Multimodal Biomarkers of Low Back Pain: A Machine Learning Approach(Presenter: Dinal Jayasekera @ Wilson Ray Lab)

Presenter: Dinal Jayasekera

BME 4th year Ph.D. student

Dr. Wilson Ray Lab

Watch the recorded presentation

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. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. In addition, we report on 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.

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