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Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

机译:正在进行的脑部活动中的单频或多频发生器:经验性MEG数据的机械全脑模型

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摘要

During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies.Considering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12 Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.
机译:在休息期间,有限带宽的正在进行的MEG信号的包络以一致的模式在大脑中变化,这与使用fMRI测量的静止状态网络有关。为了研究这种包络相关性的起源,我们考虑了一个全脑网络模型,该模型假设了两种截然不同的基本情况:一种是每个大脑区域都在一个频率下产生振荡,另一个是新颖的其中每个大脑区域可以在多个频带内产生振荡的情况。 。这些模型作为阻尼振荡的常见发生器,在稳态和振荡状态之间的临界边界处运行的超临界Hopf分叉的正常形式。使用新方法将模拟信号的包络与经验MEG数据进行比较,以分析它们在载波频率频谱上的相位相干性和稳定性方面的包络动力学。将全脑模型与每个大脑区域中的单个频率发生器一起考虑,当基频调整为12 Hz时,我们将根据经验MEG数据获得最佳拟合。但是,当在每个局部大脑区域放置多个频率发生器时,我们获得了在所有频带上进行中的MEG数据的时空结构的改进拟合。我们的结果表明,大脑在休息期间可能会在多个频率上运行,从而为未来大规模大脑活动模型引入了新的维度。

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