A quantitative study of the medial surface dynamics of an in vivo canine vocal fold during phonation.

OBJECTIVES/HYPOTHESIS: The purpose of this study was to measure the medial surface dynamics of a canine vocal fold during phonation. In particular, displacements, velocities, accelerations, and relative phase velocities of vocal fold fleshpoints were reported across the entire medial surface. Although the medial surface dynamics have a profound influence on voice production, such data are rare because of the inaccessibility of the vocal folds.

STUDY DESIGN

Medial surface dynamics were investigated during both normal and fry-like phonation as a function of innervation to the recurrent laryngeal nerve for conditions of constant glottal airflow.

METHODS

An in vivo canine model was used. The larynx was dissected similar to methods described in previous excised hemilarynx experiments. Phonation was induced with artificial airflow and innervation to the recurrent laryngeal nerve. The recordings were obtained using a high-speed digital imaging system. Three dimensional coordinates were computed for fleshpoints along the entire medial surface. The trajectories of the fleshpoints were preprocessed using the method of Empirical Eigenfunctions.

RESULTS

Although considerable variability existed within the data, in general, the medial-lateral displacements and vertical displacements of the vocal fold fleshpoints were large compared with anterior-posterior displacements. For both normal and fry-like phonation, the largest displacements and velocities were concentrated in the upper medial portion. During normal phonation, the mucosal wave propagated primarily in a vertical direction. Above a certain threshold of subglottal pressure (or stimulation to the recurrent laryngeal nerve), an abrupt transition from chest-like to fry-like phonation was observed.

CONCLUSIONS

The study reports unique, quantitative data regarding the medial surface dynamics of an in vivo canine vocal fold during phonation, capturing both chest-like and fry-like vibration patterns. These data quantify a complex set of dynamics. The mathematical modeling of such complexity is still in its infancy and requires quantitative data of this nature for development, validation, and testing.