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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.

Author information

Lee H1,2, Kim E3,4, Ha S5, Kang H5,6, Huh Y5,7, Lee Y8,9, Lim S10, Lee DS11,12,13,14.
1
Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea. hklee.brain@gmail.com.
2
Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, South Korea. hklee.brain@gmail.com.
3
Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea.
4
Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, South Korea.
5
Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, South Korea.
6
BK21 Plus Global Translational Research on Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, South Korea.
7
Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, South Korea.
8
Data Science for Knowledge Creation Research Center, Seoul National University, Seoul, South Korea.
9
Department of Statistics, College of Natural Sciences, Seoul National University, Seoul, South Korea.
10
Department of Mathematical Sciences, Seoul National University, Seoul, South Korea. slim@snu.ac.kr.
11
Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, South Korea. dsl@snu.ac.kr.
12
Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, South Korea. dsl@snu.ac.kr.
13
Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea. dsl@snu.ac.kr.
14
Korea Brain Research Institute, Daegu, Republic of Korea. dsl@snu.ac.kr.