Learning a continuous progression trajectory of amyloid in Alzheimer's disease

BACKGROUND Understanding of early Alzheimer's disease (AD) progression is critical for timely diagnosis and treatment evaluation, but traditional diagnostic groups often lack sensitivity to subtle early‐stage changes. METHODS We developed a Self‐supervised Longitudinal Progression Embedding (SLOPE) method, an unsupervised dimensionality reduction method that models amyloid progression in AD on a continuous scale that preserves the temporal sequence of follow‐up visits. Applied to longitudinal amyloid positron emission tomography data, SLOPE generates a two‐dimensional trajectory capturing global amyloid accumulation across the AD continuum. RESULTS SLOPE‐derived pseudotime scores better preserved temporal consistency across diagnostic groups and longitudinal follow‐up visits and can be generalized to subjects. The learned trajectory revealed biologically consistent amyloid spreading patterns and greater sensitivity to early progression than global amyloid standardized uptake value ratio. DISCUSSION SLOPE provides a continuous staging of amyloid pathology that complements global amyloid measures by capturing early localized progression.