Impact Factor 3.648 | CiteScore 3.99
More on impact ›

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2019.00856

States representation of dynamic functional networks in resting based on node centrality and test-retest clustering analysis

 Xin Zhao1,  Qiong Wu1,  Yuanyuan Chen1, Xizi Song1, Hongyan Ni1* and  Dong Ming1*
  • 1Tianjin University, China

The spontaneous dynamic characteristics of resting-state functional connectivity can reflect the internal brain characteristics and reveal the physiological or pathological changes of brain. The microstate of the brain dynamic functional network is a quantitative method to characterize the essence of the dynamic functional network. However, there is a lack of easily understandable and highly reliable microstate detection method. In this study, we presented a microstate detection method based on dynamic node centrality clustering and designed a test-retest experiment. Twenty-three healthy young volunteers (ages 21–26) were recruited and retested with one week interval. The results showed high reliability between two scans in the microstate representation of spontaneous dynamic functional networks, and the microstate reflected the modularization of the intrinsic functional systems. What’s more, this study explored the coupling relationship between these states and the structural network based on fiber connection, which revealed that the spontaneous dynamic functional network were highly coupled with the structural network. We found that the brain functional networks can not only spatially but also temporally reflect the segregation and integration of intrinsic functional system.

Keywords: Microstate, Dynamic Functional Connectivity, Structural network, Clustering analysis, Node centrality, hubs

Received: 14 Dec 2018; Accepted: 30 Jul 2019.

Copyright: © 2019 Zhao, Wu, Chen, Song, Ni and Ming. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Dr. Hongyan Ni, Tianjin University, Tianjin, 300072, China, nihyan@sina.com
Dr. Dong Ming, Tianjin University, Tianjin, 300072, China, richardming@tju.edu.cn