A new study published in JAMA Neurology reports two biomarkers that can reliably predict pain sensitivity in a person. They can even predict how bad the pain gets.
Pain changes the brain. Some regions become overexcited. If doctors can measure these signals safely and reliably, they can measure pain sensitivity. That can also help them judge how severe the pain is and treat it properly.
Pain is hard to measure. So doctors mostly rely on how patients describe their pain. That method is shaky, because some people call fairly moderate pain severe. Others tolerate pain better.
Pain is the most common symptom in clinics. Yet there is no reliable way to measure it. At any given time, about 1.7 billion adults live with muscle and joint pain alone. And there are many other kinds of pain, too. Pain causes great distress. Poorly managed acute pain turns chronic and does real harm to health.
Researchers say they have found a way to measure pain sensitivity for the first time. It may also help predict the risk of chronic pain. Then timely treatment may cut that burden.
For this study, researchers enrolled 150 patients with jaw pain from temporomandibular disorders. They were fairly young, aged 18 to 44 years. Researchers used electroencephalography (EEG) to measure pain sensitivity. It is a safe test that needs no needles or cuts. EEG places electrodes on the head and records brain wave patterns. Better computers, gear, and math now let researchers spot even small changes in those patterns. This study focused on two measures: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME).
A slow PAF followed by low CME was an excellent predictor of long pain episodes or chronic pain. People with that pattern were more likely to have worse pain lasting weeks or more.
In low back pain patients, low CME also signaled a much greater risk of chronic pain. These patients were more likely to have low back pain lasting more than six months.
This is the first study to show that measuring PAF and CME may help predict pain severity and the risk of chronic pain. Researchers call it a major breakthrough in pain medicine.
These biomarkers had 88% accuracy. That is high. Spotting pain sensitivity and chronic pain risk early may let doctors step in early. That may lower the risk of chronic pain.
Because the accuracy is so high, researchers are now testing these biomarkers in the real world. If they hold up there, doctors may soon use them to understand pain and predict the risk of chronic health problems.
Frequently asked questions
What is pain sensitivity?
Pain sensitivity is how strongly a person feels pain from the same trigger. It varies widely. Some people call fairly moderate pain severe. Others tolerate pain better. Pain also changes the brain itself and leaves some regions overexcited. Those brain signals are what researchers are now learning to measure.
Can pain sensitivity be measured?
Researchers say yes, for the first time. The study had 150 adults aged 18 to 44 with jaw pain from temporomandibular disorders. EEG electrodes on the head tracked two brain signals: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME). Together these biomarkers reached 88% accuracy, as reported in JAMA Neurology.
Can doctors predict who will develop chronic pain?
This research points that way. People with a slow PAF followed by low CME were far more likely to have worse pain lasting weeks or more. In people with low back pain, low CME signaled a much greater risk of pain lasting more than six months. Spotting that risk early opens the door to treatment before pain turns chronic.
Why is pain so hard to measure?
Because doctors mostly rely on how patients describe their pain, and that is subjective. Two people can rate the same pain very differently. Pain is the most common symptom in clinics. Yet there has been no reliable, objective way to measure it. That is why brain-based biomarkers from a simple EEG are drawing so much attention.
Source:
Chowdhury, N. S., Bi, C., Furman, A. J., Chiang, A. K. I., Skippen, P., Si, E., Millard, S. K., Margerison, S. M., Spies, D., Keaser, M. L., Da Silva, J. T., Chen, S., Schabrun, S. M., & Seminowicz, D. A. (2025). Predicting Individual Pain Sensitivity Using a Novel Cortical Biomarker Signature. JAMA Neurology. https://doi.org/10.1001/jamaneurol.2024.4857
Dr. Gurpreet Singh Padda, MD, MBA, MHP
