Given the increasing number of older adults in society, there is a growing need for studies on changes in the aging brain. The aim of this research is to investigate the effective connectivity of different age groups using resting-state functional magnetic resonance imaging (fMRI) and graph theory. By examining connectivity in different age groups, a better understanding of age-related changes can be achieved. Lifespan pilot data from the Human Connectome Project (HCP) were used to examine dynamic effective connectivity (dEC) changes across different age groups. The Granger causality method with time windowing was employed to calculate dEC. After extracting graph measures, statistical analyses were performed to compare the age groups. Support vector machine and decision tree classifiers were used to classify the different age groups based on the extracted graph measures. Based on the obtained results, it can be concluded that there are significant differences in the effective connectivity among the three age groups. Statistical analyses revealed disassortativity. The global efficiency exhibited a decreasing trend, and the transitivity measure showed an increasing trend with the advancing age. The decision tree classifier showed an accuracy of \(86.67\%\) with Kruskal–Wallis selected features. This study demonstrates that changes in effective connectivity across different age brackets can serve as a tool for better understanding brain function during the aging process.

鉴于社会中老年人数量的增加，对衰老大脑变化的研究需求越来越大。本研究的目的是使用静息态功能磁共振成像 （fMRI） 和图论来研究不同年龄组的有效连接性。通过检查不同年龄组的连通性，可以更好地了解与年龄相关的变化。来自人类连接组项目 （HCP） 的寿命试点数据用于检查不同年龄组的动态有效连接 （dEC） 变化。采用具有时间窗口的 Granger 因果关系法计算 dEC。提取图形测量后，进行统计分析以比较年龄组。支持向量机和决策树分类器用于根据提取的图形度量对不同的年龄组进行分类。根据获得的结果，可以得出结论，三个年龄组之间的有效连接存在显着差异。统计分析显示分类不分类。整体效率呈下降趋势，传递性指标随着年龄的增长呈上升趋势。决策树分类器显示 Kruskal-Wallis 选择特征的准确率为 \（86.67\%\）。这项研究表明，不同年龄段之间有效连接的变化可以作为更好地了解衰老过程中大脑功能的工具。