| Literature DB >> 31330196 |
Thomas Wolfers1, Dorothea L Floris2, Richard Dinga3, Daan van Rooij2, Christina Isakoglou2, Seyed Mostafa Kia2, Mariam Zabihi2, Alberto Llera2, Rajanikanth Chowdanayaka4, Vinod J Kumar5, Han Peng6, Charles Laidi7, Dafnis Batalle8, Ralica Dimitrova8, Tony Charman9, Eva Loth10, Meng-Chuan Lai11, Emily Jones12, Sarah Baumeister13, Carolin Moessnang13, Tobias Banaschewski13, Christine Ecker14, Guillaume Dumas15, Jonathan O'Muircheartaigh16, Declan Murphy16, Jan K Buitelaar17, Andre F Marquand18, Christian F Beckmann19.
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.Entities:
Keywords: Autism spectrum disorder; Biotypes; Classification; Clustering; Machine learning; Pattern recognition; Precision medicine; Stratification
Mesh:
Year: 2019 PMID: 31330196 DOI: 10.1016/j.neubiorev.2019.07.010
Source DB: PubMed Journal: Neurosci Biobehav Rev ISSN: 0149-7634 Impact factor: 8.989