Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors

Abstract

The ongoing activity of the brain at rest, i.e., under no stimulation and in absence of any task, is astonishingly highly structured into spatiotemporal patterns. These spatiotemporal patterns, called resting state networks, display low-frequency characteristics (Ͻ0.1 Hz) observed typically in the BOLD-fMRI signal of human subjects. We aim here to understand the origins of resting state activity through modeling via a global spiking attractor network of the brain. This approach offers a realistic mechanistic model at the level of each single brain area based on spiking neurons and realistic AMPA, NMDA, and GABA synapses. Integrating the biologically realistic diffusion tensor imaging/diffusion spectrum imaging-based neuroanatomical connectivity into the brain model, the resultant emerging resting state functional connectivity of the brain network fits quantitatively best the experimentally observed functional connectivity in humans when the brain network operates at the edge of instability. Under these conditions, the slow fluctuating (Ͻ0.1 Hz) resting state networks emerge as structured noise fluctuations around a stable low firing activity equilibrium state in the presence of latent "ghost" multistable attractors. The multistable attractor landscape defines a functionally meaningful dynamic repertoire of the brain network that is inherently present in the neuroanatomical connectivity.

Stimulus-dependent variability and noise correlations in cortical MT neurons

“…Nevertheless, the low level of spontaneous correlations (∼0.1) reported here may be an indication that the network operates in a single state, but near the multistability. This view is supported by recent studies showing that spiking networks represent better both the FF reduction and the resting-state correlations of fMRI signals near criticality (51,52). This scenario is functionally meaningful as, at this working point, the network can rapidly react to an external stimulus and represent it in one attractor.…”

Synchronization, non-linear dynamics and low-frequency fluctuations: Analogy between spontaneous brain activity and networked single-transistor chaotic oscillators

“…These models yield synthetic low-frequency BOLD time-series which, to varying accuracy levels, predict functional connectivity from structural connectivity through diverse dynamical phenomena including synchronization of chaotic activity, reverberation, metastability, and noise-induced exploration of "ghost" attractors. 7,8,30,66 Our experimental observations of stronger lowfrequency activity and non-linear structure in highly synchronized (high node degree) cortical areas, if confirmed with higher-temporal resolution techniques such as magnetoencephalography, may help set further constraints to the validity of these models beyond the prevalent Pearson coefficient-based comparison of functional connectivity matrices. [6][7][8][9][10]58 To our knowledge, across existing simulation studies elements of this correspondence have been reported heterogeneously.…”

“…19,28,29 At the same time, the fact that realistic RSNs emerge even in highly simplified simulation scenarios, for example, from networked phase (Kuramoto) oscillators or discrete excitable units, raises the intriguing possibility of recapitulating some dynamical phenomena underlying brain function in other physical systems, where direct manipulation of connectivity is possible and causal relationships between connectivity and non-linear dynamics can be explored experimentally. [8][9][10]30 In particular, it has recently been shown that singletransistor oscillators can exhibit strikingly complex activity depending on an easily tunable control parameter (DC voltage source series resistance), oscillating periodically, chaotically, or close to criticality. 31 An experimental investigation of a ring of 30 diffusively coupled such oscillators, each consisting of a bipolar junction transistor, three reactive components and a resistor, has furthermore demonstrated the spontaneous formation of multi-scale community structure as a function of coupling strength, with elements of similarity to the modular organization observed in brain networks.…”

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

“…Approaches that are based on spiking neuron models use firing rates as input for BOLD signal estimation. Authors motivated that by the fact that in most cases, the firing rate is well correlated with excitatory synaptic activity (Deco and Jirsa, 2012). In the present study, however, the Stefanescu-Jirsa model provides six state variables with different biophysical counterparts; futhermore, each of them is described by the sum of the activity of three modes which model the activity of different neuronal subclusters of the inhibitory and the excitatory populations.…”