Department of Electrical and Computer Engineering, Texas Tech University, 2500 Broadway, Lubbock, TX, 79409, USA.
Arthur and Hinda Marcus Institute for Aging Research and Center for Memory Health, Hebrew SeniorLife, and Department of Neurology, Harvard Medical School, Boston, MA, USA.
Brain Topogr. 2021 Jan;34(1):19-28. doi: 10.1007/s10548-020-00802-4. Epub 2020 Oct 23.
Re-activations of task-dependent patterns of neural activity take place during post-encoding periods of wakeful rest and sleep. However, it is still unclear how the temporal dynamics of brain states change during these periods, which are shaped by prior conscious experiences. Here, we examined the very brief periods of wakeful rest immediately after encoding and recognition of auditory and visual stimuli, by applying the EEG microstate analysis, in which the global variance of the EEG is explained by only a few prototypical topographies. We identified the dominant brain states of sub-second duration during the tasks-dependent periods of rest, finding that the temporal dynamics-represented here by two temporal parameters: the frequency of occurrence and the fraction of time coverage-of three task-related microstate classes changed compared to wakeful rest. This study provides evidence of experience-dependent temporal changes in post-encoding periods of resting brain activity, which can be captured using the EEG microstates approach.
在清醒休息和睡眠的后编码期间,与任务相关的神经活动模式会重新激活。然而,目前尚不清楚在这些由先前有意识的经验塑造的期间内,大脑状态的时间动态如何变化。在这里,我们通过应用 EEG 微状态分析,检查了在编码和识别听觉和视觉刺激后立即进行的非常短暂的清醒休息期,在 EEG 微状态分析中,EEG 的全局方差仅由少数几个典型的拓扑结构来解释。我们确定了与任务相关的休息期间亚秒级持续时间的主导脑状态,发现与清醒休息相比,三个与任务相关的微状态类别的时间动态(这里由两个时间参数表示:出现频率和时间覆盖分数)发生了变化。这项研究提供了在休息时大脑活动的后编码期间,依赖于经验的时间变化的证据,这可以使用 EEG 微状态方法来捕捉。