Brain networks can be divided into two categories: structural and functional networks. Many studies of neuroscience have reported that the complex brain networks are characterized by small-world or scale-free properties. The identification of nodes is the key factor in studying the properties of networks on the macro-, micro- or mesoscale in both structural and functional networks. In the study of brain networks, nodes are always determined by atlases. Therefore, the selection of atlases is critical, and appropriate atlases are helpful to combine the analyses of structural and functional networks. Currently, some problems still exist in the establishment or usage of atlases, which are often caused by the segmentation or the parcellation of the brain. We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent. In the process of building atlases, the influences of single subjects and groups should be balanced. In this article, we focused on the effects of atlases on the analysis of brain networks and the improved divisions based on the tractography or connectivity in the parcellation of atlases.

中文翻译：

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结构和功能性大脑网络的回顾：小世界和地图集。

脑网络可以分为两类：结构网络和功能网络。神经科学的许多研究都报告说，复杂的大脑网络具有小世界或无标度的特征。在结构和功能网络中，节点的识别是研究宏观，微观或中尺度网络特性的关键因素。在脑网络的研究中，结点总是由地图集决定的。因此，图集的选择至关重要，适当的图集有助于将结构和功能网络的分析相结合。当前，在地图集的建立或使用中仍然存在一些问题，这通常是由于大脑的分割或碎裂引起的。我们建议大脑网络的量化可能在很大程度上受地图集选择的影响。在建立地图集的过程中，应该平衡单个主题和组的影响。在本文中，我们将重点放在地图集对脑网络分析的影响以及基于地图集或地图集分割中的连接性的改进划分方面。