How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest

Abstract

In recent years the study of the intrinsic brain dynamics in a relaxed awake state in the absence of any specific/ntask has gained increasing attention, as spontaneous neural activity has been found to be highly structured at a/nlarge scale. This so called resting-state activity has been found to be comprised by nonrandom spatiotemporal/npatterns and fluctuations, and several Resting-State Networks (RSN) have been found in BOLD-fMRI as well as/nin MEG signal power envelope correlations. The underlying anatomical connectivity structure between areas of/nthe brain has been identified as being a key to the observed functional network connectivity, but the mechanisms/nbehind this are still underdetermined. Theoretical large-scale brain models for fMRI data have corroborated the/nimportance of the connectome in shaping network dynamics,while the importance of delays and noise differ between/nstudies and depend on the models' specific dynamics. In the current study, we present a spiking neuron/nnetworkmodel that is able to produce noisy, distributed alpha-oscillations, matching the power peak in the spectrumof/ngroup resting-stateMEG recordings.We studied howwell the model captured the inter-node correlation/nstructure of the alpha-band power envelopes for different delays between brain areas, and found that the model/nperforms best for propagation delays inside the physiological range (5–10 m/s). Delays also shift the transition/nfrom noisy to bursting oscillations to higher global coupling values in the model. Thus, in contrast to the/nasynchronous fMRI state, delays are important to consider in the presence of oscillation.