Unsupervised data-driven stratification of mentalizing heterogeneity in autism

作者：Lombardo Michael V; Lai Meng Chuan; Auyeung Bonnie; Holt Rosemary J; Allison Carrie; Smith Paula; Chakrabarti Bhismadev; Ruigrok Amber N V; Suckling John; Bullmore Edward T; Ecker Christine; Craig Michael C; Murphy Declan G M; Happe Francesca; Baron Cohen Simon

来源：Scientific Reports, 2016, 6(1): 35333.

DOI：10.1038/srep35333

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.

• 出版日期2016-10-18