The optimal organization for functional segregation and integration in brain is made evident by the “small-world” feature of functional connectivity (FC) networks, and is further supported by the loss of this feature that has been described in many types of brain disease. However, it remains unknown how such optimally organized FC networks arise from the brain’s structural constrains. On the other hand, an emerging literature suggests that brain function may be supported by critical neural dynamics, which is believed to facilitate information processing in brain. Though previous investigations have shown that the critical dynamics plays an important role in understanding the relation between whole brain structural connectivity and functional connectivity, it is not clear if the critical dynamics could be responsible for the optimal FC network configuration in human brains. Here, we show that the long-range temporal correlations (LRTCs) in the resting state fMRI blood-oxygen-level dependent (BOLD) signals are significantly correlated with the topological matrices of the FC brain network. Using structure-dynamics-function modeling approach that incorporates diffusion tensor imaging (DTI) data and simple cellular automata dynamics, we showed that the critical dynamics could optimize the whole brain FC network organization by, e.g., maximizing the clustering coefficient while minimizing the characteristic path length. We also demonstrated with a more detailed excitation-inhibition neuronal network model that loss of local excitation-inhibition (E/I) balance causes failure of critical dynamics, therefore disrupts the optimal FC network organization. The results highlighted the crucial role of the critical dynamics in forming an optimal organization of FC networks in the brain, and have potential application to the understanding and modeling of abnormal FC configurations in neuropsychiatric disorders.

中文翻译：

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功能连接网络的最佳组织，用于人脑大规模关键动力学的分离和集成

功能连接（FC）网络的“小世界”功能证明了大脑中功能隔离和整合的最佳组织，而丧失这种功能的能力得到了进一步的支持，这在许多类型的脑病中都有描述。然而，仍然不清楚如何从大脑的结构约束中产生这种最佳组织的FC网络。另一方面，新兴的文献表明，关键的神经动力学可能支持大脑功能，据信这有助于大脑中的信息处理。尽管以前的研究表明，关键动力学在理解全脑结构连通性和功能连通性之间的关系方面起着重要作用，目前尚不清楚关键动力学是否可以导致人脑中最佳的FC网络配置。在这里，我们显示静止状态fMRI血氧水平依赖性（BOLD）信号中的长期时间相关性（LRTC）与FC脑网络的拓扑矩阵显着相关。使用结合扩散张量成像（DTI）数据和简单细胞自动机动力学的结构动力学功能建模方法，我们证明了临界动力学可以通过例如最大化聚类系数同时最小化特征路径来优化整个大脑FC网络的组织。长度。我们还通过更详细的激发抑制神经网络模型证明，失去局部激发抑制（E / I）平衡会导致关键动力学失效，因此破坏了最佳的FC网络组织。结果强调了关键动力学在大脑中形成FC网络的最佳组织中的关键作用，并且在理解和建模神经精神疾病中异常FC构型方面具有潜在的应用价值。