# Uncovering the Heterogeneity of Neurodegeneration Trajectories in Alzheimer's Disease Using a Network Guided Reaction-Diffusion Model

> **NIH NIH R03** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $165,383

## Abstract

Project Summary/Abstract
Alzheimer’s disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder. Due to the multiplicity
of clinical symptoms, standard neuropsychological assessments inadequately reflect the underlying
pathophysiological mechanisms, which renders a significant gap between neurobiological examinations of AD
pathology and clinical diagnoses. Mounting evidence shows that AD is caused by the build-up of two abnormal
proteins, beta-amyloid and tau. Over time, these AD-related neuropathological burdens begin to spread
throughout the brain, which results in the characteristic progression of symptoms in AD. Although striking efforts
have been made to investigate the neurobiological factors behind the acquisition of amyloid (A), protein tau (T),
and neurodegeneration [N] biomarkers, a system-level understanding of how these neuropathological burdens
promote neurodegeneration and why AD exhibits characteristic progression is still largely elusive. In this study,
we will combine the power of systems biology and network neuroscience to disentangle the heterogeneous
trajectories of cognitive decline in AD population by understanding the dynamic interaction and diffusion process
of AT[N] biomarkers from an unprecedented amount of longitudinal neuroimaging data. The backbone of this
project is our recently developed network guided reaction-diffusion model that characterizes not only the
interaction of AT[N] biomarkers at each brain region but also their propagation pattern across the brain networks
using PDEs (partial differential equations). Given its promising results in predicting the evolution of AT[N]
biomarkers, we will further develop our current PDE-based model by incorporating spatiotemporal-adaptive
mechanistic pathways of AT[N] biomarkers. Then, we will investigate the system behaviors that steer the
trajectory of cognitive decline in Aim 1. After that, we will develop a novel deep learning approach to stratify
aging brains into a set of fine-grained categories (aka. subtypes) with distinct neurobiological underpinnings,
where individuals within the same subtype are expected to have very similar trajectories of cognitive decline. We
will evaluate the novel population stratification result using the longitudinal imaging data from the ADNI database
in Aim 2. The success of this project will allow us to have a new understanding of the neurodegeneration process
in the cognitive continuum spectrum. This is an important step because slowing down this spread at an early
stage might prevent or halt the symptoms of AD.

## Key facts

- **NIH application ID:** 10288783
- **Project number:** 1R03AG073927-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Guorong Wu
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $165,383
- **Award type:** 1
- **Project period:** 2021-08-15 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10288783

## Citation

> US National Institutes of Health, RePORTER application 10288783, Uncovering the Heterogeneity of Neurodegeneration Trajectories in Alzheimer's Disease Using a Network Guided Reaction-Diffusion Model (1R03AG073927-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10288783. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*