An unsupervised learning approach for clustering joint trajectories of Alzheimer's disease biomarkers: An application to ADNI Data

Developed a novel distance measure and clustering approach for AD biomarker trajectories. Identified distinct subgroups with different biomarker progression patterns in ADNI data. Findings challenge the traditional amyloid cascade hypothesis and suggest AD heterogeneity. Clustering approach accounts for shifts in time and emphasizes progression patterns. Results have implications for AD diagnosis, targeted interventions, and clinical trials.