IMPROVED MONITORING OF PREMANIFEST AND EARLY HUNTINGTONS USING 7T MULTIMODAL MRI

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PROJECT ABSTRACT Huntington's disease (HD) is a genetic neurodegenerative disorder with a long latent period that usually lasts into early adulthood, until motor, cognitive and psychiatric symptoms overtly impact functional capacity and then gradually advance with time. The age of onset, severity of symptoms, and rate of progression vary significantly across affected individuals, and there is a strong need to develop objective metrics to characterize disease status in order to appropriately counsel patients, design clinical trials, and evaluate the efficacy of putative therapeutic interventions. Existing markers for prognosis based on age and genetic testing lack predictive power, and indicators of disease progression use clinical and neuropsychological evaluation that suffer from poor sensitivity and reproducibility. Longitudinal changes in striatal volume are established markers for disease progression, but fail to capture the heterogeneity seen during the disease course across patients. The goal of this study is to increase the sensitivity to regional brain changes in premanifest and early symptomatic HD multi-contrast 7T MRI based on quantitative susceptibility mapping (QSM), quantitative morphometry of structural MRI, and brain connectivity analysis with diffusion tensor imaging in order to develop spatially varying, time-dependent, multi-modal models capable of predicting disease course in HD. We propose to longitudinally study patients using anatomic, susceptibility-sensitive, and diffusion-weighted MRI. Using measurements of striatal volume and shape, regional values of quantitative susceptibility, and tract-specific white matter diffusion derived from 7T MRI examinations, we aim to detect and characterize the regional distribution and temporal course of neuronal loss, disease-related iron deposition, and white matter injury in this disorder. High-field 7T MRI will be used to enhance measurements of microscopic tissue susceptibility, and recently developed techniques for striatal shape analysis and white matter diffusion alterations are applied to increase the sensitivity to disease progression. Data will be collected in 45 individuals with premanifest HD and 45 individuals with early symptomatic HD, as well as 30 healthy controls. Aim 1 will evaluate multi-contrast 7T MRI for monitoring progression of subclinical and early HD by estimating regional cross-sectional differences and within-patient longitudinal changes in imaging parameters. Aim 2 will determine whether including QSM in a multivariate model of disease burden improves predictive accuracy of the manifestation of symptoms by generating a patient-level multivariate model and voxel-level spatial map of affected brain regions that discriminates each disease stage, and relating imaging metrics to measures of disease burden, cognitive function, and clinical impairment. The proposed study will ultimately result in an understanding of the complicated relationship between iron deposition and atrophy in HD, enhance the ability to predict proximity to clinical onset of symptoms, and create a framework for developing multivariate risk prediction models in HD.