Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
Department of Nursing, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
Geroscience. 2024 Feb;46(1):1303-1318. doi: 10.1007/s11357-023-00900-8. Epub 2023 Aug 5.
The effects of age and gender on large-scale resting-state networks (RSNs) reflecting within- and between-network connectivity in the healthy brain remain unclear. This study investigated how age and gender influence the brain network roles and topological properties underlying the ageing process. Ten RSNs were constructed based on 998 participants from the REST-meta-MDD cohort. Multivariate linear regression analysis was used to examine the independent and interactive influences of age and gender on large-scale RSNs and their topological properties. A support vector regression model integrating whole-brain network features was used to predict brain age across the lifespan and cognitive decline in an Alzheimer's disease spectrum (ADS) sample. Differential effects of age and gender on brain network roles were demonstrated across the lifespan. Specifically, cingulo-opercular, auditory, and visual (VIS) networks showed more incohesive features reflected by decreased intra-network connectivity with ageing. Further, females displayed distinctive brain network trajectory patterns in middle-early age, showing enhanced network connectivity within the fronto-parietal network (FPN) and salience network (SAN) and weakened network connectivity between the FPN-somatomotor, FPN-VIS, and SAN-VIS networks. Age - but not gender - induced widespread decrease in topological properties of brain networks. Importantly, these differential network features predicted brain age and cognitive impairment in the ADS sample. By showing that age and gender exert specific dispersion of dynamic network roles and trajectories across the lifespan, this study has expanded our understanding of age- and gender-related brain changes with ageing. Moreover, the findings may be useful for detecting early-stage dementia.
年龄和性别对反映健康大脑内联网和外联网连接的大尺度静息态网络 (RSN) 的影响尚不清楚。本研究探讨了年龄和性别如何影响大脑网络角色和拓扑特性,这些特性是衰老过程的基础。基于来自 REST-meta-MDD 队列的 998 名参与者,构建了 10 个 RSN。多元线性回归分析用于检查年龄和性别对大尺度 RSN 及其拓扑特性的独立和交互影响。支持向量回归模型整合了全脑网络特征,用于预测整个生命周期的大脑年龄和阿尔茨海默病谱 (ADS) 样本中的认知能力下降。研究表明,年龄和性别对大脑网络角色的影响在整个生命周期内存在差异。具体来说,扣带-顶叶、听觉和视觉 (VIS) 网络表现出与衰老相关的不连贯特征,表现为内联网连接性降低。此外,女性在中早期表现出独特的大脑网络轨迹模式,表现为额顶网络 (FPN) 和突显网络 (SAN) 内的网络连接增强,以及 FPN-运动、FPN-VIS 和 SAN-VIS 网络之间的网络连接减弱。年龄——而不是性别——导致大脑网络拓扑性质的广泛下降。重要的是,这些差异网络特征预测了 ADS 样本中的大脑年龄和认知障碍。通过显示年龄和性别在整个生命周期内对动态网络角色和轨迹施加特定的分散影响,本研究扩展了我们对与衰老相关的年龄和性别相关的大脑变化的理解。此外,这些发现可能有助于检测早期痴呆症。