Tao Yan, Liu Bing, Zhang Xiaolong, Li Jin, Qin Wen, Yu Chunshui, Jiang Tianzi
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.
Department of Radiology, Tianjin Medical University General Hospital Tianjin, China.
Front Neuroanat. 2015 Nov 26;9:152. doi: 10.3389/fnana.2015.00152. eCollection 2015.
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.
默认模式网络（DMN）是研究最为广泛的静息态功能网络之一。DMN的结构基础备受关注，已有多位研究人员使用扩散张量成像（DTI）对其进行研究。此前的大多数研究聚焦于DMN的少数区域或白质束，因此DMN的整体结构连接模式和网络特性仍不明确。此外，有证据表明DMN与记忆和情绪均有关联，但从整个DMN结构网络的角度来看，DMN如何调节记忆和焦虑仍不清楚。我们使用多模态神经成像方法，对205名年龄在18至29岁之间的年轻健康受试者的DMN结构连接模式及其网络特性与记忆和焦虑的关联进行了研究。使用组独立成分分析（Group ICA）方法从功能磁共振成像（fMRI）数据中提取DMN成分，并应用基于DTI数据的概率纤维束成像技术构建DMN的整体结构连接模式。然后我们使用图论方法分析DMN结构网络，发现记忆商数（MQ）得分与DMN的全局和局部效率显著正相关，而焦虑与效率呈负相关。DMN内多个脑区之间强大的结构连接可能反映出DMN具有一定的结构基础。同时，我们发现DMN的网络效率与记忆和焦虑指标相关这一结果表明，DMN可能在记忆和焦虑中发挥作用。
