Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations

By K. Linkenkaer-Hansen, Vadim V. Nikouline, J. Matias Palva, Risto J. Ilmoniemi. Journal of Neuroscience, 2001

1,173 citations recorded by the source corpus.

The human brain spontaneously generates neural oscillations with a large variability in frequency, amplitude, duration, and recurrence. Little, however, is known about the long-term spatiotemporal structure of the complex patterns of ongoing activity. A central unresolved issue is whether fluctuations in oscillatory activity reflect a memory of the dynamics of the system for more than a few seconds. We investigated the temporal correlations of network oscillations in the normal human brain at time scales ranging from a few seconds to several minutes. Ongoing activity during eyes-open and eyes-closed conditions was recorded with simultaneous magnetoencephalography and electroencephalography. Here we show that amplitude fluctuations of 10 and 20 Hz oscillations are correlated over thousands of oscillation cycles. Our analyses also indicated that these amplitude fluctuations obey power-law scaling behavior. The scaling exponents were highly invariant across subjects. We propose that the large variability, the long-range correlations, and the power-law scaling behavior of spontaneous oscillations find a unifying explanation within the theory of self-organized criticality, which offers a general mechanism for the emergence of correlations and complex dynamics in stochastic multiunit systems. The demonstrated scaling laws pose novel quantitative constraints on computational models of network oscillations. We argue that critical-state dynamics of spontaneous oscillations may lend neural networks capable of quick reorganization during processing demands.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/8e9351c8ad6406e46eeb1b877f56db96194186ef

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Citations in the corpus

Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. (2005, background): “These scale free fluctuations of synchronization levels have been demonstrated for local (Linkenkaer-Hansen et al., 2001; Nikulin and Brismar, 2004, 2005) as well as global dynamics (Stam and de Bruin, 2004), and migh be due to self-organized criticality or critical dynamics near a phase transition.”
Neuronal Avalanches in Neocortical Circuits (2003, result): “, 2002), and EEG time series in humans (Linkenkaer-Hansen et al., 2001; Worrell et al., 2002)] our results go beyond the phenomenological description of a power law only.”
Complex brain networks: graph theoretical analysis of structural and functional systems (2009, background): “This timescale invariance of wavelet correlations and the brain functional network parameters derived from them is a theoretically predictable corollary of the long-range autocorrelations of neurophysiological time series 82–84 .”
Spike avalanches in vivo suggest a driven, slightly subcritical brain state (2014, background): “…(Bertschinger and Natschläger, 2004; Haldeman and Beggs, 2005; Boedecker et al., 2012; Lizier, 2013), from non-oscillatory to oscillatory regimes (Linkenkaer-Hansen et al., 2001; Poil et al., 2012), from replay to non-replay of spatiotemporal patterns (Scarpetta and de Candia, 2013), and from a…”
A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs (2006, background): “Moreover, long period or very low-frequency coherent oscillations have been described recently in both monkey and human electrophysiological data (Linkenkaer-Hansen et al., 2001; Leopold et al., 2003).”
Self-organized criticality as a fundamental property of neural systems (2014, background): “We believe that these perspectives provide a strong incentive for more experimental and theoretical work in the area of self-organized criticality.”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, methodology): “Employing an analysis called detrended fluctuation analysis (DFA), Linkenkaer-Hauser and colleagues reported that the fluctuating amplitudes of the two dominant oscillations of the human brain – the alpha and beta rhythms – exhibited scale-free temporal statistics (Linkenkaer-Hansen et al., 2001).”
Functional and Effective Connectivity: A Review (2011, neutral): “…many people argue for criticality and power law effects in large-scale cortical activity (e.g., Freyer et al., 2009; Kitzbichler et al., 2009; Linkenkaer-Hansen et al., 2001; Stam and de Bruin, 2004), other people do not (Bedard et al., 2006; Miller et al., 2007; Touboul and Des-texhe, 2009).”
Graph theoretical analysis of complex networks in the brain (2007, background): “Several studies have shown that time series of brain activity demonstrate scaling with characteristic exponents, suggesting critical dynamics near a phase transition [12-15].”
The criticality hypothesis: how local cortical networks might optimize information processing (2008, background): “…for many years in neuroscience in the temporal correlations of single time-series data (e.g. the power spectrum from the electroencephalogram (Linkenkaer-Hansen et al. 2001; Worrell et al. 2002), Fano or Allan factors in spike count statistics (Teich et al. 1997), neurotransmitter secretion…”
Generative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model (2010, positive): “These non-classic statistics in the amplitude fluctuations supplement prior findings of longtailed distributions in the temporal statistics of low frequency (below 30 Hz) rhythmic cortical activity (Linkenkaer-Hansen et al., 2001; Stam and de Bruin, 2004).”
Can structure predict function in the human brain? (2010, positive): “rapid fluctuations (Linkenkaer-Hansen et al., 2001; Stam and de Bruin, 2004; Freyer et al., 2009), possibly indicative of a complex dynamic”
Scaling laws of human interaction activity (2009, background): “5 establishes a missing link between studies of growth processes in economic or social systems (16–18) and studies of long-term correlations, such as in finance and the economy (23), Ethernet traffic (24), and human brain (25) or motor activity (26).”