Jaw tremor as a physiological biomarker of bruxism.

OBJECTIVE: To determine if sleep bruxism is associated with abnormal physiological tremor of the jaw during a visually-guided bite force control task. METHODS: Healthy participants and patients with sleep bruxism were given visual feedback of their bite force and asked to trace triangular target trajectories (duration=20s, peak force <35% maximum voluntary force). Bite force control was quantified in terms of the power spectra of force fluctuations, masseter EMG activity, and force-to-EMG coherence. RESULTS: Patients had greater jaw force tremor at ∼8 Hz relative to controls, along with increased masseter EMG activity and force-to-EMG coherence in the same frequency range. Patients also showed lower force-to-EMG coherence at low frequencies (<3 Hz), but greater coherence at high frequencies (20-40 Hz). Finally, patients had greater 6-10 Hz force tremor during periods of descending vs. ascending force, while controls showed no difference in tremor with respect to force dynamics. CONCLUSION: Patients with bruxism have abnormal jaw tremor when engaged in a visually-guided bite force task. SIGNIFICANCE: Measurement of jaw tremor may aid in the detection/evaluation of bruxism. In light of previous literature, our results also suggest that bruxism is marked by abnormal or mishandled peripheral feedback from the teeth.

Task-related changes in sensorimotor integration influence the common synaptic input to motor neurones.

AIM: The purpose of this investigation was to understand how visual information, when used to guide muscle activity, influences the frequency content of the neural drive to muscles and the gain of afferent feedback. METHODS: Subjects maintained static, isometric contractions of the tibialis anterior muscle by matching a visual display of their ankle dorsiflexion force to a target set at 10% of their maximum voluntary contraction level. Two visual feedback conditions were studied. The first was a high-sensitivity feedback, in which small changes in force were of large on-screen visual magnitude. The second was a low-sensitivity feedback, in which the on-screen scaling of feedback was reduced by a factor of 10, making small force fluctuations difficult to perceive. Force tremor and Hoffmann reflex (H-reflex) amplitudes were compared between the two conditions, as well as coherence among single motor unit spike trains derived from high-density EMG recordings. RESULTS: The high-sensitivity feedback condition was associated with lower error, larger force tremor (4-12 Hz) and larger H-reflex amplitudes relative to the low-sensitivity feedback condition. In addition, the use of high-sensitivity feedback was associated with lower 1-5 Hz coherence among pairs of motor units, but larger coherence at high frequencies (6-12, approx. 20, >30 Hz). CONCLUSION: Alteration of visual feedback influences nearly the entire frequency spectrum of common input to motor neurones, as well the gain of afferent feedback. We speculate that task-related modulation of afferent feedback could be the origin of many of the observed changes in the neural drive to muscles.