  We investigate the influence of indirect connections, interregional distance
and collective effects on the large-scale functional networks of the human
cortex. We study topologies of empirically derived resting state networks
(RSNs), extracted from fMRI data, and model dynamics on the obtained networks.
The RSNs are calculated from mean time-series of blood-oxygen-level-dependent
(BOLD) activity of distinct cortical regions via Pearson correlation
coefficients. We compare functional-connectivity networks of simulated BOLD
activity as a function of coupling strength and correlation threshold. Neural
network dynamics underpinning the BOLD signal fluctuations are modelled as
excitable FitzHugh-Nagumo oscillators subject to uncorrelated white Gaussian
noise and time-delayed interactions to account for the finite speed of the
signal propagation along the axons. We discuss the functional connectivity of
simulated BOLD activity in dependence on the signal speed and correlation
threshold and compare it to the empirical data.
