Chapter title |
Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder
|
---|---|
Chapter number | 47 |
Book title |
Biomarkers in Psychiatry
|
Published in |
Current topics in behavioral neurosciences, January 2018
|
DOI | 10.1007/7854_2018_47 |
Pubmed ID | |
Book ISBNs |
978-3-31-999641-7, 978-3-31-999642-4
|
Authors |
Derek Sayre Andrews, Andre Marquand, Christine Ecker, Grainne McAlonan, Andrews, Derek Sayre, Marquand, Andre, Ecker, Christine, McAlonan, Grainne |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 1 | 33% |
Italy | 1 | 33% |
United States | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 60 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 10 | 17% |
Student > Master | 7 | 12% |
Student > Bachelor | 7 | 12% |
Student > Doctoral Student | 5 | 8% |
Student > Postgraduate | 5 | 8% |
Other | 9 | 15% |
Unknown | 17 | 28% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 11 | 18% |
Psychology | 10 | 17% |
Medicine and Dentistry | 8 | 13% |
Social Sciences | 4 | 7% |
Arts and Humanities | 2 | 3% |
Other | 7 | 12% |
Unknown | 18 | 30% |