Understanding Alzheimer's Disease through Brain Scans and Genetics
Official title: Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
Funded at University of Texas Hlth Sci Ctr Houston · NIH-11167727 · Data updated 2026-11-12
This project uses advanced computer analysis of brain scans and genetic information to better understand Alzheimer's disease in older adults.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas Hlth Sci Ctr Houston NIH-funded |
| Lab location | 1 site (Houston, United States) |
| Project start | July 1, 2021 |
| Project end | June 30, 2027 |
| Funding year | 2025 |
| Award (this year) | $1,134,253 |
| NIH project number | 5U01AG070112-05 |
| Project ID | NIH-11167727 on NIH RePORTER |
Who could benefit from this research
✓Could help
This research is relevant for older adults, particularly those aged 65 and over, who are affected by or at risk for Alzheimer's disease.
✕Less likely to help
Patients not affected by Alzheimer's disease or related cognitive impairments would not directly benefit from this specific research.
What this research studies
Alzheimer's disease significantly impacts memory and thinking in older adults, and we still have much to learn about its causes. This project uses advanced computer methods, called deep learning, to look closely at brain images from people with Alzheimer's. By combining these detailed brain scan insights with genetic information, researchers hope to find new ways to identify the disease earlier and understand who might be at risk. This approach could help us uncover hidden patterns in the brain and our genes that contribute to Alzheimer's.
Why it matters
Potential benefit: If successful, this work could lead to earlier and more accurate ways to detect Alzheimer's disease and help develop new treatments.
How similar studies have gone: While genetic studies have identified some risk factors for Alzheimer's, combining deep learning with neuroimaging and whole-genome sequencing for this purpose is a relatively new and promising approach.
Where this research is happening
-
University of Texas Hlth Sci Ctr Houston
Houston, United States Active
Researchers
- Principal investigator: Zhi, Degui — University of Texas Hlth Sci Ctr Houston
- Study coordinator: Zhi, Degui
Related research
Other NIH-funded projects on the same topics.
-
AIM-AI: an actionable, integrated, multiscale genetic map of Alzheimer’s disease powered by deep learning
A research project uses advanced AI to connect genetic data, biology, and brain scans to better understand Alzheimer’s disease and its risk factors.
-
Deep Learning Retina Tool to Find Early Alzheimer’s
A new AI-based retinal imaging tool aims to help identify people at risk for Alzheimer’s before symptoms appear using a quick, noninvasive eye scan.
-
Interpretable deep learning to map genetics and epigenetics of Alzheimer's at single-cell level
A new project will use advanced computer models to understand how genetic and epigenetic changes drive Alzheimer's disease at the level of individual brain cells.
-
Deep learning to uncover neurovascular disruptions in Alzheimer's disease
A neuron-and-blood-vessel focused study uses advanced computer methods to find molecular changes in brain blood vessels of people with Alzheimer’s disease.
-
Global shared-AI network to improve Alzheimer's detection and prediction
This project builds a secure, shared AI system that learns from brain scans and medical data from people worldwide to help spot and predict Alzheimer's for patients and those at risk.
-
AI-built genetic map to understand Alzheimer's disease
Using artificial intelligence to combine genetic, molecular, and brain imaging data to find genetic clues that could help people with or at risk for Alzheimer's disease.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.