Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer’s Disease Progression

Early prediction of diseased brain conditions is critical for curing illness and preventing irreversible neuronal dysfunction and loss. Generically regarding the different neuroimaging modalities as filtered, complementary insights of brain’s anatomical and functional organization, multimodal data fusion could be hypothesized to enhance the predictive power as compared to a unimodal prediction of disease progression. More recently, deep learning...