Human Brain Mapping, Volume 29 Issue 7, Pages 770-777

Simon-Shlomo Poil, Arjen van Ooyen, Klaus Linkenkaer-Hansen
Department of Experimental Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR)
BioMag Laboratory, HUSLAB, Helsinki University Central Hospital

Abstract: Human brain oscillations fluctuate erratically in amplitude during rest and exhibit power-law decay of temporal correlations. It has been suggested that this dynamics reflects self-organized activity near a critical state. In this framework, oscillation bursts may be interpreted as neuronal avalanches propagating in a network with a critical branching ratio. However, a direct comparison of the temporal structure of ongoing oscillations with that of activity propagation in a model network with critical connectivity has never been made. Here, we simulate branching processes and characterize the activity propagation in terms of avalanche life-time distributions and temporal correlations. An equivalent analysis is introduced for characterizing ongoing oscillations in the alpha-frequency band recorded with magnetoencephalography (MEG) during rest. We found that models with a branching ratio near the critical value of one exhibited power-law scaling in life-time distributions with similar scaling exponents as observed in the MEG data. The models reproduced qualitatively the power-law decay of temporal correlations in the human data; however, the correlations in the model appeared on time scales only up to the longest avalanche, whereas human data indicate persistence of correlations on time scales corresponding to several burst events. Our results support the idea that neuronal networks generating ongoing alpha oscillations during rest operate near a critical state, but also suggest that factors not included in the simple classical branching process are needed to account for the complex temporal structure of ongoing oscillations during rest on time scales longer than the duration of individual oscillation bursts. Hum Brain Mapp 29:770–777, 2008. (c) 2008 Wiley-Liss, Inc.

Citations: 22 (Related articles)

Gonzalez et al., External Drive to Inhibitory Cells Induces Alternating Episodes of High- and Low-Amplitude Oscillations, PLoS Comput Biol 8(8): e1002666. doi:10.1371/journal.pcbi.1002666 (2012)

Palva and Palva, Infra-slow fluctuations in electrophysiological recordings, blood-oxygenation-level-dependent signals, and psychophysical time series, NeuroImage, Volume 62, Issue 4, 1 October 2012, Pages 2201–2211

Poil et al, Critical-State dynamics of avalanches and oscillations jointly emerge from balanced excitation/inhibition in neuronal networks. J Neurosci. 32(29):9817-9823 (2012)

Larremore et al, Statistical properties of avalanches in networks, Phys. Rev. E 85, 066131 (2012)

Hartley et al, Long-Range Temporal Correlations in the EEG Bursts of Human Preterm Babies, PLoS ONE 7(2): e31543.

Beggs and Timme, Being Critical of Criticality in the Brain, Front Physiol. 2012; 3: 163

Shew and Plenz, The Functional Benefits of Criticality in the Cortex, Neuroscientist May 24, 2012

Smit et al, cale-Free Modulation of Resting-State Neuronal Oscillations Reflects Prolonged Brain Maturation in Humans, J. Neurosci, 31(37): 13128-13136, 2011

Poil et al, Fast network oscillations in vitro exhibit a slow decay of temporal auto-correlations, Eur J Neurosci, 2011

Hobbs et al, Aberrant Neuronal Avalanches in Cortical Tissue Removed From Juvenile Epilepsy Patients, J. Clin. Neurophysiol, December 2010 – Volume 27 – Issue 6 – pp 380-386

Werner, Fractals in the Nervous System: Conceptual Implications for Theoretical Neuroscience, Front Physiol. 2010; 1: 15.

Ihlen EAF & Vereijken B, Interaction-Dominant Dynamics in Human Cognition: Beyond 1/fαFluctuationJournal of Experimental Psychology: General, Volume 139, Issue 3, August 2010, Pages 436-463

Ebisch et al, Altered intrinsic functional connectivity of anterior and posterior insula regions in high-functioning participants with autism spectrum disorder, Hum Brain Mapp.,  2010

Kello et al, Scaling laws in cognitive sciences, Trends in Cognitive Sciences, 14(5), 2010

Freyer et al., Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity, Journal of Neuroscience, 2009, 29(26):8512-8524; doi:10.1523/JNEUROSCI.0754-09.2009

Shew et al., Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality,  Journal of Neuroscience, 9 December 2009, 29(49): 15595-15600

Linkenkaer-Hansen K. Complex fluctuations in neuronal oscillations: From a qualitative hallmark to disease-sensitive quantitative traits. Frontiers in Neuroinformatics. 2009 Conference Abstract: 2nd INCF Congress of Neuroinformatics. doi: 10.3389/conf.neuro.11.2009.08.130

Montez et al, Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease, PNAS, 2009,

Ghosh et al, Noise during Rest Enables the Exploration of the Brain’s Dynamic Repertoire, PLoS Comput Biol. 2008 October; 4(10), doi: 10.1371/journal.pcbi.1000196

Milstein et al, Neuronal Shot Noise and Brownian 1/f2 Behavior in the Local Field Potential, PLoS ONE, 2009, 4(2), doi: 10.1371/journal.pone.0004338.

Kaulakys B and Alaburda M, Modeling scaled processes and 1/f(beta) noise using nonlinear stochastic differential equations, JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, P02051FEB 2009