Evidence map›Paper›PMID 41787373›Full record

ArticleBMC psychiatry2026

Exploring autism spectrum profiles via network analysis of parent-reported ASSQ patterns.

Mingwan Zhou, Honghe Zhang

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Article in BMC psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Mingwan ZhouPsychological Assessment Center, Xiamen Xianyue Hospital, Xianyue Hospital Affiliated with Xiamen Medical College, Fujian Psychiatric Center, Fujian Clinical Research Center for Mental Disorders, No. 387, Xianyue Road, Siming District, Xiamen, Fujian Province, 360102, China.
Honghe ZhangPsychological Assessment Center, Xiamen Xianyue Hospital, Xianyue Hospital Affiliated with Xiamen Medical College, Fujian Psychiatric Center, Fujian Clinical Research Center for Mental Disorders, No. 387, Xianyue Road, Siming District, Xiamen, Fujian Province, 360102, China. xiaojiujiuo123@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe behavioral profile of autism spectrum disorder (ASD) is highly heterogeneous. Analyses relying on total scores often fail to capture the associative patterns among individual behavioral items. This study applied network analysis to parent-reported Autism Spectrum Screening Questionnaire (ASSQ) data to explore the internal associative structure among core ASD behavioral domains and to identify key items within the network.

methodsThis retrospective study included 995 children and adolescents aged 7-16 years diagnosed with ASD according to ICD-10 criteria at Xiamen Xianyue Hospital between 2019 and 2025, whose parents completed the ASSQ. Given the 3-point Likert scale of the ASSQ, two parallel network modeling strategies were employed to enhance robustness: a dichotomized (Ising model) and an ordinal (Gaussian Graphical Model, GGM) approach to explore associations among ASSQ items. Comorbidities and psychiatric medication history were included as covariates. Central and bridge nodes were identified using expected influence and bridge expected influence, respectively, and the optimal model was selected. Network Comparison Tests were conducted to examine differences in global strength and structure between sex (male/female) and age groups (7-11/12-16 years).

results(1) The ordinal network model (ASSQ-GGM network) demonstrated better centrality stability and was therefore selected for reporting. (2) The ASSQ-GGM network identified the following central nodes: Q11 (inability to adjust, EI = 1.238), Q27 (unusual posture, EI = 1.154), and Q15 (fails to make relationships with peers, EI =  1.047). Bridge nodes were: Q26 (unusual facial expression, bEI = 0.806), Q11 (inability to adjust, bEI = 0.719) and Q12 (lacks empathy, bEI=0.710). (3) Network Comparison Tests found no significant differences in network structure by sex or age, which may be due to insufficient or imbalanced subsample sizes. The possibility of sex- or age-specific network patterns cannot be ruled out.

conclusionsThis study presents a network of interrelated parent-reported ASD-related behavioral features based on ASSQ items. No significant sex or age differences in network structure were found. These findings provide insights into the associative patterns among ASSQ items. As this study is based on cross-sectional, guardian-reported data, the findings are hypothesis-generating and do not support causal inferences. Any implications for intervention require validation through longitudinal or experimental studies. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Autism Spectrum DisorderAdolescentChildFemaleHumansMaleParentsRetrospective StudiesSurveys and QuestionnairesASSQAutism spectrum disorderChildren and adolescentsNetwork analysis

Identifiers

PMID41787373
PMCPMC13023168

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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.