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

By Gustavo Deco, Viktor Jirsa. Journal of Neuroscience, 2012

705 citations recorded by the source corpus.

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 (<0.1 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 (<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. The multistable attractor landscape defines a functionally meaningful dynamic repertoire of the brain network that is inherently present in the neuroanatomical connectivity.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b58862a8ae56312d8dd85bceb264ec5bf667a72e

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Citations in the corpus

Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations (2014, neutral): “, 2008a, 2008b) or even realistic spiking/synaptic models (Deco and Jirsa, 2012).”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “Recent modeling studies in this line of research can be thought of as particular realizations of the generic model presented here (Ghosh et al., 2008a; Knock et al., 2009; Honey et al., 2009; Alstott et al., 2009; Cabral et al., 2012; Deco et al., 2009; Deco and Jirsa, 2012).”
Time-resolved resting-state brain networks (2014, background): “Evidence for this hypothesis is provided by the coordinated fluctuations in network measures, such as hub centrality (31), that have been observed in simulated rsfMRI data (32, 33).”
The Metastable Brain (2014, neutral): “Perhaps for convenience of dealing with orderly collective behaviors, more often than not they emphasize strong coupling: the system’s parameter space is divided between stable and ‘‘unstable’’ regions often with a focus on the former (but see recent work that focuses on the interface between stable”
The Virtual Brain: a simulator of primate brain network dynamics (2013, methodology): “The current implementation of this model is based on the approach used in (Deco and Jirsa, 2012).”
The graphical brain: Belief propagation and active inference (2017, neutral): “…notions like metastability, itinerancy, and self-organized criticality (Bak, Tang, & Wiesenfeld, 1987; Breakspear, 2004; Breakspear & Stam, 2005; Deco & Jirsa, 2012; Jirsa, Friedrich, Haken, & Kelso, 1994; Kelso, 1995; Kitzbichler, Smith, Christensen, & Bullmore, 2009; Shin & Kim, 2006; Tsuda &…”
EEG microstates as a tool for studying the temporal dynamics of whole‐brain neuronal networks: A review (2017, background): “, 1986)) or by noise (during resting conditions) (Deco and Jirsa, 2012) (Ghosh et al.”
The human connectome: Origins and challenges (2013, background): “These studies have given rise to the idea that the resting brain resides in a regime that is close to instability which allows it to continually explore a dynamic repertoire of noisy brain states (Deco and Jirsa, 2012; Deco et al., 2011; Senden et al., 2012).”
Dynamic models of large-scale brain activity (2017, background): “A further scenario rests on the role of ghost attractors 109 , a dynamic landscape of remnant attractors each of which has an incomplete basin, hence allowing the system to ‘wander’ through large swathes of the phase space under the influence of weak noise 110 .”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “, 2012) and computational (Deco and Jirsa, 2012) analyses points in favour of this proposal.”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, methodology): “Several studies have successfully done so for both rs-MRI (Deco and Jirsa, 2012; Honey et al., 2007) and rs-MEG (Cabral et al., 2014b), and has even been used to model important features of sleep (Deco et al., 2013a) (see Figure 5).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, neutral): “…low-pass filtering properties of the haemodynamic response render subsecond dynamics impervious to measurement with fMRI, recent research has nonetheless suggested that such dynamics may lie at the heart of large-scale resting state fMRI networks across slower time scales (Deco and Jirsa, 2012).”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, methodology): “Many of these models (Deco and Jirsa, 2012; Deco et al., 2013a; Ghosh et al., 2008) operate at a working point close to the critical edge of instability (Deco et al., 2013b).”
Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations (2013, methodology): “2A), whereas in Deco and Jirsa (2012) both bifurcations overlapped.”