Network representation of fMRI timeseries using visibility graphs

Timeseries can be mapped into graphs by linking visibility of the signal at each time-point with respect to others – an approach known as visibility graph. We used visibility graph analysis to convert fMRI data into complex networks. We then evaluated the graph theoretical properties of this network and characterised their robustness to motion and test-retest reliability between sessions. We show that time-series network features such as average degree, average path length, and clustering coefficient are highly sensitive to motion. However, on a low-motion dataset they have a good reliability between sessions.