Network analysis of human brain connectivity based on graph theory has consistently identified sets of regions that are critically important for enabling efficient information integration and communication, especially for the understanding of cognitive functions, the discoveries of aging effects and the network change due to brain diseases. Two major approaches, hub measurement (HM) and vulnerability measurement (VM), have been proposed to detect these 'important nodes' within brain network organization. However, the relationship between the spatial localization and the number of these identified nodes found using HM and VM approaches respectively is still unknown. In this study, we aim to figure out the relationships between the identified critical nodes of brain network based on various HM and VM methods with DTI-based structural brain network. Two factors of parcellation atlases and level of scale are also considered to address the effects in the definition of these nodes. From the results, the great consistency is existed between the node identification using HM and VM approaches in the same atlases, but the divergence between different atlases and level of node scale.
Date:
2015-08
Relation:
37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015 Aug:422-425.
