Li Yang, Xu Hu, Wang Ranchao, Shen Yu, Yang Yu, Yu Yue, Chen Xingbing, Su Hui
Department of Radiology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
J Magn Reson Imaging. 2025 Jun 6. doi: 10.1002/jmri.29835.
Hypertension-induced alterations in brain network topology remain poorly understood, and diffusion tensor imaging (DTI) offers a promising approach for detecting early structural changes.
Rich-club organization undergoes progressive disruption from prehypertension to hypertension, and these alterations may serve as potential imaging biomarkers for hypertension.
Cross-sectional.
Five hundred thirteen participants (150 healthy controls, 175 prehypertensive individuals, and 188 hypertensive patients).
DTI with an echo planar imaging sequence at 3.0 T.
Whole-brain structural networks were constructed using deterministic fiber tracking. Modularity, rich-club organization (rich-club, feeder and local connections), small-world property, global efficiency, local efficiency, clustering coefficient, and nodal efficiency were quantified using graph-theoretical analysis. Network-based statistics (NBS) were applied to identify significant group differences in white matter connectivity.
Analysis of variance for group comparisons, with post hoc least significant difference t-testing. Logistic regression assessed the predictive power of network features, while Pearson correlation evaluated relationships between blood pressure and network disruptions. Area under the receiver operating characteristic (ROC) curve (AUC) was used to assess diagnostic performance. A significance threshold of p < 0.05 was applied.
Prehypertensive individuals exhibited significant early reductions in feeder connections, whereas hypertensive patients demonstrated widespread significant deterioration in rich-club connections. A statistically significant compensatory increase in local connection strength was observed in prehypertension but declined in hypertension. Logistic regression confirmed that rich-club connection strength and density effectively differentiated hypertensive individuals, with ROC analysis showing good discriminatory power (AUC: 0.803 and 0.816, respectively).
This study showed progressive disruption of rich-club organization in prehypertension and hypertension. This disruption has the potential to be an early neuroimaging biomarker for identifying individuals at risk of hypertension-related brain dysfunction.
Stage 2.
高血压引起的脑网络拓扑结构改变仍知之甚少,而扩散张量成像(DTI)为检测早期结构变化提供了一种很有前景的方法。
从高血压前期到高血压,富俱乐部组织会逐渐遭到破坏,这些改变可能成为高血压潜在的影像学生物标志物。
横断面研究。
513名参与者(150名健康对照者、175名高血压前期个体和188名高血压患者)。
采用3.0 T的回波平面成像序列进行DTI。
使用确定性纤维追踪构建全脑结构网络。使用图论分析对模块化、富俱乐部组织(富俱乐部、馈线和局部连接)、小世界特性、全局效率、局部效率、聚类系数和节点效率进行量化。应用基于网络的统计方法(NBS)来识别白质连接性上的显著组间差异。
采用方差分析进行组间比较,并进行事后最小显著差t检验。逻辑回归评估网络特征的预测能力,而Pearson相关分析评估血压与网络破坏之间的关系。使用受试者工作特征(ROC)曲线下面积(AUC)来评估诊断性能。采用p < 0.05的显著性阈值。
高血压前期个体的馈线连接显著早期减少,而高血压患者的富俱乐部连接普遍显著恶化。在高血压前期观察到局部连接强度有统计学意义的代偿性增加,但在高血压时下降。逻辑回归证实,富俱乐部连接强度和密度能有效区分高血压个体,ROC分析显示出良好的鉴别能力(AUC分别为0.803和0.816)。
本研究显示高血压前期和高血压患者的富俱乐部组织逐渐遭到破坏。这种破坏有可能成为识别有高血压相关脑功能障碍风险个体的早期神经影像学生物标志物。
2级。
2期。