A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms.
Górriz, Juan M
Martínez, Francisco J
Lombardo, Michael V
MRC AIMS Consortium,
International journal of neural systems
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Górriz, J. M., Ramírez, J., Segovia, F., Martínez, F. J., Lai, M., Lombardo, M. V., Baron-Cohen, S., et al. (2019). A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms.. International journal of neural systems, 29 (7), 1850058. https://doi.org/10.1142/s0129065718500582
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 investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with 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 participants. 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. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above 80% on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism (N = 120, n = 30/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.
MRC AIMS Consortium, Humans, Magnetic Resonance Imaging, Autistic Disorder, Phenotype, Databases, Factual, Adult, Female, Male, Young Adult, Machine Learning, Support Vector Machine
This work was partly supported by the MINECO under the TEC2015-64718-R project, the Salvador de Madariaga Mobility Grants 2017 and the Consejer´ıa de Econom´ıa, Innovaci´on, Ciencia y Empleo (Junta de Andaluc´ıa, Spain) under the Excellence Project P11-TIC-7103. The study was conducted in association with the National Institute for Health Research Collaborations for Leadership in Applied Health Research and Care (NIHR CLAHRC) East of England (EoE). The project was supported by the UK Medical Research Council (grant number GO 400061) and European Autism Interventions—a Multicentre Study for Developing New Medications (EU-AIMS); EU-AIMS has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n◦ 115300, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007 - 2013) and EFPIA companies’ in-kind contribution. During the period of this work M-CL was supported by the O’Brien Scholars Program in the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health (CAMH) and The Hospital for Sick Children, Toronto, the Academic Scholar Award from the Department of Psychiatry, University of Toronto, the Slaight Family Child and Youth Mental Health Innovation Fund, CAMH Foundation, and the Ontario Brain Institute via the Province of Ontario Neurodevelopmental Disorders (POND) Network; MVL was supported by the British Academy, Jesus College Cambridge,Wellcome Trust, and an ERC Starting Grant (ERC-2017- STG; 755816); SB-C was supported by the Autism Research Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health, UK
External DOI: https://doi.org/10.1142/s0129065718500582
This record's URL: https://www.repository.cam.ac.uk/handle/1810/288693