Department of Radiology, AOU of Cagliari, University of Cagliari, Italy.
Department of Vascular Surgery, AOU of Cagliari, University of Cagliari, Italy.
Neuroradiol J. 2020 Dec;33(6):486-493. doi: 10.1177/1971400920959323. Epub 2020 Sep 21.
White-matter lesions (WMLs) are frequently found in magnetic resonance imaging (MRi), and the WML load tends to be higher in patients affected by cervical internal carotid artery (cICA) stenosis.
This study aimed to investigate whether and how WMLs influence cerebral networking in patients with asymptomatic cICA stenosis eligible for carotid endarterectomy (CEA) by exploiting the connectometry technique.
The study was designed as a cross-sectional exploratory investigation, and 28 patients with cICA stenosis eligible for CEA were enrolled. All patients received an MRI scan, including a T1-weighted, a FLAIR and a diffusion-weighted (DW) sequence. The T1 and FLAIR sequences were analysed for quantification of WML burden (WMLB) and total number of WMLs (TNWMLs). The DW data were reconstructed in the MNI space using q-space diffeomorphic reconstruction, and were grouped to create a connectometry database. The connectometry analysis evaluated the influence of both the WMLB and TNWMLs to local connectivity in a multiple regression model that included age, WMLB and TNWMLs, adopting three different T-score thresholds (1, 2 and 3). A -value corrected for false discovery rate of <0.05 was adopted as a threshold to identify statistically significant results.
The connectometry analysis identified several white-matter bundles negatively correlated with WMLB; no statistically significant correlation was found for TNWMLs.
Results of our study suggest that WMLs influence brain connectivity measured by the connectometry technique in patients with cICA stenosis eligible for CEA. Further studies are warranted to understand the role of WMLs better as a marker of disease in patients with cICA stenosis.
磁共振成像(MRI)中常可见到脑白质病变(WML),而颈内动脉(ICA)狭窄患者的 WML 负荷往往更高。
本研究旨在通过连接测量技术,探讨无症状性颈内动脉狭窄患者的 WML 是否以及如何影响脑网络。
该研究设计为横断面探索性研究,共纳入 28 例适合颈动脉内膜切除术(CEA)的颈内动脉狭窄患者。所有患者均接受 MRI 扫描,包括 T1 加权、FLAIR 和弥散加权(DW)序列。T1 和 FLAIR 序列用于量化 WML 负荷(WMLB)和总 WML 数(TNWMLs)。DW 数据在 MNI 空间使用 q 空间弥散重建重建,并分组创建连接测量数据库。连接测量分析采用多元回归模型评估 WMLB 和 TNWMLs 对局部连通性的影响,该模型包括年龄、WMLB 和 TNWMLs,并采用三个不同的 T 分数阈值(1、2 和 3)。采用校正错误发现率的<0.05 的 - 值作为统计学显著结果的阈值。
连接测量分析确定了几个与 WMLB 呈负相关的白质束;TNWMLs 则没有统计学显著相关性。
本研究结果表明,WML 可通过连接测量技术影响适合 CEA 的颈内动脉狭窄患者的脑连接。需要进一步研究以更好地了解 WML 作为颈内动脉狭窄患者疾病标志物的作用。