A novel cortical biomarker signature predicts individual pain sensitivity

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

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). Design In this cohort study (recruitment period: November 2020-October 2022), participants experienced a model of prolonged temporomandibular pain with outcomes collected over 30 days. Electroencephalography (EEG) 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 1-30. Setting Data collection occurred at a single centre: Neuroscience Research Australia. Participants We enrolled 159 healthy participants (through notices placed online and at universities across Australia), aged 18-44 with no history of chronic pain, neurological or psychiatric condition. 150 participants completed the protocol. 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 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.

The final sample consisted of 66 females and 84 males with a mean age of 25.1 ± 6.2. 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[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-excellent test-retest reliability.

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. 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. Meaning A novel cortical biomarker can accurately distinguish high and low pain sensitive individuals, and may predict the transition from acute to chronic pain Competing Interest Statement The authors have declared no competing interest. Funding Statement This project was funded by the National Institute of Health (R61 NS113269/NS/NINDS NIH HHS/United States). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval was obtained from the University of New South Wales (HC190206) and the University of Maryland Baltimore (HP-00085371). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Data Availability The corresponding author had full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis. Code and relevant diary, questionnaire, and demographic data are available at https://github.com/DrNahianC/PREDICT_Scripts. The raw data supporting the findings of this study are available on OpenNeuro (https://openneuro.org/datasets/ds005486/versions/1.0.0) for the EEG data and OSF (https://osf.io/r3m9g/) for the TMS data. https://openneuro.org/datasets/ds005486/versions/1.0.0)