This study aims at assessing the impact of age on the short-term temporal dynamics of the topological properties of the undirected and weighted whole-brain functional connectivity (FC) networks. We studied the association between the participant's age and the number of significant change points detected through the Network Change Point Detection algorithm. Secondary, we defined state as the resting-state functional magnetic resonance imaging (rs-fMRI) subsequence between two significant change points, obtaining the FC network in each state and participant and characterized their network topological properties. The data comprise the rs-fMRI sequences of 114 healthy individuals combined from 3 different studies conducted at the Department of Medicine, School of Medicine and Health Sciences, University of Barcelona. Participants were healthy people in the absence of any pathology that could interfere with the scanning procedures, as well as any chronic illness that implied a short-lived situation. Topological properties of everyone's FC networks were characterized by their network strength, transitivity, characteristic path length, and small-worldness, analyzing the effect of age in those observed distributions. To that effect, we constructed a mixed linear model for each network topological property with age, state, and state duration as the linear predictors. Several statistically significant relationships have been estimated between the indicators of the FC networks that show a certain regular pattern of change in the networks during the time of registration at the resting fMRI paradigm. These dynamic changes seem to be related to the age of each group studied. Healthy aging could be characterized by FC dynamics patterns.

中文翻译：

---

健康老化中的静态功能连接动力学：通过网络变化点检测的一种方法。

这项研究旨在评估年龄对无定向和加权全脑功能连接（FC）网络的拓扑特性的短期时间动态的影响。我们研究了参与者的年龄与通过网络更改点检测算法检测到的重要更改点数量之间的关联。其次，我们将状态定义为两个重要变化点之间的静止状态功能磁共振成像（rs-fMRI）子序列，获得每种状态和参与者的FC网络并表征其网络拓扑特性。数据包含114例健康个体的rs-fMRI序列，这些序列是在巴塞罗那大学医学与健康科学学院医学系进行的3项不同研究相结合的。参与者为健康人，没有任何可能干扰扫描程序的病理以及任何暗示短暂生命的慢性疾病。每个人的FC网络的拓扑特性都以其网络强度，可传递性，特征路径长度和小世界性为特征，并分析了年龄在这些观测分布中的影响。为此，我们为每个网络拓扑属性构建了一个混合线性模型，其中年龄，状态和状态持续时间为线性预测变量。已经估计了FC网络的指标之间的几个统计上显着的关系，这些关系在静态fMRI范式注册期间显示了网络中某些规则的变化模式。这些动态变化似乎与每个研究组的年龄有关。健康衰老可以通过FC动态模式来表征。