Predicting early Alzheimer's disease using advanced data analysis techniques
Official title: Early Alzheimers Forecasting from Multimodal Data via Deep Transfer Learning, Evaluated on a Large-Scale Prospective Cohort Study
Funded at University of Massachusetts Amherst · NIH-10732306 · Data updated 2026-11-12
This study is looking for new ways to spot Alzheimer's disease early by using different types of information, like genetics and brain scans, to help create smart tools that can predict how the disease might progress, making it easier for everyone to get the right care sooner.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Massachusetts Amherst NIH-funded |
| Lab location | 1 site (Hadley, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $287,118 |
| NIH project number | 1R03AG083708-01 |
| Project ID | NIH-10732306 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals at risk for Alzheimer's disease, particularly those with a family history or genetic predisposition.
✕Less likely to help
Patients with advanced Alzheimer's disease or those who do not have any risk factors for the disease may not benefit from this research.
What this research studies
This research aims to develop innovative methods for the early detection of Alzheimer's disease by analyzing various types of data, including genetic information, brain MRIs, and cognitive assessments. By utilizing deep transfer learning techniques, the project seeks to create models that can accurately predict the progression of Alzheimer's disease without needing extensive expert input on MRI scans. The study will evaluate these models on a large, diverse group of participants to ensure the findings are applicable to the general population. This approach could significantly enhance the ability to identify Alzheimer's disease in its early stages, potentially leading to better management and treatment options.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate detection of Alzheimer's disease, allowing for timely interventions and improved patient outcomes.
How similar studies have gone: Previous research has shown promise in using multimodal data and machine learning techniques for predicting Alzheimer's disease, indicating that this approach could be effective.
Where this research is happening
-
University of Massachusetts Amherst
Hadley, United States Active
Researchers
- Principal investigator: Fiterau Brostean, Madalina — University of Massachusetts Amherst
- Study coordinator: Fiterau Brostean, Madalina
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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.