Meta-Network Analysis of Structural Correlation Networks Provides Insights Into Brain Network Development - Xu, Xiaohua; He, Ping; Yap, Pew-Thian; Zhang, Han; Nie, Jingxin; Shen, Dinggang (Frontiers in Human Neuroscience, 2019)
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.