首页> 美国卫生研究院文献>Frontiers in Computational Neuroscience >Neurocomputational Model of EEG Complexity during Mind Wandering
【2h】

Neurocomputational Model of EEG Complexity during Mind Wandering

机译:脑电漂移过程中脑电复杂性的神经计算模型

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Mind wandering (MW) can be understood as a transient state in which attention drifts from an external task to internal self-generated thoughts. MW has been associated with the activation of the Default Mode Network (DMN). In addition, it has been shown that the activity of the DMN is anti-correlated with activation in brain networks related to the processing of external events (e.g., Salience network, SN). In this study, we present a mean field model based on weakly coupled Kuramoto oscillators. We simulated the oscillatory activity of the entire brain and explored the role of the interaction between the nodes from the DMN and SN in MW states. External stimulation was added to the network model in two opposite conditions. Stimuli could be presented when oscillators in the SN showed more internal coherence (synchrony) than in the DMN, or, on the contrary, when the coherence in the SN was lower than in the DMN. The resulting phases of the oscillators were analyzed and used to simulate EEG signals. Our results showed that the structural complexity from both simulated and real data was higher when the model was stimulated during periods in which DMN was more coherent than the SN. Overall, our results provided a plausible mechanistic explanation to MW as a state in which high coherence in the DMN partially suppresses the capacity of the system to process external stimuli.
机译:精神流浪(MW)可以理解为一种瞬态状态,其中注意力从外部任务转移到内部自我产生的思想上。 MW已与默认模式网络(DMN)的激活相关联。另外,已经表明,DMN的活性与与外部事件的处理有关的大脑网络(例如,Salience网络,SN)的激活是反相关的。在这项研究中,我们提出了基于弱耦合仓本振荡器的平均场模型。我们模拟了整个大脑的振荡活动,并探讨了DMN和SN在MW状态下节点之间相互作用的作用。在两个相反的条件下,将外部刺激添加到网络模型中。当SN中的振荡器显示出比DMN中更高的内部相干性(同步性)时,或者相反,当SN中的相干性低于DMN中的相干性时,可能会出现刺激。分析了振荡器的相位,并将其用于模拟EEG信号。我们的结果表明,在DMN比SN更连贯的时期内刺激模型时,来自模拟数据和实际数据的结构复杂性都更高。总体而言,我们的结果为MW提供了一种可能的机械解释,因为DMN中的高相干性部分抑制了系统处理外部刺激的能力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号