# Biomechanical Framework to Integrate Structural MRI Information in White Matter

> **NIH NIH R03** · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2020 · $155,000

## Abstract

PROJECT SUMMARY
Sensitive and non-invasive detection of changes in brain structure or function provides more means to diagnose,
monitor, prevent or delay the progression of diseases. Recently, longitudinal magnetic resonance imaging (MRI)
has been applied to large neuroimaging studies of Alzheimer’s disease (AD) and traumatic brain injury (TBI) to
track brain changes over time. Diffusion-weighted MRI (dMRI) is commonly used to measure the physical
properties of white matter (WM) of the brain. WM is mainly made up of nerve fibers that act as a relay and
coordinate communication between different brain regions, and exhibits changes in shape and diffusion of water
molecules across the life span. However, conventional dMRI analysis methods have failed to properly consider
shape change in brain tissue, limiting the detection of small WM changes and investigation of its clinical
significance. To address limitations of current MRI analysis approaches, in this project, we will model complex
WM damage patterns on a biomechanical framework using multi-modality MRI acquired from ongoing
longitudinal TBI cohorts of youth football players (funded by current NINDS R01s but not processed using the
proposed methods).
Our scientific hypotheses are that (a) participation in a season of contact sports is associated with WM changes
and the degree of change is correlated with accumulated head impact or clinical symptoms and cognitive change,
and that (b) the WM changes along the fiber pathway estimated from the proposed biomechanical approach will
improve statistical power in detecting abnormal WM changes compared to conventional diffusion MRI measures.
To test the hypotheses, we propose two Specific Aims: (1) To model white matter damages along the pathway
of fibers in youth football players: We will identify abnormal brain development patterns in youth football players
who experienced repetitive subconcussive head impacts during a season of play compared to normal non-
contact sports players. New MRI measures derived from the proposed method will be correlated with head impact
exposure. (2) To validate the clinical utility of the new white matter modeling approach: Correlation analyses
between MRI measures and post-traumatic cognitive/symptom measures will be conducted to determine
whether these new MRI measures demonstrate higher statistical power in detecting abnormal WM changes.
Through the Specific Aims proposed above, we will explain the underlying brain injury mechanisms of repetitive
head impacts even in the absence of diagnosed concussion. This novel approach promises to develop valuable
new tools with the potential to broadly impact the medical care of contact sports players and early AD adults by
addressing more natural and realistic changes of WM on a biomechanical framework. All source codes related
to the proposed analyses will be well documented and available at software sharing platforms for users to apply
the method to various MRI studies on white m...

## Key facts

- **NIH application ID:** 10043013
- **Project number:** 1R03NS118259-01
- **Recipient organization:** WAKE FOREST UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Jeongchul Kim
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $155,000
- **Award type:** 1
- **Project period:** 2020-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10043013

## Citation

> US National Institutes of Health, RePORTER application 10043013, Biomechanical Framework to Integrate Structural MRI Information in White Matter (1R03NS118259-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10043013. Licensed CC0.

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