About the research

About 5-8% of individuals over the age of 60 have dementia. With our aging population this number is likely to increase, making dementia one of the most important threats to public health in the 21st century. Studies have highlighted the complexity of dementia and predicted that therapeutic approaches based on isolated disease features will not be successful. Better characterization of brain atrophy due to individual causes is urgently required to select biomarkers and therapeutic targets that are meaningful to each disease. In this work we will focus on Parkinson-plus syndromes, a group of neurodegenerative diseases that present with the classical features of Parkinson's disease (PD) but with additional features that distinguish them from PD. These include dementia with Lewy Bodies (DLB), progressive supranuclear palsy (PSP), and multiple system atrophy (MSA), with DLB reported as the second most common form of dementia following Alzheimer’s disease.

Here we aim to

1. Develop a deep learning based automated brain segmentation and labeling algorithm specific for DLB, PSP, and MSA imaging biomarkers
2. Compute volumetric measures as biomarkers that robustly characterize the brain atrophy seen in DLB, PSP, and MSA;
3. Carry out pilot studies on Parkinson-plus patients with a known diagnosis and compare our biomarkers with current gold standard. If successful, our work can lead to novel diagnostic strategies and insights into the pathophysiology of dementia

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