title: The Structural Connectivity Pattern of the Default Mode Network and Its Association with Memory and Anxiety.
abstract: The default mode network (DMN) is one of the most widely studied resting state functional networks. The structural basis for the DMN is of particular interest and has been studied by several researchers using diffusion tensor imaging (DTI). Most of these previous studies focused on a few regions or white matter tracts of the DMN so that the global structural connectivity pattern and network properties of the DMN remain unclear. Moreover, evidences indicate that the DMN is involved in both memory and emotion, but how the DMN regulates memory and anxiety from the perspective of the whole DMN structural network remains unknown. We used multimodal neuroimaging methods to investigate the structural connectivity pattern of the DMN and the association of its network properties with memory and anxiety in 205 young healthy subjects with age ranging from 18 to 29 years old. The Group ICA method was used to extract the DMN component from functional magnetic resonance imaging (fMRI) data and a probabilistic fiber tractography technique based on DTI data was applied to construct the global structural connectivity pattern of the DMN. Then we used the graph theory method to analyze the DMN structural network and found that memory quotient (MQ) score was significantly positively correlated with the global and local efficiency of the DMN whereas anxiety was found to be negatively correlated with the efficiency. The strong structural connectivity between multiple brain regions within DMN may reflect that the DMN has certain structural basis. Meanwhile, the results we found that the network efficiency of the DMN were related to memory and anxiety measures, indicated that the DMN may play a role in the memory and anxiety.
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Tao
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Liu
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Radiology, Tianjin Medical University General Hospital Tianjin, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Qin
authors.affiliation: Department of Radiology, Tianjin Medical University General Hospital Tianjin, China.
authors.forename: Chunshui
authors.initials: C
authors.lastname: Yu
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China ; Queensland Brain Institute, The University of Queensland Brisbane, QLD, Australia ; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Tianzi
authors.initials: T
authors.lastname: Jiang
keywords: DTI; anxiety; default mode network; memory; structural network