RT Journal Article
SR Electronic
T1 Unsupervised data-driven stratification of mentalizing heterogeneity in autism
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 034454
DO 10.1101/034454
A1 Michael V. Lombardo
A1 Meng-Chuan Lai
A1 Bonnie Auyeung
A1 Rosemary J. Holt
A1 Carrie Allison
A1 Paula Smith
A1 Bhismadev Chakrabarti
A1 Amber N. V. Ruigrok
A1 John Suckling
A1 Edward T. Bullmore
A1 MRC AIMS Consortium
A1 Christine Ecker
A1 Michael C. Craig
A1 Declan G. M. Murphy
A1 Francesca Happé
A1 Simon Baron-Cohen
YR 2016
UL http://biorxiv.org/content/early/2016/04/29/034454.abstract
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.