Brain Dynamics at Rest: How Structure Shapes Dynamics

Neural activity at rest exhibits prominent alpha oscillations, which has been well established by using electroencephalography (EEG) and magnetoencephalography (MEG) techniques. More recently in humans, data obtained by using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) revealed the existence of spatial structures across the brain called functional connectivity (FC) patterns, and the so-called resting-state networks (RSNs). FC patterns have also been found in EEG and MEG studies. Lately, the RSNs detected by BOLD fMRI have also been observed in the alpha and beta bands by using MEG technique. Although the alpha oscillations and the RSNs are now well characterized experimentally, their neural origin remains a matter of debate. To study this issue, we introduce a model of the spontaneous neural activity of the brain, comprising local excitatory and inhibitory neural networks connected via white matter fibers. Theoretical analysis and numerical simulations of this model reveal that neural activity exhibits various modes. Many of these modes are found to be oscillatory, and the most dominant ones can be identified with different alpha oscillations. These modes are responsible for correlated activity in the alpha band as well as in the BOLD signal. Comparison with intracranial EEG in humans validates the dynamical scenario proposed by the model.