PROJECT SUMMARY / ABSTRACT Laryngeal dystonia (LD), or spasmodic dysphonia, is isolated focal dystonia characterized by selective impairment of speech production due to involuntary spasms in the laryngeal muscles. Despite well- characterized clinical features of LD, its clinical management remains challenging due, in part, to the absence of objective measures (biomarkers) for early detection and differential diagnosis. This results in diagnostic inaccuracies, which have a negative impact on the patient’s quality of life and healthcare costs. Importantly, delayed diagnosis leads to deferred treatment. The objective of this application is to conduct a series of studies that combine advanced machine-learning with neuroimaging and genetics to (1) identify the neural markers that accurately differentiate LD between its clinical phenotypes (adductor vs. abductor), genotypes (sporadic vs. familial), and comorbid disorders (voice tremor and muscle tension dysphonia); (2) determine the early predictive neural markers of LD development in at-risk individuals, and (3) validate associated LD gene mutations. Supported by our preliminary data, our central hypothesis is that brain abnormalities are shaped, in part, by underlying genetic factors and exhibit LD form-characteristic features, which can be used as differential diagnostic and early predictive biomarkers of this disorder. This research is innovative both conceptually and methodologically because it uses a cross-disciplinary approach to focus on the neural pathophysiology and genetic susceptibility factors for LD diagnostic and predictive biomarker discovery. The proposed research is significant because it will directly contribute to closing the critically existing gap in the clinical management of LD. Identification of LD neural and genetic markers is expected to have a positive translational impact by establishing enhanced criteria for accurate differential diagnosis and screening of persons at risk. In short, the successful completion of these studies will open new horizons for the clinical management of LD patients.