Psychological Medicine

  • Psychological Medicine / Volume 42 / Issue 05 / May 2012, pp 1037-1047
  • Copyright © Cambridge University Press 2011 The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution-NonCommercial-ShareAlike licence <http://creativecommons.org/licenses/by-nc-sa/2.5/>. The written permission of Cambridge University Press must be obtained for commercial re-use.
  • DOI: http://dx.doi.org/10.1017/S0033291711002005 (About DOI), Published online: 07 November 2011
  • OPEN ACCESS

Original Articles

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study

J. Mourao-Mirandaa1a2, A. A. T. S. Reindersa3a4, V. Rocha-Regoa1, J. Lappina3, J. Rondinaa1, C. Morgana3, K. D. Morgana3, P. Fearona3, P. B. Jonesa5, G. A. Doodya6, R. M. Murraya3, S. Kapura3 and P. Dazzana3a7 c1

a1 Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, UK

a2 Centre for Computational Statistics and Machine Learning, UCL, London, UK

a3 Department of Psychosis Studies, Institute of Psychiatry, King's College London, UK

a4 Department of Neuroscience, University Medical Center Groningen, and BCN Neuroimaging Center, University of Groningen, The Netherlands

a5 Department of Psychiatry, University of Cambridge, UK

a6 Division of Psychiatry, University of Nottingham, UK

a7 NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, King's College London, UK

Abstract

Background To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.

Method One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls.

Results At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035).

Conclusions We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.

(Received June 16 2011)

(Revised August 17 2011)

(Accepted August 22 2011)

(Online publication November 07 2011)

Correspondence

c1 Address for correspondence: Dr P. Dazzan, Department of Psychosis Studies, Box 40, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UK. (Email: paola.dazzan@kcl.ac.uk)

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