Name
Abstract | ||
|---|---|---|
A variety of network analysis methods that can reveal the neural mechanism underlying the course of dealing information in the brain by characterizing the topology and properties of brain networks have been applied to investigate the complexity of brain activities.. Working memory refers to the maintaining and handling of information in high-level cognition. It has been demonstrated that working memory performances can be enhanced by training. However, how working memory training affects the brain network topology and behavioral performance remains unclear. In this study, independent component analysis and graph theory were applied to the study of brain networks during real time fMRI based working memory training. The results showed that the training not only recruited the central execution network, the default-mode network, and the salience network, but also exerted lasting effects on the brain minimum spanning tree structure. These results demonstrated that the organization and working pattern of brain networks were altered by the training and provide new insights into the neural mechanisms underlying working memory training. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59081-3_67 | ADVANCES IN NEURAL NETWORKS, PT II |
Keywords | Field | DocType |
Working memory,Independent component analysis,Minimum spanning tree,Network topology,Training | Graph theory,Topology,Salience (neuroscience),Computer science,Working memory,Network topology,Working memory training,Artificial intelligence,Network analysis,Cognition,Machine learning,Minimum spanning tree | Conference |
Volume | ISSN | Citations |
10262 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongping Zhao | 1 | 0 | 0.68 |
| Qiushi Zhang | 2 | 0 | 0.34 |
| Li Yao | 3 | 53 | 20.09 |
| Xiao-Jie Zhao | 4 | 37 | 14.54 |