title: Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach.
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.
  
  
  METHODS
  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.affiliation: Department of Neuroimaging, Institute of Psychiatry, King's College London, UK.
authors.forename: V
authors.initials: V
authors.lastname: Rocha-Rego
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, King's College London, UK.
authors.forename: J
authors.initials: J
authors.lastname: Jogia
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, King's College London, UK.
authors.forename: A F
authors.initials: AF
authors.lastname: Marquand
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, King's College London, UK.
authors.forename: J
authors.initials: J
authors.lastname: Mourao-Miranda
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, King's College London, UK.
authors.forename: A
authors.initials: A
authors.lastname: Simmons
authors.affiliation: Psychosis Research Program, Icahn School of Medicine at Mount Sinai, Icahn Medical Institute, New York, NY, USA.
authors.forename: S
authors.initials: S
authors.lastname: Frangou
mesh_terms: D000328:Adult; D000465:Algorithms; D001714:Bipolar Disorder / Q000175:diagnosis* / Q000188:drug therapy / Q000473:pathology; D001921:Brain / Q000473:pathology*; D016022:Case-Control Studies; D057210:Delayed Diagnosis / Q000009:adverse effects; D003937:Diagnosis, Differential; D039721:Diagnostic and Statistical Manual of Mental Disorders; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000145:classification / Q000379:methods*; D008297:Male; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000145:classification / Q000379:methods*; D011237:Predictive Value of Tests