Muscle Control Mechanism of Voice Production in Vocal Fold Paralysis

点击翻译按钮获取中文摘要

Project Summary After nearly five decades of human voice research, much remains unknown regarding muscle control mechanism of voice production. Studies confirmed that vocal fold pre-phonatory posture (geometry, position, tension and stiffness) is among the primary factors controlling vocal fold vibratory dynamics and voice type. However, very limited data is available on muscle control of these properties, primarily due to experimental difficulties. Muscle control of glottic geometry and dynamics has been studied in in vivo human and animal models, but these studies were limited to an endoscopic superior view, thus cannot to provide 3D deformation/movement of the vocal folds. Moreover, tension and stiffness of the vocal fold tissues are very difficult to obtain in vivo due to a lack of reliable techniques. A significant knowledge gap remains regarding how intrinsic laryngeal muscles (ILMs) control voice production through vocal fold posturing as well as how ILM dysfunctions, such as vocal fold paralysis/paresis (VFP), affect voice production by altering vocal fold posturing. In this project, we propose to use an innovative approach that integrates experimental data and state-of-the-art computer modeling techniques to produce a complete dataset of 3D vocal fold postures (geometry, position, tension, stiffness), 3D vocal fold vibratory dynamics, and voice outcomes in the full muscle control space including symmetric, asymmetric and compensative muscle activations. The PI’s group recently developed a state-of-the-art, physics based, 3D computer model that integrates realistic laryngeal anatomy, physiologically quantifiable inputs, inverse material parameterization and machine learning for simulating vocal fold posturing and flow-structure-acoustics interaction (FSAI) during voice production. For this project, we propose to combine this embodied model with experimental data to generate a holistic view of vocal fold posturing and FSAI with great temporal and spatial details in the full muscle control space. In particular, the model will reveal the 3D complexity of vocal fold postures and vibrations and provide measures of tension and stiffness with muscle activations, which have not been available. We propose to use the dataset to elucidate causal links among muscle activity, vocal fold posturing, vocal fold vibration and voice outcome. Muscle combinations with distinct posturing, vibration and acoustic patterns will be identified. The new knowledge is expected to elucidate the muscle control mechanism of voice production through vocal fold posturing. The proposed work has the potential to improve insight into the function and dysfunction of the ILMs and to serve as a foundation for novel, targeted therapeutic approaches. We hope to develop possible biomechanical metrics for the diagnosis and optimal methodology for treatment of VFP.