Unsupervised data-driven stratification of mentalizing heterogeneity in autism

Lombardo, M. V., Lai, M.-C., Auyeung, B., Holt, R. J., Allison, C., Smith, P., Chakrabarti, B. ORCID: https://orcid.org/0000-0002-6649-7895, Ruigrok, A. N. V., Suckling, J., Bullmore, E. T., Bailey, A. J., Baron-Cohen, S., Bolton, P. F., Bullmore, E. T., Carrington, S., Catani, M., Chakrabarti, B. ORCID: https://orcid.org/0000-0002-6649-7895, Craig, M. C., Daly, E. M., Deoni, S. C. L., Ecker, C., Happé, F., Henty, J., Jezzard, P., Johnston, P., Jones, D. K., Lai, M.-C., Lombardo, M. V., Madden, A., Mullins, D., Murphy, C. M., Murphy, D. G. M., Pasco, G., Ruigrok, A. N. V., Sadek, S. A., Spain, D., Stewart, R., Suckling, J., Wheelwright, S. J., Williams, S. C., Ellie Wilson, C., Ecker, C., Craig, M. C., Murphy, D. G. M., Happé, F. and Baron-Cohen, S. (2016) Unsupervised data-driven stratification of mentalizing heterogeneity in autism. Scientific Reports, 6. p. 35333. ISSN 2045-2322 doi: 10.1038/srep35333

Abstract/Summary

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.