Neuroimage-driven biophysical inverse problems for atrophy and tau propagation

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PROJECT SUMMARY/ABSTRACT Early detection and stratification of Alzheimer’s disease (AD) offers numerous medical, emotional and financial benefits. A critical research direction is to develop methods for earlier diagnosis and patient classification, with the hope of developing treatment— before cognitive damage sets in. Neuroimaging has made it possible to derive key biomarkers in vivo: measures of atrophy using Magnetic Resonance Imaging (MRI) and accumulation of misfolded Aβ and tau deposits using Positron Emission Tomography (PET). Community efforts have created high-quality datasets with 1000s of cases that comprise multimodal imaging scans, cognitive evaluations, lab work, and genetic information. However, the heterogeneity of the data is a challenge for traditional statistical methods. Complementary to existing quantitative analysis techniques, we propose to use biophysical mathematical models of disease progression. Prior work has shown that mathematical models of protein misfolding in degenerative disorders can capture spatiotemporal disease dynamics and can enhance image interpretation by providing clinically useful biomarkers in terms of model parameters and disease progression. In this project, we will integrate a novel partial differential equation model of tau propagation with state-of-the-art parameter calibration methods developed in our group. Our hypothesis is that this model will provide novel diagnostic and prognostic value. First, we will work on the development of a new tau propagation simulator. This model will account for the progression of tau and its coupling to atrophy. Second, we will develop model calibration algorithms that use multiparametric Magnetic Resonance Imaging, Diffusion Tensor Imaging, and tau PET. We will conduct a retrospective validation study using images from the Alzheimer’s Disease Neuroimaging Initiative and the Harvard Aging Brain Study datasets.