Journal
PSYCHOLOGICAL MEDICINE
Volume 44, Issue 3, Pages 519-532Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0033291713001013
Keywords
Bipolar disorder; diagnosis; Gaussian process classifiers; imaging; pattern recognition
Categories
Funding
- European College of Neuropsychopharmacology, Networks Initiative, Neuroimaging Network
- King's College London Centre of Excellence in Medical Engineering
- Wellcome Trust
- Engineering and Physical Sciences Research Council (EPSRC) [WT088641/Z/09/Z]
- Wellcome Trust Career Development Fellowship [WT086565/Z/08/Z]
- National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health at the South London
- Maudsley NHS Foundation Trust and Institute of Psychiatry, King's College London
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available