Research Trends in Autism Spectrum Disorder: Evidence from Scopus-Indexed Bibliometric Data
Article Sidebar
Main Article Content
Abstract
This study aims to analyze the intellectual structure and research trends in Autism Spectrum Disorder through a bibliometric approach based on Scopus-indexed publications. Data were retrieved using relevant keywords and filtered based on document type, language, and publication period to ensure quality and consistency. The analysis was conducted using VOSviewer to examine co-occurrence of keywords, co-authorship networks, and citation relationships. The results reveal a significant increase in ASD-related publications over time, indicating growing global research interest. The co-occurrence analysis identifies several dominant research clusters, including clinical and cognitive studies, early childhood diagnosis, genetic and biological mechanisms, and technology-based diagnostic approaches such as neuroimaging. The density visualization further confirms that core research themes are concentrated around clinical intervention and behavioral understanding, while emerging topics such as functional connectivity and advanced diagnostic imaging are gaining attention. Additionally, collaboration patterns show that research output is largely dominated by developed countries, suggesting the need for broader global participation. Despite the rapid development of the field, several gaps remain, particularly in underrepresented regions and longitudinal research designs. This study provides a comprehensive overview of the evolution of ASD research and offers insights into future directions, emphasizing the importance of interdisciplinary integration and technological innovation in advancing the field.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
[1] S. Graham et al., “Artificial intelligence for mental health and mental illnesses: an overview,” Curr. Psychiatry Rep., vol. 21, pp. 1–18, 2019.
[2] S. B. Teasdale et al., “Dietary intake of people with severe mental illness: systematic review and meta-analysis,” Br. J. Psychiatry, vol. 214, no. 5, pp. 251–259, 2019.
[3] L. Wing and J. Gould, “Severe impairments of social interaction and associated abnormalities in children: Epidemiology and classification,” J. Autism Dev. Disord., vol. 9, no. 1, pp. 11–29, 1979.
[4] M. N. Morrisey, C. L. Reed, D. N. McIntosh, and M. D. Rutherford, “Brief report: Attentional cueing to images of social interactions is automatic for neurotypical individuals but not those with ASC,” J. Autism Dev. Disord., vol. 48, pp. 3233–3243, 2018.
[5] J. D. Steinmetz et al., “Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021,” Lancet Neurol., vol. 23, no. 4, pp. 344–381, 2024.
[6] S. Morabito, F. Reese, N. Rahimzadeh, E. Miyoshi, and V. Swarup, “hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data,” Cell reports methods, vol. 3, no. 6, 2023.
[7] K. A. Shaw, “Prevalence and early identification of autism spectrum disorder among children aged 4 and 8 years—Autism and Developmental Disabilities Monitoring Network, 16 Sites, United States, 2022,” MMWR. Surveill. Summ., vol. 74, 2025.
[8] Q. Wang, Q. Yang, and X. Liu, “The microbiota–gut–brain axis and neurodevelopmental disorders,” Protein Cell, vol. 14, no. 10, pp. 762–775, 2023.
[9] A. Abi‐Dargham et al., “Candidate biomarkers in psychiatric disorders: state of the field,” World psychiatry, vol. 22, no. 2, pp. 236–262, 2023.
[10] C. Yen, C.-L. Lin, and M.-C. Chiang, “Exploring the frontiers of neuroimaging: a review of recent advances in understanding brain functioning and disorders,” Life, vol. 13, no. 7, p. 1472, 2023.
[11] V. Khachadourian et al., “Comorbidities in autism spectrum disorder and their etiologies,” Transl. Psychiatry, vol. 13, no. 1, p. 71, 2023.
[12] S. T. Schafer et al., “An in vivo neuroimmune organoid model to study human microglia phenotypes,” Cell, vol. 186, no. 10, pp. 2111–2126, 2023.
[13] C. Li et al., “Single-cell brain organoid screening identifies developmental defects in autism,” Nature, vol. 621, no. 7978, pp. 373–380, 2023.
[14] M. S. Farooq, R. Tehseen, M. Sabir, and Z. Atal, “Detection of autism spectrum disorder (ASD) in children and adults using machine learning,” Sci. Rep., vol. 13, no. 1, p. 9605, 2023.