Combining brain scans and tests from the US and Japan to spot Alzheimer's earlier
Large-scale harmonization and integration of multi-modal ADNI data for the early detection of Alzheimer's disease and related dementias
This project brings together and harmonizes brain scans, fluid tests, and genetic data from U.S. and Japanese Alzheimer's cohorts to help spot Alzheimer's and related dementias earlier.
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 ID | NIH-11085107 on NIH RePORTER |
What this research studies
You can think of this as researchers cleaning and matching large sets of brain MRIs, PET scans, spinal fluid results, and genetic data so differences from machines or sites don't hide disease signals. They will harmonize data across ADNI versions and build machine-learning models for each type of test, then combine those models into an ensemble predictor. The work focuses on identifying features and patterns that show up early in Alzheimer's and related dementias. The goal is to create tools that make early signs clearer for doctors and for selecting people for future treatments or trials.
Who could benefit from this research
Good fit: People with early memory problems or mild cognitive impairment, and those who have brain imaging, PET scans, cerebrospinal fluid tests, or genetic results (for example APOE status), are the most relevant candidates for this kind of analysis.
Not a fit: Those without any imaging or biomarker data, people whose cognitive issues are caused by non‑AD conditions, or people at very late disease stages are less likely to see direct benefit from these analyses.
Why it matters
Potential benefit: If successful, this work could make early detection of Alzheimer's more accurate and help match patients to treatments and clinical trials sooner.
How similar studies have performed: Previous work using ADNI data and machine learning has shown promise in predicting Alzheimer's, but site- and scanner-related variability has limited clinical reliability, so this harmonization approach builds on and seeks to improve earlier efforts.
Where this research is happening
Los Angeles, UNITED STATES
- University of Southern California — Los Angeles, United States (Active)
Researchers
- Principal investigator: Varghese, Bino Abel — University of Southern California
- Study coordinator: Varghese, Bino Abel
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 below.