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

• DOI: 10.1523/jneurosci.21-04-01370.2001
• Publication Date: 2018-04-05T00:12:27Z
• Authors: Klaus Linkenkaer-...; Vadim V. Nikouline; J. Matias Palva; Risto J. Ilmoniemi
• Keywords: Adult; Male; 0301 basic medicine; Stochastic Processes; Time Factors; Models, Neurological; Brain; Magnetoencephalography; Electroencephalography; Signal Processing, Computer-Assisted; 03 medical and health sciences; Fractals; Biological Clocks; Parietal Lobe; Visual Perception; Humans; Computer Simulation; Female; Occipital Lobe; Nerve Net

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.