Background & Aims

Awake bruxism—a behaviour characterized by repetitive clenching—is highly prevalent in myogenic temporomandibular disorders (mTMD). Individuals with mTMD also present with functional abnormalities in masticatory muscle activity and oxygenation. Here, we tested whether oxygen tissue saturation (StO2) and electromyographic (EMG) signal from the masseter muscle predict facial pain evoked by a standardized repetitive clenching task.

Methods

Seventy healthy participants (34F; 26±5.92 years) performed fifteen clenching trials, each lasting 30 seconds, while targeting 20-30% of their maximum voluntary contraction (MVC) with EMG visual feedback. Thirty seconds rests were interspersed in between the trials. Next, participants clenched at MVC until task failure. Pain intensity ratings were collected every minute. StO2 and EMG signals were recorded from the right masseter. Predictive models were developed using features extracted from StO2 and EMG signals. Features included the absolute value of the slope and the slope sign (+/-) of the fitted line to the vector of difference of StO2 medians during clench and the difference between average medians of every three trials and the rest period before starting clenching repetitions. From the EMG signal we extracted the sign of the slope of the sample entropy over trials, the absolute value of entropy change from the first to the last clenching trial, and the slope of wavelet transformed signal power. A nested k-fold cross-validation with sequential feature selection and a Partial Least Squares regression model with cross-validation was used to predict post-MVC pain and the pain change from the first to the last trials.

Results

Models trained on EMG features successfully predicted both post-MVC pain (r=0.42, p=0.0005) and pain change (r=0.45, p=0.0001), while models trained on StO2 trajectories did not (post-MVC pain: r=0.21, p=0.078; pain change: r=0.23, p=0.055). Compared to the models including EMG features only, models combining both StO2 trajectories and EMG features predicted slightly better post-MVC facial pain (r=0.49, p=0.0001) and pain change (r=0.51, p=0.0001).

Conclusions

We demonstrate that the intensity of pain evoked by repeated clenching can be predicted by features of masseter oxygenation and electrophysiological activity. These findings lay the groundwork toward developing a muscular biomarker of facial pain, e.g., in mTMD.

References

Facial Pain. https://www.nidcr.nih.gov/research/data-statistics/facial-pain.

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Presenting Author

Pedram Mouseli

Poster Authors

Pedram Mouseli

MSc

University of Toronto

Lead Author

Suha Sagheer; HBSc

Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto

Lead Author

W. Darlene Reid; PhD

Department of Physical Therapy, University of Toronto

Lead Author

Igor Jurisica

PhD

Departments of Medical Biophysics and Computer Science, Faculty of Dentistry, University of Toronto

Lead Author

Massieh Moayedi; PhD

Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto

Lead Author

Iacopo Cioffi; DDS

PhD

Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto

Lead Author

Topics

  • Models: Acute Pain