Abstract Background Biomarkers 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]). Methods 150 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. Results 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). 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-excellent test-retest reliability. Conclusions 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. Key Points Question Can individuals be accurately classified as high or low pain sensitive based on two features of cortical activity: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME)? Findings In a cohort study of 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, and inclusion of covariates did not improve model performance Meaning A novel cortical biomarker comprised of PAF and CME can accurately distinguish high and low pain sensitive individuals
Temporomandibular disorder (TMD) is a common condition that frequently transitions to chronic symptoms. Experimental pain models that mimic the symptoms of clinical TMD may be useful in understanding the mechanisms, and sex differences, present in this disorder. Here we aimed to comprehensively characterise the nature and time-course of pain, functional impairment and hyperalgesia induced by repeated intramuscular injection of nerve growth factor (NGF) into the masseter muscle, and to investigate sex differences in the NGF-induced pain experience.94 healthy individuals participated in a longitudinal study with 30-day follow-up. NGF was injected into the right masseter muscle on Day 0 and Day 2. Participants attended laboratory sessions to assess pain (Numerical Rating Scale; NRS), functional limitation (mouth opening distance, Jaw Functional Limitation Scale; JFLS) and mechanical sensitization (pressure pain thresholds; PPTs) on Days 0, 2 and 5 and completed twice daily electronic pain dairies from Day 0 to day 30.Peak pain averaged 2.0/10 (95 % CI: 1.6-2.4) at rest and 4.3/10 (95 % CI: 3.9-4.8) on chewing. Pain-free mouth opening distance reduced from 5.0 cm (95 % CI: 4.8-5.1 cm) on Day 0 to 3.7 cm (95 % CI: 3.5-3.9 cm) on Day 5. The greatest reduction in PPTs was observed over the masseter muscle. Females experienced higher pain, greater functional impairment, and greater sensitivity to mechanical stimuli than males.Intramuscular injection of NGF is a useful model with which to explore the mechanisms, and sex differences, present in clinical TMD.
Abstract Background Transcranial magnetic stimulation (TMS) evoked potentials (TEPs) can be used to index cortical excitability. However, it remains unclear to what extent TEPs reflect somatosensory and auditory-evoked potentials which arise from the scalp sensation and click of the TMS coil, as opposed to transcranial stimulation of cortical circuits. Objectives The present study had two aims; a) to determine the extent to which sensory potentials contaminate TEPs using a spatially matched sham condition, and b) to determine whether sensory potentials reflect auditory or somatosensory potentials alone, or a combination of the two. Methods Twenty healthy participants received active or sham stimulation, with the latter consisting of the click of a sham coil combined with scalp electrical stimulation. Earplugs/headphones were used to suppress the TMS click noise. Two additional control conditions i) electrical stimulation alone and ii) auditory stimulation alone were included in a subset of 13 participants. Results Signals from active and sham stimulation were correlated in spatial and temporal domains, especially >70ms post-stimulation. Relative to auditory or electrical stimulation alone, combined (sham) stimulation resulted in a) larger evoked responses b) stronger correlations with active stimulation and c) a signal that could not be explained by the linear sum of electrical and auditory stimulation alone. Conclusions Sensory potentials can confound data interpretations of TEPs at timepoints >70ms post-TMS, while earlier timepoints appear reflective of cortical excitability. Furthermore, contamination of TEPs cannot be explained by auditory or somatosensory potentials alone, but instead reflects a non-linear interaction between both sources. Future studies may benefit from controlling for sensory contamination using sham conditions that are spatially matched to active TMS, and which consist of combined auditory and somatosensory stimulation.
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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