Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston (K.-H.J., K.A.S., K.M.Y., J.M.R., C.M.K., D.H.S.).
Department of Neurology, Seoul National University Hospital, Republic of Korea (K.-H.J.).
Stroke. 2021 Jan;52(2):620-630. doi: 10.1161/STROKEAHA.120.031641. Epub 2021 Jan 7.
Cerebral white matter signal abnormalities (WMSAs) are a significant radiological marker associated with brain and vascular aging. However, understanding their clinical impact is limited because of their pathobiological heterogeneity. We determined whether use of robust reliable automated procedures can distinguish WMSA classes with different clinical consequences.
Data from generally healthy participants aged >50 years with moderate or greater WMSA were selected from the Human Connectome Project-Aging (n=130). WMSAs were segmented on T1 imaging. Features extracted from WMSA included total and regional volume, number of discontinuous clusters, size of noncontiguous lesion, contrast of lesion intensity relative to surrounding normal appearing tissue using a fully automated procedure. Hierarchical clustering was used to classify individuals into distinct classes of WMSA. Radiological and clinical variability was evaluated across the individual WMSA classes.
Class I was characterized by multiple, small, lower-contrast lesions predominantly in the deep WM; class II by large, confluent lesions in the periventricular WM; and class III by higher-contrast lesions restricted to the juxtaventricular WM. Class II was associated with lower myelin content than the other 2 classes. Class II was more prevalent in older subjects and was associated with a higher prevalence of hypertension and lower physical activity levels. Poor sleep quality was associated with a greater risk of class I.
We classified heterogeneous subsets of cerebral white matter lesions into distinct classes that have different clinical risk factors. This new method for identifying classes of WMSA will be important in understanding the underlying pathophysiology and in determining the impact on clinical outcomes.
脑白质信号异常（WMSA）是与脑和血管老化相关的重要影像学标志物。然而，由于其病理生物学异质性，对其临床影响的了解有限。我们确定是否可以使用可靠的自动化程序来区分具有不同临床后果的 WMSA 类别。
从人类连接组计划-衰老（Human Connectome Project-Aging，HCP-Aging）中选择了年龄>50 岁且有中度或更严重 WMSA 的一般健康参与者的数据（n=130）。在 T1 成像上对 WMSA 进行分割。从 WMSA 中提取的特征包括总体积和区域体积、不连续簇的数量、非连续病变的大小、病变强度相对于周围正常组织的强度对比，这些都是使用完全自动化的程序提取的。使用层次聚类将个体分类为不同的 WMSA 类别。评估个体 WMSA 类别之间的放射学和临床变异性。
I 类的特征是多个小的、低对比度的病变，主要位于深部白质；II 类的特征是大的、融合的病变位于脑室周围白质；III 类的特征是高对比度的病变局限于脑室旁白质。II 类的髓鞘含量低于其他 2 类。II 类在年龄较大的患者中更为常见，与高血压的患病率更高和身体活动水平较低有关。睡眠质量差与 I 类的风险增加有关。
我们将脑白质病变的异质亚组分类为具有不同临床危险因素的不同类别。这种识别 WMSA 类别的新方法对于了解潜在的病理生理学和确定对临床结果的影响将是重要的。
