Original Research ARTICLE
Meta-network Analysis of Structural Correlation Networks Provides Insights into Brain Network Development
- 1Yangzhou University, China
- 2University of North Carolina at Chapel Hill, United States
- 3South China Normal University, China
Analysis of developmental brain networks is fundamentally important for basic developmental neuroscience. In this paper, we focus on the temporally-covarying connection patterns, called meta-networks, and develop a new mathematical model for meta-network decomposition. With the proposed model, we decompose the developmental structural correlation networks of cortical thickness into five meta-networks. Each meta-network exhibits distinct spatial connection pattern, and its covarying trajectory highlights the temporal contribution of the meta-network along development. Systematic analysis of the meta-networks and covarying trajectories provides insights into three important aspects of brain network development.
Keywords: Brain network development, cortical thickness, meta-network analysis, low rank, temporal smoothness
Received: 24 Jul 2018;
Accepted: 27 Feb 2019.
Edited by:Jessica A. Turner, Georgia State University, United States
Reviewed by:Haijing Niu, Beijing Normal University, China
Ling-Li Zeng, National University of Defense Technology, China
Xiangyu Long, University of Calgary, Canada
Copyright: © 2019 Xu, He, Yap, Zhang, Nie and Shen. 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: Prof. Ping He, Yangzhou University, Yangzhou, China, firstname.lastname@example.org