# Longitudinal predictive modeling for tau in Alzheimer's disease

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $545,868

## Abstract

PROJECT SUMMARY
Alzheimer’s disease, the most common cause of dementia in the elderly, is characterized by a cognitively
asymptomatic preclinical stage which is identified and monitored via longitudinal tracking of pathophysiological
biomarkers, e.g., tau and amyloid. Since the aggregation of tau protein tangles in the medial temporal lobe is a
key driver of memory impairment, accurate image-based longitudinal prediction of tau burden could fill a critical
gap in biomarker development for preclinical Alzheimer’s disease. Tau tangles exhibit stereotypical
neuroanatomical patterns of spatiotemporal spread that correlate strongly with the progression of
neurodegeneration. Studies in animal models have suggested that the characteristic patterns of tau spread
associated with Alzheimer’s progression are determined by neural connectivity rather than physical proximity
between different brain regions. Graph-theoretic methods that utilize macroscale structural connectivity mapping
in humans to predict future tau burden could lead to valuable prognostic tools for Alzheimer’s disease. The
overarching research goal of this R01 Research Project Grant is to develop an interpretable machine learning
model that uses individual structural connectomics to make personalized predictions of differential measures of
tau from multimodal baseline data. Our approach relies on longitudinal 18F-Flortaucipir positron emission
tomography (PET) for the imaging of tau tangles, 11C-Pittsburgh Compound B (PiB) for the imaging of amyloid
plaques, and high-angular-resolution diffusion magnetic resonance (MR) imaging for individualized structural
connectomics in human subjects. We will develop a physics-informed and interpretable graph neural network to
predict the annual rate of change of the regional tau burden from multimodal inputs, including baseline tau, Aβ,
and an array of structural connectivity metrics. We will also develop novel physics-based analytic models for tau
progression, which will be used to effectively guide the machine learning framework. Finally, we will apply the
machine learning model to investigate the earliest cortical site of tau aggregation, to examine the connectomic
basis of early tau spread, and to leverage our model’s interpretability to discover and validate novel connectomic
biomarkers to characterize preclinical Alzheimer’s disease. To validate the machine learning model, we will use
serial tau PET data at two and three timepoints from the Harvard Aging Brain Study, one of the largest
longitudinal imaging resources for preclinical Alzheimer’s disease. To ensure scientific rigor, secondary
validation of the models will be performed using data from the Alzheimer’s Disease Neuroimaging Initiative
database. The proposed personalized predictive model could significantly impact preclinical Alzheimer’s
prognosis, facilitate ongoing clinical trials, and shed light on the neuroconnectomic and biological underpinnings
of Alzheimer’s disease.

## Key facts

- **NIH application ID:** 10471298
- **Project number:** 5R01AG072669-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Joyita Dutta
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $545,868
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10471298

## Citation

> US National Institutes of Health, RePORTER application 10471298, Longitudinal predictive modeling for tau in Alzheimer's disease (5R01AG072669-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10471298. Licensed CC0.

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