Iordan Alexandru D, Cooke Katherine A, Moored Kyle D, Katz Benjamin, Buschkuehl Martin, Jaeggi Susanne M, Jonides John, Peltier Scott J, Polk Thad A, Reuter-Lorenz Patricia A
Department of Psychology, University of Michigan, Ann Arbor, MI, United States.
Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Front Aging Neurosci. 2018 Jan 4;9:419. doi: 10.3389/fnagi.2017.00419. eCollection 2017.
Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on "resting-state" networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.
越来越多的证据表明，健康衰老会影响大脑大规模功能网络的结构。这包括降低网络模块化程度和局部效率。然而，这些影响随时间的稳定性及其在学习中的潜在作用仍知之甚少。本研究的目的是进一步阐明先前报道的年龄对“静息态”网络的影响，测试其随时间的可靠性，并评估其与训练期间后续学习的关系。对23名年轻人（YA）和20名老年人（OA）的静息态功能磁共振成像数据进行了采集，分两个阶段进行，间隔两周。图论分析确定了YA和OA之间网络结构的一致性以及模块组成的差异，表明随着年龄增长，功能网络配置存在拓扑变化且稳定性降低。在全脑范围内，与YA相比，OA的模块化程度和局部效率较低，这与年龄相关的功能去分化观点一致，并且这些影响随时间可重复。在个体网络层面，OA在扣带回-岛盖网络中始终表现出更大的参与度、更低的局部效率和网络内连通性，在默认模式网络中网络内连通性较低，而躯体感觉运动网络的参与度更高，表明在专门的脑模块层面存在年龄相关的差异效应。最后，全脑网络属性与学习率存在关联，尽管有限，这是在静息态阶段后进行10天的计算机化工作记忆训练评估得出的，表明基线网络配置可能会影响后续的学习结果。识别与学习诱导可塑性相关的神经机制对于进一步阐明这些变化是否以及如何预测训练收益的大小和维持，以及年轻人和老年人认知转移的程度和限度非常重要。
