Michael V. Lombardo a , 1 , 2 , Meng-Chuan Lai 2 , 3 , 4 , Bonnie Auyeung 2 , 5 , Rosemary J. Holt 2 , Carrie Allison 2 , Paula Smith 2 , Bhismadev Chakrabarti 2 , 6 , Amber N. V. Ruigrok 2 , John Suckling 7 , 8 , Edward T. Bullmore 7 , 8 , MRC AIMS Consortium, Christine Ecker 9 , 12 , Michael C. Craig 9 , 13 , Declan G. M. Murphy 9 , Francesca Happé 11 , Simon Baron-Cohen 2 , 8
18 October 2016
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.