Skip to main content
Log in

Network Structure of Autism Spectrum Disorder Behaviors and Its Evolution in Preschool Children: Insights from a New Longitudinal Network Analysis Method

  • Original Paper
  • Published:
Journal of Autism and Developmental Disorders Aims and scope Submit manuscript

Abstract

Network modeling of the social, communication and restrictive/repetitive behaviors (RRBs) included in the definition of Autism Spectrum Disorder was performed. The Autism Diagnostic Interview-Revised (ADI-R) assessed behaviors in 139 pre-school cases at two cross-sections that averaged 34.8 months apart. Cross-sectional networks were based on the correlation matrix of the ADI-R behavioral items and the “bootCross” method was developed and enabled the estimation of a longitudinal network. At both stages, RRB items/nodes formed a consistent peripheral cluster, while social and communication nodes formed a core cluster that diverged with time. These differences in the nature and evolution of the RRB and socio-communicative dimensions indicate that their inter-behavior dynamics are very different. The most central behaviors across stages are proposed as prime targets for efficient therapeutic intervention.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. RMSEA is a measure of absolute fit that assesses the discrepancy related to approximation in the population, and corrected for model complexity through the division by the degrees of freedom. CFI and TLI measure incremental fit that assess how much the specified model is superior to an alternative “baseline” model in reproducing the observed covariance matrix. The baseline model is usually a null model of uncorrelated variables.

References

  • American Psychiatric Association. (1980). Diagnostic and Statistical Manual of Mental Disorders (DSM-3®). American Psychiatric Pub.

  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub.

  • Anderson, G. M., Montazeri, F., & de Bildt, A. (2015). Network approach to autistic traits: Group and subgroup analyses of ADOS item scores. Journal of Autism and Developmental Disorders, 45(10), 3115–3132.

    Article  PubMed  Google Scholar 

  • Baker, J. P. (2013). Autism at 70–redrawing the boundaries. The New England Journal of Medicine, 369(12), 1089.

    Article  PubMed  Google Scholar 

  • Beard, C., Millner, A. J., Forgeard, M. J., Fried, E. I., Hsu, K. J., Treadway, M. T., Leonard, C. V., Kertz, S. J., & Björgvinsson, T. (2016). Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychological Medicine, 46(16), 3359–3369.

    Article  PubMed  PubMed Central  Google Scholar 

  • Berument, S. K., Rutter, M., Lord, C., Pickles, A., & Bailey, A. (1999). Autism screening questionnaire: Diagnostic validity. The British Journal of Psychiatry, 175(5), 444–451.

    Article  PubMed  Google Scholar 

  • Bollen, K. A. (1989). Structural equations with latent variables (Vol. 210). Wiley

  • Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5–13.

    Article  PubMed  PubMed Central  Google Scholar 

  • Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608

    Article  PubMed  Google Scholar 

  • Boschloo, L., van Borkulo, C. D., Rhemtulla, M., Keyes, K. M., Borsboom, D., & Schoevers, R. A. (2015). The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS ONE, 10(9), e0137621.

    Article  PubMed  PubMed Central  Google Scholar 

  • Briganti, G., Kempenaers, C., Braun, S., Fried, E. I., & Linkowski, P. (2018). Network analysis of empathy items from the interpersonal reactivity index in 1973 young adults. Psychiatry Research, 265, 87–92.

    Article  PubMed  Google Scholar 

  • Bringmann, L. F., & Eronen, M. I. (2018). Don’t blame the model: Reconsidering the network approach to psychopathology. Psychological Review, 125(4), 606.

    Article  PubMed  Google Scholar 

  • Cramer, A. O., Waldorp, L. J., van der Maas, H. L., & Borsboom, D. (2010). Complex realities require complex theories: Refining and extending the network approach to mental disorders. Behav Brain Sciences, 33, 178–193.

    Article  Google Scholar 

  • Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.

    Google Scholar 

  • De Beurs, D., Fried, E. I., Wetherall, K., Cleare, S., O’ Connor, D. B., Ferguson, E., & O’ Connor, R. C. (2019). Exploring the psychology of suicidal ideation: A theory driven network analysis. Behaviour Research and Therapy, 120, 103419.

    Article  PubMed  Google Scholar 

  • Dietz, C., Swinkels, S., van Daalen, E., van Engeland, H., & Buitelaar, J. K. (2006). Screening for autistic spectrum disorder in children aged 14–15 months. II: Population screening with the Early Screening of Autistic Traits Questionnaire (ESAT). Design and general findings. Journal of Autism and Developmental Disorders., 36(6), 713–722.

    Article  PubMed  Google Scholar 

  • Epskamp, S. (2020). Psychometric network models from time-series and panel data. Psychometrika, 1–26.

  • Epskamp, S., Borsboom, D., & Fried, E. I. (2018a). Estimating Psychological Networks and their Accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212.

    Article  PubMed  Google Scholar 

  • Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617.

    Article  PubMed  Google Scholar 

  • Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018b). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480.

    Article  PubMed  Google Scholar 

  • Frazier, T. W., Youngstrom, E. A., Speer, L., Embacher, R., Law, P., Constantino, J., Robert, L., Findling, M. D., Antonio, Y., Hardan, M. D., & Eng, C. (2012). Validation of proposed DSM-5 criteria for autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 51(1), 28–40.

    Article  Google Scholar 

  • Goekoop, R., & Goekoop, J. G. (2014). A network view on psychiatric disorders: Network clusters of symptoms as elementary syndromes of psychopathology. PLoS ONE, 9(11), e112734.

    Article  PubMed  PubMed Central  Google Scholar 

  • Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12(6), e0174035.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hu, L. T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). Sage Publications Inc.

    Google Scholar 

  • Isvoranu, A. M., & Epskamp, S. (2021). Continuous and ordered categorical data in network psychometrics: Which estimation method to choose? Deriving Guidelines for Applied Researchers Psychological Methods. https://doi.org/10.1037/met0000439

    Article  PubMed  Google Scholar 

  • Lai, K., Yuen, E., Hung, S. F., & Leung, P. (2021). Autism diagnostic interview-revised within DSM-5 framework: Test of reliability and validity in chinese children. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-021-05079-5

    Article  PubMed  PubMed Central  Google Scholar 

  • Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659–685.

    Article  PubMed  Google Scholar 

  • McNally, R. J. (2016). Can network analysis transform psychopathology? Behaviour Research and Therapy, 86, 95–104.

    Article  PubMed  Google Scholar 

  • McNally, R. J., Robinaugh, D. J., Wu, G. W. Y., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: A network approach to posttraumatic stress disorder. Clinical Psychological Science., 3(6), 836–849.

    Article  Google Scholar 

  • Monk, N., McLeod, G., Mulder, R., Spittlehouse, J., & Boden, J. (2021). Childhood anxious/withdrawn behaviour and later anxiety disorder: A network outcome analysis of a population cohort. Psychological Medicine. https://doi.org/10.1017/S0033291721002889

    Article  PubMed  PubMed Central  Google Scholar 

  • Montazeri, F., de Bildt, A., Dekker, V., & Anderson, G. M. (2019). Network analysis of anxiety in the autism realm. Journal of Autism and Developmental Disorders, 49(6), 2219–2230.

    Article  PubMed  Google Scholar 

  • Montazeri, F., de Bildt, A., Dekker, V., & Anderson, G. M. (2020). Network analysis of behaviors in the depression and autism realms: Inter-relationships and clinical implications. Journal of Autism and Developmental Disorders, 50(5), 1580–1595.

    Article  PubMed  Google Scholar 

  • Oosterling, I. J., Wensing, M., Swinkels, S. H., Van Der Gaag, R. J., Visser, J. C., Woudenberg, T., Minderaa, R., Steenhuis, M.-P., & Buitelaar, J. K. (2010). Advancing early detection of autism spectrum disorder by applying an integrated two-stage screening approach. Journal of Child Psychology and Psychiatry, 51(3), 250–258.

    Article  PubMed  Google Scholar 

  • Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.

    Article  Google Scholar 

  • Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74(1), 016110.

    Article  Google Scholar 

  • Revelle, W., & Revelle, M. W. (2015). Package ‘psych.’ The Comprehensive R Archive Network, 337, 338.

    Google Scholar 

  • Rigdon, E. E. (2010). Polychoric correlation coefficient. In N. Salkind (Ed.), Encyclopedia of research design (pp. 1046–1049). Sage.

    Google Scholar 

  • Rigdon, E. E., & Ferguson, C. E., Jr. (1991). The performance of the polychoric correlation coefficient and selected fitting functions in confirmatory factor analysis with ordinal data. Journal of Marketing Research, 28, 491–497.

    Article  Google Scholar 

  • Robinaugh, D. J., Hoekstra, R. H., Toner, E. R., & Borsboom, D. (2020). The network approach to psychopathology: A review of the literature 2008–2018 and an agenda for future research. Psychological Medicine, 50(3), 353–366.

    Article  PubMed  Google Scholar 

  • Rodebaugh, T. L., Tonge, N. A., Piccirillo, M. L., Fried, E., Horenstein, A., Morrison, A. S., Goldin, P., Gross, J. J., Lim, M. H., Fernandez, K. C., Blanco, C., Schneier, F. R., Bogdan, R., Thompson, R. J., & Heimberg, R. G. (2018). Does centrality in a cross-sectional network suggest intervention targets for social anxiety disorder? Journal of Consulting and Clinical Psychology, 86(10), 831–844.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ruzzano, L., Borsboom, D., & Geurts, H. M. (2015). Repetitive behaviors in autism and obsessive-compulsive disorder: New perspectives from a network analysis. Journal of Autism and Developmental Disorders., 45(1), 192–202.

    Article  PubMed  Google Scholar 

  • Sideridis, G. D., & Simos, P. (2010). What is the actual correlation between expressive and receptive measures of vocabulary? Approximating the sampling distribution of the correlation coefficient using the bootstrapping method. The International Journal of Educational and Psychological Assessment, 5, 117–133.

    Google Scholar 

  • Swinkels, S. H., Dietz, C., van Daalen, E., Kerkhof, I. H., van Engeland, H., & Buitelaar, J. K. (2006). Screening for autistic spectrum in children aged 14 to 15 months. I: The development of the Early Screening of Autistic Traits Questionnaire (ESAT). Journal of Autism and Developmental Disorders, 36(6), 723–732.

    Article  PubMed  Google Scholar 

  • van Borkulo, C. D. (2018). Symptom network models in depression research: From methodological exploration to clinical application. University of Groningen.

  • Van Borkulo, C. D., Boschloo, L., Kossakowski, J., Tio, P., Schoevers, R. A., Borsboom, D., & Waldorp, L. J. (2017). Comparing network structures on three aspects: A permutation test. Manuscript submitted for publication, 10.

  • Visser, J. C., Rommelse, N. N., Lappenschaar, M., Servatius-Oosterling, I. J., Greven, C. U., & Buitelaar, J. K. (2017). Variation in the early trajectories of autism symptoms is related to the development of language, cognition, and behavior problems. Journal of the American Academy of Child & Adolescent Psychiatry, 56(8), 659–668.

    Article  Google Scholar 

  • Wang, X., Shi, L., Ou, J., Shen, Y., Wang, Y., Lin, J., Cui, X., Liu, R., Wu, R., Xia, K., & Zhao, J. (2020). Network analysis of core and associated symptoms in preschool children with autism spectrum disorder.

  • Wei, T., Chesnut, S. R., Barnard-Brak, L., & Richman, D. (2015). Psychometric analysis of the Social Communication Questionnaire using an item-response theory framework: Implications for the use of the lifetime and current forms. Journal of Psychopathology and Behavioral Assessment, 37(3), 469–480.

    Article  Google Scholar 

  • Wodka, E. L., Mathy, P., & Kalb, L. (2013). Predictors of phrase and fluent speech in children with autism and severe language delay. Pediatrics, 131(4), e1128–e1134.

    Article  PubMed  Google Scholar 

  • Yang, Z., Algesheimer, R., & Tessone, C. J. (2016). A comparative analysis of community detection algorithms on artificial networks. Scientific Reports, 6(1), 1–18.

    Google Scholar 

  • Zwaigenbaum, L., Bryson, S., Lord, C., Rogers, S., Carter, A., Carver, L., Chawarska, K., Constantino, J., Dawson, G., Dobkins, K., Fein, D., Iverson, J., Klin, A., Landa, R., Messinger, D., Ozonoff, S., Sigman, M., Stone, W., Tager-Flusberg, H., & Yirmiya, N. (2009). Clinical assessment and management of toddlers with suspected autism spectrum disorder: Insights from studies of high-risk infants. Pediatrics, 123(5), 1383–1391.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

We specially thank families and individuals included in the study, and the authorized personnel at the Karakter Child and Adolescent Psychiatry Center at the University of Nijmegen for providing the DIANE dataset.

Funding

This research received no specific grant from any funding agency, commercial or not-for-profit sectors. JKB was supported by the European Union Horizon2020 grant CANDY (No. 847818).

Author information

Authors and Affiliations

Authors

Contributions

FM and GMA conceived the project. FM performed the data analysis and wrote the paper. FM and GMA revised the paper. AdB and IJO provided the DIANE database. AdB, JKB and IJO offered advice on revision of the paper.

Corresponding author

Correspondence to Farhad Montazeri.

Ethics declarations

Conflict of interest

JKB has been in the past three years a consultant to/member of advisory board of/and/or speaker for Takeda/Shire, Roche, Medice, Angelini, Janssen, and Servier. He is not an employee of any of these companies, and not a stock shareholder of any of these companies. He has no other financial or material support, including expert testimony, patents, royalties. AdB is author on the Dutch ADOS and ADI-R manuals for which Accare receives remuneration. FM, IO and GMA report no apparent or real conflicts of interest.

Ethical Approval

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 436 kb)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Montazeri, F., Buitelaar, J.K., Oosterling, I.J. et al. Network Structure of Autism Spectrum Disorder Behaviors and Its Evolution in Preschool Children: Insights from a New Longitudinal Network Analysis Method. J Autism Dev Disord 53, 4293–4307 (2023). https://doi.org/10.1007/s10803-022-05723-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10803-022-05723-8

Keywords

Navigation