eprintid: 10071403
rev_number: 19
eprint_status: archive
userid: 608
dir: disk0/10/07/14/03
datestamp: 2019-04-02 16:12:09
lastmod: 2021-09-28 22:30:52
status_changed: 2019-04-02 16:12:09
type: article
metadata_visibility: show
creators_name: Lombardo, MV
creators_name: Lai, M-C
creators_name: Auyeung, B
creators_name: Holt, RJ
creators_name: Allison, C
creators_name: Smith, P
creators_name: Chakrabarti, B
creators_name: Ruigrok, ANV
creators_name: Suckling, J
creators_name: Bullmore, ET
creators_name: MRC AIMS Consortium, .
creators_name: Ecker, C
creators_name: Craig, MC
creators_name: Murphy, DGM
creators_name: Happé, F
creators_name: Baron-Cohen, S
title: Unsupervised data-driven stratification of mentalizing heterogeneity in autism
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F42
keywords: autism spectrum disorders, human behaviour
note: © The Author(s) 2016. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license,
unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.
abstract: Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n = 694; n = 249), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 45-62% of ASC adults show evidence for large impairments (Cohen's d = -1.03 to -11.21), while other subgroups are effectively unimpaired. These findings delineate robust natural subdivisions within the ASC population that may allow for more individualized inferences and accelerate research towards precision medicine goals.
date: 2016-10-18
date_type: published
official_url: https://doi.org/10.1038/srep35333
oa_status: green
full_text_type: pub
pmcid: PMC5067562
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1198040
doi: 10.1038/srep35333
pii: srep35333
lyricists_name: Henty, Julian
lyricists_id: JHENT20
actors_name: Henty, Julian
actors_id: JHENT20
actors_role: owner
full_text_status: public
publication: Scientific Reports
volume: 6
article_number: 35333
issn: 2045-2322
citation:        Lombardo, MV;    Lai, M-C;    Auyeung, B;    Holt, RJ;    Allison, C;    Smith, P;    Chakrabarti, B;                                     ... Baron-Cohen, S; + view all <#>        Lombardo, MV;  Lai, M-C;  Auyeung, B;  Holt, RJ;  Allison, C;  Smith, P;  Chakrabarti, B;  Ruigrok, ANV;  Suckling, J;  Bullmore, ET;  MRC AIMS Consortium, .;  Ecker, C;  Craig, MC;  Murphy, DGM;  Happé, F;  Baron-Cohen, S;   - view fewer <#>    (2016)    Unsupervised data-driven stratification of mentalizing heterogeneity in autism.                   Scientific Reports , 6     , Article 35333.  10.1038/srep35333 <https://doi.org/10.1038/srep35333>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10071403/1/Unsupervised%20data-driven%20stratification%20of%20mentalizing%20heterogeneity%20in%20autism.pdf