Two for tau: Automated model discovery reveals two-stage tau aggregation dynamics in Alzheimer’s disease

Abstract

ABSTRACTAlzheimer’s disease is a neurodegenerative disorder characterized by the presence of amyloid-βplaques and the accumulation of misfolded tau proteins and neurofibrillary tangles in the brain. A thorough understanding of the local accumulation of tau is critical to develop effective therapeutic strategies. Tau pathology has traditionally been described using reaction-diffusion models, which succeed in capturing the global spread, but fail to accurately describe the local aggregation dynamics. Current mathematical models enforce a single-peak behavior in tau aggregation, which does not align well with clinical observations. Here we identify a more accurate description of tau aggregation that reflects the complex patterns observed in patients. We propose an innovative approach that uses constitutive neural networks to autonomously discover bell-shaped aggregation functions with multiple peaks from clinical positron emission tomography (PET) data of misfolded tau protein. Our method reveals previously overlooked two-stage aggregation dynamics by uncovering a twoterm ordinary differential equation that links the local accumulation rate to the tau concentration. When trained on data from amyloid-βpositive and negative subjects, the neural network clearly distinguishes between both groups and uncovers a more subtle relationship between amyloid-βand tau than previously postulated. In line with the amyloid-tau dual pathway hypothesis, our results show that the presence of toxic amyloid-βinfluences the accumulation of tau, particularly in the earlier disease stages. We expect that our approach to autonomously discover the accumulation dynamics of pathological proteins will improve simulations of tau dynamics in Alzheimer’s disease and provide new insights into disease progression.