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

项目摘要/摘要 退行性脊髓型颈椎病(DCM)是成人最常见的脊髓损伤形式。DCM是 以颈椎多节段退行性改变为特征，造成SC受压和损伤， 这会导致神经功能障碍恶化。手的虚弱和协调性的减弱 严重的脊柱病理指标，增加了脊柱手术的可能性。而恢复手功能是 作为外科手术的主要目标，扩张型心肌病的外科治疗具有挑战性，因为诊断的确定性很低。 潜在的病理和缺乏预测因素，以确定哪些患者可以通过手术改善。 DCM的损伤超出了SC压迫的范围，并影响到整个神经肌肉系统。 需要对大脑、SC和肌肉之间的相互作用进行表征，以充分理解 扩张型心肌病手功能障碍的机制、病理进展及影响因素 促进经济复苏。在这里，我们将使用磁共振成像(MRI)来非侵入性地表征 DCM患者手无力和协调性减弱的脑、SC和肌肉机制。我们会 然后结合大脑、SC和肌肉测量来开发手功能和肌肉功能的神经肌肉特征 评估它们在预测扩张型心肌病手术结果中的价值。我们最重要的假设是 神经肌肉健康将跟踪DCM病理的进展并预测手部的手术恢复 功能(较小的脑、SC和肌肉损伤将预示更好的手术结果)。要做到这一点， 我们将招募60例右撇子DCM患者(年龄40-80岁，女性30例，男性30例)，右手 虚弱和协调能力减弱，计划进行手术，60岁和性别匹配的健康 志愿者。我们将使用力匹配和手指敲击任务同时执行脑-SC功能磁共振成像 静息状态的功能连接，以表征大脑和手部功能障碍的SC机制。 我们还将捕捉皮质脊髓通路上的灰质形态测量和白质完整性 由我们团队开发和使用的方法。然后我们将进行脂肪-水和弥散张量磁共振成像 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和 肌肉成像通过预测握力和灵巧度来开发手功能的神经肌肉特征。 然后，我们将跟踪DCM患者术后1年的临床结果，并评估其价值。 用于预测手术结果和建立临床临界值的术前标志性反应。经过验证 以神经生物学为基础的手术反应预测指标可能导致对那些可能发生 康复，防止那些不太可能做出反应的人暴露于风险和并发症中，并阐明因素 基础恢复以改善治疗。

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.