Muscle Control Mechanism of Voice Production in Vocal Fold Paralysis

声带麻痹发声的肌肉控制机制

• 批准号: 10696434
• 负责人: Qian Xue
• 金额: $ 36.1万
• 依托单位: ROCHESTER INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-09 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10696434
• 关键词: Muscle; Control; Mechanism; Voice; Production

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.

项目摘要 经过近50年的人类声音研究，关于肌肉的许多事情仍然未知 发声的控制机制。研究证实，声带发声前姿势 （几何形状、位置、张力和刚度）是控制声带的主要因素之一 振动动力学和声音类型。然而，关于肌肉控制的数据非常有限， 这些性能，主要是由于实验困难。声门几何形状的肌肉控制 和动力学已经在体内人类和动物模型中进行了研究，但这些研究 局限于内窥镜上级视图，因此不能提供内窥镜的3D变形/移动。 声带此外，声带组织的张力和刚度很难获得， 由于缺乏可靠的技术，关于如何解决这一问题， 喉内肌也通过声带姿势控制发声 ILM功能障碍，如声带麻痹/轻瘫（VFP），如何通过以下方式影响发声： 改变声带的姿势在这个项目中，我们建议使用一种创新的方法， 结合实验数据和最先进的计算机建模技术， 3D声带姿势（几何形状、位置、张力、刚度）的完整数据集，3D声带 振动动力学，以及在整个肌肉控制空间中的语音结果，包括对称， 不对称和补偿性肌肉激活。PI的团队最近开发了一种状态- 最先进的，基于物理学的，3D计算机模型，集成了逼真的喉部解剖结构， 生理可量化的输入，逆材料参数化和机器学习， 模拟声带姿态和语音过程中的流-结构-声学相互作用（FSAI） 生产对于这个项目，我们建议将联合收割机这个具体模型与实验数据相结合 以产生具有大的时间和空间的声带姿势和FSAI的整体视图 在整个肌肉控制空间的细节。特别是，该模型将揭示3D复杂性， 声带姿势和振动，并提供肌肉紧张和僵硬的措施 激活，这是不可用的。我们建议使用数据集来阐明因果关系 肌肉活动，声带姿势，声带振动和声音结果之间的联系。肌肉 将识别具有不同姿势、振动和声学模式的组合。新 知识有望阐明发声的肌肉控制机制， 声带姿势拟议的工作有可能提高对功能的洞察力 和功能障碍的ILMs，并作为一个新的基础，有针对性的治疗 接近。我们希望开发出可能的生物力学指标，用于诊断和优化。 VFP的治疗方法。