STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU, Leuven, Belgium; imec, Leuven, Belgium.
Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom.
Neuroimage. 2019 Jan 15;185:72-82. doi: 10.1016/j.neuroimage.2018.09.082. Epub 2018 Oct 1.
Resting state brain activity has become a significant area of investigation in human neuroimaging. An important approach for understanding the dynamics of neuronal activity in the resting state is to use complementary imaging modalities. Electrophysiological recordings can access fast temporal dynamics, while functional magnetic resonance imaging (fMRI) studies reveal detailed spatial patterns. However, the relationship between these two measures is not fully established. In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub-second time-scales, accessible with EEG, link to the slower time-scales and higher spatial detail of fMRI. We found that the fMRI correlates of fast transient EEG dynamic networks show highly reproducible spatial patterns, and that their spatial organization exhibits strong similarity with traditional fMRI resting state networks maps. This further demonstrates the potential of electrophysiology as a tool for understanding the fast network dynamics that underlie fMRI resting state networks.
静息态脑活动已成为人类神经影像学研究的重要领域。理解静息态神经元活动动态的一个重要方法是使用互补的成像模式。电生理记录可以获取快速的时间动态,而功能磁共振成像 (fMRI) 研究则揭示了详细的空间模式。然而,这两种测量方法之间的关系尚未完全确定。在这项研究中,我们使用同时记录的脑电图 (EEG) 和 fMRI,以及隐马尔可夫模型,来研究快速亚秒级时间尺度的网络动态如何与 fMRI 的较慢时间尺度和更高的空间细节相关联。我们发现,快速瞬态 EEG 动态网络的 fMRI 相关物具有高度可重复的空间模式,并且它们的空间组织与传统的 fMRI 静息状态网络图谱具有很强的相似性。这进一步证明了电生理学作为一种理解 fMRI 静息状态网络背后的快速网络动态的工具的潜力。