Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold.

ABSTRACT: Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the su

SUBMITTER: Bordier C 

PROVIDER: S-EPMC5540956 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature