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人类连接组中隔离与整合之间动态波动的结构决定因素。

Structural determinants of dynamic fluctuations between segregation and integration on the human connectome.

机构信息

Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.

Data Science Center, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.

出版信息

Commun Biol. 2020 Oct 23;3(1):606. doi: 10.1038/s42003-020-01331-3.

Abstract

While segregation and integration of neural information in the neocortex are thought to be important for human behavior and cognition, the neural substrates enabling their dynamic fluctuations remain elusive. To tackle this problem, we aim to identify specific network features of the connectome that are responsible for the emergence of dynamic fluctuations between segregated and integrated patterns in human resting-state functional connectivity. Here we examine the contributions of network features to dynamic fluctuations by constructing rewired surrogate connectome in which network features of interest are selectively preserved, and then by assessing the magnitude of fluctuations simulated with these surrogates. Our analysis demonstrates significant contributions from global geometry and topology of the connectome, as well as from localized structural connections involving visual areas. By providing structural accounts of dynamic fluctuations in functional connectivity, this study offers new insights into generative mechanisms driving temporal changes in segregation and integration in the brain.

摘要

虽然皮质中的神经信息的分离和整合被认为对人类行为和认知很重要,但支持其动态波动的神经基质仍然难以捉摸。为了解决这个问题,我们旨在确定连接组中特定的网络特征,这些特征负责在人类静息状态功能连接的分离和整合模式之间出现动态波动。在这里,我们通过构建重新布线的替代连接组来检查网络特征对动态波动的贡献,在替代连接组中,有选择地保留了感兴趣的网络特征,然后评估使用这些替代连接组模拟的波动幅度。我们的分析表明,连接组的全局几何形状和拓扑结构以及涉及视觉区域的局部结构连接都有显著的贡献。通过提供功能连接中动态波动的结构解释,这项研究为驱动大脑中分离和整合的时间变化的生成机制提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca26/7584581/50e095505d9d/42003_2020_1331_Fig1_HTML.jpg

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