Early Alzheimers Forecasting from Multimodal Data via Deep Transfer Learning, Evaluated on a Large-Scale Prospective Cohort Study

项目总结

Project Summary Alzheimer's Disease, a debilitating and degenerative brain disease that has no cure, affects ~5.8 million people in the United States. This project will develop techniques to train, adapt and transfer models for the early detection of Alzheimer’s disease from multimodal data, including genetic information, brain MRIs and cognitive tests, with a focus on screening for AD in the general population (i.e., evaluated on a cross-sectional, prospective cohort study, representative of the populations). We will introduce new techniques, based on deep transfer learning, to extract representations from brain MRIs, applicable to prospectively collected data which is unaccompanied by expert annotations. We will incorporate the feature extraction in an end-to-end predictive framework using multimodal deep learning. Such methods will be useful for modeling, monitoring, and forecasting the progression of Alzheimer's disease, where MRIs accompany the clinical information collected at different levels of granularity. We will start with a model that predicts the evolution of AD, trained on multimodal longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Models trained on ADNI data typically rely on specialized engineered features from the brain MRIs requiring a considerable amount of domain knowledge and pre-processing and which would not be generally available if a patient were to obtain an MRI scan in the hospital. Thus, we train CNN-based models that work directly with brain MRIs, optimized to capture the predictive capabilities of the engineered features present in ADNI. We integrate the brain MRI network with a forecasting model that uses deep learning to extract abstract representations of the subjects' health status based on their multimodal information at a given point, including demographics, genetic information (e.g., the ApoE genes), cognitive test scores and brain MRIs. The method learns health status transitions, as well as how to map the health status abstraction to a diagnosis. An important innovation is the incorporation of an image extraction component in an end-to-end manner in the framework using hybrid convolutional layers, visual attention guided by domain knowledge and information theoretical measurements to extract different features from images. Moreover, we introduce methodology for the seamless transfer of the models between datasets collected as part of different studies, where the recorded information, including clinical tests, images collected and subject questionnaires, differs across study cohorts. The methods mitigate the challenges presented by this otherwise rich and varied data by using fused signals and mappings between abstractions. At the end of this study, we will have created a general forecasting framework, capable of predicting the onset of Alzheimer’s years before symptoms arise, a striking advance that will enable clinicians to identify new prevention strategies and prepare for, rather that respond to, Alzheimer’s.