title: Accuracy of automated classification of major depressive disorder as a function of symptom severity.
abstract: Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers.
  
  
  
  Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were sorted into three groups: mild to moderate depression (HRSD 14-19), severe depression (HRSD 20-23), and very severe depression (HRSD ≥ 24). We collected functional magnetic resonance imaging (fMRI) data during both resting-state and an emotional-face matching task. Patients in each of the three severity groups were compared against controls in separate analyses, using either the resting-state or task-based fMRI data. We use each of these six datasets with linear support vector machine (SVM) binary classifiers for identifying individuals as patients or controls.
  
  
  
  The resting-state fMRI data showed statistically significant classification accuracy only for the very severe depression group (accuracy 66%, p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. With task-based fMRI data, the automated classifier performed at chance in all three severity groups.
  
  
  
  Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.
authors.affiliation: Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Department of Clinical Neuroscience, University of Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
authors.forename: Rajamannar
authors.initials: R
authors.lastname: Ramasubbu
authors.affiliation: Department of Psychiatry, University of Alberta, Edmonton, AB, Canada; Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Alberta Innovates Centre for Machine Learning, Edmonton, AB, Canada.
authors.forename: Matthew R G
authors.initials: MR
authors.lastname: Brown
authors.affiliation: Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
authors.forename: Filmeno
authors.initials: F
authors.lastname: Cortese
authors.affiliation: Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
authors.forename: Ismael
authors.initials: I
authors.lastname: Gaxiola
authors.affiliation: Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
authors.forename: Bradley
authors.initials: B
authors.lastname: Goodyear
authors.affiliation: Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Greenshaw
authors.affiliation: Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.
authors.forename: Serdar M
authors.initials: SM
authors.lastname: Dursun
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Alberta Innovates Centre for Machine Learning, Edmonton, AB, Canada.
authors.forename: Russell
authors.initials: R
authors.lastname: Greiner
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods; D003865:Major Depressive Disorder / Q000145:classification* / Q000000981:diagnostic imaging* / Q000503:physiopathology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D011569:Psychiatric Status Rating Scales; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D060388:Support Vector Machine
keywords: Classification; Diagnosis; Functional magnetic resonance imaging; Machine learning; Major depression; Severity of symptoms; Support vector machine