Abstract
Recent modeling and empirical studies support the hypothesis that large-scale brain networks function near a critical state. Similar functional connectivity patterns derived from resting state empirical data and brain network models at criticality provide further support. However, despite the strong implication of a relationship, there has been no principled explanation of how criticality shapes the characteristic functional connectivity in large-scale brain networks. Here, we hypothesized that the network science concept of partial phase locking is the underlying mechanism of optimal functional connectivity in the resting state. We further hypothesized that the characteristic connectivity of the critical state provides a theoretical boundary to quantify how far pharmacologically or pathologically perturbed brain connectivity deviates from its critical state, which could enable the differentiation of various states of consciousness with a theory-based metric.To test the hypothesis, we used a neuroanatomically informed brain network model with the resulting source signals projected to electroencephalogram (EEG)-like sensor signals with a forward model. Phase lag entropy (PLE), a measure of phase relation diversity, was estimated and the topography of PLE was analyzed. To measure the distance from criticality, the PLE topography at a critical state was compared with those of the EEG data from baseline consciousness, isoflurane anesthesia, ketamine anesthesia, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state.We demonstrate that the partial phase locking at criticality shapes the functional connectivity and asymmetric anterior-posterior PLE topography, with low (high) PLE for high (low) degree nodes. The topographical similarity and the strength of PLE differentiates various pharmacologic and pathologic states of consciousness. Moreover, this model-based EEG network analysis provides a novel metric to quantify how far a pharmacologically or pathologically perturbed brain network is away from critical state, rather than merely determining whether it is in a critical or non-critical state.
Highlights
Criticality, the state of a system at the boundary between order and disorder, has long been proposed to play an important role in neural dynamics and brain function
Recent studies have revealed that the functional connectivity of spontaneous dynamics is highly correlated to the structural connectivity when the system is at criticality, and less correlated when the system is at sub- or super-criticality (Kim et al, 2017; Stam et al, 2016; Tagliazucchi et al, 2016)
The maximal correlation between the functional connectivity of the empirical data and the brain network model at criticality is direct evidence suggesting that the resting-state brain functions at criticality (Deco and Jirsa, 2012). fMRI studies demonstrated that the correlation is state specific, with higher values in the conscious state and lower values in the unconscious state (Tagliazucchi et al, 2016)
Summary
Criticality, the state of a system at the boundary between order and disorder, has long been proposed to play an important role in neural dynamics and brain function. It has been suggested that criticality in the brain promotes optimal information processing and storage in neural networks. Empirical evidence supports the hypothesis that the brain operates at or near the critical point, at the neuronal network. Most studies have focused on scale-free behavior, showing power law distribution of empirically observed variables as the evidence of criticality. Recent studies have revealed that the functional connectivity of spontaneous dynamics is highly correlated to the structural connectivity when the system is at criticality, and less correlated when the system is at sub- or super-criticality (Kim et al, 2017; Stam et al, 2016; Tagliazucchi et al, 2016). Despite the obvious implication of a relationship between network structure, connectivity, and criticality, there has been no principled explanation of how a correlation between functional and structural networks emerges at a critical state
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