Abstract
Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or “virtual brains”, whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs) and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic), in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states display lower complexity than virtual brains modeling normal neural function. We finally discuss the implications of our results for the neurobiology of health and disease.
Highlights
Recent studies of electroencephalography (EEG) and magnetoencephalography (MEG) as well as of extracellular recordings in acute brain slices have demonstrated that both macroscopic and microscopic neural networks exhibit multiple activity rhythms [1,2,3,4,5]. These rhythms appear as a number of frequency bands which are evenly spaced on a logarithmic scale, thereby reducing the potential for cross-talk or mutual entrainment between frequency bands
Since the multi-oscillatory activity of the brain is known to be altered in disease, we set out to reconstruct virtual brains that reproduce altered EEG and MEG patterns such as those observed in epilepsy [5,7] and schizophrenia [8,9]
Oscillations of different frequency bands tend to minimize interference. This is similar to radio stations avoiding overlap between frequency bands to ensure clear transmission
Summary
Recent studies of electroencephalography (EEG) and magnetoencephalography (MEG) as well as of extracellular recordings (local field potentials) in acute brain slices have demonstrated that both macroscopic and microscopic neural networks exhibit multiple activity rhythms [1,2,3,4,5]. The virtual brains are not meant to model specific anatomical pathways or synaptic connections Instead, they model functional coupling between elements (nodes) of an abstract network. That the virtual brains would share common features, such as functional (not necessarily anatomical or synaptic) connectivity and dynamics, among themselves and with actual brains. Characterizing these commonalities would in turn help us identify parameters and physiological features of healthy brains, such as the balance between functional excitation and inhibition, the relative number of highly connected nodes (hubs), the probability of finding certain structural motifs, etc. We expected to find alterations of connectivity and dynamics in these altered virtual brains that would give us some insight into structural and physiological changes associated with the above pathologies
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