Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning

Jeff Boissoneault, Landrew Sevel, Janelle Letzen, Michael Robinson, Roland Staud

Research output: Contribution to journalReview articlepeer-review

Abstract

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70–92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Original languageEnglish (US)
Article number5
JournalCurrent rheumatology reports
Volume19
Issue number1
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

Keywords

  • Chronic pain
  • Classification
  • Machine learning
  • Magnetic resonance imaging (MRI)
  • Musculoskeletal

ASJC Scopus subject areas

  • Rheumatology

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