Heterogeneity of Cerebral White Matter Lesions and Clinical Correlates in Older Adults

医学 白质 病变 对比度(视觉) 高强度 病理 磁共振成像 放射科 计算机科学 人工智能
作者
Keun‐Hwa Jung,Kimberly A. Stephens,Kathryn Morrison Yochim,Joost M. Riphagen,Chan Mi Kim,Randy L. Buckner,David H. Salat
出处
期刊:Stroke [Ovid Technologies (Wolters Kluwer)]
卷期号:52 (2): 620-630 被引量:15
标识
DOI:10.1161/strokeaha.120.031641
摘要

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.
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