Personalized brain maps to track Alzheimer's progression
Official title: Mapping Trajectories of Alzheimer's Progression via Personalized Brain Anchor-nodes
Funded at University of Texas Arlington · NIH-11258537 · Data updated 2026-11-12
Using advanced MRI and AI to create personalized brain maps that predict how Alzheimer's may progress for people from no symptoms through mild impairment to dementia.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas Arlington NIH-funded |
| Lab location | 1 site (Arlington, United States) |
| Project start | February 15, 2022 |
| Project end | January 31, 2027 |
| Funding year | 2026 |
| Award (this year) | $465,722 |
| NIH project number | 5R01AG075582-05 |
| Project ID | NIH-11258537 on NIH RePORTER |
Who could benefit from this research
✓Could help
People across the Alzheimer's spectrum—older adults with no symptoms but at risk, those with mild cognitive impairment, or people diagnosed with Alzheimer's—would be the ideal candidates for related imaging participation or future clinical applications.
✕Less likely to help
Individuals with memory problems caused by non-Alzheimer conditions or those without access to advanced MRI scans may not see direct benefit from this particular approach.
What this research studies
You would be connected to research that mines large collections of brain scans to find reliable 'anchor-node' landmarks that appear across many people's brains. Researchers will train deep-learning tools to translate those population landmarks onto individual MRI scans by combining different types of brain images and anatomical features. They will then build a progression tree from those individualized measures to show common paths from normal aging to mild cognitive problems to Alzheimer dementia. The goal is to give a clearer, personalized picture of how the disease may unfold for you.
Why it matters
Potential benefit: If successful, this work could enable earlier, more personalized warnings about disease course and help guide decisions about monitoring and treatment planning.
How similar studies have gone: MRI and PET biomarkers have previously helped subgroup risk and progression, but using personalized anchor-node mapping with deep learning to build a progression tree is a relatively new and innovative approach.
Where this research is happening
-
University of Texas Arlington
Arlington, United States Active
Researchers
- Principal investigator: Zhu, Dajiang — University of Texas Arlington
- Study coordinator: Zhu, Dajiang
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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.