Temporal and instantaneous connectivity of default mode network estimated using Gaussian Bayesian network frameworks

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• 作者：Juan Li^a ; Rui Li^d ; Kewei Chen^a ; ^c ; Li Yao^a ; ^b ; Xia Wu^a ; ^b ; ^{wuxia@bnu.edu.cn}
• 关键词：Default mode network (DMN) ; Effective connectivity ; Instantaneous connectivity ; Temporal connectivity ; Bayesian networks (BN) ; Posterior cingulate cortex (PCC)
• 刊名：Neuroscience Letters
• 出版年：2012
• 期刊代码：52_03043940
• 类别：neu
• 出版时间：28 March, 2012
• 卷：513
• 期：1
• 页码：62-66
• 文件大小：424 K

摘要

By probing its functional anatomy, the default mode network (DMN) can be considered consisting of two interacting hub and non-hub subsystems. The hub subsystem includes posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC) and bilateral inferior parietal cortex (IPC). The non-hub subsystem contains inferior temporal cortex (ITC) and (para) hippocampus (HC). In this study, Gaussian Bayesian Network (BN) and Gaussian Dynamic Bayesian Network (DBN) were applied separately to detect the instantaneous and temporal connection relationship within each and between the two DMN subsystems. It was found that the directional instantaneous interactions between the two subsystems were primarily 鈥渇rom non-hub to hub鈥? The temporal interactions between hub and non-hub regions, on the other hand, are less presented between the two subsystems. The hub subsystem demonstrated both strong instantaneous and temporal interactions among the hub regions, while the non-hub regions were only strongly inter-connected instantaneously but temporally isolated with each other. In addition, one of the hub regions, PCC, appears to be a confluent node and important in the functional integration within the network.