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Related Concept Videos

Pain01:20

Pain

443
Pain serves as a critical warning signal that alerts the body to potential or actual harm. When mechanical pressure on the skin is intense, such as from a sharp pinch, the sensation transitions from touch to pain. Similarly, extreme temperatures, like a hot pot handle, convert the sensation of heat into pain. Pain can also result from overstimulation of other senses, such as blinding light, loud noise, or the intense heat from habañero peppers. This ability to sense pain is essential for...
443

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Related Experiment Video

Updated: May 30, 2025

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
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Predicting Individual Pain Sensitivity Using a Novel Cortical Biomarker Signature.

Nahian S Chowdhury1,2, Chuan Bi3,4, Andrew J Furman5,6

  • 1Center for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia.

JAMA Neurology
|January 27, 2025
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Summary

A novel sensorimotor cortical biomarker signature, combining peak alpha frequency (PAF) and corticomotor excitability (CME), shows high accuracy in predicting pain sensitivity. This reliable biomarker has potential for clinical use in managing acute to chronic pain transitions.

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Last Updated: May 30, 2025

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Area of Science:

  • Neuroscience
  • Pain Research
  • Biomarker Development

Background:

  • Biomarkers are crucial for effective chronic pain management, aiding in diagnosis, prevention, and treatment decisions.
  • Current diagnostic methods for pain lack objective measures, highlighting the need for reliable biomarkers.

Purpose of the Study:

  • To analytically validate a sensorimotor cortical biomarker signature for pain.
  • The signature comprises two measures: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME).

Main Methods:

  • A cohort study involving 150 healthy adults aged 18-44 years was conducted.
  • Prolonged temporomandibular pain was induced using nerve growth factor (NGF) injections.
  • Electroencephalography (EEG) for PAF and transcranial magnetic stimulation (TMS) for CME were recorded; pain was assessed over 30 days.

Main Results:

  • The developed machine learning model achieved an Area Under the Curve (AUC) of 1.00 in the training set and 0.88 in the test set.
  • The PAF/CME biomarker signature demonstrated excellent predictive accuracy and good to excellent test-retest reliability.
  • Model performance was robust, with no improvement when including sex or pain catastrophizing as covariates.

Conclusions:

  • The study provides evidence for a validated sensorimotor cortical biomarker signature for pain sensitivity.
  • The PAF/CME signature exhibits high accuracy, reproducibility, and reliability, indicating substantial potential for clinical translation.
  • This biomarker signature may be valuable in predicting the transition from acute to chronic pain states.