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    From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder

    Wolfers, Thomas and Floris, D.L. and Dinga, R. and Rooij, D. and Isakoglou, C. and Kia, S.M. and Zabihi, M. and Llera, A. and Chowdanayaka, R. and Kumar, V.J, and Peng, H. and Laidi, C. and Batalle, D. and Dimitrova, R. and Charman, T. and Loth, E. and Lai, M.-C. and Jones, Emily J.H. and Baumeister, S. and Moessnang, C. and Banaschewski, T. and Ecker, C. and Dumas, G. and O'Muircheartaigh, J. and Murphy, D. and Buitelaar, J. and Marquand, A.F. and Beckmann, C.F. (2019) From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder. Neuroscience and Biobehavioral Reviews 104 , pp. 240-254. ISSN 0149‐7634.

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    Abstract

    Pattern classification and stratification approaches have increasingly been used in research on Autism Spectrum Disorder (ASD) over the last ten years with the goal of translation towards clinical applicability. Here, we present an extensive scoping literature review on those two approaches. We screened a total of 635 studies, of which 57 pattern classification and 19 stratification studies were included. We observed large variance across pattern classification studies in terms of predictive performance from about 60% to 98% accuracy, which is among other factors likely linked to sampling bias, different validation procedures across studies, the heterogeneity of ASD and differences in data quality. Stratification studies were less prevalent with only two studies reporting replications and just a few showing external validation. While some identified strata based on cognition and intelligence reappear across studies, biology as a stratification marker is clearly underexplored. In summary, mapping biological differences at the level of the individual with ASD is a major challenge for the field now. Conceptualizing those mappings and individual trajectories that lead to the diagnosis of ASD, will become a major challenge in the near future.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Autism spectrum disorder, Machine learning, Pattern recognition, Classification, Clustering, Stratification, Biotypes, Precision medicine
    School: Birkbeck Schools and Departments > School of Science > Psychological Sciences
    Research Centre: Brain and Cognitive Development, Centre for (CBCD)
    Depositing User: Emily Jones
    Date Deposited: 31 Jul 2019 10:28
    Last Modified: 01 Mar 2020 11:25
    URI: http://eprints.bbk.ac.uk/id/eprint/28380

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