Volume entropy for modeling information flow in a brain graph.
Sci Rep
; 9(1): 256, 2019 01 22.
Article
in En
| MEDLINE
| ID: mdl-30670725
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain
/
Aging
/
Entropy
/
Models, Neurological
/
Nerve Net
Type of study:
Health_economic_evaluation
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
Sci Rep
Year:
2019
Document type:
Article
Affiliation country:
South Korea