From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder. (September 2019)

• Record Type: Journal Article
• Authors: Wolfers, Thomas; Floris, Dorothea L.; Dinga, Richard; van Rooij, Daan; Isakoglou, Christina; Kia, Seyed Mostafa; Zabihi, Mariam; Llera, Alberto; Chowdanayaka, Rajanikanth; Kumar, Vinod J.; Peng, Han; Laidi, Charles; Batalle, Dafnis; Dimitrova, Ralica; Charman, Tony; Loth, Eva; Lai, Meng-Chuan; Jones, Emily; Baumeister, Sarah; Moessnang, Carolin; Banaschewski, Tobias; Ecker, Christine; Dumas, Guillaume; O'Muircheartaigh, Jonathan; Murphy, Declan; Buitelaar, Jan K.; Marquand, Andre F.; Beckmann, Christian F.
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 104(2019)
• Page Start: 240
• Page End: 254
• Publication Date: 2019-09
• Subjects: Autism spectrum disorder -- Machine learning -- Pattern recognition -- Classification -- Clustering -- Stratification -- Biotypes -- Precision medicine; Psychophysiology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Neurology -- Periodicals; Behavior -- Periodicals; Ethology -- Periodicals; Neurology -- Periodicals; Psychophysiologie -- Périodiques; Comportement humain -- Périodiques; Animaux -- Mœurs et comportement -- Périodiques; Neurologie -- Périodiques; Animal behavior; Human behavior; Neurology; Psychophysiology; Periodicals; Electronic journals; 573.8
• DOI: 10.1016/j.neubiorev.2019.07.010
• Languages: English
• ISSNs: 0149-7634

Abstract

Highlights: Extensive overview on pattern classification and stratification studies in ASD. Compares pattern classification and stratifications approaches head-on. Presents potential future directions for both approaches in ASD research. Suggest promising avenues for clinical translation of these two approaches. 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.