Multi-modal biomarkers of low back pain: A machine learning approach

Bidhan Lamichhane, Dinal Jayasekera, Rachel Jakes, Matthew F. Glasser, Justin Zhang, Chunhui Yang, Derayvia Grimes, Tyler L. Frank, Wilson Z. Ray, Eric C. Leuthardt, Ammar H. Hawasli

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66–0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy.

Original languageEnglish
Article number102530
JournalNeuroImage: Clinical
Volume29
DOIs
StatePublished - Jan 2021

Keywords

  • Cortical thickness
  • Emotion processing
  • Low back pain
  • Pain processing
  • Resting state connectivity
  • Support vector machine

Fingerprint

Dive into the research topics of 'Multi-modal biomarkers of low back pain: A machine learning approach'. Together they form a unique fingerprint.

Cite this