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

TY - CHAP

T1 - Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder

AU - Andrews, Derek Sayre

AU - Marquand, Andre

AU - Ecker, Christine

AU - McAlonan, Grainne Mary

PY - 2018/4/7

Y1 - 2018/4/7

N2 - Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction and communication, as well as repetitive and restrictive behaviors. The etiological and phenotypic complexity of ASD have 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.

AB - Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction and communication, as well as repetitive and restrictive behaviors. The etiological and phenotypic complexity of ASD have 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.

U2 - 10.1007/7854_2018_47

DO - 10.1007/7854_2018_47

M3 - Chapter

SP - 1

EP - 24

BT - Current Topics in Behavioral Neuroscience

PB - Springer

ER -