TY - JOUR
T1 - From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder
AU - Wolfers, Thomas
AU - Floris, Dorothea L
AU - Dinga, Richard
AU - van Rooij, Daan
AU - Isakoglou, Christina
AU - Kia, Seyed Mostafa
AU - Zabihi, Mariam
AU - Llera, Alberto
AU - Chowdanayaka, Rajanikanth
AU - Kumar, Vinod J
AU - Peng, Han
AU - Laidi, Charles
AU - Batalle, Dafnis
AU - Dimitrova, Ralica
AU - Charman, Tony
AU - Loth, Eva
AU - Lai, Meng-Chuan
AU - Jones, Emily
AU - Baumeister, Sarah
AU - Moessnang, Caroline
AU - Banaschewski, Tobias
AU - Ecker, Christine
AU - Dumas, Guillaume
AU - O'Muircheartaigh, Jonathan
AU - Murphy, Declan
AU - Buitelaar, Jan K
AU - Marquand, Andre F
AU - Beckmann, Christian F
N1 - Copyright © 2019. Published by Elsevier Ltd.
PY - 2019/9
Y1 - 2019/9
N2 - 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. 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.
AB - 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. 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.
KW - Autism spectrum disorder
KW - Biotypes
KW - Classification
KW - Clustering
KW - Machine learning
KW - Pattern recognition
KW - Precision medicine
KW - Stratification
UR - http://www.scopus.com/inward/record.url?scp=85069633471&partnerID=8YFLogxK
U2 - 10.1016/j.neubiorev.2019.07.010
DO - 10.1016/j.neubiorev.2019.07.010
M3 - Review article
C2 - 31330196
SN - 0149-7634
VL - 104
SP - 240
EP - 254
JO - Neuroscience and biobehavioral reviews
JF - Neuroscience and biobehavioral reviews
ER -