Characterization of clinical phenotypes of laryngeal dystonia and voice tremor

项目摘要/摘要 摘要局灶性喉肌张力障碍(LD)是一种罕见的神经性发声障碍，会间歇性地中断说话。 嗓音开始紧张而窒息，或导致发声停止或产生突然的喘息声。那些 LD患者通常在获得准确诊断前5年报告症状出现， 尽管看了很多专家。声音震颤(VT)是另一种神经性声音障碍，由 听众的声音质量不稳定。严重的室上性心动过速可导致声音中断，听起来类似于LD导致的 被专家误诊。最近的研究表明，在区分患有学习障碍的人和其他人方面可靠性较差 嗓音障碍，很大程度上是由于依赖感知评估方法和广泛的临床 没有证据的标准来指导准确的诊断方法。对于VT，一种声音，尤其如此 没有明确记录的临床特征的障碍，因此无法使用电流进行分类 以运动障碍共识为基础的震颤综合征标准。LD与VT的准确鉴别诊断 对于有效的治疗计划和管理以及准确的临床和流行病学是必不可少的 表征和分类。这个项目的目标是系统地描述个体的特征。 LD和VT使用当前可用的和新的临床工具来确定区分临床 这些特征对他们的正确诊断具有高度的预测性。将对65人进行三项研究 每个诊断都符合多学科共识，以满足LD和VT的标准以及35个神经典型 正常对照组。所有参与者将接受彻底的筛选和测试，以确保就以下问题达成共识 他们由言语病理学、神经病学和耳鼻喉科的专家进行小组作业。 此后，临床表型特征将在不同组之间进行比较，使用声学、空气动力学、 喉部肌电和鼻内窥镜以量化声音模式的周期性和任务特异性。小说 评估工具和措施也将用于研究身体分布、言语症状状况、 以及语音结构运动(运动学)模式的规律性或间歇性/音素特异性 高速视频内窥镜(HSV)、实时磁共振成像(RtMRI)和鼻内窥镜 持续发声期间的录音与有声和无声加载的句子进行比较。计算型 建模将用于评估空气动力学、喉部肌电和语音结构的运动模式，以 将患者特定的声学输出模拟为VT或LD来预测组成员。这样做的结果是 研究将显著提高我们关于最佳临床工具和科学知识的临床和科学知识 测量LD和VT的临床特征，从而准确诊断这些神经性语言 精神错乱。

PROJECT SUMMARY / ABSTRACT Focal laryngeal dystonia (LD) is a rare neurological voice disorder that interrupts speaking with intermittent onset of a strained-strangled voice quality or causes voicing to stop or produce sudden breathiness. Those suffering from LD commonly report onset of symptoms 5 years prior to achieving an accurate diagnosis, despite seeing multiple experts. Voice tremor (VT) is another neurological voice disorder that is perceived by listeners as a shaky voice quality. Severe VT can result in voice interruptions that sound similar to LD resulting in misdiagnosis by experts. Recent research shows poor reliability in distinguishing those with LD from other voice disorders, largely due to the reliance on perceptual assessment methods and a wide range of clinical criteria without evidence to guide accurate diagnostic approaches. This is particularly true of VT, a voice disorder without clearly documented clinical features such that classification is not possible using current movement disorder consensus-based tremor syndrome criteria. Accurate differential diagnosis of LD from VT is essential to effective treatment planning and management as well as for accurate clinical and epidemiologic characterization and classification. The goal of this project is to systematically characterize individuals with LD and VT using currently available and novel clinical tools to determine distinguishing clinical features highly predictive of their correct diagnosis. Three studies will be conducted with 65 individuals each diagnosed by multi-disciplinary consensus to meet criteria for LD and VT as well as 35 neurotypical normal controls. All participants will undergo thorough screening and testing to assure consensus regarding their group assignment by experts from speech-language pathology, neurology, and otolaryngology. Thereafter, clinical phenotypic features will be compared between groups using acoustic, aerodynamic, laryngeal EMG, and nasoendoscopy to quantify periodicity and task-specificity of voice patterns. Novel assessment tools and measures will also be used to study body distribution, condition of speech symptoms, and regularity or intermittency/phoneme-specificity of speech structure movement (kinematic) patterns using high speed videoendoscopy (HSV), real-time magnetic resonance imaging (rtMRI), and nasoendoscopy recordings during sustained phonation compared to voice- and voiceless-loaded sentences. Computational modeling will be used to assess aerodynamic, laryngeal EMG and speech structure kinematic patterns to simulate patient-specific acoustic output predictive of group membership as VT or LD. Outcomes of this research will significantly advance our clinical and scientific knowledge regarding optimal clinical tools and measures of LD and VT clinical features that result in precise diagnosis of these neurological speech disorders.