title: Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies.
abstract: Multiple studies have examined functional and structural brain alteration in patients diagnosed with major depressive disorder (MDD). The introduction of multivariate statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy control subjects (HCs). However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies.
  
  
  
  We conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1-weighted images, task-based functional magnetic resonance imaging [MRI], resting-state MRI, or diffusion tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from HCs.
  
  
  
  Thirty-three (k = 33) samples including 912 patients with MDD and 894 HCs were included in the meta-analysis. Across all studies, patients with MDD were separated from HCs with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (85% sensitivity, 83% specificity) and on diffusion tensor imaging data (88% sensitivity, 92% specificity) outperformed classifications based on structural MRI (70% sensitivity, 71% specificity) and task-based functional MRI (74% sensitivity, 77% specificity).
  
  
  
  Our results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome.
authors.affiliation: Department of Psychiatry, Ludwig-Maximilians University Munich, Munich. Electronic address: joseph.kambeitz@med.uni-muenchen.de.
authors.forename: Joseph
authors.initials: J
authors.lastname: Kambeitz
authors.affiliation: Department of Psychiatry, Ludwig-Maximilians University Munich, Munich.
authors.forename: Carlos
authors.initials: C
authors.lastname: Cabral
authors.affiliation: Neurosciences Program and Department of Psychology, Stanford University, Stanford, California.
authors.forename: Matthew D
authors.initials: MD
authors.lastname: Sacchet
authors.affiliation: Neurosciences Program and Department of Psychology, Stanford University, Stanford, California.
authors.forename: Ian H
authors.initials: IH
authors.lastname: Gotlib
authors.affiliation: Institute of Psychiatry, King's College London, London, United Kingdom.
authors.forename: Roland
authors.initials: R
authors.lastname: Zahn
authors.affiliation: Laboratory of Psychiatric Neuroimaging, Institute and Department of Psychiatry, Sao Paulo, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of Sao Paulo, Sao Paulo, Brazil.
authors.forename: Mauricio H
authors.initials: MH
authors.lastname: Serpa
authors.affiliation: Clinical Affective Neuroimaging Laboratory, Department of Behavioural Neurology, Leibniz Institute for Neurobiology, Magdeburg; Department of Psychiatry and Psychotherapy, Eberhard Karls University, Tubingen, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Walter
authors.affiliation: Department of Psychiatry, Ludwig-Maximilians University Munich, Munich.
authors.forename: Peter
authors.initials: P
authors.lastname: Falkai
authors.affiliation: Department of Psychiatry, Ludwig-Maximilians University Munich, Munich.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Koutsouleris
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging*; D003865:Major Depressive Disorder / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D015999:Multivariate Analysis; D059906:Neuroimaging / Q000379:methods
keywords: Affective disorder; Classification; Diagnosis; Prediction; Sensitivity; Specificity