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Predicting Age From Large-Scale Brain Networks: Evidence From the Cam-CAN Dataset Across the Lifespan

机译:预测大规模脑网络的年龄:来自CAM-CAN DataSet的证据跨越寿命

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摘要

Changes in cognition observed in aging (e.g. a shift from prioritization of fluid cognition in young adulthood toward an emphasis on crystalized knowledge and semantic cognition in older adulthood) are believed to reflect alterations in neural connectivity in aging. Recent work specifically highlights how increased connectivity between executive control (EC) regions and default mode network (DMN) may underlie characteristic shifts in cognitive abilities between younger and older adults. However, the contribution of the salience network, which plays a crucial role in mediating the dynamic interplay between EC and DMN, is relatively overlooked. To extend previous work, we used a large cohort (N = 547) of participants from the Cam-CAN database (18-88 years old) to examine whether resting-state functional connectivity between EC and DMN can reliably predict participant age. We further examined how addition of the salience network impacts the hypothesized increased connectivity between EC and DMN as a result of aging. A series of multiple regression analyses using functional connectivity and age as variables revealed that connectivity between EC and DMN regions (specifically between dorsolateral and ventromedial prefrontal cortex and parietal regions, including the precuneus) accounted for a significant portion of age variability and that the inclusion of the salience network improved the models’ explanatory power. Follow-up analyses by age cohort further highlighted that these relationships dynamically change across the lifespan. We will discuss these findings in the context of default-executive coupling hypothesis for aging and propose avenues for future research in refinement of this model.
机译:观察到老化的认知变化(例如,从年轻的成年期间从事流体认知的优先顺序转移到强调老年人的年龄较旧的结晶知识和语义认知)反映了衰老中神经连接的改变。最近的工作明确突出了行政控制(EC)地区和默认模式网络(DMN)之间的连接程度增加了如何在年轻人和老年人之间的认知能力方面的特征变化。然而,在调解EC和DMN之间的动态相互作用中发挥着至关重要的作用的宣传网络的贡献相对忽略了。为了扩展以前的工作,我们使用了来自CAM-CAN数据库(18-88岁)的大型队列(N = 547),以检查EC和DMN之间的休息状态功能连接是否可以可靠地预测参与者年龄。我们进一步研究了显着网络的增加如何影响EC和DMN之间的假设增加的连通性。使用功能连通性和年龄作为变量的一系列多元回归分析显示EC和DMN区(特别是在背侧和口腔前列前额叶皮层和包括前跖)之间的连接之间的连接占年龄变异性的大部分变异性,并且包含在内显着网络改善了模型的解释性力量。按年龄群组进行后续分析进一步强调了这些关系在寿命中动态地改变。我们将在违约 - 行政耦合假设的背景下讨论这些调查结果,以便衰老,并提出途径,以便将来的改进本模型的研究。

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