Improving the Assessment of Myelin and Axonal Integrity in Early Multiple Sclerosis

改善早期多发性硬化症髓磷脂和轴突完整性的评估

基本信息

项目摘要

PROJECT SUMMARY Neurodegeneration, characterized by myelin and axonal injury, is a key driver in multiple sclerosis (MS) pathology and a major determinant of patients’ disability and outcome. Currently, it is not possible to assess neurodegeneration in vivo because there are no magnetic resonance imaging (MRI) biomarkers sensitive and specific to myelin and axonal injury. Finding this biometric is set as a major priority by an MS International Panel of Experts, as a path toward halting neurodegeneration and promoting neuroprotection in MS. Accordingly, the long-term goal of the current lines of investigations is to identify MRI biomarkers of neurodegeneration and repair that can be used to monitor disease and predict likelihood of progression. The overall objective of this work is to establish the sensitivity to disease, neurological dysfunction and outcome, of two novel MRI methods in patients at the time of diagnosis. The central hypothesis of this proposal is that metrics derived from selective inversion recovery quantitative magnetization transfer imaging (SIR-qMT) and multi-compartment microscopic diffusion imaging using the spherical mean technique (SMT) are sensitive hallmarks of myelin and axonal neurodegenerative tissue injury early in the disease course, and relate to and predict disease progression more accurately than currently favored MRI measures. The central hypothesis will be tested by pursuing three specific aims: 1) Establish that metrics derived from SIR-qMT (Aim 1) and SMT (Aim 2) are sensitive hallmarks of neurodegenerative injury and reflect neurological dysfunction cross-sectionally, early in the disease course; 2) and explore if metrics derived from SIR-qMT and SMT predict clinical outcome and radiological progression, longitudinally, more accurately than currently favored MRI measures (Aim 3). Longitudinal data will also be used for sample size computations for proof of concept clinical trials on neuroprotection and repair (corollary analysis). The research proposed in this application is innovative because, through the use of two advanced and novel MRI techniques, this project 1) assesses and measures progression of neurodegeneration in Veterans with MS; and 2) delivers sample computations for proof of concept clinical trials on neuroprotection. The proposed research is significant because 1) it will lay the foundation for future larger studies providing a more accurate understanding of MS progression in Veterans; 2) it will help untangle the effects of modifiable and non-modifiable Veteran-related risk factors on disease progression and mortality; and 3) it has the potential to support new interventions and treatments.
项目摘要 神经变性以髓磷脂和轴突损伤为特征,是多发性硬化症(MS)病理学和 患者残疾和预后的主要决定因素。目前,无法评估体内神经退行性 因为没有磁共振成像(MRI)生物标志物敏感,并且对髓磷脂和轴突损伤具有特异性。 MS International专家小组将发现这种生物识别作为主要的优先事项,以此作为停止的途径 神经变性并促进MS中的神经保护作用。彼此之间,当前线的长期目标 研究是为了鉴定可用于监测疾病的神经变性和修复的MRI生物标志物 进展的可能性。这项工作的总体目的是建立对疾病,神经系统的敏感性 诊断时患者的两种新型MRI方法的功能障碍和结果。 该提议的中心假设是从选择性反演恢复定量的指标 磁化转移成像(SIR-QMT)和使用球形平均值 技术(SMT)是疾病病程初期髓磷脂和轴突神经退行性组织损伤的敏感标志, 并与当前有利于MRI测量值更准确地与和预测疾病进展。中心假设 将通过追求三个特定目标来测试:1)确定源自SIR-QMT(AIM 1)和SMT(AIM 2)的指标 是神经退行性损伤的敏感标志,并反映神经功能障碍横截面,早期 疾病课程; 2)并探索是否源自SIR-QMT和SMT的指标预测临床结果和放射学 纵向,比目前有利于MRI测量的进展更准确(AIM 3)。纵向数据也将是 用于样本量计算,用于概念证明神经保护和修复的临床试验(推论分析)。 本应用程序中提出的研究具有创新性,因为通过使用两个高级和新颖的MRI 技术,该项目1)评估和衡量MS退伍军人的神经退行性的进展; 2)交付 样本计算,用于神经保护概念临床试验证明。拟议的研究很重要,因为 1)它将为将来的大型研究奠定基础,从而更准确地了解退伍军人的MS进展; 2)它将有助于解开可修改和不可验证的退伍军人相关风险因素对疾病进展和 死亡; 3)它有可能支持新的干预措施和治疗。

项目成果

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Francesca Rosaria Bagnato其他文献

Francesca Rosaria Bagnato的其他文献

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{{ truncateString('Francesca Rosaria Bagnato', 18)}}的其他基金

Improving the Assessment of Myelin and Axonal Integrity in Early Multiple Sclerosis
改善早期多发性硬化症髓磷脂和轴突完整性的评估
  • 批准号:
    10662233
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:
Longitudinal Measurement of Neurodegeneration in two Murine Models of Multiple Sclerosis: A Clinical and Histopathologic Validation Study
两种多发性硬化症小鼠模型神经变性的纵向测量:临床和组织病理学验证研究
  • 批准号:
    10328569
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:

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