Literature DB >> 31330196

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

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.
Copyright © 2019 Elsevier Ltd. All rights reserved.

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


  18 in total

1.  Analysing brain networks in population neuroscience: a case for the Bayesian philosophy.

Authors:  Danilo Bzdok; Dorothea L Floris; Andre F Marquand
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-02-24       Impact factor: 6.237

2.  Future Prospects for Epigenetics in Autism Spectrum Disorder.

Authors:  Logan A Williams; Janine M LaSalle
Journal:  Mol Diagn Ther       Date:  2022-08-13       Impact factor: 4.476

3.  Features that best define the heterogeneity and homogeneity of autism in preschool-age children: A multisite case-control analysis replicated across two independent samples.

Authors:  Lisa D Wiggins; Lin H Tian; Eric Rubenstein; Laura Schieve; Julie Daniels; Karen Pazol; Carolyn DiGuiseppi; Brian Barger; Eric Moody; Steven Rosenberg; Chyrise Bradley; Melanie Hsu; Cordelia Robinson Rosenberg; Deborah Christensen; Tessa Crume; Juhi Pandey; Susan E Levy
Journal:  Autism Res       Date:  2021-12-29       Impact factor: 4.633

Review 4.  Ethical dimensions of translational developmental neuroscience research in autism.

Authors:  Arianna Manzini; Emily J H Jones; Tony Charman; Mayada Elsabbagh; Mark H Johnson; Ilina Singh
Journal:  J Child Psychol Psychiatry       Date:  2021-08-18       Impact factor: 8.982

5.  Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models.

Authors:  Yu Yao; Klaas E Stephan
Journal:  Hum Brain Mapp       Date:  2021-04-07       Impact factor: 5.038

6.  Clinical and Translational Implications of an Emerging Developmental Substructure for Autism.

Authors:  John N Constantino; Tony Charman; Emily J H Jones
Journal:  Annu Rev Clin Psychol       Date:  2021-02-12       Impact factor: 22.098

7.  A Metabolomics Approach to Screening for Autism Risk in the Children's Autism Metabolome Project.

Authors:  Alan M Smith; Marvin R Natowicz; Daniel Braas; Michael A Ludwig; Denise M Ney; Elizabeth L R Donley; Robert E Burrier; David G Amaral
Journal:  Autism Res       Date:  2020-06-18       Impact factor: 5.216

8.  Towards robust and replicable sex differences in the intrinsic brain function of autism.

Authors:  Dorothea L Floris; José O A Filho; Meng-Chuan Lai; Steve Giavasis; Marianne Oldehinkel; Maarten Mennes; Tony Charman; Julian Tillmann; Guillaume Dumas; Christine Ecker; Flavio Dell'Acqua; Tobias Banaschewski; Carolin Moessnang; Simon Baron-Cohen; Sarah Durston; Eva Loth; Declan G M Murphy; Jan K Buitelaar; Christian F Beckmann; Michael P Milham; Adriana Di Martino
Journal:  Mol Autism       Date:  2021-03-01       Impact factor: 6.476

Review 9.  Brain imaging-based machine learning in autism spectrum disorder: methods and applications.

Authors:  Ming Xu; Vince Calhoun; Rongtao Jiang; Weizheng Yan; Jing Sui
Journal:  J Neurosci Methods       Date:  2021-06-24       Impact factor: 2.390

10.  Fractionating autism based on neuroanatomical normative modeling.

Authors:  Mariam Zabihi; Dorothea L Floris; Seyed Mostafa Kia; Thomas Wolfers; Julian Tillmann; Alberto Llera Arenas; Carolin Moessnang; Tobias Banaschewski; Rosemary Holt; Simon Baron-Cohen; Eva Loth; Tony Charman; Thomas Bourgeron; Declan Murphy; Christine Ecker; Jan K Buitelaar; Christian F Beckmann; Andre Marquand
Journal:  Transl Psychiatry       Date:  2020-11-06       Impact factor: 7.989

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.