Institute of Neuroscience and Medicine 4 (INM-4), Research Center Jülich, Jülich, Germany.
Department of Physics, Georgian Technical University, Tbilisi, Georgia.
Neuroimage. 2019 Nov 15;202:116077. doi: 10.1016/j.neuroimage.2019.116077. Epub 2019 Aug 6.
Interest in white matter hyperintensities (WMH), a radiological biomarker of small vessel disease, is continuously increasing. This is, in most part, due to our better understanding of their association with various clinical disorders, such as stroke and Alzheimer's disease, and the overlapping pathology of WMH with these afflictions. Although post-mortem histological studies have reported various underlying pathophysiological substrates, in vivo research has not been specific enough to fully corroborate these findings. Furthermore, post-mortem studies are not able to capture which pathological processes are the driving force of the WMH severity. The current study attempts to fill this gap by non-invasively investigating the influence of WMH on brain tissue using quantitative MRI (qMRI) measurements of the water content (HO), the longitudinal (T) and effective transverse relaxation times (T), as well as the semi-quantitative magnetization transfer ratio (MTR), and bound proton fraction (ƒ). In total, seventy subjects (age range 50-80 years) were selected from a population-based aging cohort study, 1000BRAINS. Normal appearing grey (NAGM) and white matter (NAWM), as well as deep (DWMH) and periventricular (PWMH) white matter hyperintensities, were segmented and characterized in terms of their quantitative properties. The subjects were then further divided into four grades according to the Fazekas rating scale of severity. Groupwise analyses of the qMRI values in each tissue class were performed. All five qMRI parameters showed significant differences between WMH and NAWM (p < 0.001). Importantly, the parameters differed between DWMH and PWMH, the latter having higher HO, T, T and lower MTR and ƒ values (p < 0.001). Following grading according to the Fazekas scale, DWMH showed an increase in the water content, T and a decrease in bound proton fraction corresponding to severity, exhibiting significant changes in grade 3 (p < 0.001), while NAWM revealed significantly higher HO values in grade 3 compared to grade 0 (p < 0.001). PWMH demonstrated an increase in T values (significant in grade 3, P < 0.001). These results are in agreement with previous histopathological studies and support the interpretation that both edema and myelin loss due to a possible breakdown of the blood-brain barrier and inflammation are the major pathological substrates turning white matter into DWMH. Edema being an earlier contributing factor to the pathology, as expressed in the elevated water content values in NAWM with increasing severity. In the case of PWMH, an altered fluid dynamic and cerebrospinal fluid leakage exacerbate the changes. It was also found that the pathology, as monitored by qMRI, evolves faster in DWMH than in the PWMH following the severity.
人们对脑白质高信号（WMH）作为小血管疾病的影像学生物标志物的兴趣持续增加。这主要是因为我们对其与各种临床疾病（如中风和阿尔茨海默病）之间的关联，以及与这些疾病的重叠病理有了更好的了解。尽管尸检组织学研究报告了各种潜在的病理生理基础，但体内研究还不够具体，无法充分证实这些发现。此外，尸检研究无法捕捉到哪些病理过程是 WMH 严重程度的驱动因素。本研究试图通过使用定量 MRI（qMRI）测量水含量（HO）、纵向（T）和有效横向弛豫时间（T）以及半定量磁化传递比（MTR）和结合质子分数（ƒ），无创性地研究 WMH 对脑组织的影响，从而填补这一空白。总共从一项基于人群的老龄化队列研究（1000BRAINS）中选择了 70 名年龄在 50-80 岁之间的受试者。对正常表现的灰质（NAGM）和白质（NAWM）以及深部（DWMH）和脑室周围（PWMH）脑白质高信号进行分割和特征描述，根据 Fazekas 严重程度评分量表对其进行分级。然后，根据 Fazekas 量表将受试者进一步分为四个等级。对每个组织类别的 qMRI 值进行组间分析。所有 5 个 qMRI 参数在 WMH 和 NAWM 之间均有显著差异（p<0.001）。重要的是，DWMH 和 PWMH 之间的参数不同，后者的 HO、T 和 T 较高，而 MTR 和ƒ值较低（p<0.001）。根据 Fazekas 量表分级后，DWMH 的水含量增加，T 降低，结合质子分数相应降低，与严重程度呈正相关，在 3 级时发生显著变化（p<0.001），而 NAWM 在 3 级时与 0 级相比，HO 值显著升高（p<0.001）。PWMH 的 T 值增加（3 级时显著，P<0.001）。这些结果与之前的组织病理学研究一致，并支持这样的解释，即血脑屏障和炎症导致的水肿和髓鞘丢失是将白质转化为 DWMH 的主要病理基础。水肿是病理学的早期影响因素，这反映在 NAWM 中随着严重程度的增加而升高的水含量值上。在 PWMH 的情况下，改变的流体动力学和脑脊液漏加剧了这种变化。研究还发现，qMRI 监测到的病理学在 DWMH 中的进展速度比 PWMH 更快。
