Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles, Brussels 1070, Belgium.
Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles, Brussels 1070, Belgium; Magnetoencephalography Unit, Service of Translational Neuroimaging, CUB - Hôpital Erasme, Brussels, Belgium.
Neuroimage. 2022 Feb 15;247:118850. doi: 10.1016/j.neuroimage.2021.118850. Epub 2021 Dec 22.
State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. To investigate whether microstates and power envelope HMM states describe the same neural dynamics, we used simultaneous MEG/EEG recordings at rest and compared the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differ both spatially and temporally. Microstates reflect sharp events of neural synchronization, whereas power envelope HMM states disclose network-level activity with 100-200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.
全脑脑电(EEG)或脑磁图(MEG)的状态建模可用于研究瞬态、反复出现的神经动力学事件。两种广泛使用的技术是 EEG 信号的微状态分析和 MEG 功率包络的隐马尔可夫建模(HMM)。据报道,这两种方法在 100ms 的时间尺度上产生相似的状态寿命,表明存在共同的神经基础。为了研究微状态和功率包络 HMM 状态是否描述相同的神经动力学,我们使用静息状态下的同时 MEG/EEG 记录,并比较了从 EEG 和 MEG 分别获得的微状态和功率包络 HMM 状态的空间特征和时间激活动力学。结果表明,微状态和功率包络 HMM 状态在空间和时间上都存在差异。微状态反映了神经同步的急剧事件,而功率包络 HMM 状态揭示了具有 100-200ms 寿命的网络级活动。此外,MEG 微状态与典型的 EEG 微状态不同,而是更好地解释为分裂的 HMM 状态。另一方面,MEG 和 EEG HMM 状态都涉及到相似功能网络的(去)激活。因此,微状态分析和功率包络 HMM 对发生在不同空间和时间尺度上的神经事件敏感。因此,它们代表了探索自发人脑活动中快速、亚秒级突发电生理动力学的互补方法。