Estimating the Pathophysiology of Phonotraumatic Vocal Hyperfunction Using Ambulatory Data and a Computational Model

This study uses a voice production model to estimate muscle activation levels and subglottal pressure (P_S) in patients with phonotraumatic vocal hyperfunction (PVH), based on ambulatory measurements of sound pressure level (SPL) and spectral tilt (H₁-H₂). In addition, variations in these physiological parameters are evaluated with respect to different values of the Daily Phonotrauma Index (DPI).

The study obtained ambulatory voice data from patients diagnosed with PVH and a matched control group. To infer physiological parameters, ambulatory data were mapped onto synthetic data generated by a physiologically relevant voice production model. Inverse mapping strategies involved selecting model simulations that represented ambulatory distributions using stochastic (random) sampling weighted by probability with which different vowels occur in English. A categorical approach assessed the relationship between different values of DPI and changes in estimated physiological parameters.

Results showed significant differences between the PVH and control groups in key parameters, including statistical moments of H₁-H₂, SPL, P_S, and muscle activity of lateral cricoarytenoid (LCA) and cricothyroid (CT) muscles. Higher DPI values, reflecting more severe PVH, were associated with increased mean LCA activation and decreased LCA variability, along with decreased mean CT activation and increased median P_S. These findings highlight the relationship between muscle activation patterns, P_S, and the severity of vocal pathology as indicated by the DPI. It is hypothesized that a major driver of muscle activation and P_S changes is the variation in maladaptive adjustments (vocal effort) when compensating for the presence of vocal pathology.

This study demonstrated that noninvasive ambulatory voice data could be used to drive a voice production modeling process, providing valuable insights into underlying physiological parameters associated with PVH. Future research will focus on refining the predictive power of the modeling process and exploring the implications of these findings in further delineating the etiology and pathophysiology of PVH, with the ultimate goal to develop improved methods for the prevention, diagnosis, and treatment of PVH.

https://doi.org/10.23641/asha.28352720.