From the School of Life Science (T.L., L.W., D.S., K.W., J.W., D.C., T.Y.) and Intelligent Robotics Institute, School of Mechatronical Engineering (J.Z.), Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China.
Radiology. 2022 Sep;304(3):624-632. doi: 10.1148/radiol.211762. Epub 2022 May 3.
Background The aging brain is typically associated with aberrant interactions of large-scale intrinsic networks. However, the dynamic variation of these networks' coactivation or deactivation across the adult lifespan remains unclear. Purpose To promote the interpretation of dynamic brain network variations underlying the complex aging process by quantifying activation levels and obtaining a clear definition of coactivation patterns (CAPs) with resting-state functional MRI (rsfMRI). Materials and Methods In a retrospective study (October 2010 to September 2013), rsfMRI data from healthy participants in the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository were used to generate CAPs by applying single-volume temporal clustering analysis. Spatial clustering analysis was then performed to capture dynamic coactivation and deactivation within or between primary sensory networks and high-order cognitive networks (including the default mode network [DMN], attentional network [AN], and frontoparietal network [FPN]). Linear relationships between dynamic metrics and age were revealed with Spearman partial correlations. Results A total of 614 participants (mean age, 54 years ± 18 [SD]; 311 women) ranging in age from 18 to 88 years were evaluated. There was a negative correlation of the CAPs (Spearman correlations: = -0.98, < .001) with loss of coactivation (partial correlations: = -0.17, < .001) and deactivation (partial correlations: = 0.216, < .001) with aging. The CAPs, characterized by negative correlation patterns between the DMN and AN, occurred (partial correlations: = 0.14, = .003) and dwelled (partial correlations: = 0.10, = .04) more with aging. Moreover, the AN and DMN CAP transitioned more to the AN and FPN CAP with aging (partial correlations: = 0.17, < .001). Conclusion The dynamics of the healthy aging brain are characterized mainly by more flexibility of the high-order cognitive networks while maintaining primary sensory functions (networks). © RSNA, 2022 See also the editorial by Holodny in this issue.
背景 大脑老化通常与大规模固有网络的异常相互作用有关。然而，这些网络在成年期的共激活或去激活的动态变化仍不清楚。 目的 通过量化激活水平并使用静息态功能磁共振成像(rsfMRI)获得共激活模式(CAPs)的清晰定义，来促进对复杂衰老过程中大脑网络变化的理解。 材料与方法 在一项回顾性研究中(2010 年 10 月至 2013 年 9 月)，使用剑桥老龄化与神经科学中心(Cam-CAN)数据存储库中的健康参与者的 rsfMRI 数据，通过应用单容积时间聚类分析生成 CAPs。然后进行空间聚类分析，以捕获主要感觉网络和高级认知网络(包括默认模式网络[DMN]、注意网络[AN]和额顶网络[FPN])内或之间的动态共激活和去激活。用 Spearman 偏相关分析显示动态指标与年龄之间的线性关系。 结果 共评估了 614 名年龄在 18 至 88 岁之间的参与者(平均年龄 54 岁±18[标准差];311 名女性)。CAPs 与共激活丧失呈负相关(Spearman 相关系数： = -0.98，<.001)，与去激活呈负相关(偏相关系数： = -0.17，<.001)。随着年龄的增长，DMN 和 AN 之间具有负相关模式的 CAPs 出现(偏相关系数： = 0.14， =.003)并持续存在(偏相关系数： = 0.10， =.04)。此外，随着年龄的增长，AN 和 DMN CAP 更倾向于向 AN 和 FPN CAP 转变(偏相关系数： = 0.17，<.001)。 结论 健康大脑衰老的特点主要是高级认知网络更具灵活性，同时保持初级感觉功能(网络)。 © RSNA，2022 也可参见本期 Holodny 的社论。
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