ASD.

Comparison of clinical and demographic characteristics between HDE and LDE groups

Our analysis revealed that the two groups showed no significant differences in gender, ASD severity, preterm birth, or annual household income, indicating similar baseline characteristics. Notably, the initial diagnosis age of the HDE group was significantly younger than that of the LDE group, indicating that children with higher ST exposure exhibited clinical symptoms of ASD at an earlier stage. This finding is consistent with prior research, which has highlighted the potential for earlier symptom onset in children with increased ST exposure [20]. Additionally, in the comparison of clinical characteristics between the two groups, we observed that the HDE group had lower developmental quotients (DQ) in hearing and language, suggesting that increased ST may contribute to negative developmental outcomes. This finding aligns with prior research [12, 42].

The relationship between ST and anxiety/depression symptoms

Our study observed that daily mean ST was significantly associated with anxiety/depression symptoms in children with ASD. Regression analysis indicated that ST served as a predictor for anxiety/depression symptoms. While the findings from previous studies are not entirely consistent, a substantial body of research supports our results. Studies conducted in Canada, the United States, China, and the United Kingdom have found significant associations between ST and anxiety/depression symptoms in children and adolescents [16, 43–45]. In contrast, McVeigh and Kovess-Masfety concluded that ST is not correlated with an increased risk of mental health problems [46, 47]. This might be because the associations between ST and mental health vary according to screen types: new media use (social media, online games, online videos) correlates with higher depression risk, while traditional Television shows inverse associations [48]. In addition, there is evidence suggesting that gender and social context can also have a potential impact on the relationship between ST and mental health [49].

However, much of the existing literature focuses primarily on typically developing children and school-aged children and adolescents and reports adverse mental health effects of ST in both groups. Considering that the atypical brain structural development of children with ASD increases their susceptibility to adverse environments and that the brains of young children are highly plastic [23, 50], ST may have more adverse impacts on preschool children with ASD. Besides the direct effects brought by screens, these impacts also involve the mediating role of changes in cortical volume, yet the underlying mechanisms remain unclear.

The full mediating role of the left superior frontal cortical volume between ST and anxiety/depression

To explore the underlying mechanisms, we investigated a potential mediating factor and found that the cortical volume of the left superior frontal area plays a full mediating role in the relationship between ST and anxiety/depression symptoms. This finding is consistent with a previous study examining typically developing adolescents aged 9 to 10 years. The study demonstrated that imbalances in the development of specific cortical and subcortical regions (i.e., greater cortical gray volume reduction and slower subcortical gray volume expansion) partially mediated the relationship between ST and internalizing symptoms two years later [51]. Although our results indicate that the left superior frontal cortical volume fully mediates this relationship rather than partially mediating it, we found that the direct effect of ST on anxiety/depression showed a statistically significant trend (p = 0.08). This may be attributed to the limitations of sample size and other undiscovered mediating factors, highlighting the need for future studies to be conducted in a more comprehensive manner to further validate our research findings.

One possible explanation for the observed brain changes is the “displacement hypothesis”. Previous studies have shown a clear association between ST and adverse changes in brain structure (i.e., a thinner cortex, a reduced volume, and a lower microstructural integrity of brain white matter tracts) [52, 53]. In contrast, reading habits have profound benefits for the development of brain structure [54]. When children spend a lot of time in front of electronic screens, whether it’s watching videos, playing games or browsing simple information, it will take up the time that could have been used for activities that are more beneficial to brain development, such as reading. Additionally, according to the selective-elimination hypothesis, the brain optimizes neural connections through synaptic pruning, a process that is highly specific and activity-dependent. Infrequently used neural connections are selectively eliminated, while frequently used connections are strengthened [55]. The unique characteristics of brain development in children with ASD may exacerbate these negative effects.

The superior frontal area plays a crucial role in emotion regulation [56, 57]. Damage or structural changes in this region may impair an individual’s ability to effectively regulate and manage emotions, thus increasing the risk of anxiety and depression. For instance, Juan et al. found that individuals with anxiety/depression had significantly reduced gray matter volume in the left superior frontal gyrus compared to individuals without these conditions. These structural alterations could contribute to functional impairments [58]. Furthermore, the alterations in the prefrontal cortex (PFC) of individuals with anxiety/depression might stem from the dysregulation of glutaminergic and GABAergic transmission. Hare’s research has illustrated that the levels of glutamate metabolites and GABA synthetic enzyme glutamate decarboxylase-67 in the PFC of individuals with depression are diminished, which suggests a malfunction in the neuronal transmission and communication within the PFC, consequently giving rise to impairment in their emotional and cognitive faculties [59].

Limitations

This study is the first to investigate the relationships among ST, cortical development, and mental health in preschool children with ASD. However, several limitations should be noted. First, it did not differentiate between screen types and content, potentially overlooking the benefits of certain screen activities. Second, as a cross-sectional study, it could not establish causal relationships between ST and anxiety/depression symptoms. Therefore, longitudinal studies are needed to determine causality. Third, the HDE and LDE groups showed age mismatch, which may potentially influence the observed differences in hearing and language DQ, cortical volume, and anxiety/depression symptoms between the two groups. Fourth, while cortical volume in the left superior frontal area was explored as a mediator, other factors may also contribute to anxiety/depression in children with ASD. Future research should identify additional mediators. Fifth, this study did not perform formal sample size estimation, and all data were collected from a single hospital. Although the sample size of 149 participants is comparable to previous observational studies in similar fields [20], future research should incorporate power-based sample size calculations and adopt multicenter designs to enhance the robustness of findings. Sixth, the screen time was calculated based on parental reports, which may introduce subjective bias. Future studies should incorporate tools capable of objectively measuring screen time to enhance data reliability. Finally, the use of the CBCL scale to assess emotions in preschool children with ASD has certain limitations due to its inability to fully capture the complex emotional characteristics of these children, and the inherent subjectivity of scale assessments. Future research should aim to address this issue by incorporating more objective measures, such as biological indicators, to provide a more comprehensive understanding of the relationship between screen time, anxiety, depression, and cortical volume in this population.

Conclusion

In summary, ST serves as a predictor of anxiety and depression symptoms in preschool children with ASD. The cortical volume of the left superior frontal area may act as a key neuroimaging mediator in this relationship. These findings underscore the importance of raising awareness among clinicians and parents regarding the potential impact of ST on the mental health of preschool children with ASD. It is recommended that clinicians incorporate screen time management into early interventions for ASD.

Abbreviations

ASD

Autism spectrum disorder

TD

Typical development

ST

Screen time

AAP

American academy of pediatrics

NSCH

National survey of children's health

GDS-C

Griffiths development scales-chinese language edition

DQ

Developmental quotient

CARS

Childhood autism rating scale

LDE

Low-dose exposure

HDE

High-dose exposure

ADHD

Attention-deficit/hyperactivity disorder

vmPFC

Ventromedial prefrontal cortex

DSM-5

The fifth edition of the diagnostic and statistical manual of mental disorders

CBCL

Child behavior checklist

3D-SPGR

Three-dimensional spoiled gradient echo

MRI

Magnetic resonance imaging

FOV

Field of view

TR

Repetition time

TE

Echo time

FA

Flip angle

Authors’ contributions

RC: Writing-Original draft preparation, Visualization, Investigation, Writing-review & editing. MSB: Writing-Original draft preparation, Data curation, Investigation, Writing-review & editing. TZ: Investigation, Writing-review & editing. YX: Investigation, Data curation, Writing-review & editing. ZAM: Investigation, Writing-review & editing. FYJ: Supervision, Conceptualization, Writing-review & editing.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data availability

All the data and materials are available. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the first hospital of Jilin university’s institutional ethics committee. Written informed consent was obtained from parents/legal guardians before including children in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.