Du Yuhui, Guo Yating, Calhoun Vince D
School of Computer and Information Technology, Shanxi University, Taiyuan, China.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States.
Front Aging Neurosci. 2023 May 18;15:1159054. doi: 10.3389/fnagi.2023.1159054. eCollection 2023.
Numerous studies have shown that aging has important effects on specific functional networks of the brain and leads to brain functional connectivity decline. However, no studies have addressed the effect of aging at the whole-brain level by studying both brain functional networks (i.e., within-network connectivity) and their interaction (i.e., between-network connectivity) as well as their joint changes.
In this work, based on a large sample size of neuroimaging data including 6300 healthy adults aged between 49 and 73 years from the UK Biobank project, we first use our previously proposed priori-driven independent component analysis (ICA) method, called NeuroMark, to extract the whole-brain functional networks (FNs) and the functional network connectivity (FNC) matrix. Next, we perform a two-level statistical analysis method to identify robust aging-related changes in FNs and FNCs, respectively. Finally, we propose a combined approach to explore the synergistic and paradoxical changes between FNs and FNCs.
Results showed that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network, and cerebellum. Aging also greatly affects the connectivity within FNs, and the increased within-network connectivity along with aging are mainly within the sensorimotor network, while the decreased within-network connectivity significantly involves the default mode network. More importantly, many significant joint changes between FNs and FNCs involve default mode and sub-cortical networks. Furthermore, most synergistic changes are present between the FNCs with reduced amplitude and their linked FNs, and most paradoxical changes are present in the FNCs with enhanced amplitude and their linked FNs.
In summary, our study emphasizes the diversity of brain aging and provides new evidence via novel exploratory perspectives for non-pathological aging of the whole brain.
众多研究表明,衰老对大脑特定功能网络有重要影响,并导致脑功能连接性下降。然而,尚无研究通过同时研究脑功能网络(即网络内连接性)及其相互作用(即网络间连接性)以及它们的联合变化,来探讨衰老在全脑水平上的影响。
在这项工作中,基于来自英国生物银行项目的6300名年龄在49至73岁之间的健康成年人的大量神经影像数据样本,我们首先使用我们先前提出的先验驱动独立成分分析(ICA)方法,即NeuroMark,来提取全脑功能网络(FNs)和功能网络连接性(FNC)矩阵。接下来,我们执行一种两级统计分析方法,分别识别FNs和FNCs中与衰老相关的稳健变化。最后,我们提出一种综合方法来探索FNs和FNCs之间的协同和矛盾变化。
结果表明,增强的FNCs主要发生在不同功能域之间,涉及默认模式和认知控制网络,而减少的FNCs不仅来自不同域之间,也来自同一域内,主要与视觉网络、认知控制网络和小脑有关。衰老也极大地影响FNs内的连接性,随着衰老增加的网络内连接性主要在感觉运动网络内,而减少的网络内连接性显著涉及默认模式网络。更重要的是,FNs和FNCs之间许多显著的联合变化涉及默认模式和皮层下网络。此外,大多数协同变化出现在幅度降低的FNCs与其相连的FNs之间,而大多数矛盾变化出现在幅度增强的FNCs与其相连的FNs之间。
总之,我们的研究强调了脑衰老的多样性,并通过新颖的探索性视角为全脑的非病理性衰老提供了新证据。