Project Summary/Abstract Vocal hyperfunction (VH) refers to chronic conditions resulting from repeated detrimental patterns of vocal behavior and it is implicated in the most commonly occurring types of voice disorders. Our center aims to address the pressing need to increase the understanding of the etiological and pathophysiological mechanisms associated with VH, to improve the prevention, diagnosis and treatment of VH-related disorders. Building upon the progress made by our group to determine key underlying physical mechanisms, additional efforts are needed to better understand the role of auditory-motor impairments in VH as well as the physical mechanisms underpinning long-term of voice use in phonotrauma. Identifying what triggers a VH vicious cycle and differentiating cause from reaction in these disorders is critical. The first aim of the project is to determine the role of auditory-motor control in the laryngeal biomechanics of individuals with VH. A set of lumped and finite element vocal fold models will be incorporated into an established neurocomputational framework of speech motor control, to investigate neural control of voice in terms of pitch, responses to environmental noise, and voice quality. Previous efforts to simulate onset and compensatory mechanisms of VH will be extended to account for the proposed physiologically relevant auditory-motor control model and subject-specific representations will be developed using a Bayesian framework. The proposed auditory-motor framework will allow for the investigation of causal effects and the interrelation between laryngeal motor control and laryngeal biomechanics, which are not directly observable from the behavioral responses. The second aim is to determine the physical mechanisms that underlie VH statistical classifiers that are based on ambulatory voice monitoring. Our lumped element, finite element, and physical models will be used to ascertain how the VH mechanisms modeled for sustained phonation relate to the long-term differences between groups and conditions. In addition, numerical models will mimic population distributions in the ambulatory data to determine the underlying physical mechanisms behind the statistical classification of VH. We will also explore the role of energy dissipation dose, edema, fibrosis, and healing using structure remodeling principles in both physical and finite element models. Individual descriptions will be enhanced using Bayesian subject-specific model-based ambulatory measures that capture underlying VH pathophysiological mechanisms to assess our findings for the statistical classification of VH. Completion of the proposed aim will improve the understanding and clinical relevance of ambulatory monitoring.