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. 2014 Feb;44(3):519-32.
doi: 10.1017/S0033291713001013. Epub 2013 Jun 5.

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

V Rocha-Rego  1 , J Jogia  1 , A F Marquand  1 , J Mourao-Miranda  1 , A Simmons  1 , S Frangou  2
Affiliations
Free PMC article

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

V Rocha-Rego et al. Psychol Med. 2014 Feb.
Free PMC article

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.

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Figures

Fig. 1.
Fig. 1.
A hypothetical example based on a simplified version of the Gaussian process classifier (GPC) decision function considering two gray matter (GM) voxels per image.
Fig. 2.
Fig. 2.
Discrimination maps for (a) gray matter (GM) and (b) white matter (WM) classification in cohort 1.
Fig. 3.
Fig. 3.
Discrimination maps for (a) gray matter (GM) and (b) white matter (WM) classification in cohort 2.
See this image and copyright information in PMC

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