Analysis of brain functional connectivity (FC) could provide insight in how and why cognitive functions decline even in healthy aging (HA). Despite FC being established as fluctuating over time even in the resting state (RS), dynamic functional connectivity (DFC) studies involving healthy elderly individuals and assessing how these patterns relate to cognitive performance are yet scarce. In our recent study we showed that fractal temporal scaling of functional connections in RS is not only reduced in HA, but also predicts increased response latency and reduced task solving accuracy. However, in that work we did not address changes in the dynamics of fractal connectivity (FrC) strength itself and its plausible relationship with mental capabilities. Therefore, here we analyzed RS electroencephalography recordings of the same subject cohort as previously, consisting of 24 young and 19 healthy elderly individuals, who also completed 7 different cognitive tasks after data collection. Dynamic fractal connectivity (dFrC) analysis was carried out via sliding-window detrended cross-correlation analysis (DCCA). A machine learning method based on recursive feature elimination was employed to select the subset of connections most discriminative between the two age groups, identifying 56 connections that allowed for classifying participants with an accuracy surpassing 92%. Mean of DCCA was found generally increased, while temporal variability of FrC decreased in the elderly when compared to the young group. Finally, dFrC indices expressed an elaborate pattern of associations—assessed via Spearman correlation—with cognitive performance scores in both groups, linking fractal connectivity strength and variance to increased response latency and reduced accuracy in the elderly population. Our results provide further support for the relevance of FrC dynamics in understanding age-related cognitive decline and might help to identify potential targets for future intervention strategies.

对大脑功能连接（FC）的分析可以深入了解认知功能即使在健康老龄化（HA）中也是如何以及为何下降的。尽管 FC 被认为即使在静息状态 (RS) 下也会随着时间的推移而波动，但涉及健康老年人并评估这些模式与认知表现之间关系的动态功能连接 (DFC) 研究仍然很少。在我们最近的研究中，我们表明，RS 中功能连接的分形时间尺度不仅在 HA 中减少，而且还预测响应延迟增加和任务解决精度降低。然而，在这项工作中，我们没有解决分形连通性（FrC）强度本身的动态变化及其与心理能力的合理关系。因此，我们在这里分析了与之前相同的受试者队列的 RS 脑电图记录，该队列由 24 名年轻人和 19 名健康老年人组成，他们在数据收集后也完成了 7 种不同的认知任务。通过滑动窗口去趋势互相关分析（DCCA）进行动态分形连通性（dFrC）分析。采用基于递归特征消除的机器学习方法来选择两个年龄组之间最具区分力的连接子集，识别出 56 个连接，可以对参与者进行分类，准确率超过 92%。与年轻组相比，老年人的 DCCA 平均值普遍增加，而 FrC 的时间变异性降低。最后，dFrC 指数表达了一种复杂的关联模式（通过斯皮尔曼相关性进行评估）与两组的认知表现得分，将分形连接强度和方差与老年人群的反应延迟增加和准确性降低联系起来。我们的结果进一步支持了 FrC 动态在理解与年龄相关的认知衰退方面的相关性，并可能有助于确定未来干预策略的潜在目标。