Disease state prediction from resting state functional connectivity

R. Cameron Craddock, Paul E. Holtzheimer, Xiaoping P. Hu, Helen S. Mayberg

Research output: Contribution to journalArticlepeer-review

333 Scopus citations

Abstract

The application of multivoxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real-time functional MRI applications. Support vector classification is the most popular of these techniques, owing to reports that it has better prediction accuracy and is less sensitive to noise. Support vector classification was applied to learn functional connectivity patterns that distinguish patients with depression from healthy volunteers. In addition, two feature selection algorithms were implemented (one filter method, one wrapper method) that incorporate reliability information into the feature selection process. These reliability feature selections methods were compared to two previously proposed feature selection methods. A support vector classifier was trained that reliably distinguishes healthy volunteers from clinically depressed patients. The reliability feature selection methods outperformed previously utilized methods. The proposed framework for applying support vector classification to functional connectivity data is applicable to other disease states beyond major depression.

Original languageEnglish
Pages (from-to)1619-1628
Number of pages10
JournalMagnetic Resonance in Medicine
Volume62
Issue number6
DOIs
StatePublished - Dec 2009
Externally publishedYes

Keywords

  • Biomarker
  • Disease state prediction
  • Feature selection
  • Functional connectivity
  • Major depressive disorder (MDD)
  • Multivariate pattern analysis (MVPA)
  • Support vector classification (SVC)

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