Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchronization transitions with EEG/MEG. However, there is still little understanding of the relationship between the slow fluctuations measured with fMRI and the fast synchronization transitions with EEG/MEG. In this study, as a first step toward evaluating experimental evidence of resting state activity at two different time scales but in a unified way, we investigate connectome dynamics models that simultaneously explain resting-state functional connectivity (rsFC) and EEG microstates. Here, we introduce empirical rsFC and microstates as evaluation criteria of simulated neuronal dynamics obtained by the Larter-Breakspear model in one cortical region connected with those in other cortical regions based on structural connectivity. We optimized the global coupling strength and the local gain parameter (variance of the excitatory and inhibitory threshold) of the simulated neuronal dynamics by fitting both rsFC and microstate spatial patterns to those of experimental ones. As a result, we found that simulated neuronal dynamics in a narrow optimal parameter range simultaneously reproduced empirical rsFC and microstates. Two parameter groups had different inter-regional interdependence. One type of dynamics was synchronized across the whole brain region, and the other type was synchronized between brain regions with strong structural connectivity. In other words, both fast synchronization transitions and slow BOLD fluctuation changed based on structural connectivity in the two parameter groups. Empirical microstates were similar to simulated microstates in the two parameter groups. Thus, fast synchronization transitions correlated with slow BOLD fluctuation based on structural connectivity yielded characteristics of microstates. Our results demonstrate that a bottom-up approach, which extends the single neuronal dynamics model based on empirical observations into a neural mass dynamics model and integrates structural connectivity, effectively reveals both macroscopic fast, and slow resting-state network dynamics.

中文翻译：

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使用静息 fMRI 连接和 EEG 微状态评估神经质量模型的静息时空动力学

静息状态的大脑活动已被广泛研究，以使用非侵入性成像方法（如 fMRI、EEG 和 MEG）了解人脑的宏观网络架构。先前的研究揭示了使用连接组动力学建模方法的静息状态网络 (RSN) 的机械起源，其中模拟受结构连接约束的神经质量动力学模型以复制使用 fMRI 和/或快速同步转换测量的静息状态网络用脑电图/脑电图。然而，对 fMRI 测量的缓慢波动与 EEG/MEG 快速同步转换之间的关系仍然知之甚少。在这项研究中，作为在两个不同时间尺度上以统一的方式评估静息状态活动的实验证据的第一步，我们研究了同时解释静息状态功能连接 (rsFC) 和 EEG 微观状态的连接组动力学模型。在这里，我们引入了经验 rsFC 和微观状态作为通过 Larter-Breakspear 模型获得的模拟神经元动力学的评估标准，其中一个皮质区域与其他皮质区域基于结构连通性相连。我们通过将 rsFC 和微状态空间模式与实验的空间模式相匹配，优化了模拟神经元动力学的全局耦合强度和局部增益参数（兴奋性和抑制性阈值的方差）。结果，我们发现在狭窄的最佳参数范围内模拟神经元动力学同时再现了经验 rsFC 和微观状态。两个参数组具有不同的区域间相互依赖性。一种类型的动力学在整个大脑区域内同步，另一种类型在具有强结构连接的大脑区域之间同步。换句话说，快速同步转换和慢速 BOLD 波动都基于两个参数组中的结构连通性而改变。经验微观状态与两个参数组中的模拟微观状态相似。因此，与基于结构连通性的缓慢 BOLD 波动相关的快速同步转换产生了微观状态的特征。我们的结果表明，自下而上的方法将基于经验观察的单个神经元动力学模型扩展到神经质量动力学模型并整合结构连通性，有效地揭示了宏观快速和慢速静息状态网络动力学。