Volume entropy for modeling information flow in a brain graph View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-01-22

AUTHORS

Hyekyoung Lee, Eunkyung Kim, Seunggyun Ha, Hyejin Kang, Youngmin Huh, Youngjo Lee, Seonhee Lim, Dong Soo Lee

ABSTRACT

Brain regions send and receive information through neuronal connections in an efficient way. In this paper, we modelled the information propagation in brain networks by a generalized Markov system associated with a new edge-transition matrix, based on the assumption that information flows through brain networks forever. From this model, we derived new global and local network measures, called a volume entropy and the capacity of nodes and edges on FDG PET and resting-state functional MRI. Volume entropy of a metric graph, a global measure of information, measures the exponential growth rate of the number of network paths. Capacity of nodes and edges, a local measure of information, represents the stationary distribution of information propagation in brain networks. On the resting-state functional MRI of healthy normal subjects, these measures revealed that volume entropy was significantly negatively correlated to the aging and capacities of specific brain nodes and edges underpinned which brain nodes or edges contributed these aging-related changes. More... »

PAGES

256

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-36339-7

DOI

http://dx.doi.org/10.1038/s41598-018-36339-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111604032

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30670725


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