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.
Highlights
Brain is typically represented by a complex network, where its regions are functionally connected to each other[1]
On the resting state functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) obtained from 38 normal individuals between the ages of 20 s and 60 s, volume entropy revealed the change of volume entropy according to normal aging and the specific brain areas contributing these changes, which represented the aging-related decline of information flows in the brain
It measured the fastest growth rate of paths in a network through which the information was propagated over a brain
Summary
Brain is typically represented by a complex network, where its regions are functionally connected to each other[1]. There are sufficient alternative paths between brain regions from a geometric point of view This property is referred to being locally efficient, which is often found in a modular network. We propose a new entropy-based network measure, called a volume entropy, which is derived from an information propagation model[14]. They were based on the information entropy that measured the average negative logarithm of the probability distribution of a system in information theory[17] Both of these entropies required a procedure to approximate the probability distribution of networks. The results showed that the volume entropy distinguished the underlying graph topology and geometry better than the existing network measures such as global and local efficiencies as well as preexisting entropy-based measures. On the resting state fMRI and PET obtained from 38 normal individuals between the ages of 20 s and 60 s, volume entropy revealed the change of volume entropy according to normal aging and the specific brain areas contributing these changes, which represented the aging-related decline of information flows in the brain
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
