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Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks

机译:认知努力驱动人脑功能网络的工作区配置

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摘要

Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N = 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the β-band (16–32 Hz) and γ-band (32–63 Hz) networks, compared with both lower (α-band; 8–16 Hz) and higher (high γ-band; 63–125 Hz) frequency intervals. Workspace configuration of β-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting <2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.
机译:理论上,努力的认知表现将取决于整体神经元工作区的形成。我们使用网络拓扑的图形理论测度和同步的物理距离,测试了工作空间理论的特定预测,这些数据是从健康成人志愿者(N = 13)在执行几个难度级别的工作记忆任务期间记录的脑磁图数据中得出的。我们发现,更大的认知努力导致出现了全局效率更高,群集更少,模块化程度更低的网络配置,并且大脑区域之间的距离更远。与任务相关的工作空间配置的这种模式在β波段(16–32 Hz)和γ波段(32–63 Hz)网络中更为明显,而较低的(α波段; 8–16 Hz)和较高的网络均如此。 (高γ波段; 63–125 Hz)频率间隔。与执行速度较慢的参与者相比,执行速度更快的参与者(正确的响应等待时间小于样本中位数)的β波段网络的工作空间配置也更大。工作空间形成和放松的过程与认知力的时变要求有关,可以在持续2 s的任务试验过程中看到。这些实验结果为复杂的网络指标方面的工作空间理论提供了支持,并直接证明了认知努力如何打破模块化,以使人脑功能网络暂时采用更高效但更经济的配置。

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