From Symptomatic to Pre-Symptomatic: Adaptive Knowledge Distillation for Early Alzheimer's Detection Using Functional MRI

Alzheimer's disease (AD) progresses from asymptomatic changes to clinical symptoms, underscoring the critical need for early detection to facilitate timely treatment. Functional magnetic resonance imaging (fMRI) offers non-invasive biomarkers for detection, but current methods fail to reliably identify pre-symptomatic individuals due to two key challenges: (1) Subtle, anatomically distinct fMRI patterns in pre-symptomatic cases that resemble healthy controls more than symptomatic patients, and (2) Severe class imbalance in real-world data, where healthy controls vastly outnumber pre-symptomatic subjects. To address this, we reconceptualize AD diagnosis as a multi-stage distillation task, where insights from easier-to-detect symptomatic cases guide pre-symptomatic detection. We propose a novel margin-aware knowledge distillation (KD) framework with two innovations: (1) We leverage Unbalanced Optimal Transport (UOT) for Feature Distillation to flexibly adapt to anatomical differences in brain patterns caused by neurodegeneration and ensure effective distillation from later to earlier disease stages. (2) We propose Self-Distillation with Dynamic Margins to combat class imbalance by adaptively refining the classification boundary. We evaluate our proposed framework across four distinct base models and demonstrate its superiority over state-of-the-art KD methods. Additionally, we show the significance of various brain regions in identifying pre-symptomatic subjects, as well as how features are transferred during distillation. These contributions advance the development of more precise diagnostic tools and foster a deeper understanding of early disease manifestations, marking a significant stride towards more reliable and earlier AD diagnosis.