Functional brain networks (FBNs) coordinate brain functions and are studied in fMRI using blood-oxygen-level-dependent (BOLD) signal correlations. Previous research links FBN changes to aging and cognitive decline, but various physiological factors influnce BOLD signals. Few studies have investigated the intrinsic components of the BOLD signal in different timescales using signal decomposition. This study aimed to explore differences between intrinsic FBNs and traditional BOLD-FBN, examining their associations with age and cognitive performance in a healthy cohort without dementia. A total of 396 healthy participants without dementia (men = 157; women = 239; age range = 20–85 years) were enrolled in this study. The BOLD signal was decomposed into several intrinsic signals with different timescales using ensemble empirical mode decomposition, and FBNs were constructed based on both the BOLD and intrinsic signals. Subsequently, network features—global efficiency and local efficiency values—were estimated to determine their relationship with age and cognitive performance. The findings revealed that the global efficiency of traditional BOLD-FBN correlated significantly with age, with specific intrinsic FBNs contributing to these correlations. Moreover, local efficiency analysis demonstrated that intrinsic FBNs were more meaningful than traditional BOLD-FBN in identifying brain regions related to age and cognitive performance. These results underscore the importance of exploring timescales of BOLD signals when constructing FBN and highlight the relevance of specific intrinsic FBNs to aging and cognitive performance. Consequently, this decomposition-based FBN-building approach may offer valuable insights for future fMRI studies.

中文翻译：

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在没有痴呆的健康人群中探索特定时间尺度的功能性大脑网络及其与衰老和认知表现的关联

功能性大脑网络 (FBN) 协调大脑功能，并使用血氧水平依赖性 (BOLD) 信号相关性在功能磁共振成像中进行研究。先前的研究将 FBN 的变化与衰老和认知能力下降联系起来，但多种生理因素会影响 BOLD 信号。很少有研究利用信号分解研究不同时间尺度下 BOLD 信号的内在成分。本研究旨在探讨内在 FBN 和传统 BOLD-FBN 之间的差异，在没有痴呆的健康人群中检查它们与年龄和认知表现的关联。共有 396 名无痴呆症的健康参与者（男性 = 157 名；女性 = 239 名；年龄范围 = 20-85 岁）参加了这项研究。利用集合经验模态分解将BOLD信号分解为多个不同时间尺度的本征信号，并基于BOLD和本征信号构建FBN。随后，估计网络特征（全局效率和局部效率值）以确定它们与年龄和认知表现的关系。研究结果表明，传统 BOLD-FBN 的整体效率与年龄显着相关，特定的内在 FBN 促成了这些相关性。此外，局部效率分析表明，内在 FBN 在识别与年龄和认知表现相关的大脑区域方面比传统的 BOLD-FBN 更有意义。这些结果强调了在构建 FBN 时探索 BOLD 信号时间尺度的重要性，并强调了特定内在 FBN 与衰老和认知表现的相关性。因此，这种基于分解的 FBN 构建方法可能为未来的功能磁共振成像研究提供有价值的见解。