PT  - JOURNAL ARTICLE
AU  - Michael V. Lombardo
AU  - Meng-Chuan Lai
AU  - Bonnie Auyeung
AU  - Rosemary J. Holt
AU  - Carrie Allison
AU  - Paula Smith
AU  - Bhismadev Chakrabarti
AU  - Amber N. V. Ruigrok
AU  - John Suckling
AU  - Edward T. Bullmore
AU  - MRC AIMS Consortium
AU  - Christine Ecker
AU  - Michael C. Craig
AU  - Declan G. M. Murphy
AU  - Francesca Happé
AU  - Simon Baron-Cohen
TI  - Unsupervised data-driven stratification of mentalizing heterogeneity in autism
AID  - 10.1101/034454
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 034454
4099  - http://biorxiv.org/content/early/2016/04/29/034454.short
4100  - http://biorxiv.org/content/early/2016/04/29/034454.full
AB  - 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=715; n=251), 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 42-65% 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.