2nd Reading February 15, 2019 14:34 1850058 International Journal of Neural Systems, Vol. 29, No. 0 (2019) 1850058 (22 pages) c The Author(s) DOI: 10.1142/S0129065718500582 A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms Juan M. G´orriz∗,JavierRam´ırez, F. Segovia and Francisco J. Mart´ınez Department of Signal Theory, Networking and Communications University of Granada, Granada 18071, Spain ∗
[email protected];
[email protected] Meng-Chuan Lai Centre for Addiction and Mental Health and The Hospital for Sick Children, Toronto, Canada Department of Psychiatry University of Toronto, Toronto, ON M6J 1H4, Canada Michael V. Lombardo Department of Psychology University of Cyprus, 2109 Aglantzia, Nicosia, Cyprus Simon Baron-Cohen, MRC AIMS Consortium† and John Suckling Department of Psychiatry, University of Cambridge Cambridge, CB2 0SZ, UK Accepted 8 December 2018 Published Online 18 February 2019 Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investi- gating differences between the sexes in autism is the small sample sizes of available imaging datasets with Int. J. Neur. Syst. Downloaded from www.worldscientific.com mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing partici- pants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate.