Novel Biomarkers of Disease Progression in Huntington’s Disease

Huntington’s disease (HD) is a fatal inherited neurodegenerative disorder, causing progressive brain degeneration, cognitive and motor impairment. The course of HD progression develops gradually and consists of a premanifest HD (pre-HD) stage and a symptomatic HD stage (symp-HD). The pre-HD stage is where individuals have not yet manifested overt motor symptoms to warrant a diagnosis, but have a positive genetic test, whereas symp-HD individuals have a clinical diagnosis based on overt motor signs. Pre-HD individuals undergo significant brain changes prior to clinical diagnosis, including, but not limited, to white and grey matter abnormalities, functional changes, atrophy of the striatum and cerebral cortex, cortical thinning, and decreased cortical metabolism. The brain changes during the pre-HD stages are significant and are detectable up to 20 years before clinical diagnosis. We have a large data set consisting of clinical, neurocognitive and neuroimaging of pre-HD, symp-HD and healthy control individuals. This project will focus on understanding how neuropathology spreads across the brain and whether machine learning and artificial intelligence approaches can predict degeneration of disease over time. The project will develop and implement data-driven models to automatically identify distinct neuroimaging based factors predictive of disease progression in HD. This will enable the development of highly subject-specific biomarkers relevant for disease monitoring and personalized therapies. Computational and neuroscience/psychology background would be preferable.