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Learning a Continuous Progression Trajectory of Amyloid in Alzheimer's disease

Tong, M.; Mehfooz, F.; Zhang, S.; Wang, Y.; Fang, S.; Saykin, A. J.; Wang, X.; Yan, J.; Alzheimer's Disease Neuroimaging Initiative,

2026-02-06 bioinformatics
10.64898/2026.02.03.703568 bioRxiv
Show abstract

BACKGROUNDUnderstanding of early Alzheimer progression is critical for timely diagnosis and treatment evaluation, but traditional diagnostic groups often lack sensitivity to subtle early-stage changes. METHODSWe 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. RESULTSSLOPE-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. DISCUSSIONSLOPE provides a continuous staging of amyloid pathology that complements global amyloid measures by capturing early localized progression.

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