# Neuroimage-driven biophysical inverse problems for atrophy and tau propagation

> **NIH NIH R21** · UNIVERSITY OF TEXAS AT AUSTIN · 2022 · $222,649

## Abstract

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.

## Key facts

- **NIH application ID:** 10476510
- **Project number:** 5R21AG074276-02
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** George Biros
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $222,649
- **Award type:** 5
- **Project period:** 2021-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10476510

## Citation

> US National Institutes of Health, RePORTER application 10476510, Neuroimage-driven biophysical inverse problems for atrophy and tau propagation (5R21AG074276-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10476510. Licensed CC0.

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