Continuous amyloid staging complements global amyloid burden in early Alzheimer's disease

Early Alzheimer's disease (AD) staging remains challenging because clinical categories and global amyloid PET measures provide only coarse characterization of disease progression. We developed a continuous amyloid staging score from regional amyloid PET to complement global amyloid burden.

Using longitudinal florbetapir PET from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we trained an end-to-end deep learning model to directly estimate a continuous amyloid staging score. We evaluated longitudinal consistency, its relationship with global amyloid burden, regional contributors to early-stage variation, and whether the staging score stratified subsequent memory decline among participants matched for baseline global amyloid burden. Cognitive analyses were replicated in A4+LEARN.

The staging score better preserved longitudinal visit order than existing amyloid staging approaches. During early disease stages, it captured regional amyloid variation despite relatively stable global amyloid burden. Among participants with similar baseline global amyloid burden, higher staging scores were associated with faster subsequent memory decline in both ADNI and A4+LEARN.

A continuous amyloid staging score complements global amyloid burden for early AD staging and cognitive risk stratification.