Computational quantification of brain perivascular space morphologies: Associations with vascular risk factors and white matter hyperintensities. A study in the Lothian Birth Cohort 1936
Computational quantification of brain perivascular space morphologies: Associations with vascular risk factors and white matter hyperintensities. A study in the Lothian Birth Cohort 1936
复制标题
DOI：
10.1016/j.nicl.2019.102120
复制
发表时间：
2020-01-01
影响因子：
4.2
通讯作者：
Wardlaw, Joanna
中科院分区：
文献类型：
--
作者：
Ballerini, Lucia;Booth, Tom;Wardlaw, Joanna
背景和目的：血管周围间隙（PVS），也称为Virchow-Robin间隙，在结构性脑MRI上可见，是重要的液体引流管道，与小血管疾病（SVD）相关。可见PVS的计算量化可以在大型数据集中进行有效分析，并提高检测与脑部疾病相关性的灵敏度。我们评估了计算得出的PVS参数与血管因素和白色高信号（WMH）（SVD的标志物）的相关性。参与者：来自1936年Lothian出生队列的社区居住个体（n = 700），他们在72.6岁时进行了多模式脑MRI方法：我们在T2加权像上计算半卵圆中心和放射冠深部的PVS。计算的指标为每例受试者的总PVS体积和计数，以及每例受试者的平均个体PVS长度、宽度和大小。我们通过体积和视觉Fazekas评分评估WMH。我们比较了PVS视觉评分和PVS计算指标，并检验了每个PVS指标与血管危险因素之间的相关性（高血压、糖尿病、胆固醇）、血管病史（心血管疾病和中风）和WMH负担，使用广义线性模型，我们使用系数，置信区间和模型拟合进行比较。在533名受试者中，计算PVS测量与视觉PVS评分呈正相关（PVS计数r = 0.59; PVS体积r = 0.61; PVS平均长度r = 0.55; PVS平均宽度r = 0.52; PVS平均大小r = 0.47）。PVS大小和宽度与高血压（OR分别为1.22，95% CI [1.03 - 1.46]和1.20，95% CI [1.01 - 1.43]）和卒中（OR分别为1.34，95% CI [1.08 - 1.65]和1.36，95% CI [1.08 - 1.71]）相关。我们发现其他PVS指标与糖尿病、高胆固醇血症或心血管疾病史之间无关联。计算PVS体积、长度、宽度和大小与WMH的相关性更强（PVS平均大小与WMH Fazekas评分β = 0.66，95% CI [0.59至0.74]和与WMH体积β = 0.43，95% CI [0.38至0.48]）比计算PVS计数（WMH Fazekas评分β = 0.21，95% CI [0.11 - 0.3]; WMH体积β = 0.14，95% CI [0.09 - 0.19]）或视觉评分。个体PVS大小与WMH. Conclusions：反映个体PVS大小、长度和宽度的计算测量与WMH、中风和高血压的相关性比计算计数或视觉PVS评分更强。多维计算PVS指标可以增加检测PVS与风险暴露、脑病变和神经系统疾病的关联的灵敏度，提供更多的解剖细节，并加速对脑液和废物清除障碍的理解。
Background and Purpose: Perivascular Spaces (PVS), also known as Virchow-Robin spaces, seen on structural brain MRI, are important fluid drainage conduits and are associated with small vessel disease (SVD). Computational quantification of visible PVS may enable efficient analyses in large datasets and increase sensitivity to detect associations with brain disorders. We assessed the associations of computationally-derived PVS parameters with vascular factors and white matter hyperintensities (WMH), a marker of SVD.Participants: Community dwelling individuals (n = 700) from the Lothian Birth Cohort 1936 who had multimodal brain MRI at age 72.6 years (SD = 0.7).Methods: We assessed PVS computationally in the centrum semiovale and deep corona radiata on T2-weighted images. The computationally calculated measures were the total PVS volume and count per subject, and the mean individual PVS length, width and size, per subject. We assessed WMH by volume and visual Fazekas scores. We compared PVS visual rating to PVS computational metrics, and tested associations between each PVS measure and vascular risk factors (hypertension, diabetes, cholesterol), vascular history (cardiovascular disease and stroke), and WMH burden, using generalized linear models, which we compared using coefficients, confidence intervals and model fit.Results: In 533 subjects, the computational PVS measures correlated positively with visual PVS ratings (PVS count r = 0.59; PVS volume r = 0.61; PVS mean length r = 0.55; PVS mean width r = 0.52; PVS mean size r = 0.47). PVS size and width were associated with hypertension (OR 1.22, 95% CI [1.03 to 1.46] and 1.20, 95% CI [1.01 to 1.43], respectively), and stroke (OR 1.34, 95% CI [1.08 to 1.65] and 1.36, 95% CI [1.08 to 1.71], respectively). We found no association between other PVS measures and diabetes, hypercholesterolemia or cardiovascular disease history. Computational PVS volume, length, width and size were more strongly associated with WMH (PVS mean size versus WMH Fazekas score beta = 0.66, 95% CI [0.59 to 0.74] and versus WMH volume beta = 0.43, 95% CI [0.38 to 0.48]) than computational PVS count (WMH Fazekas score beta = 0.21, 95% CI [0.11 to 0.3]; WMH volume beta = 0.14, 95% CI [0.09 to 0.19]) or visual score. Individual PVS size showed the strongest association with WMH.Conclusions: Computational measures reflecting individual PVS size, length and width were more strongly associated with WMH, stroke and hypertension than computational count or visual PVS score. Multidimensional computational PVS metrics may increase sensitivity to detect associations of PVS with risk exposures, brain lesions and neurological disease, provide greater anatomic detail and accelerate understanding of disorders of brain fluid and waste clearance.