Department of Computer Science, University of Verona, Verona, Italy.
Center for Mind/Brain Sciences, University of Trento, Trento, Italy.
J Magn Reson Imaging. 2022 Jan;55(1):154-163. doi: 10.1002/jmri.27806. Epub 2021 Jun 30.
The mechanisms driving primary progressive and relapsing-remitting multiple sclerosis (PPMS/RRMS) phenotypes are unknown. Magnetic resonance imaging (MRI) studies support the involvement of gray matter (GM) in the degeneration, highlighting its damage as an early feature of both phenotypes. However, the role of GM microstructure is unclear, calling for new methods for its decryption.
To investigate the morphometric and microstructural GM differences between PPMS and RRMS to characterize GM tissue degeneration using MRI.
Prospective cross-sectional study.
Forty-five PPMS (26 females) and 45 RRMS (32 females) patients.
FIELD STRENGTH/SEQUENCE: 3T scanner. Three-dimensional (3D) fast field echo T1-weighted (T1-w), 3D turbo spin echo (TSE) T2-w, 3D TSE fluid-attenuated inversion recovery, and spin echo-echo planar imaging diffusion MRI (dMRI).
T1-w and dMRI data were employed for providing information about morphometric and microstructural features, respectively. For dMRI, both diffusion tensor imaging and 3D simple harmonics oscillator based reconstruction and estimation models were used for feature extraction from a predefined set of regions. A support vector machine (SVM) was used to perform patients' classification relying on all these measures.
Differences between MS phenotypes were investigated using the analysis of covariance and statistical tests (P < 0.05 was considered statistically significant).
All the dMRI indices showed significant microstructural alterations between the considered MS phenotypes, for example, the mode and the median of the return to the plane probability in the hippocampus. Conversely, thalamic volume was the only morphometric feature significantly different between the two MS groups. Ten of the 12 features retained by the selection process as discriminative across the two MS groups regarded the hippocampus. The SVM classifier using these selected features reached an accuracy of 70% and a precision of 69%.
We provided evidence in support of the ability of dMRI to discriminate between PPMS and RRMS, as well as highlight the central role of the hippocampus.
2 TECHNICAL EFFICACY STAGE: 3.
原发性进展型和复发缓解型多发性硬化症（PPMS/RRMS）的发病机制尚不清楚。磁共振成像（MRI）研究支持灰质（GM）在变性中的作用，突出了其损伤是两种表型的早期特征。然而，GM 微观结构的作用尚不清楚，需要新的方法来对其进行解码。
使用 MRI 研究原发性进展型和复发缓解型多发性硬化症之间 GM 形态和微观结构的差异，以表征 GM 组织变性。
前瞻性横断面研究。
45 例原发性进展型多发性硬化症（26 例女性）和 45 例复发缓解型多发性硬化症（32 例女性）患者。
磁场强度/序列：3T 扫描仪。三维（3D）快速场回波 T1 加权（T1-w）、3D 涡轮自旋回波（TSE）T2-w、3D TSE 液体衰减反转恢复和自旋回波-回波平面成像扩散 MRI（dMRI）。
T1-w 和 dMRI 数据分别用于提供形态和微观结构特征的信息。对于 dMRI，分别使用扩散张量成像和基于 3D 简单谐振子的重建和估计模型，从一组预设的区域中提取特征。支持向量机（SVM）用于根据所有这些测量值对患者进行分类。
使用协方差分析和统计检验（P<0.05 被认为具有统计学意义）来研究 MS 表型之间的差异。
在所考虑的 MS 表型之间，所有 dMRI 指数均显示出明显的微观结构改变，例如海马体中返回平面概率的模式和中位数。相反，丘脑体积是两个 MS 组之间唯一显著不同的形态学特征。在作为两个 MS 组之间的判别特征被选择过程保留的 12 个特征中，有 10 个与海马体有关。使用这些选定特征的 SVM 分类器达到了 70%的准确性和 69%的精度。
我们提供了证据支持 dMRI 区分原发性进展型和复发缓解型多发性硬化症的能力，并强调了海马体的核心作用。
2 技术功效阶段：3.
