Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates
Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates
复制标题
DOI：
10.3389/fncom.2019.00091
复制
发表时间：
2020-01-17
影响因子：
3.2
通讯作者：
Yamashita, Okito
中科院分区：
文献类型：
--
作者：
Endo, Hidenori;Hiroe, Nobuo;Yamashita, Okito
静息状态下的大脑活动已经被广泛研究，以了解人类大脑的宏观尺度网络结构，使用非侵入性成像方法，如功能磁共振成像，脑电图和脑磁图。以往的研究揭示了一个机制的起源静息态网络（RSNs）使用连接体动力学建模方法，其中的神经质量动力学模型的结构连接的约束下，模拟复制静息态网络测量与功能磁共振成像和/或快速同步转换与EEG/MEG。然而，仍然有很少的理解之间的关系的慢波动测量与脑电/脑磁图的快速同步过渡。在这项研究中，作为第一步评估实验证据的静息状态活动在两个不同的时间尺度，但在一个统一的方式，我们研究连接体动力学模型，同时解释静息状态功能连接（rsFC）和EEG microstates。在这里，我们引入经验rsFC和microstates作为评价标准的Larter-Breakspear模型在一个皮层区域与那些在其他皮层区域的基础上结构连接的模拟神经元动力学。我们优化了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）的模拟神经元动力学通过拟合两个rsFC和microstate空间模式的实验。其结果是，我们发现，在一个狭窄的最佳参数范围内的模拟神经元动力学同时再现经验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同步，另一种类型的动力学在具有强结构连接的大脑区域之间同步。换句话说，无论是快速的同步转换和缓慢的BOLD波动改变的基础上，在两个参数组的结构连接。经验的微观状态是相似的模拟微观状态在两个参数组。因此，快速同步过渡与缓慢BOLD波动的基础上结构连接产生的微观状态的特征。我们的研究结果表明，一个自下而上的方法，它扩展了单神经元动力学模型的基础上的经验观察到一个神经质量动力学模型，并集成了结构连接，有效地揭示了宏观快速，缓慢的静息态网络动力学。
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.