# Center for Molecular Imaging Technology and Translation (CMITT) Administrative Supplement #4

> **NIH NIH P41** · MASSACHUSETTS GENERAL HOSPITAL · 2021 · $328,969

## Abstract

Abstract
Alzheimer's Disease (AD) is characterized by the presence and distribution of intracellular
neurofibrillary tangles tau and extracellular amyloid-β. Tau pathology is deeply associated with
cognitive decline in AD in a region-specific manner. It will be highly valuable to predict how tau
burden would spread in the future given a baseline tau PET measurement. Such approach can
be used to predict how region-wise tau burden changes versus age and, therefore, stratify
potential candidates for AD therapy based on their “trajectory” of progression. It can also be used
to determine how different a tau PET distribution is after a therapy from the predicted distribution
if no therapy is applied. The recent availability of longitudinal tau PET data provides us a unique
opportunity for technology development: to model tau-propagation using state-of-the-art deep
learning methods. However, current available longitudinal tau PET datasets are relatively small,
therefore, insufficient to train a deep neural network. To solve this problem, we propose a novel
approach that incorporates a simple mathematical model in the training of the deep neural
network. We first develop a simple network diffusion model that fits part of the available
longitudinal tau PET data. We then generate a very large number of longitudinal tau PET datasets
using the fitted model to pretrain a U-net-like autoencoder deep neural network. Finally, we further
train the neural network by freezing all the parameters except those directly associated with the
bottleneck layer of the neural network. This approach makes it possible to model tau propagation
directly from measured longitudinal tau PET data while avoid overfitting caused by insufficient
training data.

## Key facts

- **NIH application ID:** 10287986
- **Project number:** 3P41EB022544-05S1
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Georges El Fakhri
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $328,969
- **Award type:** 3
- **Project period:** 2017-09-30 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10287986

## Citation

> US National Institutes of Health, RePORTER application 10287986, Center for Molecular Imaging Technology and Translation (CMITT) Administrative Supplement #4 (3P41EB022544-05S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10287986. Licensed CC0.

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