# Predicting Dependency after Traumatic Brain Injury

> **NIH NIH K23** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $229,178

## Abstract

Project Summary/Abstract
Traumatic brain injury (TBI) affects millions of people each year who recover to widely disparate levels of
independent function. It is not currently possible to reliably predict who will remain functionally dependent on
caregivers. Most patients assessed with existing models are assigned an intermediate likelihood of recovery,
remaining in a prognostic ‘grey area’. Accurate prediction is important, because withdrawal of life sustaining
therapy based on perceived prognosis is the leading cause of death after TBI. Though advances in brain
imaging have enabled precise localization of focal brain injuries, injury location is not incorporated into existing
TBI prognostic models. This is because it is not known whether dependency results from injury to specific brain
structures or networks. This knowledge gap creates ongoing heterogeneity in clinical practice and limits the
development and evaluation of targeted therapeutics. This K23 award addresses this knowledge gap, using
multi-modality neuroimaging to identify brain structures, connections and networks that produce dependency
when disrupted, and testing whether injury location improves prognostication after TBI. The principal
investigator, Dr. Samuel B. Snider, is a Neurocritical Care Neurologist at Brigham and Women’s Hospital
(BWH), whose goal is to become a translational neuroscientist using advanced imaging techniques to better
understand recovery mechanisms and predict outcomes after acute brain injuries. Dr. Snider has an
established early career track record in advanced MRI, and in measuring and predicting TBI outcomes. His
preliminary data demonstrate the feasibility of mapping focal traumatic brain injury with CT and MRI at the
scale required for this project. Through novel analysis of three existing datasets and one prospectively
enrolling TBI study at BWH, Dr. Snider will test whether the locations of hemorrhagic contusions on CT scans
(1), axonal injury on diffusion MRI (2a) and functional network disruption on resting state functional MRI (2b)
independently improve the prediction of functional dependency after moderate or severe TBI. Using multiple
sources to create one of the largest TBI imaging datasets ever assembled, this project will generate novel
insights into mechanisms of recovery from brain injury and improve existing prognostic models. The mentored
research and structured training in multi-center data harmonization and analysis, resting-state functional MRI,
and prospective clinical data collection will provide Dr. Snider with the skills, experience and preliminary data
needed to submit an NIH R01 validating the first imaging-based TBI prognostic model. His career development
plan utilizes the resources of the world-class Harvard Medical School training environment, bringing together a
diverse and multidisciplinary team of mentors and collaborators centered at the BWH. Under the guidance of
primary mentor Dr. Michael Fox, co-mentors Drs. Brian Edlo...

## Key facts

- **NIH application ID:** 10865680
- **Project number:** 1K23NS136767-01
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Samuel Snider
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $229,178
- **Award type:** 1
- **Project period:** 2024-04-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10865680

## Citation

> US National Institutes of Health, RePORTER application 10865680, Predicting Dependency after Traumatic Brain Injury (1K23NS136767-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10865680. Licensed CC0.

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