Unsupervised data-driven stratification of mentalizing heterogeneity in autism

Michael V. Lombardo, Meng Chuan Lai, Bonnie Auyeung, Rosemary J. Holt, Carrie Allison, Paula Smith, Bhismadev Chakrabarti, Amber N V Ruigrok, John Suckling, Edward T. Bullmore, MRC AIMS Consortium: Anthony J. Bailey, Simon Baron-Cohen, Patrick F. Bolton, Edward T. Bullmore, Sarah Carrington, Marco Catani, Bhismadev Chakrabarti, Michael C. Craig, Eileen M. Daly, Sean C. L. Deoni, Christine Ecker, Francesca Happé, Julian Henty, Peter Jezzard, Patrick Johnston, Derek K. Jones, Meng-Chuan Lai, Michael V. Lombardo, Anya Madden, Diane Mullins, Clodagh M. Murphy, Declan G. M. Murphy, Greg Pasco, Amber N. V. Ruigrok, Susan A. Sadek, Debbie Spain, Rose Stewart, John Suckling, Sally J. Wheelwright, Steven C. Williams & C. Ellie Wilson, Christine Ecker, Michael C. Craig, Declan G M Murphy, Francesca Happe, Simon Baron-Cohen, Simon Baron-Cohen, Patrick F. Bolton

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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.
Original languageEnglish
Article number35333
JournalScientific Reports
Volume6
Early online date18 Oct 2016
DOIs
Publication statusE-pub ahead of print - 18 Oct 2016

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