Comparison of Local Information Indices Applied in Resting State Functional Brain Network Connectivity Prediction.

ABSTRACT: Anatomical distance has been widely used to predict functional connectivity because of the potential relationship between structural connectivity and functional connectivity. The basic implicit assumption of this method is "distance penalization." But studies have shown that one-parameter model (anatomical distance) cannot account for the small-worldness, modularity, and degree distribution of normal human brain functional networks. Two local information indices-common neighbor (CN) and preferential attachment index (PA), are introduced into the prediction model as another parameter to emulate many key topological of brain functional networks in the previous study. In addition to these two indices, many other local information indices can be chosen for investigation. Different indices eval

SUBMITTER: Cheng C 

PROVIDER: S-EPMC5186779 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature