Han Xu, Wang Yao, Chen Yuewei, Qiu Yage, Gu Xiyao, Dai Yongming, Xu Qun, Sun Yawen, Zhou Yan
Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Quant Imaging Med Surg. 2024 Sep 1;14(9):6621-6634. doi: 10.21037/qims-24-238. Epub 2024 Aug 28.
White-matter hyperintensity (WMH) is the key magnetic resonance imaging (MRI) marker of cerebral small-vessel disease (CSVD). This study aimed to investigate whether habitat analysis based on physiologic MRI parameters can predict the progression of WMH and cognitive decline in CSVD.
Diffusion- and perfusion-weighted imaging data were obtained from 69 patients with CSVD at baseline and at 1-year of follow-up. The white-matter region was classified into constant WMH, growing WMH, shrinking WMH, and normal-appearing white matter (NAWM) according to the T2-fluid-attenuated inversion recovery (FLAIR) sequences images at the baseline and follow-up. We employed k-means clustering on a voxel-wise basis to delineate WMH habitats, integrating multiple diffusion metrics and cerebral blood flow (CBF) values derived from perfusion data. The WMH at the baseline and the predicted WMH from the habitat analysis were used as regions of avoidance (ROAs). The decreased rate of global efficiency for the whole brain structural connectivity was calculated after removal of the ROA. The association between the decreased rate of global efficiency and Montreal Cognitive Assessment (MoCA) and mini-mental state examination (MMSE) scores was evaluated using Pearson correlation coefficients.
We found that the physiologic MRI habitats with lower fractional anisotropy and CBF values and higher mean diffusivity, axial diffusivity, and radial diffusivity values overlapped considerably with the new WMH (growing WMH of baseline) after a 1-year follow-up; the accuracy of distinguishing growing WMH from NAWM was 88.9%±12.7% at baseline. Similar results were also found for the prediction of shrinking WMH. Moreover, after the removal of the predicted WMH, a decreased rate of global efficiency had a significantly negative correlation with the MoCA and MMSE scores at follow-up.
This study revealed that a habitat analysis combining perfusion with diffusion parameters could predict the progression of WMH and related cognitive decline in patients with CSVD.
脑白质高信号（WMH）是脑小血管病（CSVD）的关键磁共振成像（MRI）标志物。本研究旨在探讨基于生理MRI参数的栖息地分析是否能够预测CSVD中WMH的进展及认知功能衰退。
在基线期和随访1年时，从69例CSVD患者处获取扩散加权成像和灌注加权成像数据。根据基线期和随访期的T2液体衰减反转恢复（FLAIR）序列图像，将白质区域分为稳定WMH、进展性WMH、消退性WMH和正常白质（NAWM）。我们在体素水平上采用k均值聚类来描绘WMH栖息地，整合多个扩散指标和从灌注数据得出的脑血流量（CBF）值。将基线期的WMH和栖息地分析预测的WMH用作避让区域（ROA）。去除ROA后，计算全脑结构连通性的全局效率降低率。使用Pearson相关系数评估全局效率降低率与蒙特利尔认知评估（MoCA）和简易精神状态检查表（MMSE）评分之间的关联。
我们发现，在随访1年后，各向异性分数和CBF值较低且平均扩散率、轴向扩散率和径向扩散率值较高的生理MRI栖息地与新出现WMH（基线期进展性WMH）有相当大的重叠；在基线期，区分进展性WMH和NAWM的准确率为88.9%±12.7%。在预测消退性WMH方面也发现了类似结果。此外，去除预测的WMH后，全局效率降低率与随访期的MoCA和MMSE评分显著负相关。
本研究表明，结合灌注与扩散参数的栖息地分析可以预测CSVD患者WMH的进展及相关认知功能衰退。
