Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction and communication, as well as repetitive and restrictive behaviours. The etiological and phenotypic complexity of ASD has so far hindered the development of clinically useful biomarkers for the condition. Neuroimaging studies have been valuable in establishing a biological basis for ASD. Increasingly, neuroimaging has been combined with ‘machine learning’-based pattern classification methods to make individual diagnostic predictions. Moving forward, the hope is that these techniques may not only facilitate the diagnostic process but may also aid in fractionating the ASD phenotype into more biologically homogeneous sub-groups, with defined pathophysiology, predictable outcomes and/or responses to targeted treatments and/or interventions. This review chapter will first introduce ‘machine learning’ and pattern recognition methods in general, with a focus on their application to diagnostic classification. It will highlight why such approaches to biomarker discovery may have advantages over more conventional analytical methods. Magnetic resonance imaging (MRI) findings of atypical brain structure, function and connectivity in ASD will be briefly reviewed before we describe how pattern recognition has been applied to generate predictive models for ASD. Last, we will discuss some limitations and pitfalls of pattern recognition analyses in ASD and consider how the field can advance beyond the prediction of binary outcomes.
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Acknowledgements
The authors would like to acknowledge all the participants, families and researchers who contributed to the studies reviewed in this chapter. The authors acknowledge support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no 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. DA and GM are partly funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. DA acknowledges support from the National Institutes of Health (NIH) grants MH103371 and MH104438. CE acknowledges support by grants EC480/1-1 and EC480/2-1 from the German Research Foundation under the Heisenberg Programme. AFM gratefully acknowledges support from the Dutch Organisation for Scientific Research (NWO), under a Vernieuwingsimpuls ‘VIDI’ Fellowship (grant number 016.156.415).
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Andrews, D.S., Marquand, A., Ecker, C., McAlonan, G. (2018). Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder. In: Pratt, J., Hall, J. (eds) Biomarkers in Psychiatry. Current Topics in Behavioral Neurosciences, vol 40. Springer, Cham. https://doi.org/10.1007/7854_2018_47
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