Predicting recovery after traumatic brain injury using advanced imaging and machine learning
Official title: Predicting recovery after TBI: Development and comparison of MR-supplemented models using non-parametric and machine learning multimodal fusion
Funded at University of California Los Angeles · NIH-10811231 · Data updated 2026-11-12
This study is looking to help people with moderate-to-severe traumatic brain injuries by using advanced imaging and technology to better predict how well they will recover in the months after their injury.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Los Angeles NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project start | August 5, 2024 |
| Project end | July 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $389,813 |
| NIH project number | 1R21EB034428-01A1 |
| Project ID | NIH-10811231 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals who have experienced moderate-to-severe traumatic brain injuries.
✕Less likely to help
Patients with mild traumatic brain injuries or those who are not undergoing acute care for TBI may not benefit from this research.
What this research studies
This research aims to improve the prediction of recovery outcomes for patients with moderate-to-severe traumatic brain injury (TBI) by utilizing advanced imaging techniques and machine learning algorithms. By analyzing early biomarkers, clinical data, and demographic information, the study seeks to create a more accurate framework for forecasting recovery trajectories. The approach involves integrating various magnetic resonance imaging (MRI) signals to enhance the predictive capabilities of existing clinical methods, which often struggle with variability and inaccuracies. Patients will be assessed based on acute data to determine their likely functional outcomes six months post-injury.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of recovery for TBI patients, enabling better treatment planning and decision-making.
How similar studies have gone: Previous research has shown promise in using machine learning and imaging techniques for predicting outcomes in brain injury, indicating that this approach could be effective.
Where this research is happening
-
University of California Los Angeles
Los Angeles, United States Active
Researchers
- Principal investigator: Monti, Martin Max — University of California Los Angeles
- Study coordinator: Monti, Martin Max
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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.