Computational Biomarkers for Early Neurodegenerative Change

This publication examines how multimodal computational biomarkers can improve the early detection of neurodegenerative change before clear clinical symptoms appear. The work synthesizes findings from neuroimaging, cognitive assessment, and molecular profiling to describe how machine-learning models identify subtle patterns associated with disease progression. Emphasis is placed on reproducibility, transparent feature selection, and the clinical limits of predictive algorithms in longitudinal patient cohorts.