Ranking Regions, Edges and Classifying Tasks in Functional Brain Graphs by Sub-Graph Entropy.

ABSTRACT: This paper considers analysis of human brain networks or graphs constructed from time-series collected from functional magnetic resonance imaging (fMRI). In the network of time-series, the nodes describe the regions and the edge weights correspond to the absolute values of correlation coefficients of the time-series of the two nodes associated with the edges. The paper introduces a novel information-theoretic metric, referred as sub-graph entropy, to measure uncertainty associated with a sub-graph. Nodes and edges constitute two special cases of sub-graph structures. Node and edge entropies are used in this paper to rank regions and edges in a functional brain network. The paper analyzes task-fMRI data collected from 475 subjects in the Human Connectome Project (HCP) study for gambling and

SUBMITTER: Sen B 

PROVIDER: S-EPMC6527859 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature