Auditory-Motor Control of Voice in Individuals with Essential Vocal Tremor

原发性声带震颤患者声音的听觉运动控制

• 批准号: 9813695
• 负责人: Rosemary Anne Lester-Smith
• 金额: $ 14.22万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9813695
• 关键词: Accounting; Acoustics; Adverse effects; Affect; Articulators; Auditory; Auditory pitch; Behavior Therapy; Behavioral; Breathing; Cerebellar Diseases; Cerebellum; Cognitive deficits; Communication; Computer Simulation; Development; Disease; Ensure; Essential Tremor; Feedback; Frequencies; Functional disorder; Future; Hearing; Individual; Larynx; Literature; Loudness; Measures; Medical; Methods; Modeling; Motor; Movement Disorders; Neurologic; Output; Population; Prevention; Production; Productivity; Quality of life; Reflex action; Research; Speech; Speech Disorders; System; Techniques; Therapeutic; Tremor; Voice; auditory feedback; auditory reflex; base; improved; motor control; motor deficit; neural network; novel; response; sound; stem; voice therapy

Project Summary Essential tremor (ET) is the most prevalent movement disorder affecting nearly one in every 100 people worldwide. Up to sixty-two percent of individuals with ET develop vocal tremor (VT), a hyperkinetic neurological speech disorder. VT is characterized by modulation of the frequency (pitch) and amplitude (loudness) of the voice, which results in a ‘shaky’ sounding voice and increased effort during speech production. Neurogenic speech disorders like VT are detrimental to communication, professional productivity, and quality of life. Unfortunately, the current approaches for medical management of VT have inconsistent effects on voice production and often cause adverse effects. Furthermore, there is limited evidence supporting the benefit of behavioral management for VT. The challenges in managing VT stem from a lack of understanding of the underlying sensorimotor deficits in this population. Previous research on typical sensorimotor control of voice demonstrated that healthy speakers use auditory feedback for immediate correction of pitch errors and prevention of future errors. Based on the Directions into the Velocities of Articulators (DIVA) model, a computational model accounting for normal and disordered speech motor control, a neural network of feedback and feedforward mechanisms drive auditory- motor responses and ensure that any mismatch between the intended voice output and actual voice output is corrected. When individuals with VT attempt to produce a steady pitch, their voice output is characterized by a 4-8 Hz involuntary modulation of the fundamental frequency and amplitude. It is unknown whether the feedback and/or feedforward systems attempt to correct for these modulations and stabilize the voice output, or if there is dysfunction of these systems that further contributes to the modulated output. Thus, this study aims to investigate how auditory feedback influences pitch control in individuals with VT compared with healthy speakers. The findings of this study may improve the understanding of sensorimotor control in VT and guide development of sensorimotor-based treatments for the disorder. Additionally, this will be a novel application of the current models of feedforward and feedback auditory-motor control, which may help refine these computational models and motivate future research.

项目摘要 特发性震颤(ET)是最常见的运动障碍，几乎每100人中就有一人受到影响 全世界。高达62%的ET患者会出现声带震颤(VT)，这是一种多动神经疾病 言语障碍。VT的特征是对频率(音调)和幅度(响度)进行调制 声音，这会导致声音发抖，并在语音产生过程中付出更多努力。神经源性 像室上性心动过速这样的言语障碍对沟通、职业生产力和生活质量都是有害的。 不幸的是，目前VT的医疗管理方法对语音的影响不一致 生产，往往会造成不良影响。此外，支持……好处的证据有限 VT的行为管理。管理VT的挑战源于对VT的缺乏了解 这一人群潜在的感觉运动缺陷。 先前对声音的典型感觉运动控制的研究表明，健康的说话者使用 用于即时纠正音调错误和防止未来错误的听觉反馈。基于 方向进入关节速度(DIVA)模型，这是一种考虑正常和 无序的语音运动控制，反馈和前馈机制的神经网络驱动听觉- 并确保预期语音输出和实际语音输出之间的任何不匹配 已更正。当室上性心动过速患者试图产生稳定的音调时，他们的声音输出的特征是 4-8赫兹的基波频率和幅度的非自愿调制。目前尚不清楚 反馈和/或前馈系统试图校正这些调制并稳定语音输出， 或者是否存在这些系统的功能障碍，从而进一步影响调制输出。 因此，本研究旨在探讨听觉反馈如何影响室速患者的音调控制。 与健康的说话者相比。本研究的结果可能会提高对感觉运动的认识。 控制室性心动过速并指导以感觉运动为基础的疾病治疗的发展。此外，这将是 是当前前馈和反馈听觉-运动控制模型的新应用，这可能 帮助完善这些计算模型，并推动未来的研究。