Introduction: The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion-like spreading processes of neurofibrillary tangles and amyloid plaques.
Methods: Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis. We then created dynamic models with the Nathan Kline Institute-Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression.
Results: A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are associated with the medial temporal lobe and subcortical regions, together with posterior and occipital brain regions. Our model simulations suggest that changes associated with dementia begin to manifest structurally at early stages.
Discussion: Our analyses suggest that diffusion magnetic resonance imaging-based centrality measures can offer a tool for early disease detection before clinical dementia onset.
Keywords: Alzheimer's disease; Computational modeling; Diffusion MRI; Machine learning; Network centrality; Structural brain connectivity.