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

MRI 衍生的神经肌肉特征可预测退行性脊髓型颈椎病的手术反应

• 批准号: 10660889
• 负责人: Kenneth Arnold Weber
• 金额: $ 62.42万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10660889
• 关键词: Adult; Affect; Age; Architecture; Area; Atrophic; Brain; Cerebellum; Cervical; Cervical spine; Clinical; Diagnostic; Diffusion; Early Intervention; Enrollment; Exposure to; Extensor; Fatty acid glycerol esters; Female; Fingers; Flexor; Forearm; Functional Magnetic Resonance Imaging; Goals; Hand; Hand Strength; Hand functions; Health; Image; Imaging Techniques; Injury; Knowledge; Location; Magnetic Resonance Imaging; Measures; Methods; Motor; Motor Cortex; Motor Neurons; Muscle; Muscle denervation procedure; Neurobiology; Neurologic Dysfunctions; Operative Surgical Procedures; Outcome; Participant; Pathology; Pathway interactions; Patients; Peripheral; Predictive Factor; Property; Recovery; Rest; Risk; Schedule; Severities; Signal Transduction; Spinal; Spinal Cord; Spinal Cord Diseases; Spinal cord grey matter structure; Spinal cord injury; Spine surgery; Surgical Management; Technology; United States National Institutes of Health; Validation; Ventral Horn of the Spinal Cord; Vertebral column; Water; Work; dexterity; grasp; gray matter; hand dysfunction; hand rehabilitation; healthy volunteer; improved; machine learning algorithm; male; morphometry; multidisciplinary; neuromuscular; neuromuscular system; predicting response; predictive signature; prevent; response; sex; spinal cord compression; surgery outcome; white matter

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.

项目概要/摘要 退行性脊髓型颈椎病 (DCM) 是成人脊髓 (SC) 损伤最常见的形式。 DCM 是 其特点是颈椎多级退行性改变，导致颈椎受压和损伤， 从而导致神经功能障碍恶化。手部无力和协调性下降更严重 严重的脊柱病理指标，增加了脊柱手术的可能性。恢复手部功能的同时 作为手术的主要目标，DCM 的手术治疗具有挑战性，因为诊断确定性较低 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得到改善。 DCM 的损伤超出了 SC 受压水平并影响整个神经肌肉系统。 需要表征大脑、SC 和肌肉之间的相互作用，才能充分理解 DCM手部功能障碍的机制、DCM病理进展及影响因素 促进康复。在这里，我们将使用磁共振成像（MRI）来非侵入性地表征 DCM 中手部无力和协调性减弱的大脑、SC 和肌肉机制。我们将 然后结合大脑、SC 和肌肉测量来开发手部功能的神经肌肉特征 评估它们在预测 DCM 手术结果方面的价值。我们的总体假设是 神经肌肉健康将跟踪 DCM 病理学进展并预测手部手术康复 功能（较少广泛的大脑、SC 和肌肉损伤将预测更好的手术结果）。为了实现这一目标， 我们将招募60名右手DCM患者（年龄40-80岁，30名女性，30名男性），右手 无力和协调能力下降，计划接受手术的 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同时执行脑 SC 功能磁共振成像， 静息态功能连接来表征手功能障碍背后的大脑和 SC 机制。 我们还将利用皮质脊髓通路捕获灰质形态测量和白质完整性 我们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 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。我们将使用多元机器学习算法和大脑、SC 和 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 年的临床结果，并评估其价值 用于预测手术结果并建立临床临界点的术前特征反应。已验证 基于神经生物学的手术反应预测因素可能会导致对那些可能出现这种情况的患者进行早期干预 康复，防止那些不太可能做出反应的人面临风险和并发症，并阐明因素 基础康复以改善治疗。