Abstract：
:The most important goals of brain network analyses are to (a) detect pivotal regions and connections that contribute to disproportionate communication flow, (b) integrate global information, and (c) increase the brain network efficiency. Most centrality measures assume that information propagates in networks with the shortest connection paths, but this assumption is not true for most real networks given that information in the brain propagates through all possible paths. This study presents a methodological pipeline for identifying influential nodes and edges in human brain networks based on the self-regulating biological concept adopted from the Physarum model, thereby allowing the identification of optimal paths that are independent of the stated assumption. Network hubs and bridges were investigated in structural brain networks using the Physarum model. The optimal paths and fluid flow were used to formulate the Physarum centrality measure. Most network hubs and bridges are overlapped to some extent, but those based on Physarum centrality contain local and global information in the superior frontal, anterior cingulate, middle temporal gyrus, and precuneus regions. This approach also reduced individual variation. Our results suggest that the Physarum centrality presents a trade-off between the degree and betweenness centrality measures.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Kwon H,Choi YH,Lee JMdoi
10.1038/s41598-019-42322-7subject
Has Abstractpub_date
2019-04-11 00:00:00pages
5907issue
1issn
2045-2322pii
10.1038/s41598-019-42322-7journal_volume
9pub_type
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