Staging and Pseudotime Inference of Alzheimer's Disease Progression using Multimodal Imaging Data.

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline, driven by the accumulation of amyloid-beta plaques, tau tangles, and neuronal atrophy. This study analyzes three imaging modalities corresponding to the three hallmark biomarkers of AD: amyloid PET, tau PET, and structural MRI. Using cortical measurements from the ADNI dataset, we apply PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding), a dimensionality reduction technique, to uncover continuous trajectories of disease progression. We derive pseudotime values from PHATE embeddings via Slingshot, a principal-curve-based pseudotime inference algorithm. In parallel, we apply SuStaIn (Subtype and Stage Inference), a machine learning model that uncovers distinct biomarker event sequences and assigns subjects to discrete disease stages. We observe strong correspondence between SuStaIn-predicted stages and PHATE-derived pseudotimes, indicating temporal coherence across modeling frameworks. SuStaIn further reveals non-overlapping, modality-specific sequences of biomarker abnormalities, consistent with prior neuropathological models. We validate these sequences using pseudotime-aligned kernel density models and clinical measures. Together, our results support the integrative use of these approaches for evaluating AD progression. Future steps will focus on anchoring pseudotime to real-world clinical timelines and experimentally validating SuStaIn-predicted biomarker cascades.