The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan 4030030, China.
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China.
Neuroimage. 2021 Aug 15;237:118148. doi: 10.1016/j.neuroimage.2021.118148. Epub 2021 May 10.
Resting-state studies have typically assumed constant functional connectivity (FC) between brain regions, and these parameters of interest provide meaningful descriptions of the functional organization of the brain. A number of studies have recently provided evidence pointing to dynamic FC fluctuations in the resting brain, especially in higher-order regions such as the default mode network (DMN). The neural activities underlying dynamic FC remain poorly understood. Here, we recorded electrophysiological signals from DMN regions in freely behaving rats. The dynamic FCs between signals within the DMN were estimated by the phase locking value (PLV) method with sliding time windows across vigilance states [quiet wakefulness (QW) and slow-wave and rapid eye movement sleep (SWS and REMS)]. Factor analysis was then performed to reveal the hidden patterns within the DMN. We identified distinct spatial FC patterns according to the similarities between their temporal dynamics. Interestingly, some of these patterns were vigilance state-dependent, while others were independent across states. The temporal contributions of these patterns fluctuated over time, and their interactive relationships were different across vigilance states. These spatial patterns with dynamic temporal contributions and combinations may offer a flexible framework for efficiently integrating information to support cognition and behavior. These findings provide novel insights into the dynamic functional organization of the rat DMN.
静息态研究通常假设大脑区域之间的功能连接（FC）是恒定的，这些感兴趣的参数为大脑的功能组织提供了有意义的描述。最近有许多研究提供了证据，表明静息状态下大脑的 FC 存在动态波动，特别是在默认模式网络（DMN）等高级区域。动态 FC 背后的神经活动仍知之甚少。在这里，我们在自由活动的大鼠的 DMN 区域记录了电生理信号。通过相位锁定值（PLV）方法，使用在警觉状态[安静觉醒（QW）和慢波和快速眼动睡眠（SWS 和 REMS）]之间滑动的时间窗口，估计 DMN 内信号之间的动态 FC。然后进行因子分析以揭示 DMN 中的隐藏模式。我们根据其时间动态之间的相似性，确定了不同的空间 FC 模式。有趣的是，这些模式中的一些依赖于警觉状态，而另一些则在状态之间独立。这些模式的时间贡献随时间波动，它们在不同警觉状态下的相互关系也不同。这些具有动态时间贡献和组合的空间模式可能为有效整合信息以支持认知和行为提供了灵活的框架。这些发现为大鼠 DMN 的动态功能组织提供了新的见解。
