Using machine learning to better understand behavior in Alzheimer's disease models
Official title: Machine Learning Approaches for Behavioral Phenotyping of Humanized Knock-in Models of Alzheimer's Disease
Funded at J. David Gladstone Institutes · NIH-11101410 · Data updated 2026-11-12
This study is looking at how to better understand the changes in behavior caused by Alzheimer's disease using new computer technology, which could help create more effective treatments for people living with the condition.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | J. David Gladstone Institutes NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | September 15, 2023 |
| Project end | April 30, 2028 |
| Funding year | 2025 |
| Award (this year) | $150,630 |
| NIH project number | 5K01AG083732-03 |
| Project ID | NIH-11101410 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with early-stage Alzheimer's disease or those at risk of developing the condition.
✕Less likely to help
Patients with advanced Alzheimer's disease or those with other forms of dementia may not benefit from this research.
What this research studies
This research focuses on improving the understanding of behavioral changes in Alzheimer's disease by utilizing advanced machine learning techniques. It aims to address the limitations of traditional behavioral tests in animal models that do not accurately reflect human Alzheimer's pathology. By employing a novel computer vision approach, the study seeks to provide a more sensitive and comprehensive characterization of subtle behavioral phenotypes in humanized mouse models of Alzheimer's disease. This could lead to better preclinical assessments and ultimately more effective therapies for patients.
Why it matters
Potential benefit: If successful, this research could lead to more accurate models for testing Alzheimer's treatments, potentially accelerating the development of effective therapies.
How similar studies have gone: Other research has shown promise in using machine learning for behavioral analysis in various conditions, suggesting that this approach could be effective in Alzheimer's disease as well.
Where this research is happening
-
J. David Gladstone Institutes
San Francisco, United States Active
Researchers
- Principal investigator: Miller, Stephanie Regina — J. David Gladstone Institutes
- Study coordinator: Miller, Stephanie Regina
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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.