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A Machine Learning Approach to Reveal the Neurophenotypes of Autisms

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A Machine Learning Approach to Reveal the Neurophenotypes of Autisms 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. 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 by CAMBRIDGE UNIVERSITY on 03/14/19. Re-use and distribution is strictly not permitted, except for Open Access articles. 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. Keywords: MRI; multiclass classification; upper bounds; autism; spatial overlap analysis. ∗ Corresponding author. † See Appendix B. This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. 1850058-1 2nd Reading February 15, 2019 14:34 1850058 J. M. G´orriz et al. 1. Introduction implicated both regional increases and decreases. The greatest consistency that emerges from meta- The discovery of a characteristic pattern of struc- 11–13,15–18,33,80 tural brain differences associated with autism spec- analyses is the large variance of both trum conditions (ASC) would be a major advance in the primary literature and the outcomes of the our understanding of this complex and highly vari- derived meta-analyses, where the inclusion or exclu- able developmental disorder. Not only would it be a sion of just a few primary sources can significantly substrate upon which to build a systems narrative of alter the aggregated pattern of differences. autism, but from this starting point, it might be pos- Explanations for the absence of a coherent nar- sible to run development “in reverse” to disentangle rative on the structural brain differences associ- ated with autism have been suggested to arise from the roles of environmental and genetic risk factors variety of origins. Whilst methodological differences in its etiology. Unfortunately, “inconsistent” is per- vary between studies,19 similar variability in VBM haps the most common adjective associated with the pipelines has not hindered the observation of charac- extant literature in this area. teristic patterns in other disorders.8–10 Autism is also Initial MRI observations focused on increased highly heterogeneous.20 Indeed, under DSM-IV, four total brain, total tissue, and total lateral ventri- 1 categories were contained within Autism Spectrum cle volumes in adults with autism, but subsequent meta-analyses, including MRI-derived measurements Disorder: Asperger’s disorder, childhood disintegra- as well as head circumference and post-mortem brain tive disorder and pervasive developmental disorder. It has been argued that no reliable clinical diagno- weights, were only able to detect a significant case- 21 control difference amongst 4–5 year olds.2 More sis has been made with these sub-types and that recent meta-analyses based on fully automated voxel no consistent biological substrate has been discov- measures of gray and white matter volumes have con- ered that differentiates between them leading to a single diagnostic category of Autism Spectrum Dis- firmed the absence of any large difference in adults in 22 33 order in DSM-V that subsumes the sub-types. Nev- either total gray matter (GM) or total white mat- 3 4 ertheless, it is unlikely that there will be an observa- ter (WM), or in cerebellar volume. Thus, if there tion that unites these disorders, and in fact greater are differences in overall brain size, then they occur 81 diversity in phenotyping, perhaps down to the indi- very early in life and are followed by a decrease in vidual level, is argued to be more likely to identify volume towards normative values during maturation 23 2,5 an underlying neurobiology for autism. Stratifica- in adolescence and early adulthood. tion does appears to yield sensitivity improvements; Int. J. Neur. Syst. Downloaded from www.worldscientific.com for example, children with regressive autism appear 1.1. Gray and white matter to have increases in brain size, whereas those with distributions in the autistic brain nonregressive autism are not associated with a sig- Our knowledge of localized changes in brain anatomy nificant size change24; and males and females with has historically come from GM and WM dis- autism display different patterns of GM change.27 tributions estimated by voxel-based morphometry What is almost certain is that should there be a true (VBM). Undergoing several iterations and improve- effect, it is spatially diffuse and generally of low effect ments over time, VBM pipelines are automated, reli- size. 6 7 by CAMBRIDGE UNIVERSITY on 03/14/19. Re-use and distribution is strictly not permitted, except for Open Access articles. able and statistically well behaved. The success of The main demographic features of ASC are its the VBM technique can be measured by the large high prevalence, affecting 1% of the entire popu- number of wide-ranging longitudinally and cross- lation,29–31 and the significant skewing of the gen- sectionally designed studies, as well as consistent der ratio towards male individuals to give values cross-study patterns of tissue differences character- of 2–3:1 male:female,30–32 and potentially contribut- izing disorders like schizophrenia,8 Alzheimer’s dis- ing to the heterogeneous patterns of brain structure ease78 and major depressive disorder.9,10 associated with the disorder. Previously, most stud- Whilst measures of global volume have gen- ies of the biology of autism have focused predomi- erally pointed toward increases in the very early nantly on males33 with male:female ratios in research lives of those with autism, local comparisons of samples in the range 8–15:1. Studies of functional gray and white matter volume have variously imaging modalities that undertook analyses in each 1850058-2 2nd Reading February 15, 2019 14:34 1850058 A Machine Learning Approach to Reveal the Neurophenotypes of Autisms sex independently have found widespread significant to this hostile environment, otherwise control of cross-sectional differences.34 Structural MRI stud- the generalization ability of the learning process is ies of autism following similar stratification strate- arguably weak.55,58 A solution to this problem may gies have been successful in assessing the atypi- be achieved by the application of feature extrac- cal brain areas that are shared and distinct across tion/selection (FES) algorithms54 with linear clas- the sexes.3 In a recent MRI study comprising high- sifiers rather than defining a specific metric on the functioning male and female adults with autism,34 data,85 or by evaluating the role that each dimen- exploratory analyses based on a VBM univariate sta- sion plays in the development of the machine learn- tistical framework uncovered substantial heterogene- ing architecture.50 The aim is twofold: (i) to reduce ity in the neurobiology of autism. Furthermore, strat- the number of input dimensions, retaining the rel- ification by sex can provide the empirical evidence evant information by measuring the importance of to test predictions from the “extreme male brain” each dimension, and (ii) to control the complexity (EMB) theory of autism71 and other similar theo- of the selected classifiers to avoid overfitting,51,52 ries74 that predict a relationship between autism and reducing the false positive rate (type I error). In biological
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