Dynamic network model with continuous valued nodes for longitudinal brain morphometry

作者：Chen, Rong^*; Zheng, Yuanjie; Nixon, Erika; Herskovits, Edward H.

来源：NeuroImage, 2017, 155: 605-611.

DOI：10.1016/j.neuroimage.2017.05.018

• Brain network
• Continuous valued variable
• Longitudinal morphometric data
• Dynamic Bayesian network
• State space modeling

Longitudinal brain morphometry probes time-related brain morphometric patterns. We propose a method called dynamic network modeling with continuous valued nodes to generate a dynamic brain network from continuous valued longitudinal morphometric data. The mathematical framework of this method is based on state-space modeling. We use a bootstrap-enhanced least absolute shrinkage operator to solve the network-structure generation problem. In contrast to discrete dynamic Bayesian network modeling, the proposed method enables network generation directly from continuous valued high-dimensional short sequence data, being free from any discretization process. We applied the proposed method to a study of normal brain development.

• 出版日期2017-7-15
• 单位山东师范大学; 北京理工大学