The temporal structures and functional significance of scale-free brain activity

By Biyu J. He, John M. Zempel, Abraham Z. Snyder, Marcus E. Raichle. Neuron, 2010

995 citations recorded by the source corpus.

SUMMARY Scale-free dynamics, with a power spectrum following P ∝ f-β, are an intrinsic feature of many complex processes in nature. In neural systems, scale-free activity is often neglected in electrophysiological research. Here, we investigate scale-free dynamics in human brain and show that it contains extensive nested frequencies, with the phase of lower frequencies modulating the amplitude of higher frequencies in an upward progression across the frequency spectrum. The functional significance of scale-free brain activity is indicated by task performance modulation and regional variation, with β being larger in default network and visual cortex and smaller in hippocampus and cerebellum. The precise patterns of nested frequencies in the brain differ from other scale-free dynamics in nature, such as earth seismic waves and stock market fluctuations, suggesting system-specific generative mechanisms. Our findings reveal robust temporal structures and behavioral significance of scale-free brain activity and should motivate future study on its physiological mechanisms and cognitive implications.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/25055b228f68ac005752e44bfb61c3f6de365a9c

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Citations in the corpus

The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine (2019, positive): “Furthermore, as during physiological sleep the E/I balance shifts towards inhibition, EEG activity is characterized by a steeper PSD decay over a broad range of frequencies (Miskovic et al., 2018; He et al., 2010; Freeman and Zhai, 2009; Pereda et al., 1998; Freeman, 2006; Su sm akov a, 2006).”
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The Restless Brain (2011, positive): “…as well, brain electrical activity can appear largely arrhythmic (sometimes referred to as ‘‘scale free’’ because of the absence of speciﬁc oscillations in, for example, the alpha and theta bands); this frequency spectrum can be best described using a power law [i.e., 1/ f a (He et al., 2010)].”
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Parameterizing neural power spectra into periodic and aperiodic components (2020, background): “Thus, treating the aperiodic component as ‘noise’ ignores its physiological correlates, which in turn relate to cognitive and perceptual 17,27 states, while trait-like differences in aperiodic activity have been shown to be potential biological markers in development 28 and aging 18 as well as…”
A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex (2015, positive): “This may explain decreases in low-frequency power (i.e. slow modes) seen in ECoG recordings when a subject engages in a task (He et al., 2010).”
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