Zheng Jiahui, Wu Xiangxiang, Dai Jiankun, Pan Changjie, Shi Haifeng, Liu Tongqiang, Jiao Zhuqing
Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China.
GE Healthcare, MR Research China, Beijing, China.
Front Neurosci. 2022 Aug 10;16:967760. doi: 10.3389/fnins.2022.967760. eCollection 2022.
To characterize the topological properties of gray matter (GM) and functional networks in end-stage renal disease (ESRD) patients undergoing maintenance hemodialysis to provide insights into the underlying mechanisms of cognitive impairment.
In total, 45 patients and 37 healthy controls were prospectively enrolled in this study. All subjects completed resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) examinations and a Montreal cognitive assessment scale (MoCA) test. Differences in the properties of GM and functional networks were analyzed, and the relationship between brain properties and MoCA scores was assessed. Cognitive function was predicted based on functional networks by applying the least squares support vector regression machine (LSSVRM) and the whale optimization algorithm (WOA).
We observed disrupted topological organizations of both functional and GM networks in ESRD patients, as indicated by significantly decreased global measures. Specifically, ESRD patients had impaired nodal efficiency and degree centrality, predominantly within the default mode network, limbic system, frontal lobe, temporal lobe, and occipital lobe. Interestingly, the involved regions were distributed laterally. Furthermore, the MoCA scores significantly correlated with decreased standardized clustering coefficient (γ), standardized characteristic path length (λ), and nodal efficiency of the right insula and the right superior temporal gyrus. Finally, optimized LSSVRM could predict the cognitive scores of ESRD patients with great accuracy.
Disruption of brain networks may account for the progression of cognitive dysfunction in ESRD patients. Implementation of prediction models based on neuroimaging metrics may provide more objective information to promote early diagnosis and intervention.
表征接受维持性血液透析的终末期肾病（ESRD）患者灰质（GM）和功能网络的拓扑特性，以深入了解认知障碍的潜在机制。
本研究前瞻性纳入了45例患者和37名健康对照者。所有受试者均完成静息态功能磁共振成像（rs-fMRI）和扩散峰度成像（DKI）检查以及蒙特利尔认知评估量表（MoCA）测试。分析了GM和功能网络特性的差异，并评估了脑特性与MoCA评分之间的关系。通过应用最小二乘支持向量回归机（LSSVRM）和鲸鱼优化算法（WOA），基于功能网络预测认知功能。
我们观察到ESRD患者的功能和GM网络的拓扑组织均受到破坏，表现为全局测量值显著降低。具体而言，ESRD患者的节点效率和度中心性受损，主要发生在默认模式网络、边缘系统、额叶、颞叶和枕叶内。有趣的是，受累区域呈外侧分布。此外，MoCA评分与右侧脑岛和右侧颞上回的标准化聚类系数（γ）、标准化特征路径长度（λ）以及节点效率降低显著相关。最后，优化后的LSSVRM能够高度准确地预测ESRD患者的认知评分。
脑网络的破坏可能是ESRD患者认知功能障碍进展的原因。基于神经影像学指标实施预测模型可能会提供更客观的信息，以促进早期诊断和干预。
