Brain Metabolic, Structural, and Behavioral Pattern Learning for Early Predictive Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting millions of people worldwide. Laboratory research and longitudinal clinical studies have helped to reveal various information about the disease but the exact causal process is not known yet. Patterns from alteration of neurochemicals (e.g., glutathione depletion, etc.), hippocampal atrophy, and brain effective connectivity loss as well as associated behavioral changes have generated important characteristic features. These imaging-based readouts and neuropsychological outcomes along with supervised clinical review are critical for developing a comprehensive artificial intelligence strategy for early predictive AD diagnosis and therapeutic development.