Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, China; School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China.
School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China.
Neuroimage Clin. 2022;36:103165. doi: 10.1016/j.nicl.2022.103165. Epub 2022 Aug 26.
Cerebral small vessel disease (CSVD) is associated with altered cerebral perfusion. However, global and regional cerebral blood flow (CBF) are highly heterogeneous across CSVD patients. The aim of this study was to identify subtypes of CSVD with different CBF patterns using an advanced machine learning approach. 121 CSVD patients and 53 healthy controls received arterial spin label MRI, T1 structural MRI and clinical measurements. Regional CBF were used to identify distinct perfusion subtypes of CSVD via a semi-supervised machine learning algorithm. Statistical analyses were used to explore alterations in CBF, clinical measures, gray and white matter volume between healthy controls and different subtypes of CSVD. Correlation analysis was used to assess the association between clinical measures and altered CBF in each CSVD subtype. Three subtypes of CSVD with distinct CBF patterns were found. Subtype 1 showed decreased CBF in the temporal lobe and increased CBF in the parietal and occipital lobe. Subtype 2 exhibited decreased CBF in the right hemisphere of the brain, and increased CBF in the left cerebrum. Subtype 3 demonstrated decreased CBF in the posterior part of the brain, and increased CBF in anterior part of the brain. The three subtypes also differed significantly in gender (p = 0.005), the proportion of subjects with lacune (p = 0.002), with periventricular white matter hyperintensity (p = 0.043), and CSVD burden score (p = 0.048). In subtype 3, it was found that widespread decreased CBF was correlated with total CSVD burden score (r = -0.324, p = 0.029). Compared with healthy controls, the three CSVD subtypes also showed distinct volumetric patterns of white matter. The current results associate different subtypes with different clinical and imaging phenotypes, which can improve the understanding of brain perfusion alterations of CSVD and can facilitate precision diagnosis of CSVD.
脑小血管病（CSVD）与脑灌注改变有关。然而，CSVD 患者的全脑和局部脑血流（CBF）存在高度异质性。本研究旨在采用先进的机器学习方法，识别具有不同 CBF 模式的 CSVD 亚型。121 例 CSVD 患者和 53 例健康对照者接受动脉自旋标记 MRI、T1 结构 MRI 和临床测量。通过半监督机器学习算法，利用局部 CBF 来识别 CSVD 的不同灌注亚型。统计分析用于探索健康对照组和不同 CSVD 亚型之间 CBF、临床指标、灰质和白质体积的变化。相关性分析用于评估每个 CSVD 亚型中临床指标与 CBF 改变的相关性。发现了具有不同 CBF 模式的三种 CSVD 亚型。1 型表现为颞叶 CBF 降低，顶叶和枕叶 CBF 增加。2 型表现为右侧大脑半球 CBF 降低，左大脑 CBF 增加。3 型表现为大脑后部 CBF 降低，前部 CBF 增加。三种亚型在性别（p=0.005）、腔隙（p=0.002）、脑室周围白质高信号（p=0.043）和 CSVD 负担评分（p=0.048）的比例方面差异显著。在 3 型中，发现广泛的 CBF 降低与总 CSVD 负担评分相关（r=-0.324，p=0.029）。与健康对照组相比，三种 CSVD 亚型的白质体积模式也存在明显差异。目前的结果将不同的亚型与不同的临床和影像学表型相关联，这可以提高对 CSVD 脑灌注改变的认识，并有助于 CSVD 的精准诊断。
