Department of Neurology, University of Ulm, Ulm, Germany.
Curr Opin Neurol. 2018 Aug;31(4):425-430. doi: 10.1097/WCO.0000000000000578.
MRI has become a well established technical tool for parkinsonism both in the diagnostic work-up to differentiate between causes and to serve as a neurobiological marker. This review summarizes current developments in the advanced MRI-based assessment of brain structure and function in atypical parkinsonian syndromes and explores their potential in a clinical and neuroscientific setting.
Computer-based unbiased quantitative MRI analyses were demonstrated to guide in the discrimination of parkinsonian syndromes at single-patient level, with major contributions when combined with machine-learning techniques/support vector machine classification. These techniques have shown their potential in tracking the disease progression, perhaps also as a read-out in clinical trials. The characterization of different brain compartments at various levels of structural and functional alterations can be provided by multiparametric MRI, including a growing variety of diffusion-weighted imaging approaches and potentially iron-sensitive and functional MRI.
In case that the recent advances in the MRI-based assessment of atypical parkinsonism will lead to standardized protocols for image acquisition and analysis after the confirmation in large-scale multicenter studies, these approaches may constitute a great achievement in the (operator-independent) detection, discrimination and characterization of degenerative parkinsonian disorders at an individual basis.
MRI 已成为帕金森病诊断的重要技术手段,可用于区分病因,作为神经生物学标志物。本文综述了基于 MRI 的非典型帕金森综合征脑结构和功能的先进评估方法的最新进展,并探讨了其在临床和神经科学领域的应用。
基于计算机的无偏定量 MRI 分析被证明可用于指导帕金森综合征患者的个体鉴别,与机器学习技术/支持向量机分类相结合具有重要意义。这些技术在疾病进展的跟踪中显示出了潜力,也可能成为临床试验中的一种检测手段。多参数 MRI 可提供不同脑区的结构和功能改变的特征,包括越来越多的扩散加权成像方法和潜在的铁敏感和功能 MRI。
如果基于 MRI 的非典型帕金森病评估的最新进展能够在大规模多中心研究中得到确认后,为图像采集和分析制定出标准化方案,那么这些方法可能会在退行性帕金森病的个体检测、鉴别和特征描述方面取得重大突破。