Without a cure or prevention, Alzheimer’s Disease (AD) rapidly will become an overwhelming economic and social burden. With the failure of multiple clinical trials targeting reduction of beta-amyloid (A!) burden and in general the stagnation of the development of effective treatment for AD, AD community believes that underlying causes of AD must be attacked before emergence of the cognitive phenotype. In this context, the development of predictive biomarkers and models is critical for identifying patients with high risk of rapid progression along the AD continuum, i.e. transitioning from normal cognition to mild cognitive impairment (MCI) and from MCI to dementia. Previous AD research has primarily been utilizing magnetic resonance imaging (MRI) for assessing morphological changes. We aim to: 1) study the normal aging processes using imaging metrics with correlations with neuropyschological, cardiovascular, genetic evaluations; 2) develop machine learning methods for predicting changes in neuropsychological tests. The project will last for 36 months.
