Song Ming, Liu Yong, Zhou Yuan, Wang Kun, Yu Chunshui, Jiang Tianzi
Research Center of Computational Medicine, Sino-French Lab in Computer Science, Automation and Applied Mathematics, Institution of Automation, Chinese Academy of Sciences, 100190 Beijing, PR China.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2212-5. doi: 10.1109/IEMBS.2009.5334874.
In the last few years, many studies in the cognitive and system neuroscience found that a consistent network of brain regions, referred to as the default network, showed high levels of activity when no explicit task was performed. Some scientists believed that the resting state activity might reflect some neural functions that consolidate the past, stabilize brain ensembles and prepare us for the future. Here, we modeled default network as undirected weighted graph and then used graph theory to investigate the topological properties of the default network of the two groups of people with different intelligence levels. We found that, in both groups, the posterior cingulate cortex showed the greatest degree in comparison to the other brain regions in the default network, and that the medial temporal lobes and cerebellar tonsils were topologically separations from the other brain regions in the default network. More importantly, we found that the strength of some functional connectivities and the global efficiency of default network were significantly different between the superior intelligence group and the average intelligence group, which indicates that the functional integration of the default network might be related to the individual intelligent performance.
在过去几年中,认知与系统神经科学领域的许多研究发现,一个被称为默认网络的大脑区域的连贯网络,在没有执行明确任务时会表现出高水平的活动。一些科学家认为,静息状态活动可能反映了一些巩固过去、稳定大脑整体并为未来做好准备的神经功能。在此,我们将默认网络建模为无向加权图,然后使用图论来研究两组不同智力水平人群默认网络的拓扑特性。我们发现,在两组中,与默认网络中的其他脑区相比,后扣带回皮层的度数最大,并且内侧颞叶和小脑扁桃体在拓扑结构上与默认网络中的其他脑区分离。更重要的是,我们发现高智力组和平均智力组之间默认网络的一些功能连接强度和全局效率存在显著差异,这表明默认网络的功能整合可能与个体智力表现有关。