# Predicting recovery after TBI: Development and comparison of MR-supplemented models using non-parametric and machine learning multimodal fusion

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $389,813

## Abstract

Project Summary/Abstract (30 lines max)
The ability to leverage early biomarkers, clinical, and demographic data to accurately predict a patient’s likely
recovery trajectory following moderate-to-severe traumatic brain injury (TBI) is paramount to allow definition of
appropriate therapeutic strategies and to evaluate medical decision-making in the context of critical decisions
such as early withdrawal of life supporting therapies. Prognostication of neurofunctional recovery following TBI,
however, is known to be as critical as challenging. Extensive work has shown that current approaches suffer
from high variability across physicians and medical centers, as well as a tendency for overestimation of poor
outcomes and underestimation of positive outcomes, and to be affected by non-clinical factors such as
geographic region and socioeconomic variation. To overcome such gaps in prognostication, this project is aimed
at developing and assessing novel frameworks that can be employed in early post-injury care. Specifically, we
leverage non-parametric models and machine learning techniques to fuse and incorporate routine multimodal
and multiplex magnetic resonance imaging (MRI) signals into a prediction framework. In two aims, we address
the ability of univariate multimodal fusion and machine learning architectures, respectively, to predict accurately
functional outcome at six months post injury on the sole basis of acute data, and compare their performance to
existing clinical algorithms. This project is thus aimed at developing a novel tools that can be easily deployed in
the Intensive Care Unit to help guide medical decision-making in an evidence-based manner. If the development
and assessment proposed in the present project is successful, the ultimate aim of this line of work is to develop
this research into broadly accessible platform that can be used by practicing clinicians all over the world to
supplement prognosis based solely on gross clinical indicators with quantitative and spatial multimodal MR data.

## Key facts

- **NIH application ID:** 10811231
- **Project number:** 1R21EB034428-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Martin Max Monti
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $389,813
- **Award type:** 1
- **Project period:** 2024-08-05 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10811231

## Citation

> US National Institutes of Health, RePORTER application 10811231, Predicting recovery after TBI: Development and comparison of MR-supplemented models using non-parametric and machine learning multimodal fusion (1R21EB034428-01A1). Retrieved via AI Analytics 2026-06-13 from https://api.ai-analytics.org/grant/nih/10811231. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*