Department of Neurology, University of California, San Francisco, CA.
Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
J Neuroimaging. 2020 Jan;30(1):110-118. doi: 10.1111/jon.12666. Epub 2019 Sep 30.
The quantification of spinal cord (SC) atrophy by MRI has assumed an important role in assessment of neuroinflammatory/neurodegenerative diseases and traumatic SC injury. Recent technical advances make possible the quantification of gray matter (GM) and white matter tissues in clinical settings. However, the goal of a reliable diagnostic, prognostic or predictive marker is still elusive, in part due to large intersubject variability of SC areas. Here, we investigated the sources of this variability and explored effective strategies to reduce it.
One hundred twenty-nine healthy subjects (mean age: 41.0 ± 15.9) underwent MRI on a Siemens 3T Skyra scanner. Two-dimensional PSIR at the C2-C3 vertebral level and a sagittal 1 mm 3D T1-weighted brain acquisition extended to the upper cervical cord were acquired. Total cross-sectional area and GM area were measured at C2-C3, as well as measures of the vertebra, spinal canal and the skull. Correlations between the different metrics were explored using Pearson product-moment coefficients. The most promising metrics were used to normalize cord areas using multiple regression analyses.
The most effective normalization metrics were the V-scale (from SienaX) and the product of the C2-C3 spinal canal diameters. Normalization methods based on these metrics reduced the intersubject variability of cord areas of up to 17.74%. The measured cord areas had a statistically significant sex difference, while the effect of age was moderate.
The present work explored in a large cohort of healthy subjects the source of intersubject variability of SC areas and proposes effective normalization methods for its reduction.
MRI 对脊髓(SC)萎缩的定量分析在神经炎症/神经退行性疾病和创伤性 SC 损伤的评估中具有重要作用。最近的技术进步使得在临床环境中对灰质(GM)和白质组织进行定量成为可能。然而,由于 SC 区域的个体间变异性较大,可靠的诊断、预后或预测标志物的目标仍然难以实现。在这里,我们研究了这种变异性的来源,并探讨了减少这种变异性的有效策略。
129 名健康受试者(平均年龄:41.0±15.9 岁)在西门子 3T Skyra 扫描仪上进行 MRI 检查。在 C2-C3 椎体水平进行二维 PSIR,在矢状面进行 1mm3D T1 加权脑采集,延伸至上颈段脊髓。在 C2-C3 测量总横截面积和 GM 面积,以及椎体、椎管和颅骨的测量值。使用 Pearson 积矩相关系数探索不同指标之间的相关性。使用多元回归分析,选择最有前途的指标对脊髓区域进行归一化。
最有效的归一化指标是 SienaX 的 V-scale 和 C2-C3 椎管直径的乘积。基于这些指标的归一化方法可将脊髓区域的个体间变异性降低高达 17.74%。所测量的脊髓区域存在统计学上显著的性别差异,而年龄的影响中等。
本研究在一个大型健康受试者队列中探讨了 SC 区域个体间变异性的来源,并提出了有效的减少变异性的归一化方法。