A Hybrid Communication Pattern in Brain Structural Network Revealed by Evolutionary Computation

Abstract

SummaryThe human brain functional connectivity network (FCN) is constrained and shaped by the information communication processes in the structural connectivity network (SCN). The underlying communication model thus becomes a critical issue for understanding structure-function coupling in the human brain. A number of communication models featuring different point-to-point routing strategies have been proposed, with shortest path (SP), diffusion (DIF), and navigation (NAV) as the typical, respectively requiring network global knowledge, local knowledge, and their combination for path seeking. Yet these models all assumed the entire brain to use a uniform routing strategy, which ignored lumping evidence supporting the wide variety of brain regions in both terms of biological substrates and functional exhibitions. In this study, we developed a novel communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB) for maximizing the structure-function coupling. The HYB-based model outperformed the three typical models in terms of predicting FCN and supporting robust communication. In HYB, brain regions in lower-order functional modules inclined to choose the routing strategies requiring more global knowledge, while those in higher-order functional components preferred to choose DIF. Additionally, compared to regions using SP and NAV, regions using DIF had denser structural connections, participated in more functional modules, but were less dominant within them. Together, our findings revealed and evidenced the possibility and advantages of hybrid routing underpinning efficient SCN communication.