# CRCNS: Deep Learning to Discover Neurovascular Disruptions in Alzheimer's Disease

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2024 · $296,661

## Abstract

The Neurovascular unit (NVU) comprises of cells in the brain vasculature (endothelial cells and pericytes) working in 
coordination with parenchymal cells (neurons and astrocytes) to maintain brain homeostasis and cognitive function. 
Intricate functional interactions among NVU cells, referred to as neurovascular coupling, is progressively impaired 
and NVU composition is severely disrupted in Alzheimer’s disease. However, the underlying pathophysiological 
mechanisms are poorly understood due to paucity of molecular level information on the less abundant, yet 
functionally critical, cerebrovascular endothelial cells and pericytes. The fMRI imaging, widely used in the clinic to 
evaluate neurovascular coupling, may not inform molecular level changes. It is challenging to identify changes in 
NVU composition using standard histopathological methods, because they lack sensitivity and specificity to locate 
endothelial cells and pericytes in the brain tissue. Bulk RNA sequencing from postmortem Alzheimer's brain tissue 
can be used to investigate NVU components, but it measures gene expression averaged across all cells, thus making it 
difficult to define cell-specific pathways and NVU constituent interactions. Single-cell methods and linear 
deconvolution techniques are currently employed to analyze bulk RNA sequencing data to determine cell-type-specific gene expression patterns. However, these techniques struggle to capture the molecular signature of low-abundant cells like endothelial and pericytes. The objective of the current study is to develop deep-learning methods 
to accurately predict the composition and transcriptomic signature of NVU cells, and to map interactions among them. 
Our central hypothesis is that data-driven deep-learning models, which have the flexibility to capture underlying 
gene-gene and cell-cell interactions in the brain tissue, will predict the composition and transcriptomic signature of 
NVU cells more effectively than the conventional methods. In Aim 1, we will design NUGENT, a novel deep-learning framework, to identify cell-type composition and predict cell-type-specific gene expression patterns. In Aim 
2, we will validate NUGENT using new scRNA-seq data of NVU constituent cells harvested from Alzheimer’s 
disease transgenic mice (APPswe/PSEN1dE9) and their non-transgenic littermates. Employing the data generated in 
Aim 2 and publicly available patient and mouse data on the NUGENT framework, in Aim 3 we will investigate 
molecular pathways regulating neurovascular coupling in cognitively normal and Alzheimer’s patients. It is highly 
likely that the proposed studies will help identify molecular determinants of neurovascular dysfunction underlying 
age-related cognitive decline and Alzheimer’s dementia and facilitate the discovery of novel biomarkers and 
therapeutic targets.

## Key facts

- **NIH application ID:** 10917302
- **Project number:** 5R01AG085900-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Carlos Fernandez-Granda
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $296,661
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10917302

## Citation

> US National Institutes of Health, RePORTER application 10917302, CRCNS: Deep Learning to Discover Neurovascular Disruptions in Alzheimer's Disease (5R01AG085900-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10917302. Licensed CC0.

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