Multi-modal neuroimaging biomarkers of Parkinson’s disease progression
Aperçu du projet
Parkinson’s disease (PD) is one of the most common neurodegenerative disorders in the world. PD can manifest and develop in a variety of ways for different patients, and it is currently not possible to reliably determine, for a given patient, which subtype of PD they have and what course their disease will follow. Previous research has shown that magnetic resonance imaging (MRI) can reveal brain characteristics – known as “biomarkers” – associated with PD severity or progression. However, a large amount of data is needed to ensure that these findings are meaningful and can be generalized to individuals from diverse populations. This project aims to identify and validate MRI biomarkers for PD subtyping and progression using data from hundreds of PD patients from longitudinal studies, where data from the same patient at different timepoints are available. I will use advanced machine learning methods to identify PD subtypes and stages, and I plan to develop a robust and accurate tool that can predict a patient’s subtype and estimate the time it will take for them to transition to the next stage of their disease. Overall, this project is expected to provide a predictive tool that informs clinicians about the subtype and disease trajectory of each patient, which could lead to more accurate diagnosis, prognosis and treatment of PD.
Chef d'équipe
Michelle Wang , McGill University
Partenaire et Donateurs
Mireille and Steinberg Foundation and the Growling Beaver Brevet