Learning a Continuous Progression Trajectory of Amyloid in Alzheimer disease

Understanding of early Alzheimer progression is critical for timely diagnosis and treatment evaluation, but traditional diagnostic groups often lack sensitivity to subtle early-stage changes.

We developed SLOPE, an unsupervised dimensionality reduction method that models the amyloid progression in AD on a continuous scale which preserves the temporal sequence of follow-up visits. Applied to longitudinal amyloid PET data, SLOPE generated a two-dimensional trajectory capturing global amyloid accumulation across the AD continuum.

SLOPE-derived pseudotime scores better preserved temporal consistency across diagnostic groups and longitudinal follow-up visits and can be generalized to held-out subjects. The learned trajectory revealed biologically consistent amyloid spreading patterns and greater sensitivity to early progression than global amyloid SUVR.

SLOPE provides a continuous staging of amyloid pathology that complements global amyloid measures by capturing early localized progression.