Broadband Criticality of Human Brain Network Synchronization

By Manfred G. Kitzbichler, Marie L. Smith, Søren R. Christensen, E. Bullmore. PLoS Comput. Biol., 2009

546 citations recorded by the source corpus.

Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth.

Subfields: Cognitive Neuroscience, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e0f4e6a1368263582df142b025a0bc14d8a2b6dc

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Citations in the corpus

Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, unknown): “A particular case of this differential coupling is when S is a sine function, as in the Kuramoto model (Kuramoto, 1975; Honey and Sporns, 2008; Martens et al., 2009; Kitzbichler et al., 2009; Cabral et al., 2011; Mirollo, 2012; Hong and Strogatz, 2012).”
EEG microstate sequences in healthy humans at rest reveal scale-free dynamics (2010, result): “This result also extends the finding that synchronization metrics during rest between different channels in MEG and between brain regions in fMRI show power law scaling behavior (62).”
Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors (2012, result): “This is consistent with the recent analyses of Kitzbichler et al. (2009), who demonstrated criticality in empirical resting state data.”
Cognitive relevance of the community structure of the human brain functional coactivation network (2013, background): “The possible configurations of a brain functional network will dynamically change over time, either in response to changing external contingencies or endogenously (34, 35).”
Being Critical of Criticality in the Brain (2012, background): “In addition, the size of phase locking intervals in human fMRI has been reported to follow a power law, and the authors have related this to criticality in the awake, healthy human brain (Kitzbichler et al., 2009).”
Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks (2011, methodology): “The window size Lwin expressed in units of wavelet scale determines the number of cycles over which the average is taken and was chosen as Lwin 16 (Kitzbichler et al., 2009).”
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, background): “…(FC), is modulated by learning (Bassett et al., 2011), cognitive and affective states (Cribben et al., 2012; Ekman et al., 2012; Eryilmaz et al., 2011; Richiardi et al., 2011; Shirer et al., 2012) and also spontaneously (Britz et al., 2010; Chang and Glover, 2010; Kitzbichler et al., 2009).”
Functional and Effective Connectivity: A Review (2011, unknown): “They also speak to larger questions about how the brain maintains itself near phase-transitions (i.e., self-organized criticality and gain control; Abbott et al., 1997; Kitzbichler et al., 2009).”
Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations (2014, unknown): “, 2001; Strogatz, 2003) and, more recently, neural dynamics (Breakspear et al., 2010; Cabral et al., 2011; Kitzbichler et al., 2009; Yan and Li, 2013).”
Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits (2010, background): “Functional patterns on hierarchically modular architectures have specifically been shown to display computationally advantageous dynamics characterized by stability and diversity [33], unlike simulated dynamics on either random or non-hierarchically small-world architectures [34].”
The Kuramoto model in complex networks (2015, methodology): “Other aspects of dynamical criticality in human brain functional networks were studied in [400] by comparing times series generated with the Kuramoto model and fMRI data recorded from normal subjects under resting conditions.”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, methodology): “Research in these areas has accelerated dramatically and now yields a stream of important discoveries spanning from the neuronal (Gal and Marom, 2013; Gollo et al., 2013) to the whole-brain scale (Kitzbichler et al., 2009).”
Consciousness in the universe: a review of the 'Orch OR' theory. (2014, background): “Neurons have a rich and uniquely organized cytoskeleton, the major components being microtubules, well-positioned and uniquely arrayed (e.g. in dendrites and soma) to mediate consciousness and regulate firing.”
Cognitive fitness of cost-efficient brain functional networks (2009, result): “This conservation of topological properties across frequency scales is broadly compatible with other recent demonstrations of scale invariance in human brain functional networks (7, 22).”
The graphical brain: Belief propagation and active inference (2017, unknown): “…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 & Fujii, 2004).”