# MRI-Derived Neuromuscular Signatures to Predict Surgical Response in Degenerative Cervical Myelopathy

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $600,926

## Abstract

PROJECT SUMMARY/ABSTRACT
Degenerative cervical myelopathy (DCM) is the most common form of spinal cord (SC) injury in adults. DCM is
characterized by multilevel degenerative changes in the cervical spine, causing SC compression and injury,
which leads to worsening neurological dysfunction. Hand weakness and diminished coordination are more
severe spinal pathology indicators, increasing the likelihood of spinal surgery. While restoring hand function is
a primary goal of surgery, surgical management of DCM is challenging due to the low diagnostic certainty of
the underlying pathology and lack of predictive factors to determine which patients may improve with surgery.
The injury in DCM extends beyond the level of SC compression and affects the entire neuromuscular system.
The interplay among the brain, SC, and muscles needs to be characterized to fully understand the
mechanisms underlying hand dysfunction in DCM, the progression of DCM pathology, and the factors
promoting recovery. Here we will use magnetic resonance imaging (MRI) to non-invasively characterize the
brain, SC, and muscular mechanisms underlying hand weakness and diminished coordination in DCM. We will
then combine brain, SC, and muscle measures to develop neuromuscular signatures of hand function and
assess their value in predicting surgical outcomes in DCM. Our overarching hypothesis is that signatures of
neuromuscular health will track the progression of DCM pathology and predict surgical recovery of hand
function (less extensive brain, SC, and muscle injury will predict better surgical outcome). To accomplish this,
we will enroll 60 right-handed DCM patients (age 40–80 years, 30 females, 30 males) with right hand
weakness and diminished coordination, who are scheduled for surgery, and 60 age- and sex-matched healthy
volunteers. We will perform simultaneous brain-SC fMRI using force-matching and finger-tapping tasks and
resting-state functional connectivity to characterize the brain and SC mechanisms underlying hand dysfunction.
We will also capture gray matter morphometry and white matter integrity along corticospinal pathways using
methods developed and in use by our team. Then we will perform fat-water and diffusion tensor MRI of the
right forearm providing measures of muscle volume and quality to characterize the downstream effects of SC
injury on the forearm muscles. We will use multivariate machine-learning algorithms and the brain, SC, and
muscle imaging to develop neuromuscular signatures of hand function by predicting grip strength and dexterity.
We will then track clinical outcomes at 1-year post-surgery in the DCM patients, and we will assess the value
of the pre-surgical signature responses for predicting surgical outcomes and establish clinical cutoffs. Validated
neurobiologically-based predictors of surgical response could lead to earlier intervention in those likely to
recover, prevent exposure to risks and complications in those unlikely to respond, and elucidate th...

## Key facts

- **NIH application ID:** 10836535
- **Project number:** 5R01NS128478-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Kenneth Arnold Weber
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $600,926
- **Award type:** 5
- **Project period:** 2023-05-15 → 2028-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10836535

## Citation

> US National Institutes of Health, RePORTER application 10836535, MRI-Derived Neuromuscular Signatures to Predict Surgical Response in Degenerative Cervical Myelopathy (5R01NS128478-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10836535. Licensed CC0.

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