Lombardo, MV;
Lai, M-C;
Auyeung, B;
Holt, RJ;
Allison, C;
Smith, P;
Chakrabarti, B;
... Baron-Cohen, S; + view all
(2016)
Unsupervised data-driven stratification of mentalizing heterogeneity in autism.
Scientific Reports
, 6
, Article 35333. 10.1038/srep35333.
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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.
Type: | Article |
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Title: | Unsupervised data-driven stratification of mentalizing heterogeneity in autism |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1038/srep35333 |
Publisher version: | https://doi.org/10.1038/srep35333 |
Language: | English |
Additional information: | © 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/. |
Keywords: | autism spectrum disorders, human behaviour |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10071403 |




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