Wang Yuanyuan, Ji Yu, Liu Jie, Lv Lianjiang, Xu Zihe, Yan Meimei, Chen Jialu, Luo Zhijun, Zeng Xianjun
Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Heliyon. 2024 Nov 2;10(23):e37890. doi: 10.1016/j.heliyon.2024.e37890. eCollection 2024 Dec 15.
and purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients.
We recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs.
All participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937.
The alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs.
视网膜脱离（RD）患者大脑功能可塑性及重塑的研究已受到越来越多的关注并得到验证。然而，动态功能网络拓扑结构的精确改变仍未完全明晰。本研究旨在探究RD患者动态脑功能网络的拓扑结构。
我们招募了32例RD患者和33名健康对照者（HCs）参与静息态功能磁共振成像。采用滑动时间窗分析和K均值聚类方法，我们试图识别两组中的动态功能连接（dFC）变异性模式。对全脑功能网络拓扑结构的研究采用了图论方法。此外，我们进行了机器学习分析，选择改变的拓扑属性作为分类特征以区分RD患者和HCs。
所有参与者均表现出四种不同的动态功能连接状态。与健康对照组（HC）相比，RD患者在这四种状态之间的转换次数显著减少。此外，RD患者的动态拓扑属性在全局和节点特异性特征方面均表现出显著变化，且这些变化与临床参数相关。用于分类的支持向量机（SVM）模型的准确率为0.938，曲线下面积（AUC）为0.988，灵敏度和特异度均为0.937。
RD患者大脑拓扑属性的改变可能表明全脑网络的整合功能和信息交换效率降低。此外，拓扑属性在区分RD和HCs方面具有很大潜力。
