Commute Time as a Method to Explore Brain Functional Connectomes.

Graph theory has been extensively applied to investigate the brain complex networks in current neuroscience research. Many metrics derived from graph theory, such as local and global efficiencies, are based on the path length between nodes. These approaches are commonly used in the analyses of brain networks assessed by resting-state fMRI, though relying on the strong assumption that information flow throughout the network is restricted to the shortest paths. In this study, we propose the utilization of the commute time as a tool to investigate regional centrality on the functional Connectome. Our initial hypothesis was that an alternative approach that considers alternative routes (such as the commute time) could provide further information into the organization of functional networks. However, our empirical findings on the ADHD-200 database suggest that, at the group level, the commute time and shortest path are highly correlated. In contrast, at the subject level, we discovered that the commute time is much less susceptible to head motion artifacts when compared to metric based on shortest paths. Given the overall similarity between the measures, we argue that commute time might be advantageous particularly for connectomic studies in populations where motion artifacts are a major issue.