![]() The best match between simulated and experimental resting state functional connectivity as measured in humans with fMRI occurs right below the threshold separating the trivial low firing activity equilibrium state from the multistable landscape of attractors with high firing activity in certain brain areas. With this in mind, we here demonstrate the emergence of the slowly fluctuating (<0.1 Hz) RSNs as noise-driven transient fluctuations around the stable equilibrium state corresponding to low firing activity in all neurons in all areas. To this aim, we formulate a detailed global attractor model of the brain network, which offers a realistic mechanistic model at the level of each single brain area based on spiking neurons and realistic AMPA, NMDA, and GABA synapses. A more detailed and complete physiological model for the dynamics of individual brain areas will allow making the link between neurophysiological parameters and RSN dynamics. Other RSN modeling efforts included more detailed physiological models for the dynamics of brain areas, but then again imposed specific constraints upon the network dynamics, mostly for reasons of computational efficiency. (2009) were able to explain the formation and dissolution of slowly fluctuating RSNs by considering a simple local oscillatory dynamics at each node. Theoretical models allowed us to study the relation between anatomical structure and RSN. ![]() The missing link for understanding the formation and dissolution of RSNs is the dynamics ( Deco et al., 2011). RSNs reflect the anatomical connectivity between brain areas in a network but cannot be understood in those terms alone ( Bullmore and Sporns, 2009). During the last decade, numerous experimental investigations have shown that spontaneous brain activity during rest is highly structured into characteristic spatiotemporal patterns, the so-called resting-state networks (RSNs) ( Biswal et al., 1995 Greicius et al., 2003 Fox et al., 2005, 2007 Fransson, 2005 Raichle and Mintun, 2006 Rogers et al., 2007 Vincent et al., 2007). ![]() Nevertheless, the equilibrium state of the brain, i.e., the spontaneous, not stimuli- or task-evoked brain activity during rest, does not reflect just a trivial random activity as one may naively expect. The brain is a particular case of a noisy physical system composed of neurons interconnected through synapses in brain areas, whose activity is mainly characterized by the level of spiking activity in those areas. Eventually, if noise is inherent in the system, the fluctuations drive the system out of its equilibrium state resulting in low-amplitude random activity. In the absence of external stimulation, any physical system is in its equilibrium state, which is often characterized by the lowest level of activity of that system. The multistable attractor landscape defines a functionally meaningful dynamic repertoire of the brain network that is inherently present in the neuroanatomical connectivity. Under these conditions, the slow fluctuating (<0.1 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. ![]() 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. 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. We aim here to understand the origins of resting state activity through modeling via a global spiking attractor network of the brain. These spatiotemporal patterns, called resting state networks, display low-frequency characteristics (<0.1 Hz) observed typically in the BOLD-fMRI signal of human subjects. 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.
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