A novel cortical biomarker signature accurately and reliably predicts individual pain sensitivity: The PREDICT longitudinal analytical validation study

Abstract

AbstractBackgroundBiomarkers would greatly assist chronic pain management. The present study aimed to undertake analytical validation of a sensorimotor cortical biomarker signature for pain consisting of two measures: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME), using a human model of prolonged temporomandibular pain (masseter intramuscular injection of nerve growth factor [NGF]).Methods150 participants received an injection of NGF to the right masseter muscle on Days 0 and 2, inducing prolonged pain lasting up to 4 weeks. Electroencephalography (EEG) to assess PAF and transcranial magnetic stimulation (TMS) to assess CME were recorded on Days 0, 2 and 5. We determined the predictive accuracy of the PAF/CME biomarker signature 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.ResultsThe 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). Results were reproduced across a range of methodological parameters and inclusion of covariates in the modelling. PAF and CME biomarkers showed good-excellent test-retest reliability.ConclusionsThis study provides evidence for a sensorimotor cortical biomarker signature for an episode of prolonged pain. The combination of accuracy, reproducibility, and reliability, suggests the PAF/CME biomarker signature has substantial potential for clinical translation.