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A consistent organizational structure across multiple functional subnetworks of the human brain

机译:跨人脑的多个功能子网的一致组织结构

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A recurrent theme of both cognitive and network neuroscience is that the brain has a consistent subnetwork structure that maps onto functional specialization for different cognitive tasks, such as vision, motor skills, and attention. Understanding how regions in these subnetworks relate is thus crucial to understanding the emergence of cognitive processes. However, the organizing principles that guide how regions within subnetworks communicate, and whether there is a common set of principles across subnetworks, remains unclear. This is partly due to available tools not being suited to precisely quantify the role that different organizational principles play in the organization of a subnetwork. Here, we apply a joint modeling technique - the correlation generalized exponential random graph model (cGERGM) - to more completely quantify subnetwork structure. The cGERGM models a correlation network, such as those given in functional connectivity, as a function of activation motifs - consistent patterns of coactivation (i.e., connectivity) between collections of nodes that describe how the regions within a network are organized (e.g., clustering) - and anatomical properties - relationships between the regions that are dictated by anatomy (e.g., Euclidean distance). By jointly modeling all features simultaneously, the cGERGM models the unique variance accounted for by each feature, as well as a point estimate and standard error for each, allowing for significance tests against a random graph and between graphs. Across eight functional subnetworks, we find remarkably consistent organizational properties guiding subnetwork architecture, suggesting a fundamental organizational basis for subnetwork communication. Specifically, all subnetworks displayed greater clustering than would be expected by chance, but lower preferential attachment (i.e., hub use). These findings suggest that human functional subnetworks follow a segregated highway structure, in which tightly clustered subcommunities develop their own channels of communication rather than relying on hubs.
机译:认知和神经网络的反复出现的主题是,大脑有一个映射到不同的认知任务,如视力,运动技能,注意力和功能专业化一致的子网结构。了解这些子网地区如何与因此,至关重要的理解认知过程的出现。然而,组织原则子网内引导如何沟通的区域,以及是否有原则跨子网一组通用的,目前还不清楚。这部分是由于可用的工具不适合于精确量化不同的组织原则,在一个子网的组织发挥的作用。在这里,我们采用了联合建模技术 - 相关广义指数随机图模型(cGERGM) - 更彻底子网进行量化结构。所述cGERGM模型的相关性网络,如那些在功能连接给定的,作为活化基序的函数 - 的节点的集合之间共活化的一致的模式(即,连接),描述了如何在网络内的区域被组织(例如,聚类) - 和解剖性质 - 由解剖(例如,欧几里德距离)所规定的区域之间的关系。通过同时联合建模的所有功能,该机型cGERGM独特变异占各项功能,以及一个点估计和标准差每个,允许对一个随机图和图表之间的显着性检验。在八个功能子网,我们发现非常一致的组织特性的指导子网架构,表明子网通信的基本组织基础。具体而言,所有的子网络显示得比将偶然预期更大聚类,但较低的优先连接(即,轮毂使用)。这些结果表明,人性化的功能子网遵循分离公路结构,其中紧密集群子社区发展自己的沟通渠道,而不是依赖于集线器。

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