Abstract
Neuroimaging techniques are increasingly being explored as potential tools for clinical prediction in psychiatry. There are a wide range of approaches which can be applied to make individual predictions for various aspects of disorders such as diagnostic status, symptom severity scores, identification of patients at risk of developing disorders and estimation of the likelihood of response to treatment. This selective review highlights a popular group of pattern recognition techniques, support vector machines (SVMs) for use with structural magnetic resonance imaging scans. First, however, we outline various practical issues, limitations and techniques which need to be considered before SVM’s can be applied. We begin with a discussion on the practicalities of scanning children and adolescent participants and the importance of acquiring high quality images. Scan processing required for inter-subject comparisons is then discussed. We then briefly discuss feature selection and other considerations when applying pattern recognition techniques. Finally, SVMs are described and various studies highlighted to indicate the potential of these techniques for child and adolescent psychiatric research.
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Acknowledgments
BAJ and BMI were partially supported by SINAPSE (www.sinapse.ac.uk) studentships. BAJ support includes a SINAPSE-SPIRIT industry partnership with Siemens Medical.
Conflict of interest
KM has received research funding from St Jude Medical (BROADEN study), is on the advisory board of the Medtronic OCD DBS PM Study, has received educational grants from Cyberonics and Schering Plough, has received travel/meetings support from Medtronic/SJM and is the clinical lead of the Advanced Interventions Service for NHS Tayside. DC has received research funding from Lilly, Shire, Janssen and Vifor, honoraria for consultancy, advisory boards and speaker fees from Lilly, Shire, Janssen, Medice, Flynn, Novartis and Vifor. JDS has received research funding via an honorarium associated with a lecture from Wyeth. All other authors reported no potential conflicts of interest.
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The data included in the current study are available online as part of the ADHD-200 Sample and the International Neuroimaging Data sharing Initiative. They can be downloaded through http://fcon_1000.projects.nitrc.org/indi/adhd200. We included data from the NeuroImage, Oregon Health & Science University, and New York University Child Study Centre samples that are available within the larger ADHD-200 sample.
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Johnston, B.A., Mwangi, B., Matthews, K. et al. Predictive classification of individual magnetic resonance imaging scans from children and adolescents. Eur Child Adolesc Psychiatry 22, 733–744 (2013). https://doi.org/10.1007/s00787-012-0319-0
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DOI: https://doi.org/10.1007/s00787-012-0319-0