Predicting brain atrophy from tau pathology: a summary of clinical findings and their translation into personalized models

For more than 25 years, the amyloid hypothesis–the paradigm that amyloid is the primary cause of Alzheimer’s disease–has dominated the Alzheimer’s community. Now, increasing evidence suggests that tissue atrophy and cognitive decline in Alzheimer’s disease are more closely linked to the amount and location of misfolded tau protein than to amyloid plaques. However, the precise correlation between tau pathology and tissue atrophy remains unknown. Here we integrate multiphysics modeling and Bayesian inference to create personalized tau-atrophy models using longitudinal clinical images from the Alzheimer’s Disease Neuroimaging Initiative. For each subject, we infer three personalized parameters, the tau misfolding rate, the tau transport coefficient, and the tau-induced atrophy rate from four consecutive annual tau positron emission tomography scans and structural magnetic resonance images. Strikingly, the tau-induced atrophy coefficient of 0.13/year (95% CI: 0.097-0.189) was fairly consistent across all subjects suggesting a strong correlation between tau pathology and tissue atrophy. Our personalized whole brain atrophy rates of 0.68-1.68%/year (95% CI: 0.5-2.0) are elevated compared to healthy subjects and agree well with the atrophy rates of  1-3%/year reported for Alzheimer’s patients in the literature. Once comprehensively calibrated with a larger set of longitudinal images, our model has the potential to serve as a diagnostic and predictive tool to estimate future atrophy progression from clinical tau images on a personalized basis.

Statement of Significance

Developing predictive, patient-specific models of Alzheimer’s disease pathology and progression is of paramount importance for effective patient care and potential treatment. Tissue atrophy, the reduction of brain volume, is an important biomarker for Alzheimer’s disease. Similarly, the pathology associated with tau proteins is thought to play a central role in Alzheimer’s disease progression, local atrophy, and a patient’s cognitive decline. The main question is: how do combine the mechanisms of tau propagation and atrophy in a single model that can make the best use of existing data? Here, we first review the dynamics of atrophy in for Alzheimer’s disease and describe a mathematical model that couples tau propagation and atrophy. We then investigate how to fit the model parameters using the longitudinal structural neuroimaging data of four subjects, from the ADNI database, and a Bayesian Markov Chain Monte Carlo inference method. Our approach shows that network neurodegeneration models may hold promise for the predictive, patient-specific modeling of AD using AV-1451 tau PET and T1 structural MRI data.