petVAE: A Data-Driven Model for Identifying Amyloid PET Subgroups Across the Alzheimer's Disease Continuum

Abstract

Amyloid-{beta} (A{beta}) PET imaging is a core biomarker and is sufficient for the biological diagnosis of Alzheimers disease (AD). Here, we aimed to identify biologically meaningful subgroups across the continuum of A{beta} accumulation using a data-driven deep learning approach, without imposing predefined thresholds for A{beta} negativity or positivity. We analyzed 3,110 A{beta} PET scans from the Alzheimers Disease Neuroimaging Initiative and Anti-Amyloid Treatment in Asymptomatic Alzheimers Disease studies to develop petVAE, a two-dimensional variational autoencoder. The model accurately reconstructed scans without prior labeling, selection by scanner or region of interest. Latent representations of scans extracted from petVAE were used to visualize and cluster the AD continuum. Clustering yielded four groups: two predominantly A{beta} negative (A{beta}-, A{beta}-+) and two predominantly A{beta} positive (A{beta}+, A{beta}++). All clusters differed in standardized uptake value ratio (p < 1.64x10-) and cerebrospinal fluid (CSF) A{beta} (p < 0.02), demonstrating petVAEs ability to assign scans along the A{beta} continuum. Extreme clusters (A{beta}-, A{beta}++) resembled conventional A{beta} negative and positive groups and differed in cognition, APOE {varepsilon}4 prevalence, A{beta} and tau CSF biomarkers (p < 3x10-). Intermediate clusters (A{beta}-+, A{beta}+) showed higher odds of carrying at least one APOE {varepsilon}4 allele versus A{beta}- (p < 0.03). Participants in A{beta}+ or A{beta}++ clusters exhibited faster progression to AD (A{beta}+ hazard ratio = 2.42, A{beta}++ HR = 9.43; p < 1.17x10-). Thus, petVAE was capable of reconstructing PET scans while extracting latent features that capture the AD continuum and define biologically meaningful subgroups, enabling data-driven characterization of preclinical disease stages.