Unsupervised data-driven stratification of mentalizing heterogeneity in autism

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Lombardo, M. V.; Lai, M-C; Auyeung, B.; Holt, R. J.; Allison, C.; Smith, P.; Chakrabarti, B.; Ruigrok, A. N. V.; Suckling, J.; Bullmore, E. T.; MRC AIMS Consortium; Ecker, C.; Craig, M. C.; Murphy, D. G. M.; Happé, F.; Baron-Cohen, S.;
(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 b... View more
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