Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder. Interventions at the preclinical and prodromal stages are appealing targets for slowing or halting disease progression. It is desired to achieve accurate prognosis of AD dementia and cognitive decline for people with mild cognitive impairment who have increased risk to develop AD. In order to achieve fast and accurate prognosis of AD dementia based on neuroimaging data, we will develop and validate novel deep learning techniques. Particularly, we will develop unsupervised deep learning methods for segmenting brain images and reconstructing cortical surfaces from structural magnetic resonance imaging data. These fast and accurate image processing methods will be used in conjunction with advanced deep learning methods to build prognosis models of AD dementia and cognitive decline in a time-to-event analysis framework using large-scale imaging datasets. Finally, we will develop and disseminate a user friendly, open source, modular, and extensible software package to improve prognosis of AD dementia. Source code, standalone programs, and web-application interfaces of all the algorithms will be made available on GitHub and NITRC. Our tools will enable real-time neuroimaging data analysis and can find applications in diverse fields, including quantifying brain changes associated with aging and development.
Accurate, time cognitive fast, and robust brain image analysis and pattern recognition methods will be developed for real- neuroimaging data analysis and computer aided prognosis of Alzheimer's disease dementia and decline.