Lv Ke, Liu Yanzhen, Chen Yongsheng, Buch Sagar, Wang Ying, Yu Zhuo, Wang Huiying, Zhao Chenxi, Fu Dingwei, Wang Huapeng, Wang Beini, Zhang Shengtong, Luo Yu, Haacke E Mark, Shen Wen, Chai Chao, Xia Shuang
Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China.
Department of Radiology, Tianjin Chest Hospital, Tianjin, China.
Neuroimage. 2023 Nov 1;281:120370. doi: 10.1016/j.neuroimage.2023.120370. Epub 2023 Sep 15.
The goal of this work was to explore the total iron burden of cerebral microbleeds (CMBs) using a semi-automatic quantitative susceptibility mapping and to establish its effect on brain atrophy through the mediating effect of white matter hyperintensities (WMH). A total of 95 community-dwelling people were enrolled. Quantitative susceptibility mapping (QSM) combined with a dynamic programming algorithm (DPA) was used to measure the characteristics of 1309 CMBs. WMH were evaluated according to the Fazekas scale, and brain atrophy was assessed using a 2D linear measurement method. Histogram analysis was used to explore the distribution of CMBs susceptibility, volume, and total iron burden, while a correlation analysis was used to explore the relationship between volume and susceptibility. Stepwise regression analysis was used to analyze the risk factors for CMBs and their contribution to brain atrophy. Mediation analysis was used to explore the interrelationship between CMBs and brain atrophy. We found that the frequency distribution of susceptibility of the CMBs was Gaussian in nature with a mean of 201 ppb and a standard deviation of 84 ppb; however, the volume and total iron burden of CMBs were more Rician in nature. A weak but significant correlation between the susceptibility and volume of CMBs was found (r = -0.113, P < 0.001). The periventricular WMH (PVWMH) was a risk factor for the presence of CMBs (number: β = 0.251, P = 0.014; volume: β = 0.237, P = 0.042; total iron burden: β = 0.238, P = 0.020) and was a risk factor for brain atrophy (third ventricle width: β = 0.325, P = 0.001; Evans's index: β = 0.323, P = 0.001). PVWMH had a significant mediating effect on the correlation between CMBs and brain atrophy. In conclusion, QSM along with the DPA can measure the total iron burden of CMBs. PVWMH might be a risk factor for CMBs and may mediate the effect of CMBs on brain atrophy.
这项工作的目标是使用半自动定量磁化率成像来探究脑微出血（CMB）的总铁负荷，并通过白质高信号（WMH）的中介作用来确定其对脑萎缩的影响。共纳入了95名社区居民。采用定量磁化率成像（QSM）结合动态规划算法（DPA）来测量1309个CMB的特征。根据Fazekas量表评估WMH，并使用二维线性测量方法评估脑萎缩。采用直方图分析来探究CMB的磁化率、体积和总铁负荷的分布，同时采用相关性分析来探究体积与磁化率之间的关系。采用逐步回归分析来分析CMB的危险因素及其对脑萎缩的影响。采用中介分析来探究CMB与脑萎缩之间的相互关系。我们发现，CMB的磁化率频率分布本质上呈高斯分布，均值为201 ppb，标准差为84 ppb；然而，CMB的体积和总铁负荷本质上更呈莱斯分布。发现CMB的磁化率与体积之间存在微弱但显著的相关性（r = -0.113，P < 0.001）。脑室周围WMH（PVWMH）是CMB存在的危险因素（数量：β = 0.251，P = 0.014；体积：β = 0.237，P = 0.042；总铁负荷：β = 0.238，P = 0.020），也是脑萎缩的危险因素（第三脑室宽度：β = 0.325，P = 0.001；埃文斯指数：β = 0.323，P = 0.001）。PVWMH对CMB与脑萎缩之间的相关性具有显著的中介作用。总之，QSM结合DPA可以测量CMB的总铁负荷。PVWMH可能是CMB的危险因素，并可能介导CMB对脑萎缩的影响。
