Chronic pain is a major health burden associated with immense economic and social costs. Predictive biomarkers that can identify individuals at risk of developing severe and persistent pain, which is associated with worse disability and greater reliance on opioids, would promote aggressive, early intervention that could halt the transition to chronic pain. Our team has uncovered evidence of a unique cortical biomarker signature that predicts pain susceptibility (severity and duration). The biomarker signature combines resting state sensorimotor peak alpha frequency (PAF) measured using electroencephalograph (EEG) and corticomotor excitability (CME) measured using transcranial magnetic stimulation (TMS). This PAF/CME biomarker signature could be capable of predicting the severity of pain experienced by an individual minutes to months in the future, as well as the duration of pain (time to recovery). In the R61 phase of the current proposal, we aim to undertake analytical validation of this biomarker in healthy participants using a standardized model of the transition to sustained myofascial temporomandibular pain (masseter intramuscular injection of nerve growth factor, n=150). We will record PAF/CME at multiple time points before and during the development of pain and use online diaries and in-laboratory assessments of pain, sleep, stress, and other psychosocial variables. Specifically, we will test if the biomarker signature predicts an individual's pain sensitivity (high- or low-pain sensitive) with at least 75% accuracy. We will also test whether the biomarker signature predicts pain severity (on a 0-10 scale) and pain duration (number of days until pain resolves). We will use multiple statistical approaches to optimize and test the performance of the predictive biomarker. In the R33 phase we then aim to perform initial clinical validation to determine whether the optimized PAF/CME biomarker signature predicts pain severity and duration in patients with new onset myofascial temporomandibular disorder (TMD; n=30). We expect our work to result in the delivery of a candidate biomarker signature ready for advanced prospective clinical validation studies.

Public Health Relevance

Because of the difficulty in treating chronic pain, development of brain signal predictive biomarkers is of growing interest. Here we propose to develop a predictive biomarker signature of pain severity and duration using two commonly available techniques ? electroencephalogram (EEG) and transcranial magnetic stimulation (TMS) ? and perform initial clinical validation in first onset temporomandibular disorder (TMD). The biomarker could have utility in identifying patients at high risk of transitioning from acute to chronic pain, and has additional potential for clinical application in the treatment and prevention of chronic pain.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
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Special Emphasis Panel (ZRG1)
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Pelleymounter, Mary A
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University of Maryland Baltimore
Other Basic Sciences
Schools of Dentistry/Oral Hygn
United States
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