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    Understanding the nature of face processing in early autism: a prospective study

    Tye, C. and Bussu, G. and Gliga, Teodora and Elsabbagh, Mayada and Pasco, G. and Johnsen, K. and Charman, T. and Jones, Emily J.H. and Buitelaar, J. and Johnson, Mark H. (2022) Understanding the nature of face processing in early autism: a prospective study. Journal of Abnormal Psychology 131 (6), pp. 542-555. ISSN 0021-843X.

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    Dimensional approaches to psychopathology interrogate the core neurocognitive domains interactingat the individual level to shape diagnostic symptoms. Embedding this approach in prospective longitudinal studies couldtransform our understanding of the mechanisms underlying neurodevelopmental disorders. Such designs require us to move beyond traditional group comparisons and determine which domain-specific alterations apply at the level of the individual, and whether they vary across distinct phenotypic subgroups. As a proof of principle, this studyexamineshow the domain of face processingcontributes to the emergenceof Autism Spectrum Disorder (ASD). We used an event-related potentials (ERPs) task in a cohort of 8-month-oldinfants with (n=148) and without (n=68) an older sibling withASD, andcombined traditional case-control comparisonswith machine-learningtechniques for prediction of social traits and ASD diagnosisat 36 months,and Bayesian hierarchical clustering for stratification into subgroups. Abroad profile of alterations in the time-course of neural processing of faces in infancy was predictive oflaterASD, with a strong convergence in ERP features predicting social traits and diagnosis.We identified two main subgroups in ASD,defined by distinct patternsof neural responsestofaces,which differed on latersensory sensitivity. Taken together, our findings suggest that individual differences between infantscontribute to the diffuse pattern of alterations predictive of ASD in the first year of life. Moving from group-level comparisons to pattern recognition and stratification can help to understand and reduce heterogeneity in clinical cohorts, and improve our understanding of the mechanisms that lead to later neurodevelopmental outcomes.


    Item Type: Article
    Additional Information: ©American Psychological Association 2020. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at the DOI cited above.
    Keyword(s) / Subject(s): autism, ERP, face processing, machine learning, prospective longitudinal study
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Psychological Sciences
    Research Centres and Institutes: Brain and Cognitive Development, Centre for (CBCD)
    Depositing User: Emily Jones
    Date Deposited: 24 Sep 2020 12:32
    Last Modified: 02 Aug 2023 18:04


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