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Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity
Open accessAbstract
The brain is widely assumed to be a paradigmatic example of a complex, self-organizing system. As such, it should exhibit the classic hallmarks of nonlinearity, multistability, and "nondiffusivity" (large coherent fluctuations). Surprisingly, at least at the very large scale of neocortical dynamics, there is little empirical evidence to support this, and hence most computational and methodological frameworks for healthy brain activity have proceeded very reasonably from a purely linear and diffusive perspective. By studying the temporal fluctuations of power in human resting-state electroencephalograms, we show that, although these simple properties may hold true at some temporal scales, there is strong evidence for bistability and nondiffusivity in key brain rhythms. Bistability is manifest as nonclassic bursting between high- and low-amplitude modes in the alpha rhythm. Nondiffusivity is expressed through the irregular appearance of high amplitude "extremal" events in beta rhythm power fluctuations. The statistical robustness of these observations was confirmed through comparison with Gaussian-rendered phase-randomized surrogate data. Although there is a good conceptual framework for understanding bistability in cortical dynamics, the implications of the extremal events challenge existing frameworks for understanding large-scale brain systems.
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“Neural activity often exhibits multimodal distributions, reflecting transitions between distinct dynamical states rather than continuous fluctuations around a single equilibrium (Deco et al. 2011; Freyer et al. 2009; Kinouchi and Copelli 2006).”
From The myth of the Bayesian brain (2025) · cites this paper as Freyer et al. 2009
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“Neural fluctuations frequently follow heavy-tailed distributions, including power-law, log-normal, and Lévy-stable distributions rather than Gaussian profiles (Beggs and Plenz 2003; Chialvo 2010; Freyer et al. 2009; Metzler and Klafter 2000; Touboul and Destexhe 2010).”
From The myth of the Bayesian brain (2025) · cites this paper as Freyer et al. 2009
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Metadata
- Authors
- Freyer, F., Aquino, K., Robinson, P. A., Ritter, P., & Breakspear, M.
- Affiliations
- Charité – Universitätsmedizin Berlin (DE)
- The University of Sydney (AU)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2009-07-01
- Volume
- 29
- Issue
- 26
- Pages
- 8512-8524
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6665653
- OpenAlex
- W2060524608
- Cited by
- 189
- References
- 65
- Citations by year
- 2026: 10 · 2025: 18 · 2024: 10 · 2023: 9 · 2022: 5 · 2021: 8 · 2020: 9 · 2019: 6 · 2018: 11 · 2017: 13 · 2016: 15 · 2015: 12 · 2014: 14 · 2013: 16 · 2012: 16
- FWCI
- 5.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- James S. McDonnell Foundation (JSMF22002082)
- Robert Bosch Stiftung
- Bundesministerium für Bildung und Forschung
- Australian Research Council (DP0667065, TS0669860)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- bistability, non-Gaussian fluctuations, spontaneous cortical activity, resting-state EEG, alpha rhythm, beta oscillations, neural oscillations, burst firing, nonlinear brain dynamics, self-organization
- MeSH terms
- Adult, Bayes Theorem, Brain Mapping, Cerebral Cortex, Electroencephalography, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Oxygen, Rest, Spectrum Analysis, Stochastic Processes, Time Factors, Models, Statistical, Normal Distribution, Nonlinear Dynamics, Principal Component Analysis, Young Adult
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