Alzheimer’s Disease (AD) tau pathology originates in the brainstem and subsequently spreads to the entorhinal cortex, hippocampus and finally to temporal, parietal and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to trans-neuronal spread of misfolded tau protein along the projection pathways of affected neurons. The aggregation of tau is being increasingly recognized as a trustworthy biomarker preceding the appearance of Alzheimer’s disease (AD) symptoms. One major goals of disease modifying therapies has been to stop or slow down the tau aggregation process. In order to evaluate drug efficacy, it would be desirable to have an accurate model predictive of a patient’s future tau burden, against which the tau measurements from drug-receiving cohorts could be compared. Here we report the development of such a model, evaluated in a cohort of 88 subjects clinically diagnosed as Mild Cognitively Impaired (MCI = 60) or Alzheimer’s disease (AD = 28) and tracked over a period of 18 months. Our approach combined data-driven and model-based methodologies, with the goal of predicting changes in tau within suitably specified target regions. We show that traditional statistical methods, allied to a network diffusion model for tau propagation in the brain, provide a remarkable prediction of the magnitude of incremental tau deposited in particular cortical areas of the brain over this period (MCI: R2 = 0.65+-0.16; AD: R2 = 0.71+-0.11) from baseline data. Our work has the potential to greatly strengthen the repertoire of analysis tools used in AD clinical trials, opening the door to future interventional trials with far fewer sample sizes than currently required.
bioRxiv Subject Collection: Neuroscience