Predicting Individual Pain Sensitivity Using a Novel Cortical Biomarker Signature, 2025, Nahian S. Chowdhury, PhD

Key Points

Question Can individuals be accurately classified as having high or low pain sensitivity based on 2 features of cortical activity, sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME)?

Findings In a cohort study involving 150 healthy participants, the performance of a logistic regression model was outstanding in a training set (n = 100) and excellent in a test set (n = 50), with the combination of slower PAF and CME depression predicting higher pain. Results were reproduced across a range of methodological parameters.

Meaning A novel cortical biomarker can accurately distinguish high and low pain-sensitive individuals and may predict the transition from acute to chronic pain.

Abstract
Importance Biomarkers would greatly assist decision-making in the diagnosis, prevention, and treatment of chronic pain.

Objective To undertake analytical validation of a sensorimotor cortical biomarker signature for pain consisting of 2 measures: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME).

Design, Setting, and Participants This cohort study at a single center (Neuroscience Research Australia) recruited participants from November 2020 to October 2022 through notices placed online and at universities across Australia. Participants were healthy adults aged 18 to 44 years with no history of chronic pain or a neurological or psychiatric condition. Participants experienced a model of prolonged temporomandibular pain with outcomes collected over 30 days. Electroencephalography to assess PAF and transcranial magnetic stimulation (TMS) to assess CME were recorded on days 0, 2, and 5. Pain was assessed twice daily from days 1 through 30.

Exposure Participants received an injection of nerve growth factor (NGF) to the right masseter muscle on days 0 and 2 to induce prolonged temporomandibular pain lasting up to 4 weeks.

Main Outcomes and Measures The predictive accuracy of the PAF/CME biomarker signature was determined using a nested control-test scheme: machine learning models were run on a training set (n = 100), where PAF and CME were predictors and pain sensitivity was the outcome. The winning classifier was assessed on a test set (n = 50) comparing the predicted pain labels against the true labels.

Results Among the final sample of 150 participants, 66 were female and 84 were male; the mean (SD) age was 25.1 (6.2) years. The winning classifier was logistic regression, with an outstanding area under the curve (AUC = 1.00). The locked model assessed on the test set had excellent performance (AUC = 0.88; 95% CI, 0.78-0.99). Results were reproduced across a range of methodological parameters. Moreover, inclusion of sex and pain catastrophizing as covariates did not improve model performance, suggesting the model including biomarkers only was more robust. PAF and CME biomarkers showed good to excellent test-retest reliability.

Conclusions and Relevance This study provides evidence for a sensorimotor cortical biomarker signature for pain sensitivity. The combination of accuracy, reproducibility, and reliability suggests the PAF/CME biomarker signature has substantial potential for clinical translation, including predicting the transition from acute to chronic pain.
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Why pain feels different for everyone? Reveals a brain biomarker that predicts individual pain sensitivity and could identify chronic pain risk.

 

I’m not an expert but can’t this just as well have detected actual correlates with pain, rather than sensitivity to pain.

 

I agree with Yann04 that they're not really sure what they're measuring. Do people vary in their nerve signals strengths in response to the injection? Do the brain waves correlate to the pain levels the subjects are expecting to experience, or to memories of previous pain experiences?

To me that sounds like the other data didn't match their precious theory, so it should be discarded. Other theories to explain all the data might not be as likely to get citations.