TY - JOUR T1 - Unsupervised data-driven stratification of mentalizing heterogeneity in autism JF - bioRxiv DO - 10.1101/034454 SP - 034454 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 Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/04/29/034454.abstract N2 - 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. ER -