Evidence map›Paper›PMID 41254934›Full record

ArticleInternational journal of methods in psychiatric research2025

A Network Analysis of ADHD and ASD Symptoms in Chinese Children: Insights on Age and Gender Differences.

Jie Luo, Wen Shao, Yuanzhen Wu, Huanhuan Huang, Gaoyang Xu, Yanjie Qi, Paul Overton, Yi Zheng, Fan He

Abstract read
In one paragraph

Article in International journal of methods in psychiatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jie LuoThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.ORCID 0000-0001-7926-054X
Wen ShaoDepartment of Psychology, University of Sheffield, Sheffield, UK.ORCID 0000-0002-8716-5312
Yuanzhen WuThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Huanhuan HuangThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Gaoyang XuThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Yanjie QiThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Paul OvertonDepartment of Psychology, University of Sheffield, Sheffield, UK.ORCID 0000-0003-4334-261X
Yi ZhengThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Fan HeThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.

Funding

Beijing Hospitals Authority Clinical Medicine Development of Special ZYLX202128Research on prevention and control of major chronic non-communicable diseases in the Ministry of Science and Technology 2016YFC1306100
6 · The paper itself

Abstract

objectiveThis study aimed to explore the transdiagnostic interactions between ADHD and ASD symptoms in Chinese children, identifying core and bridging symptoms, and examining differences in symptom networks across gender and age subgroups.

methodUsing data from a nationwide mental health survey of 71,217 Chinese children (mean age = 11.49, SD = 2.82), a symptom network analysis was conducted. ADHD and ASD symptoms were assessed via the Child Behavior Checklist (CBCL), with ASD items selected from an empirically derived CBCL autism scale. Network estimation was performed using the Glasso algorithm, and community detection was achieved through exploratory graph analysis (EGA). Network comparison tests (NCT) were used to evaluate differences in network structure and connectivity between gender (male vs. female) and age (younger vs. older) subgroups.

resultsInattentive symptoms (I8 "Cannot concentrate," I78 "Inattentive/easily distracted") emerged as central bridging nodes linking ADHD and ASD symptoms. Three stable communities were identified: (1) an inattentive/internalizing cluster reflecting overlapping ADHD-inattentive and ASD features, (2) a hyperactive/impulsive and immature behavior cluster, and (3) a social withdrawal/low energy cluster representing core ASD features. Females exhibited significantly higher network connectivity than males (global strength: S = 1.05, p = 0.03), with tighter symptom interplay. Older children showed greater ADHD-ASD symptom overlap, though global strength differences were non-significant (S = 0.70, p = 0.13).

conclusionTwo inattentive symptoms (I8 and I78) emerged as among the most strongly connected items in the combined ADHD-ASD symptom profile, suggesting that attentional difficulties could play an important role in the psychopathological mechanisms underlying both conditions. Nevertheless, this cross-sectional finding does not establish causality; longitudinal and intervention studies remain necessary. Screening approaches tailored to gender differences and local cultural context could improve identification of these symptoms in Chinese children and adolescents. The present findings may also guide future refinements in school mental-health support and related policy development.

Indexed as

Attention Deficit Disorder with HyperactivityAutism Spectrum DisorderAdolescentAge FactorsChildChinaEast Asian PeopleFemaleHumansMaleSex FactorsADHDASDChinese childrencomorbiditygender differencessymptom network analysis

Identifiers

PMID41254934
PMCPMC12627236

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.