Whole-brain propagating patterns in human resting-state brain activities
Whole-brain propagating patterns in human resting-state brain activities
复制标题
人类静息态大脑活动的全脑传播模式
DOI：
10.1016/j.neuroimage.2021.118711
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发表时间：
2021
期刊：
影响因子：
5.7
通讯作者：
Yamashita Okito
中科院分区：
文献类型：
--
作者：
Takeda Yusuke;Hiroe Nobuo;Yamashita Okito
静息态脑活动中的重复性传播活动广泛存在于不同物种和区域。因为它们类似于任务中之前的大脑活动，所以它们被认为反映了嵌入神经元回路中的过去经验。“全脑”传播活动也可能反映了整合分布在整个大脑上的信息的过程，例如视觉和运动信息。在这里，我们揭示了全脑传播活动，从人类静息态脑磁图（MEG）和脑电图（EEG）数据。我们同时记录了脑磁图和脑电图，并从两个测量值中估计了源电流。然后使用我们最近提出的算法，我们提取重复的时空模式的源电流。估计的模式由多个频率分量组成，每个频率分量都短暂地表现出功能性MRI（fMRI）的频率特异性静息状态网络（RSN），例如默认模式和感觉运动网络。模拟测试表明，时空模式反映了由沿着沿着解剖连接的传播活动引起的多频率振荡器的相位对准。这些结果表明，全脑传播活动在其多个频率分量中瞬时表现出多个RSN，这表明它们反映了整合分布在频率和网络上的信息的过程。
Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. “Whole-brain” propagating activities may also reflect a process that integrates information distributed over the entire brain, such as visual and motor information. Here we reveal whole-brain propagating activities from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. We simultaneously recorded the MEGs and EEGs and estimated the source currents from both measurements. Then using our recently proposed algorithm, we extracted repetitive spatiotemporal patterns from the source currents. The estimated patterns consisted of multiple frequency components, each of which transiently exhibited the frequency-specific resting-state networks (RSNs) of functional MRIs (fMRIs), such as the default mode and sensorimotor networks. A simulation test suggested that the spatiotemporal patterns reflected the phase alignment of the multiple frequency oscillators induced by the propagating activities along the anatomical connectivity. These results argue that whole-brain propagating activities transiently exhibited multiple RSNs in their multiple frequency components, suggesting that they reflected a process to integrate the information distributed over the frequencies and networks.
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信息通过MEG来源估计和多元模式分类的组合扩散。
影响因子：
3.7
作者：
Sato M;Yamashita O;Sato MA;Miyawaki Y
通讯作者：
Miyawaki Y
影响因子：
2.9
作者：
Henson RN;Wakeman DG;Litvak V;Friston KJ
通讯作者：
Friston KJ
影响因子：
5.7
作者：
Woolrich, Mark W.;Baker, Adam;Luckhoo, Henry;Mohseni, Hamid;Barnes, Gareth;Brookes, Matthew;Rezek, Iead
通讯作者：
Rezek, Iead
DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
影响因子：
3.7
作者：
Izhikevich, EM;Gally, JA;Edelman, GM
通讯作者：
Edelman, GM