Structure–function relationship in complex brain networks expressed by hierarchical synchronization
Structure–function relationship in complex brain networks expressed by hierarchical synchronization
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DOI：
10.1088/1367-2630/9/6/178
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发表时间：
2007-06
影响因子：
3.3
通讯作者：
Changsong Zhou;L. Zemanova;G. Zamora-López;C. Hilgetag;J. Kurths
中科院分区：
文献类型：
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作者：
Changsong Zhou;L. Zemanova;G. Zamora-López;C. Hilgetag;J. Kurths
大脑是自然界中最复杂的系统之一，具有结构化的复杂连接。近年来，大规模的皮层皮层连接，无论是结构上的还是功能上的，都得到了大量的研究关注，特别是使用复杂网络分析的方法。了解结构和功能连接之间的关系在神经科学中至关重要。在这里，我们试图通过研究猫皮层连接的现实解剖网络中的同步动力学来阐明这种关系。我们通过神经质量模型（群体模型）或通过相互作用的可兴奋神经元的子网络（多级模型）对节点（皮层区域）进行建模。我们表明，如果动力学的特点是定义明确的振荡（神经质量模型和子网络强耦合），同步模式主要是由节点强度（总输入强度的节点）和详细的网络拓扑结构是相当无关紧要的。另一方面，弱耦合的多级模型显示出更不规则的，生物学上合理的动力学，同步模式揭示了网络结构中的分层集群组织。在不同水平的同步结构和功能的连接之间的关系进行了探讨。因此，研究皮层多级复杂网络模型中的同步性可以为复杂脑网络的拓扑结构和功能组织之间的关系提供新的视角。
The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks.