Characterization of Clinical Phenotypes of Laryngeal Dystonia and Voice Tremor

Focal laryngeal dystonia (LD) is a rare neurological voice disorder that interrupts speaking with the intermittent onset of strained-strangled voice quality or causes voicing to stop or produce sudden breathiness. Those suffering from LD commonly report the 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 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 research 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. Clinical phenotypic features will be compared between groups using acoustic, aerodynamic, laryngeal electromyography (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 real-time magnetic resonance imaging 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 a precise diagnosis of these neurological speech disorders.