系统性红斑狼疮异常脑功能网络:图论、基于网络的统计和机器学习研究。

IF 4.1 Q1 CLINICAL NEUROLOGY
Brain communications Pub Date : 2025-04-02 eCollection Date: 2025-01-01 DOI:10.1093/braincomms/fcaf130
Yifan Yang, Ru Bai, Shuang Liu, Shu Li, Ruotong Zhao, Xiangyu Wang, Yuqi Cheng, Jian Xu
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引用次数: 0

摘要

系统性红斑狼疮患者的脑功能网络损伤尚不完全清楚。本研究探讨了系统性红斑狼疮的脑功能网络拓扑改变,并将机器学习应用于系统性红斑狼疮和健康对照的分类。采用127例系统性红斑狼疮患者和102例健康对照者的静息状态功能MRI数据。预处理过程包括使用自动解剖标记图谱来计算116个大脑区域的时间序列数据。然后,通过评估这些大脑区域时间序列之间的皮尔逊相关性,创建了一个功能连接网络。使用GRETNA工具箱计算组间拓扑属性的差异。使用依赖于基于网络的统计分析的非参数排列测试来评估群体之间区域网络的变化。以功能连通性网络指标为特征,以基于网络的统计分析为特征选择方法,采用基于网络的统计分析预测软件,利用支持向量机对系统性红斑狼疮与对照组进行分类。对系统性红斑狼疮分类贡献最大的子网也被确定。对于全局指标,系统性红斑狼疮组的归一化聚类系数值明显较低(P = 0)。(0317)和小世界指数(P = 0.0364)。错误发现率校正后,不保留两组间between Centrality、Degree Centrality、Node Efficiency、Node Local Efficiency等局部指标的差异。临床数据与网络指标无相关性。系统性红斑狼疮组与12节点,11边缘子网络的连接明显减少(P = 0.024)。总之,系统性红斑狼疮患者表现出次优的整体脑功能连接网络拓扑结构和连接异常减少的子网络的存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Abnormal brain functional networks in systemic lupus erythematosus: a graph theory, network-based statistic and machine learning study.

Systemic lupus erythematosus patients' brain functional network impairments are incompletely clarified. This study investigates the brain functional network topological alterations in systemic lupus erythematosus and the application of machine learning to the classification of systemic lupus erythematosus and healthy controls. Resting-state functional MRI data from 127 systemic lupus erythematosus patients and 102 healthy controls were used. The pre-processing process involved using automated anatomical labelling atlas to compute time series data for 116 brain regions. A functional connectivity network was then created by assessing the Pearson correlation between the time series of these brain regions. The GRETNA toolbox was used to compute the difference in topological attributes between groups. Variations in regional networks among groups were evaluated using non-parametric permutation tests that rely on network-based statistical analysis. With the functional connectivity network metrics as features and network-based statistical analysis as the feature selection method, network-based statistical analysis Predict software was used to classify systemic lupus erythematosus from controls by support vector machine. The subnets that contributed the most to systemic lupus erythematosus classification were also identified. For global indicators, the systemic lupus erythematosus group exhibited significantly lower values for the normalized clustering coefficient (P = 0. 0317) and small-world index (P = 0.0364) compared to the healthy controls group. After false discovery rate correction, the differences in Betweeness Centrality, Degree Centrality, Node Efficiency, Node Local Efficiency and other local indexes between the two groups were not retained. No correlation was found between clinical data and network indicators. Systemic lupus erythematosus group had a significantly reduced connection with a 12-node, 11-edge subnetwork (P = 0.024). In conclusion, systemic lupus erythematosus patients exhibit suboptimal global brain functional connectivity network topology and the presence of a subnetwork with abnormally reduced connectivity.

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CiteScore
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