Network entropy analysis reveals high heterogeneity of human functional networks

Abstract

We investigated the structure of the functional networks of the human brain using data from the OASIS dataset. To construct functional networks from BOLD signals, we used the method of dynamic time warping (DTW), which allows one to take into account possible distortions and nonlinear effects when comparing two time series. We investigated the resulting functional networks in terms of graph entropy, a recently proposed thermodynamic approach to describing dynamics in complex networks. The graph entropy approach provides tools to investigate the information flows in networks on different timescales. We showed a high heterogeneity of the resulting individual functional networks, expressed in a significant mismatch of the characteristic excitation diffusion times between subjects. We also constructed an artificial network model with a hierarchy of temporal scales to explain the detected multiscale nature of some functional networks. No differences were found between the healthy subjects and subjects with different levels of clinical dementia rating. We hypothesize that the heterogeneity we found may be related to the personality traits of the subjects, and we intend to investigate this issue further.