Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: A pattern classification approach

Mon Ju Wu, Hanjing Emily Wu, Benson Mwangi, Marsal Sanches, Sudhakar Selvaraj, Giovana B. Zunta-Soares, Jair C. Soares

Research output: Contribution to journalArticle

16 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)84-91
Number of pages8
JournalJournal of Psychiatric Research
Volume62
DOIs
StatePublished - Mar 1 2015

Keywords

  • Machine learning
  • Neuroimaging
  • Pediatric unipolar depression
  • Support vector machine

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry

Fingerprint Dive into the research topics of 'Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: A pattern classification approach'. Together they form a unique fingerprint.

Cite this