Understanding how genetic variations affect brain imaging in Alzheimer's disease
Official title: Bridging the gap between genetic variants and radiomic phenotypes via genomic large language models
Funded at Stanford University · NIH-10948002 · Data updated 2026-11-12
This study is looking at how certain genes might affect brain changes in people with Alzheimer's, using smart technology to help find better ways to diagnose and treat the disease based on individual genetic information.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | September 1, 2024 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $131,085 |
| NIH project number | 1K99HG013661-01 |
| Project ID | NIH-10948002 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with Alzheimer's disease or those at risk due to genetic factors.
✕Less likely to help
Patients with non-genetic forms of dementia or those without a familial history of Alzheimer's may not benefit from this research.
What this research studies
This research investigates the connection between genetic variants and brain imaging traits in Alzheimer's disease using advanced artificial intelligence techniques. By developing genomic large language models, the study aims to decode how specific genetic changes influence the biological processes that lead to Alzheimer's. Patients may benefit from improved diagnostic tools and personalized treatment strategies based on their genetic profiles. The research will involve analyzing genetic data alongside brain imaging results to uncover these relationships.
Why it matters
Potential benefit: If successful, this research could lead to more accurate diagnoses and targeted therapies for Alzheimer's disease based on individual genetic information.
How similar studies have gone: Previous research has shown promise in using artificial intelligence to analyze genetic data, suggesting that this approach could yield significant insights into Alzheimer's disease.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Liu, Qiao — Stanford University
- Study coordinator: Liu, Qiao
Related research
Other NIH-funded projects on the same topics.
-
Multimodal AI to predict CAR-T patient response and guide safer, smarter cell design
A Stanford team is building AI tools that use patient and CAR-T cell data to predict how well a CAR-T therapy will work and what design changes might boost effectiveness and safety for people with cancer.
-
Community-driven model to reduce overdose deaths through smarter resource use
A Stanford-led project uses a data-driven model to help counties pick the best ways to prevent opioid and stimulant overdoses and save lives.
-
Center for Dissemination and Implementation at Stanford: Research Core
A Stanford-based program is advancing how evidence-based addiction treatments are implemented across settings to help people with substance use disorders receive better care.
-
Center for Dissemination and Implementation At Stanford (C-DIAS)
A center-based program at Stanford aims to speed people who need addiction treatments to get evidence-based care.
-
Mitochondrial structure and neuron health in Alzheimer's and ALS
This grant studies how mitochondrial cristae structure in neurons is controlled and how its disruption may contribute to diseases like Alzheimer's and ALS.
-
Pediatric EMS seizures: simpler age-based midazolam dosing
A nationwide trial at EMS agencies tests whether using fixed, age-based dosing of midazolam for seizures in children improves treatment speed and safety.
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.