Neuronal Avalanches in the Resting MEG of the Human Brain

Abstract

What constitutes normal cortical dynamics in healthy human subjects is a major question in systems neuroscience. Numerous in vitro and in vivo animal studies have shown that ongoing or resting cortical dynamics are characterized by cascades of activity across many spatial scales, termed neuronal avalanches. In experiment and theory, avalanche dynamics are identified by two measures: (1) a power law in the size distribution of activity cascades with an exponent of −3/2 and (2) a branching parameter of the critical value of 1, reflecting balanced propagation of activity at the border of premature termination and potential blowup. Here we analyzed resting-state brain activity recorded using noninvasive magnetoencephalography (MEG) from 124 healthy human subjects and two different MEG facilities using different sensor technologies. We identified large deflections at single MEG sensors and combined them into spatiotemporal cascades on the sensor array using multiple timescales. Cascade size distributions obeyed power laws. For the timescale at which the branching parameter was close to 1, the power law exponent was −3/2. This relationship was robust to scaling and coarse graining of the sensor array. It was absent in phase-shuffled controls with the same power spectrum or empty scanner data. Our results demonstrate that normal cortical activity in healthy human subjects at rest organizes as neuronal avalanches and is well described by a critical branching process. Theory and experiment have shown that such critical, scale-free dynamics optimize information processing. Therefore, our findings imply that the human brain attains an optimal dynamical regime for information processing.

Assessing the sensitivity of diffusion MRI to detect neuronal activity directly

“…Comparison of intracellular and extracellular spike activity demonstrated that during up-state periods, all neurons are depolarized and pyramidal neurons display average firing rates of ∼1 Hz, typical found for resting activity in vivo (24,48). In fact, the spatiotemporal organization of the neuronal population activity during up-states (25,28) is characteristic of neuronal avalanches, the resting state organization identified in vivo in rodents, nonhuman primates (27), and humans (49). We therefore conclude that up-state activity measured in organotypic cultures best matches ongoing resting activity in vivo.…”

Landau–Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization

“…As a matter of fact, such models have been reported to achieve the best performance -e.g. reproducing empirically-observed resting-state networks (68)-when operating close to the synchronization phase transition point (64,87,88).Within our framework, it is possible to define a protocol to analyze avalanches, resembling very closely the experimental one (7, 8, 11,16,17). Thus, in contrast with other computational models, causal information is not explicitly needed/employed here to determine avalanches -they are determined from raw data-and results can be straightforwardly compared to experimental ones for neuronal avalanches, without conceptual gaps (40).…”

“…to be organized in a scale-free way, limited only by network size (7). Furthermore, they obey finite-size scaling (8), a trademark of scale invariance (9), and the corresponding exponents are compatible with those of an unbiased branching process (10).Scale-free avalanches of neuronal activity have been consistently reported to occur across neural tissues, preparation types, experimental techniques, scales, and species (11)(12)(13)(14)(15)(16)(17)(18). This has been taken as empirical evidence backing the criticality hypothesis, i.e.…”

Mean-field behavior as a result of noisy local dynamics in self-organized criticality: Neuroscience implications

“…Our main motivation is due to the recent observation of neuronal avalanches and their presumed relation to SOC. In a wide range of recent experiments [14][15][16][17][18][19][20][21] , neuronal avalanches have been shown to exhibit power law behavior with mean-field exponents. Whether neural dynamics [22] is dissipative or not is still debated, but their noisy dynamics [23] is a certainty, as the post-synaptic neurons receive more or less than their fair share of the ion distributed by the pre-synaptic neuron in the ionic plasma, which permeates the space between synapses.…”