论文标题

功能性脑网络的相互作用是由人类结构连接组中的K型渗透渗透形成的

Interaction of functional brain networks is formed by k-clique percolation in the human structural connectome

论文作者

Tiselko, V., Dogonasheva, O., Myshkin, A., Valba, O.

论文摘要

人类结构连接组具有一个复杂的内部社区组织,其特征是高度重叠,并且与功能和认知现象有关。我们探索了Connectome网络中的连接性能,并表明$ k $ clique的渗透异常高阶是人类结构连接组的特征。由此产生的结构组织保持了整个连接组分布的高局部连接密度,同时保留了网络的整体稀疏性。为了分析这些发现,我们提出了一个新型模型,用于在网络形成过程中高阶渗透的出现,并在连接长度的约束下具有相变动态。在研究功能性脑子网的结构基础上,我们确定了它们的相互作用与集团集团在其结构连接中的形成之间的直接关系。基于这些发现,我们假设渗透集团群集在相互作用的功能子网之间充当分布式边界,显示其结构连接的复杂,互补性。我们还研究了个体特异性和共同的结构连接之间的差异,发现后者在结构社区的连通性中起着持续的作用。同时,与共同的连接相比,各个连接的优越性在功能性脑部子网络的相互作用中造成了可变性。

The human structural connectome has a complex internal community organization, characterized by a high degree of overlap and related to functional and cognitive phenomena. We explored connectivity properties in connectome networks and showed that $k$-clique percolation of an anomalously high order is characteristic of the human structural connectome. The resulting structural organization maintains a high local density of connectivity distributed throughout the connectome while preserving the overall sparsity of the network. To analyze these findings, we proposed a novel model for the emergence of high-order clique percolation during network formation with a phase transition dynamic under constraints on connection length. Investigating the structural basis of functional brain subnetworks, we identified a direct relationship between their interaction and the formation of clique clusters within their structural connections. Based on these findings, we hypothesize that the percolating clique cluster serves as a distributed boundary between interacting functional subnetworks, showing the complex, complementary nature of their structural connections. We also examined the difference between individual-specific and common structural connections and found that the latter plays a sustaining role in the connectivity of structural communities. At the same time, the superiority of individual connections, in contrast to common ones, creates variability in the interaction of functional brain subnetworks.

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