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

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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 the factors underlying recovery to improve treatment.