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Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder

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Biomarkers in Psychiatry

Part of the book series: Current Topics in Behavioral Neurosciences ((CTBN,volume 40))

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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Authors and Affiliations

  1. The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioural Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA

    Derek Sayre Andrews

  2. Department of Forensic and Neurodevelopmental Sciences, The Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Derek Sayre Andrews, Christine Ecker & Grainne McAlonan

  3. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands

    Andre Marquand

  4. Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Andre Marquand

  5. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Universitätsklinikum Frankfurt am Main, Goethe-University Frankfurt am Main, Frankfurt, Germany

    Christine Ecker

  6. South London and Maudsley NHS Foundation Trust, London, UK

    Grainne McAlonan

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  1. Derek Sayre Andrews
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  2. Andre Marquand
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  3. Christine Ecker
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  4. Grainne McAlonan
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Corresponding author

Correspondence to Grainne McAlonan .

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Editors and Affiliations

  1. Strathclyde Institute of Pharmacy and Biomedical Science, University of Strathclyde, Glasgow, UK

    Judith Pratt

  2. College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK

    Jeremy Hall

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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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