人脑默认网络方向图:基于7TfMRI的动态因果模型

Directed Graph of Human Brain's Default Network:a DCM With 7T fMRI

近年来,默认网络是认知神经科学领域的研究热点之一,已有研究报告它可能参与了多种认知活动,而且某些精神疾病也与其异常活动相关.但默认网络内主要脑区之间的有向连接关系(有效连接模式)尚不明确.本研究使用国际前沿的谱动态因果模型算法,基于7T高分辨率静息态功能磁共振数据,对默认网络4个核心脑区之间的有效连接模式进行了探索.实验结果发现,默认网络内后扣带回接受内侧前额叶、双侧顶下叶的信息输入,可能扮演着信息集合中心的角色,而双侧顶下叶对内侧前额叶、后扣带回都有信息输入,在默认网络内可能起到信息驱动和调节的功能.本研究首次报道了基于7T功能磁共振数据得到的默认网络有向连接图谱,对于我们更深入理解默认网络的功能具有帮助,对相关精神疾病的研究具有潜在的参考应用价值.

The default mode network（DMN） has been reported to be involved in a variety of important cognitive functions and received increasing attention in neuroscience recently. Its dysfunction is also reported to be associated with multiple psychiatric disorders. However, the causal information flow（effective connectivity） within the default mode network remains poorly understood. In this study, we explored the effective connectivity pattern between 4 key DMN brain areas based on a high resolution 7 T resting state f MRI dataset using a cutting-edge spectral dynamic causal modelling technique. Results showed that there was a distinct effective connectivity pattern among the DMN nodes. We found medial prefrontal cortex（MPFC） and bilateral inferior parietal cortex（IPC） sent information to the posterior cingulate cortex（PCC）, which suggested that the PCC might be a hub region that collected information from other DMN areas. Besides, a causal influence was found from bilateral IPC to MPFC, and from left IPC to right IPC. This work was the first 7 T f MRI study that investigated effective connectivity pattern among DMN nodes, which may promote our understandings about the functions of DMN and benefit future research in DMN-related psychiatric disorders.