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DOI码：10.1371/journal.pone.0062789

发表刊物：PLOS ONE

关键字：GRAPH-THEORETICAL ANALYSISSMALL-WORLDFUNCTIONAL CONNECTIVITYCORTICAL NETWORKSMODULAR ORGANIZATIONANATOMICAL NETWORKSARCHITECTUREPARCELLATIONSTABILITYPATTERNS

摘要：Recently, a combination of non-invasive neuroimaging techniques and graph theoretical approaches has provided a unique opportunity for understanding the patterns of the structural and functional connectivity of the human brain (referred to as the human brain connectome). Currently, there is a very large amount of brain imaging data that have been collected, and there are very high requirements for the computational capabilities that are used in high-resolution connectome research. In this paper, we propose a hybrid CPU-GPU framework to accelerate the computation of the human brain connectome. We applied this framework to a publicly available resting-state functional MRI dataset from 197 participants. For each subject, we first computed Pearson's Correlation coefficient between any pairs of the time series of gray-matter voxels, and then we constructed unweighted undirected brain networks with 58 k nodes and a sparsity range from 0.02% to 0.17%. Next, graphic properties of the functional brain networks were quantified, analyzed and compared with those of 15 corresponding random networks. With our proposed accelerating framework, the above process for each network cost 80 similar to 150 minutes, depending on the network sparsity. Further analyses revealed that high-resolution functional brain networks have efficient small-world properties, significant modular structure, a power law degree distribution and highly connected nodes in the medial frontal and parietal cortical regions. These results are largely compatible with previous human brain network studies. Taken together, our proposed framework can substantially enhance the applicability and efficacy of high-resolution (voxel-based) brain network analysis, and have the potential to accelerate the mapping of the human brain connectome in normal and disease states.

合写作者：Haixiao Du, Mingrui Xia, Ling Ren, Mo Xu, Teng Xie, Gaolang Gong, Ningyi Xu,杨华中, He yong,Constans, J.

第一作者：汪玉,Weibei Dou

论文类型：会议论文

通讯作者：Yong He,Weibei Dou,Weibei Dou

卷号：8

期号：5

ISSN号：1932-6203

是否译文：否

发表时间：2013-05-10