کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5631165 | 1580855 | 2017 | 7 صفحه PDF | دانلود رایگان |
- To generate a dynamic brain network from longitudinal morphometric data.
- Network generation from continuous valued high-dimensional short sequence data.
- Use a bootstrap-enhanced LASSO to solve the network generation problem.
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.
Journal: NeuroImage - Volume 155, 15 July 2017, Pages 605-611