Amyloid positron emission tomography (PET) is commonly used to classify individuals as amyloid-β (Aβ) negative or positive, but binary approach may overlook intermediate stages of pathology. Here, we aimed to identify data-driven subgroups along the Alzheimer's disease (AD) continuum using Aβ PET imaging to enable a more nuanced characterization of amyloid accumulation.
We analyzed 3,110 Aβ PET scans from the ADNI and A4 cohorts using petVAE, a variational autoencoder trained to reconstruct two-dimensional PET slices without diagnostic labels or predefined regions of interest. The model generated 11,648-dimensional latent representations per scan, which were used for exploratory analyses and clustering across the AD spectrum.
We identified four clusters that differed significantly in standardized uptake value ratio (p < 1.64×10⁻⁸) and cerebrospinal fluid (CSF) Aβ levels (p < 0.02), indicating that petVAE effectively positions scans along a continuous Aβ trajectory. Two clusters (Aβ−, Aβ−+) were largely amyloid-negative, while two (Aβ+, Aβ++) were predominantly amyloid-positive. The extreme clusters (Aβ−, Aβ++) aligned with conventional classifications and showed marked differences in cognition, APOE ε4 carrier frequency, and CSF Aβ, Tau, and phosphorylated Tau (p < 3×10⁻⁶). Intermediate clusters exhibited increased odds of APOE ε4 carriership (p < 0.026). Individuals in Aβ+ and Aβ++ clusters had a higher risk of progression to AD over 6 years (hazard ratios 2.42 and 9.43; p < 1.17×10⁻⁷).
Overall, petVAE reconstructs PET images accurately while learning biologically meaningful representations of Aβ pathology. This data-driven framework enables detection of subtle disease stages and supports investigation of preclinical AD.

Co-authors: Eero Vuoksimaa, Esa Pitkänen