Using machine learning to estimate brain age and understand neuroimaging genetics
Official title: Interpretable machine learning to synergize brain age estimation and neuroimaging genetics
Funded at University of Southern California · NIH-11030816 · Data updated 2026-11-12
This study is looking at how computer technology can help figure out your brain's age from MRI scans, which could help spot early signs of diseases like Alzheimer's, making it easier to tell if someone is aging normally or showing early signs of problems.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Southern California NIH-funded |
| Lab location | 1 site (Los Angeles, UNITED STATES) |
| Project start | February 1, 2023 |
| Project end | January 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $774,786 |
| NIH project number | 5R01AG079957-03 |
| Project ID | NIH-11030816 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include adults showing early signs of cognitive decline or those at risk for Alzheimer's disease.
✕Less likely to help
Patients with no cognitive concerns or those who are not within the age range of interest may not benefit from this research.
What this research studies
This research investigates how machine learning can be used to estimate brain age from MRI scans, which may help identify early signs of neurodegenerative diseases like Alzheimer's. The project aims to develop an interpretable machine learning model that not only predicts brain age accurately but also highlights the specific brain features that contribute to this estimation. By analyzing these features, researchers hope to differentiate between normal aging and abnormal aging patterns associated with conditions such as mild cognitive impairment. This approach could lead to more effective early detection and intervention strategies for at-risk individuals.
Why it matters
Potential benefit: If successful, this research could provide a noninvasive method for early detection of Alzheimer's disease and other neurodegenerative conditions.
How similar studies have gone: Previous research has shown promise in using machine learning for brain age estimation, but this project aims to enhance interpretability, making it a novel approach.
Where this research is happening
-
University of Southern California
Los Angeles, United States Active
Researchers
- Principal investigator: Irimia, Andrei — University of Southern California
- Study coordinator: Irimia, Andrei
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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.