RT Journal Article
SR Electronic
T1 Neuronal Avalanches in the Resting MEG of the Human Brain
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 7079
OP 7090
DO 10.1523/JNEUROSCI.4286-12.2013
VO 33
IS 16
A1 Shriki, Oren
A1 Alstott, Jeff
A1 Carver, Frederick
A1 Holroyd, Tom
A1 Henson, Richard N.A.
A1 Smith, Marie L.
A1 Coppola, Richard
A1 Bullmore, Edward
A1 Plenz, Dietmar
YR 2013
UL http://www.jneurosci.org/content/33/16/7079.abstract
AB 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.