# Developing an Individualized Deep Connectome Framework for ADRD Analysis

> **NIH NIH RF1** · UNIVERSITY OF TEXAS ARLINGTON · 2022 · $1,686,621

## Abstract

As the most and the second most common type of dementia, AD and Lewy body dementias (LBD), including
dementia with Lewy bodies and Parkinson’s disease dementia, account for 65% to 85% individuals with
AD/ADRD. Misdiagnosis between AD and ADRD, e.g., AD vs. LBD, will lead to non-beneficial, incomplete, or
even harmful treatment and management options. Comparing to diagnosis and prediction of AD from normal
aging, differentiation between AD and LBD is very challenging, due to both mixed pathologies and clinical
symptoms. Current MRI-based neuroimaging studies are limited to group-wise analysis between AD and LBD
patients and controls, and there are significant challenges in dealing with the remarkable heterogeneity in
AD/ADRD pathologies and clinical symptoms, and in pinpointing specific and subtle abnormalities across
different individual AD/ADRD brains. In this project, we will significantly advance and integrate our powerful
methods/tools and apply them to multiple AD/LBD datasets to discover and identify individualized connectome-
scale differences between AD and LBD, by leveraging the cutting-edge deep learning techniques. Specifically,
we will 1) discover, define and represent individual GyralNets to characterize brain connectome heterogeneity
and AD/LBD related abnormalities for individual AD/LBD patient; 2) learn a cortical surface transformation to
align GyralNets from population to individuals using unsupervised spherical networks and 3) develop a new
infrastructure to integrate multiple types of connectome data including anatomical, structural and functional
connectome, and characterize, represent and summarize their deep relationship as a “individual connectome
signature” by maximizing its prediction capability between AD and LBD.

## Key facts

- **NIH application ID:** 10515550
- **Project number:** 1RF1NS128534-01
- **Recipient organization:** UNIVERSITY OF TEXAS ARLINGTON
- **Principal Investigator:** Gang Li
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,686,621
- **Award type:** 1
- **Project period:** 2022-08-18 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10515550

## Citation

> US National Institutes of Health, RePORTER application 10515550, Developing an Individualized Deep Connectome Framework for ADRD Analysis (1RF1NS128534-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10515550. Licensed CC0.

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