Temporal and instantaneous connectivity of default mode network estimated using Gaussian Bayesian network frameworks |
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Authors: | Juan Li Rui Li Kewei Chen Li Yao Xia Wu |
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Affiliation: | 1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China;2. College of Information Science and Technology, Beijing Normal University, Beijing 100875, China;3. Banner Alzheimer''s Institute (BAI) and Banner Good Samaritan PET Center, Phoenix, AZ 85006, USA;4. Center on Aging Psychology, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100875, China |
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Abstract: | 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 “from non-hub to hub”. 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. |
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Keywords: | Default mode network (DMN) Effective connectivity Instantaneous connectivity Temporal connectivity Bayesian networks (BN) Posterior cingulate cortex (PCC) |
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