Psychological Medicine

  • Psychological Medicine / Volume 44 / Issue 03 / February 2014, pp 519-532
  • Copyright © Cambridge University Press 2013 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/3.0/>. The written permission of Cambridge University Press must be obtained for commercial re-use.
  • DOI: http://dx.doi.org/10.1017/S0033291713001013 (About DOI), Published online: 05 June 2013
  • OPEN ACCESS

Original Articles

Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach

V. Rocha-Regoa1a2 , J. Jogiaa1 , A. F. Marquanda1, J. Mourao-Mirandaa1a3, A. Simmonsa1a2a4 and S. Frangoua5 c1

a1 Department of Neuroimaging, Institute of Psychiatry, King's College London, UK

a2 NIHR Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, King's College London, UK

a3 Computer Science Department, Centre for Computational Statistics and Machine Learning, University College London, UK

a4 MRC Centre for Neurodegeneration Research, Institute of Psychiatry, King's College London, UK

a5 Psychosis Research Program, Icahn School of Medicine at Mount Sinai, Icahn Medical Institute, New York, NY, USA

Abstract

Background Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD.

Method GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n = 26; cohort 2: n = 14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls.

Results The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2; the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2; the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD.

Conclusions Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers.

(Received December 24 2011)

(Revised October 03 2012)

(Accepted April 09 2013)

(Online publication June 05 2013)

Key words

  • Bipolar disorder;
  • diagnosis;
  • Gaussian process classifiers;
  • imaging;
  • pattern recognition

Correspondence

c1 Address for correspondence: Professor S. Frangou, Professor of Psychiatry, Chief, Psychosis Research Program, Icahn School of Medicine at Mount Sinai, Icahn Medical Institute, Box 1230, 1425 Madison Avenue, New York, NY 10029, USA. (Email: sophia.frangou@mssm.edu)

Footnotes

  These authors contributed equally to this work.

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