# Continuing Tool Development for Longitudinal Network Analysis: Enriching the Diagnostic Power of Disease-Specific Connectomic Biomarkers by Deep Graph Learning

> **NIH NIH R03** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $158,733

## Abstract

Project Summary/Abstract
A plethora of neuroscience studies shows mounting evidence that neurodegenerative diseases manifest distinct
network dysfunction patterns much earlier prior to the onset of clinical symptoms. Since the subject-specific
longitudinal network changes are more relevant to the neuropathological process than topological patterns
derived from cross-sectional data, recognizing the subtle and dynamic longitudinal network biomarkers from
noisy network data is of great demand to enhance the sensitivity and specificity of computer-assisted diagnosis
in neurodegenerative diseases. However, current popular statistical inference or machine learning approaches
used for neuroimages (in a regular data structure such as grid and lattice) are not fully optimized for the learning
task on brain network data which is often encoded in a high dimensional graph (an irregular and non-linear data
structure). Such gross adaption is partially responsible for the lack of reliable biomarkers that can be used to
predict cognitive decline in routine clinical practice. To address this challenge, we aim to (1) develop a novel
GNN (graph neural network) based learning framework to hierarchically discover the multi-scale network
biomarkers that can recognize the disease-relevant network alterations over time, and (2) examine the diagnostic
power of the new network biomarkers derived from our GNN-based machine learning engine across
neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and frontotemporal dementia.
The success of this project will allow us to integrate the novel GNN-based learning component into our current
longitudinal network analysis toolbox and release the AI (artificial intelligence) based network analysis software
to the neuroscience and neuroimaging community.

## Key facts

- **NIH application ID:** 10109509
- **Project number:** 1R03AG070701-01
- **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:** $158,733
- **Award type:** 1
- **Project period:** 2021-03-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10109509

## Citation

> US National Institutes of Health, RePORTER application 10109509, Continuing Tool Development for Longitudinal Network Analysis: Enriching the Diagnostic Power of Disease-Specific Connectomic Biomarkers by Deep Graph Learning (1R03AG070701-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10109509. Licensed CC0.

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