The complexity of the human mind and how it generates a sense of an individual self is staggering - and we are discovering more and more that there is a fine line between chaos and healthy functioning brain.
Here is the abstract and author's summary from the referenced research:Our complex brains thrive on the edge of chaos
CHAOTIC thinking is rarely a recipe for success, but evidence is emerging that operating at the edge of chaos may drive our brain's astonishing capabilities.
Neuroscientists have long suspected that the network of neurons in our brains might be connected in such a way that they achieve a state of "self-organised criticality" (SOC), in which they are neither ordered nor random, but somewhere in between. In such a state, even a minor change can prompt a large reaction: for example, forest fires, earthquakes and avalanches tend to propagate under SOC.
In 2003, neuroscientists showed that the propagation of electrical signals in a slice of brain tissue from a rat followed patterns expected for a state of SOC (The Journal of Neuroscience, vol 23, p 11167). To see whether this was also true in people, Ed Bullmore of the University of Cambridge and his colleagues mapped electrical brain activity in 19 volunteers.
One mark of SOC is that signals should show similar patterns at all frequencies - a property known as scale invariance. Sure enough, when Bullmore's team measured the length of time that two electrical signals from random locations in the brain were "in phase", it was the same at all signal frequencies (PLoS Computational Biology, DOI: 10.1371/journal.pcbi.1000314).
Computer models have shown that when neural networks are in a state of SOC, they maximise information processing and storage. "It might be advantageous for the brain," Bullmore says.
Abstract
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.
Author Summary
Systems in a critical state are poised on the cusp of a transition between ordered and random behavior. At this point, they demonstrate complex patterning of fluctuations at all scales of space and time. Criticality is an attractive model for brain dynamics because it optimizes information transfer, storage capacity, and sensitivity to external stimuli in computational models. However, to date there has been little direct experimental evidence for critical dynamics of human brain networks. Here, we considered two measures of functional coupling or phase synchronization between components of a dynamic system: the phase lock interval or duration of synchronization between a specific pair of time series or processes in the system and the lability of global synchronization among all pairs of processes. We confirmed that both synchronization metrics demonstrated scale invariant behaviors in two computational models of critical dynamics as well as in human brain functional systems oscillating at low frequencies (<0.5>
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