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The fronto‐insular cortex causally mediates the default‐mode and central‐executive networks to contribute to individual cognitive performance in healthy elderly

机译:额叶皮层因果关系介导默认模式和中央执行网络以促进健康老年人的个体认知表现

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摘要

The triple network model that consists of the default‐mode network (DMN), central‐executive network (CEN), and salience network (SN) has been suggested as a powerful paradigm for investigation of network mechanisms underlying various cognitive functions and brain disorders. A crucial hypothesis in this model is that the fronto‐insular cortex (FIC) in the SN plays centrally in mediating interactions between the networks. Using a machine learning approach based on independent component analysis and Bayesian network (BN), this study characterizes the directed connectivity architecture of the triple network and examines the role of FIC in connectivity of the model. Data‐driven exploration shows that the FIC initiates influential connections to all other regions to globally control the functional dynamics of the triple network. Moreover, stronger BN connectivity between the FIC and regions of the DMN and the CEN, as well as the increased outflow connections from the FIC are found to predict individual performance in memory and executive tasks. In addition, the posterior cingulate cortex in the DMN was also confirmed as an inflow hub that integrates information converging from other areas. Collectively, the results highlight the central role of FIC in mediating the activity of large‐scale networks, which is crucial for individual cognitive function.
机译:已经提出了由默认模式网络(DMN),中央执行网络(CEN)和显着网络(SN)组成的三重网络模型,作为研究各种认知功能和脑部疾病的网络机制的强大范例。该模型中的一个关键假设是,SN中的额叶皮层(FIC)在介导网络之间的相互作用中起着核心作用。使用基于独立成分分析和贝叶斯网络(BN)的机器学习方法,本研究描述了三重网络的定向连接架构,并研究了FIC在模型连接中的作用。数据驱动的探索表明,FIC启动了与所有其他区域的有影响力的连接,以全局控制三重网络的功能动态。此外,发现FIC与DMN和CEN区域之间更强大的BN连接,以及来自FIC的流出连接增加,可以预测个人在记忆和执行任务中的表现。此外,DMN中的后扣带回皮层也被确认为是一个整合了其他区域信息融合的流入枢纽。总的来说,这些结果突出了FIC在调解大规模网络活动中的核心作用,这对于个人的认知功能至关重要。

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