Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands.
Department of Mechanical Engineering, University of California, Riverside, California, United States of America.
PLoS One. 2022 Oct 26;17(10):e0275819. doi: 10.1371/journal.pone.0275819. eCollection 2022.
Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network.
大尺度脑活动的生物物理模型是理解神经影像学观察到的模式背后机制的基本工具。这些模型结合了单个体系结构中神经元的内聚体和整体间动力学的宏观描述。这些模型面临的一个挑战是解释内聚体同步随时间的调制。这种局部同步的调制对于模拟行为任务和静息状态活动是至关重要的。另一个挑战来自于对大规模脑模型进行参数化的困难，这阻碍了对与整体间差异相关的原理的研究。在这里，我们推导出一个简洁的大规模脑模型，可以描述局部同步的波动。至关重要的是，我们没有首先将内聚体动力学简化为宏观变量，而是在保留其生理差异的同时，同样考虑内聚体和整体间的相互作用。通过一个可以调节局部连接强度的参数，可以调整内聚体同步的动力学。我们模拟了具有相同和不同局部连接的模型中的 alpha 频带包络的静息状态静态和时间分辨功能连接。我们表明，当局部和全局同步的波动同时很高时，功能连接就会出现（即亚稳态动力学）。我们还表明，对于大多数整体而言，倾向于局部异步或同步与与其他整体的功能连接相关，除了一些属于默认模式网络的区域。
