A mechanistic model of connector hubs, modularity and cognition
A mechanistic model of connector hubs, modularity and cognition
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DOI：
10.1038/s41562-018-0420-6
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发表时间：
2018-10-01
影响因子：
29.9
通讯作者：
D'Esposito, Mark
中科院分区：
文献类型：
--
作者：
Bertolero, Maxwell A.;Yeo, B. T. Thomas;D'Esposito, Mark
人脑网络是模块化的——由紧密相连的节点组成(1)。该网络包含本地集线器和连接器集线器，前者在自己的社区内有许多连接，后者的连接分布在不同的社区（2,3）。对这些中枢以及它们如何支持认知的机制理解尚未得到证实。在这里，我们利用了枢纽连通性和认知方面的个体差异。我们展示了一个中枢连接模型准确地预测了476个人在4个不同任务中的认知表现。此外，对于认知表现而言，有一个普遍的最优网络结构——无论任务如何，拥有不同连接枢纽和相应的模块化大脑网络的个体都表现出更高的认知表现。重要的是，我们发现了与一个机制模型一致的证据，在这个模型中，连接器中心调整其邻居的连接，使其更加模块化，同时允许跨社区的任务适当的信息集成，从而提高全球模块化和认知性能。
The human brain network is modular-consisting of communities of tightly interconnected nodes(1). This network contains local hubs, which have many connections within their own communities, and connector hubs, which have connections diversely distributed across communities(2,3). A mechanistic understanding of these hubs and how they support cognition has not been demonstrated. Here, we leveraged individual differences in hub connectivity and cognition. We show that a model of hub connectivity accurately predicts the cognitive performance of 476 individuals in 4 distinct tasks. Moreover, there is a general optimal network structure for cognitive performance-individuals with diversely connected hubs and consequent modular brain networks exhibit increased cognitive performance, regardless of the task. Critically, we find evidence consistent with a mechanistic model in which connector hubs tune the connectivity of their neighbours to be more modular while allowing for task appropriate information integration across communities, which increases global modularity and cognitive performance.