title: Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: a pattern classification approach.
abstract: BACKGROUND
  Diagnosis of pediatric neuropsychiatric disorders such as unipolar depression is largely based on clinical judgment - without objective biomarkers to guide diagnostic process and subsequent therapeutic interventions. Neuroimaging studies have previously reported average group-level neuroanatomical differences between patients with pediatric unipolar depression and healthy controls. In the present study, we investigated the utility of multiple neuromorphometric indices in distinguishing pediatric unipolar depression patients from healthy controls at an individual subject level.
  
  
  METHODS
  We acquired structural T1-weighted scans from 25 pediatric unipolar depression patients and 26 demographically matched healthy controls. Multiple neuromorphometric indices such as cortical thickness, volume, and cortical folding patterns were obtained. A support vector machine pattern classification model was 'trained' to distinguish individual subjects with pediatric unipolar depression from healthy controls based on multiple neuromorphometric indices and model predictive validity (sensitivity and specificity) calculated.
  
  
  RESULTS
  The model correctly identified 40 out of 51 subjects translating to 78.4% accuracy, 76.0% sensitivity and 80.8% specificity, chi-square p-value = 0.000049. Volumetric and cortical folding abnormalities in the right thalamus and right temporal pole respectively were most central in distinguishing individual patients with pediatric unipolar depression from healthy controls.
  
  
  CONCLUSIONS
  These findings provide evidence that a support vector machine pattern classification model using multiple neuromorphometric indices may qualify as diagnostic marker for pediatric unipolar depression. In addition, our results identified the most relevant neuromorphometric features in distinguishing PUD patients from healthy controls.
authors: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors: Mon-Ju
authors: MJ
authors: Wu
authors: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors: Hanjing Emily
authors: HE
authors: Wu
authors: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA. Electronic address: benson.irungu@uth.tmc.edu.
authors: Benson
authors: B
authors: Mwangi
authors: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors: Marsal
authors: M
authors: Sanches
authors: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors: Sudhakar
authors: S
authors: Selvaraj
authors: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors: Giovana B
authors: GB
authors: Zunta-Soares
authors: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors: Jair C
authors: JC
authors: Soares
mesh_terms: D000293:Adolescent; D000704:Analysis of Variance; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D002648:Child; D003866:Depressive Disorder / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D060388:Support Vector Machine
keywords: Machine learning; Neuroimaging; Pediatric unipolar depression; Support vector machine