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

项目总结/摘要 退行性颈脊髓病（DCM）是成人脊髓（SC）损伤的最常见形式。DCM是 特征为颈椎多节段退行性变化，导致SC压迫和损伤， 导致神经功能障碍恶化手部无力和协调性下降 严重的脊柱病理指标，增加了脊柱手术的可能性。虽然恢复手部功能是 作为手术的主要目标，DCM的手术治疗具有挑战性，因为DCM的诊断确定性低， 潜在的病理学和缺乏预测因素来确定哪些患者可以通过手术改善。 扩张型心肌病的损伤超出了SC压迫的水平，并影响整个神经肌肉系统。 需要描述大脑、SC和肌肉之间的相互作用，以充分了解 扩张型心肌病手功能障碍的潜在机制、扩张型心肌病病理学的进展以及影响手功能障碍的因素 促进复苏。在这里，我们将使用磁共振成像（MRI）来非侵入性地表征 脑，SC和肌肉机制的手无力和减少协调扩张型心肌病。我们将 然后结合联合收割机的大脑，SC和肌肉的措施，开发神经肌肉签名的手功能， 评估它们在预测DCM手术结果中的价值。我们的首要假设是， 神经肌肉健康将跟踪DCM病理学的进展并预测手的手术恢复 功能（较不广泛的脑、SC和肌肉损伤将预测更好的手术结果）。为了实现这一点， 我们将招募60名右利手DCM患者（年龄40-80岁，30名女性，30名男性）， 虚弱和协调能力下降，计划进行手术，以及60名年龄和性别匹配的健康人 志愿者我们将使用力匹配和手指敲击任务进行同步脑SC功能磁共振成像， 静息状态功能连接，以表征手功能障碍的大脑和SC机制。 我们还将使用以下方法捕获沿沿着皮质脊髓通路的灰质形态测量和白色物质完整性 我们的团队开发和使用的方法。然后，我们将进行脂肪水和扩散张量MRI的 右前臂提供肌肉体积和质量的测量，以表征SC的下游效应 前臂肌肉受伤我们将使用多变量机器学习算法和大脑，SC， 肌肉成像，通过预测握力和灵活性来开发手部功能的神经肌肉特征。 然后，我们将跟踪DCM患者术后1年的临床结局，并评估 用于预测手术结果并建立临床截止值。验证 基于神经生物学的手术反应预测因子可能导致对那些可能 恢复，防止那些不太可能响应的风险和并发症的暴露，并阐明因素 以改善治疗。

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.