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Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations
Open accessAbstract
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.
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“The power spectra of neural signals typically follow a power-law form (), indicating temporal correlations that persist across multiple timescales (He et al. 2010; Miller et al. 2009; Linkenkaer-Hansen et al. 2001; Touboul and Destexhe 2010).”
From The myth of the Bayesian brain (2025) · cites this paper as Linkenkaer-Hansen et al. 2001
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Cites
Self-organized criticality: An explanation of the 1/ f noise
Bak, P., Tang, C., & Wiesenfeld, K. A. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.381 · Cited by 7,644
Mosaic organization of DNA nucleotides
Peng, C., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, E., & Goldberger, A. L. (1994) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.49.1685 · Cited by 5,002 · cited 1×
“…fluctuation analysis has been developed for quantifying correlation properties in nonstationary signals, e.g., in physiological time series, because long-range correlations—revealed by an ACF analysis—can arise also as an artifact of the “patchiness” of nonstationary data (Peng et al., 1994, 1995).”
BackgroundBak, P., Tang, C., & Wiesenfeld, K. A. (1988) · Physical Review A · doi:10.1103/physreva.38.364 · Cited by 4,744 · cited 6×
“Unlike deterministic approaches aimed at finding low-dimensional chaos, the SOC framework allows for a highdimensional character of the dynamics and for the presence of stochastic effects.”
BackgroundInfluentialÜber das Elektrenkephalogramm des Menschen
Berger, H. (1929) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01797193 · Cited by 4,266
Fractal Concepts in Surface Growth
Barabási, A. L., & Stanley, H. E. (1995) · doi:10.1017/cbo9780511599798 · Cited by 3,925
Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series
Peng, C., Havlin, S., Stanley, E., & Goldberger, A. L. (1995) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.166141 · Cited by 3,865 · cited 4×
“and Marchesi, 1999), heartbeats (Peng et al., 1995), and human”
BackgroundInfluentialThalamocortical oscillations in the sleeping and aroused brain
Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993) · Science · doi:10.1126/science.8235588 · Cited by 3,564 · cited 3×
“…frequencies are a prominent feature of the spontaneous electroencephalogram (EEG) (Berger, 1929; Connors and Amitai, 1997) and are believed to reflect functional states of the brain (Llinás, 1988; Steriade et al., 1993; Arieli et al., 1996; Herculano-Houzel et al., 1999; Tsodyks et al., 1999).”
BackgroundFractal Concepts in Surface Growth
Barabási, A.-Ĺ., Stanley, H. E., & Sander, L. M. (1995) · Physics Today · doi:10.1063/1.2808215 · Cited by 3,328
Bak, P. (1996) · Springer eBooks · doi:10.1007/978-1-4757-5426-1 · Cited by 2,830
Llinás, R. R. (1988) · Science · doi:10.1126/science.3059497 · Cited by 2,285 · cited 4×
“…frequencies are a prominent feature of the spontaneous electroencephalogram (EEG) (Berger, 1929; Connors and Amitai, 1997) and are believed to reflect functional states of the brain (Llinás, 1988; Steriade et al., 1993; Arieli et al., 1996; Herculano-Houzel et al., 1999; Tsodyks et al., 1999).”
BackgroundInfluential
Cited by
Sorted by citations
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
“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 .”
BackgroundSpontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
“This 1/f distribution has also been observed in studies of spontaneous electroencephalography (EEG”
BackgroundFunctional and Effective Connectivity: A Review
Friston, K. J. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0008 · Cited by 3,415
“…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).”
Efficiency and Cost of Economical Brain Functional Networks
Achard, S., & Bullmore, E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030017 · Cited by 2,553
“On the other hand, multisecond periodicities or infraslow oscillations have been reported in both local field potential recordings from rat basal ganglia and monkey cortex [31,41,42] and the surface EEG in humans [ 43 ].”
BackgroundNeuronal Avalanches in Neocortical Circuits
Beggs, J. M., & Plenz, D. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-35-11167.2003 · Cited by 2,325
“, 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.”
ResultAchard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3874-05.2006 · Cited by 2,393
“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).”
BackgroundNonlinear dynamical analysis of EEG and MEG: Review of an emerging field
Stam, C. J. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2005.06.011 · Cited by 1,410
“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.”
BackgroundADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features
Mognon, A., Jovicich, J., Bruzzone, L., & Buiatti, M. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2010.01061.x · Cited by 1,274
Dynamic models of large-scale brain activity
Breakspear, M. (2017) · Nature Neuroscience · doi:10.1038/nn.4497 · Cited by 1,239
Emergent complex neural dynamics
Chialvo, D. R. (2010) · Nature Physics · doi:10.1038/nphys1803 · Cited by 1,165
The Temporal Structures and Functional Significance of Scale-free Brain Activity
He, B. J., Zempel, J. M., Snyder, A. Z., & Raichle, M. E. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.020 · Cited by 990
“Notably, scale-free properties have recently been described in the amplitude and synchronization of oscillatory brain activity (Linkenkaer-Hansen et al., 2001; Stam and de Bruin, 2004), and in the temporal and spatial distributions of negative LFP peaks (Plenz and Thiagarajan, 2007).”
Nonlinear multivariate analysis of neurophysiological signals
Pereda, E., Quiroga, R. Q., & Bhattacharya, J. (2005) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2005.10.003 · Cited by 1,066
“…noise-like version of the data as a control condition is not to be recommended, because the prominent autocorrelations inherent to neurophysiological signals (see, e.g., (Linkenkaer-Hansen et al., 2001)) turn this kind of surrogates into very unlikely realizations of any neurophysiological process.”
BackgroundGraph theoretical analysis of complex networks in the brain
Stam, C. J., & Reijneveld, J. C. (2007) · Nonlinear Biomedical Physics · doi:10.1186/1753-4631-1-3 · Cited by 1,036
“Several studies have shown that time series of brain activity demonstrate scaling with characteristic exponents, suggesting critical dynamics near a phase transition [12-15].”
BackgroundBrain network dynamics are hierarchically organized in time
Vidaurre, D., Smith, S. M., & Woolrich, M. W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705120114 · Cited by 883
Scale-free brain activity: past, present, and future
He, B. J. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.04.003 · Cited by 827
Power-Law Scaling in the Brain Surface Electric Potential
Miller, K. J., Sorensen, L. B., Ojemann, J. G., & den Nijs, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000609 · Cited by 742
Tognoli, E., & Kelso, J. A. S. (2014) · Neuron · doi:10.1016/j.neuron.2013.12.022 · Cited by 718
“…of a brain is marked by a vast number of ongoing electrical and chemical processes spanning multiple spatial and temporal scales (Pritchard, 1992; Linkenkaer-Hansen et al., 2001; Kozma et al., 2005; Honey et al., 2007; Plenz and Chialvo, 2009; Werner, 2010; Lowen et al., 1997) that both arise…”
Can structure predict function in the human brain?
Honey, C. J., Thivierge, J.-P., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.071 · Cited by 629
“rapid fluctuations (Linkenkaer-Hansen et al., 2001; Stam and de Bruin, 2004; Freyer et al., 2009), possibly indicative of a complex dynamic”
EEG microstate sequences in healthy humans at rest reveal scale-free dynamics.
Van De Ville, D., Britz, J., & Michel, C. M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1007841107 · Cited by 606
Romei, V., Groß, J., & Thut, G. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0160-10.2010 · Cited by 624
“Unsurprisingly, neuronal oscillations therefore display ongoing variability in power even at baseline (Linkenkaer-Hansen et al., 2001), in addition to showing sensory-driven changes (Bauer et al., 2006; Gross et al., 2007; van Dijk et al., 2008).”
BackgroundThe criticality hypothesis: how local cortical networks might optimize information processing
Beggs, J. M. (2008) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2092 · Cited by 533
“…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…”
BackgroundSpike avalanches in vivo suggest a driven, slightly subcritical brain state
Priesemann, V. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00108 · Cited by 563
“…(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…”
BackgroundInfluentialDetrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations
Hardstone, R., Poil, S., Schiavone, G., Jansen, R., Nikulin, V. V., Mansvelder, H. D., & Linkenkaer‐Hansen, K. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00450 · Cited by 536
“The discovery of LRTC in the amplitude envelope of ongoing oscillations, was based on 10 subjects recorded with EEG and MEG for 20 min during eyes-closed and eyes-open rest (Linkenkaer-Hansen et al., 2001).”
BackgroundMethodInfluentialSelf-citationBroadband criticality of human brain network synchronization
Kitzbichler, M. G., Smith, M. L., Christensen, S. R., & Bullmore, E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000314 · Cited by 527
“Power law scaling of human neurophysiological processes has been previously described in both functional MRI and MEG or electrophysiological datasets [ 14 ,15,43].”
MethodGenerative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model
Breakspear, M. J., Heitmann, S., & Daffertshofer, A. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00190 · Cited by 531
Criticality in the brain: A synthesis of neurobiology, models and cognition
Cocchi, L., Gollo, L. L., Zalesky, A., & Breakspear, M. J. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.07.002 · Cited by 555
“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).”
MethodEEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial
Enriquez‐Geppert, S., Huster, R. J., & Herrmann, C. S. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00051 · Cited by 526
Vanhatalo, S., Palva, J. M., Holmes, M., Miller, J. W., Voipio, J., & Kaila, K. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0305375101 · Cited by 517
Self-citationBeing Critical of Criticality in the Brain
Beggs, J. M., & Timme, N. M. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00163 · Cited by 505
“POWER LAWS AND NEURAL DATA Critio: Well, there were several early reports that the nervous system could produce power law distributions (Chen et al., 1997; Teich et al., 1997; Linkenkaer-Hansen et al., 2001; Worrell et al., 2002).”
BackgroundIntrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity
Engel, A. K., Gerloff, C., Hilgetag, C. C., & Nolte, G. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.038 · Cited by 471
“As shown by human and animal studies, the dynamics of envelope ICMs exhibits these characteristic features (Linkenkaer-Hansen et al., 2001; He et al., 2010; Palva and Palva, 2011; Tagliazucchi et al., 2012b).”
Self-organized criticality as a fundamental property of neural systems
Hesse, J., & Gross, T. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00166 · Cited by 440
“We believe that these perspectives provide a strong incentive for more experimental and theoretical work in the area of self-organized criticality.”
BackgroundPalva, J. M., Zhigalov, A., Hirvonen, J., Korhonen, O., Linkenkaer‐Hansen, K., & Palva, S. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216855110 · Cited by 472
“Second, the observations of LRTCs and neuronal avalanches with power-law size and lifetime distributions add to the growing body of data suggesting that the human brain operates near a critical regime (12, 30, 32, 33), which may fundamentally determine the dynamics of human perceptual, cognitive, and behavioral proces…”
BackgroundMethodResultInfluentialSelf-citationScale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task
He, B. J. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2111-11.2011 · Cited by 444
“To more robustly investigate the scalefree behavior of the fMRI signal, we used the well-established detrended fluctuation analysis (DFA) method (Linkenkaer-Hansen et al., 2001; Eke et al., 2006), which was initially introduced to study correlations in DNA sequences (Peng et al., 1994).”
MethodMonto, S., Palva, S., Voipio, J., & Palva, J. M. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.1910-08.2008 · Cited by 453
“…of 5–20 Hz oscillations are autocorrelated over tens to hundreds of seconds (LinkenkaerHansen et al., 2001; Linkenkaer-Hansen et al., 2005), and their amplitude dynamics show 1/f-type power distributions both in human (Linkenkaer-Hansen et al., 2001) and monkey (Leopold et al., 2003) cortex.”
BackgroundResultInfluentialSelf-citationAn electrophysiological marker of arousal level in humans
Lendner, J. D., Helfrich, R. F., Mander, B. A., Romundstad, L. G., Lin, J. J., Walker, M. P., Larsson, P. G., & Knight, R. T. (2020) · eLife · doi:10.7554/elife.55092 · Cited by 425
The role of alpha oscillations in temporal attention
Hanslmayr, S., Groß, J., Klimesch, W., & Shapiro, K. L. (2011) · Brain Research Reviews · doi:10.1016/j.brainresrev.2011.04.002 · Cited by 436
Poil, S., Hardstone, R., Mansvelder, H. D., & Linkenkaer‐Hansen, K. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5990-11.2012 · Cited by 397
“If the oscillations wax and wane at random (e.g., as bandpass filtered white noise), the DFA exponent will be 0.5, whereas a DFA exponent between 0.5 and 1.0 imply that oscillations have a scale-free amplitude modulation (Linkenkaer-Hansen et al., 2001).”
InfluentialSelf-citationPrestimulus Oscillations Enhance Psychophysical Performance in Humans
Linkenkaer‐Hansen, K., Nikulin, V. V., Palva, S., Ilmoniemi, R. J., & Palva, J. M. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.2584-04.2004 · Cited by 399
“1C), suggesting that the underlying networks were in a highly dynamic state (Linkenkaer-Hansen et al., 2001).”
BackgroundSelf-citationDoes the 1 / f Frequency Scaling of Brain Signals Reflect Self-Organized Critical States?
Bédard, C., Kröger, H., & Alain Destexhe, A. (2006) · Physical Review Letters · doi:10.1103/physrevlett.97.118102 · Cited by 391
Scaling laws in cognitive sciences
Kello, C. T., Brown, G. D. A., Ferrer-i-Cancho, R., Holden, J. G., Linkenkaer-Hansen, K., Rhodes, T., & Van Orden, G. C. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.02.005 · Cited by 384
Self-citationThe organizing principles of neuronal avalanches: cell assemblies in the cortex?
Plenz, D., & Thiagarajan, T. C. (2007) · Trends in Neurosciences · doi:10.1016/j.tins.2007.01.005 · Cited by 373
Spatial spectra of scalp EEG and EMG from awake humans
Freeman, W., Holmes, M., Burke, B. C., & Vanhatalo, S. (2003) · Clinical Neurophysiology · doi:10.1016/s1388-2457(03)00045-2 · Cited by 379
“The scaling behavior of temporal patterns has been well studied (Ingber, 1995), particularly in the alpha and beta ranges (Nunez et al., 1997; Linkenkaer-Hansen et al., 2001).”
BackgroundNeuronal Avalanches in the Resting MEG of the Human Brain
Shriki, O., Alstott, J., Carver, F. W., Holroyd, T., Henson, R. N., Smith, M. L., Coppola, R., Bullmore, E. T., & Plenz, D. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4286-12.2013 · Cited by 346
“, 2010), and corresponding long-range temporal correlations (Linkenkaer-Hansen et al., 2001).”
Sadaghiani, S., Hesselmann, G., & Kleinschmidt, A. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.2592-09.2009 · Cited by 325
“Yet, ongoing activity fluctuations contribute to variability of evoked responses (Arieli et al., 1996) and impact on perceptual performance (Linkenkaer-Hansen et al., 2001; Boly et al., 2007; Hesselmann et al., 2008a,b).”
BackgroundRegional Variation in Interhemispheric Coordination of Intrinsic Hemodynamic Fluctuations
Stark, D. E., Margulies, D. S., Shehzad, Z. E., Reiss, P. T., Kelly, C. A. M., Uddin, L. Q., Gee, D. G., Roy, A. K., Banich, M. T., Castellanos, F. X., et al. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.4544-08.2008 · Cited by 324
“Amplitude fluctuations of interhemispherically coherent high-frequency activity have been demonstrated at much slower time scales ranging from seconds to minutes (Linkenkaer-Hansen et al., 2001; Nikouline et al., 2001) and such variations correlate well with hemodynamic fluctuations (Logothetis et al.”
BackgroundMontez, T., Poil, S., Jones, B. F., Manshanden, I., Verbunt, J. P. A., van Dijk, B. W., Brussaard, A. B., van Ooyen, A., Stam, C. J., Scheltens, P., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811699106 · Cited by 321
“The main steps from the broadband MEG signal to the quantification of LRTC using DFA have been explained in detail elsewhere (23, 49, 50).”
BackgroundMethodSelf-citationBehavior needs neural variability
Waschke, L., Kloosterman, N. A., Obleser, J., & Garrett, D. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.023 · Cited by 317
Construct validation of a DCM for resting state fMRI
Razi, A., Kahan, J., Rees, G. E., & Friston, K. J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.027 · Cited by 287
“At low frequencies (less than 1.5 Hz) the slope is shallower and it is likely that the amplitude or power enve-lopes of faster frequencies are scale-free (Kitzbichler et al., 2009; Linkenkaer-Hansen et al., 2001) or heavy-tailed (Freyer et al., 2009).”
BackgroundInfluentialA fractal approach to dynamic inference and distribution analysis
van Rooij, M. M. J. W., Nash, B. A., Rajaraman, S., & Holden, J. G. (2013) · Frontiers in Physiology · doi:10.3389/fphys.2013.00001 · Cited by 294
“For instance, the distribution of endogenous EEG and MEG oscillations are inversely powerlaw distributed (Linkenkaer-Hansen et al., 2001).”
BackgroundDynamic BOLD functional connectivity in humans and its electrophysiological correlates
Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., & Laufs, H. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00339 · Cited by 300
On the dynamics of the spontaneous activity in neuronal networks
Mazzoni, A., Broccard, F. D., Garcia-Perez, E., Bonifazi, P., Ruaro, M. E., & Torre, V. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000439 · Cited by 279
“Neuronal activity was described in the SOC framework in human electroencephalogram [34,35] (but see also [36]) and in local field potentials from cultured and acute cortical rat slices [27], but this is the first time to our knowledge that similar phenomena are found: i - at the spike level, ii - in a random neuronal…”
BackgroundFractional Gaussian noise, functional MRI and Alzheimer's disease
Maxim, V., Sendur, L., Fadili, J. M., John Suckling, A., Gould, R. L., Howard, R. J., & Bullmore, E. T. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.10.044 · Cited by 270
Rubinov, M., Sporns, O., Thivierge, J., & Breakspear, M. J. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002038 · Cited by 265
Chialvo, D. R. (2004) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2004.05.064 · Cited by 266
Northoff, G., Wainio‐Theberge, S., & Evers, K. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2019.05.002 · Cited by 272
How critical is brain criticality?
O’Byrne, J., & Jerbi, K. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.08.007 · Cited by 283
Wavelets and functional magnetic resonance imaging of the human brain
Bullmore, E. T., Fadili, J. M., Maxim, V., Sendur, L., Whitcher, B. J., John Suckling, A., Brammer, M. J., & Breakspear, M. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.012 · Cited by 257
“Fractal dimensions and 1/f spectral properties have been measured in electroencephalographic (EEG) signals (Bullmore et al., 1994a; Linkenkaer-Hansen et al., 2001; Senhadji et al., 1995).”
BackgroundBreakdown of long-range temporal dependence in default mode and attention networks during deep sleep
Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., Jahnke, K., & Laufs, H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312848110 · Cited by 269
“Long-range temporal memory is a landmark of power fluctuations in faster EEG and magnetoencephalography frequencies, such as α (8–12 Hz) or β (13–30 Hz) (21); in contrast, a decreased H exponent relative to the exponent of wakefulness is associated with the slow rhythms that herald the onset of deep sleep (36).”
BackgroundNorthoff, G., & Lamme, V. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.07.019 · Cited by 247
Interaction-dominant dynamics in human cognition: Beyond 1/ƒα fluctuation.
Ihlen, E. A. F., & Vereijken, B. (2010) · Journal of Experimental Psychology General · doi:10.1037/a0019098 · Cited by 251
Criticality between Cortical States
Fontenele, A. J., de Vasconcelos, N. A. P., Feliciano, T., Aguiar, L. A. A., Soares-Cunha, C., Coimbra, B., Dalla Porta, L., Ribeiro, S., Rodrigues, A. J., Sousa, N., et al. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.208101 · Cited by 237
Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude
Freeman, W. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.02.029 · Cited by 248
“The importance of long-distance correlations has been emphasized by numerous brain theorists (e.g., Ingber, 1995; Hoppenstaedt and Izhkevich, 1998; Haken, 1999; Friston, 2000; Linkenkaer-Hansen et al., 2001; Vitiello, 2001; Kaneko and Tsuda, 2001; Kozma, Freeman and Erdí, 2003; Stam et al., 2003).”
Scale‐free dynamics of global functional connectivity in the human brain
Stam, C. J., & de Bruin, E. A. (2004) · Human Brain Mapping · doi:10.1002/hbm.20016 · Cited by 242
Spontaneous travelling cortical waves gate perception in behaving primates
Davis, Z. W., Muller, L., Martínez-Trujillo, J., B, T. J. S. A., & Reynolds, J. H. (2020) · Nature · doi:10.1038/s41586-020-2802-y · Cited by 251
Fractals in the nervous system: conceptual implications for theoretical neuroscience
Werner, G. (2010) · Frontiers in Physiology · doi:10.3389/fphys.2010.00015 · Cited by 252
Disturbed fluctuations of resting state EEG synchronization in Alzheimer's disease
Stam, C. J., Montez, T., Jones, B. F., Rombouts, S. A. R. B., van der Made, Y., Pijnenburg, Y. A. L., & Scheltens, P. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.09.022 · Cited by 249
Scaling laws of human interaction activity
Diego Rybski, A., Buldyrev, S. V., Havlin, S., Liljeros, F., & Makse, H. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0902667106 · Cited by 244
“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).”
BackgroundThe relation of ongoing brain activity, evoked neural responses, and cognition
Sadaghiani, S. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00020 · Cited by 242
The emergent coordination of cognitive function.
Kello, C. T., Beltz, B. C., Holden, J. G., & Orden, G. C. V. (2007) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.136.4.551 · Cited by 234
“Moreover , 1/f fluctuations have been found in electrophysiological measures that are not subject to fMRI artifacts (Bhattacharya, Edwards, Mamelak, & Schuman, 2005; Leopold, Murayama, & Logothetis, 2003; Le Van Quyen, 2003; Linkenkaer-Hansen et al., 2001).”
BackgroundAtasoy, S., Roseman, L., Kaelen, M., Kringelbach, M. L., Deco, G., & Carhart-Harris, R. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-17546-0 · Cited by 214
“Finally, although in this study we apply this technqiue to fMRI data in order to extract the neural signatures of the LSD state, application of the connectome-harmonic decomposition can also be easily extended to other functional neuroimaging datasets such as MEG or high density scalp EEG.”
InfluentialMathewson, K. E., Prudhomme, C., Fabiani, M., Beck, D. M., Lleras, A., & Gratton, G. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00288 · Cited by 228
Linkenkaer‐Hansen, K., Monto, S., Rytsälä, H. J., Suominen, K. H., Isometsä, E. T., & Kähkönen, S. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.3244-05.2005 · Cited by 227
“The evidence indicates, however, that neuronal activity patterns, including their long-range temporal correlations (Linkenkaer-Hansen et al., 2001; Nikulin and Brismar, 2004), are highly replicable within and across subjects (Gusnard and Raichle, 2001).”
InfluentialSelf-citationMeasurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics
Bruining, H., Hardstone, R., Juárez-Martinez, E. L., Sprengers, J. J., Avrămiea, A.-E., Simpraga, S., Houtman, S. J., Poil, S., Dallares, E., Palva, S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-65500-4 · Cited by 213
Self-citationDynamics of large-scale electrophysiological networks: A technical review
O’Neill, G. C., Tewarie, P. K. B., Vidaurre, D., Liuzzi, L., Woolrich, M. W., & Brookes, M. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.003 · Cited by 225
“Extracellular electrophysiological systems and their fluctuations in power have all been shown to follow power laws (He et al., 2010; Linkenkaer-Hansen et al., 2001; Miller et al., 2009; Poil et al., 2008), as does global functional connectivity (Botcharova et al.”
BackgroundInfra-Slow EEG Fluctuations Are Correlated with Resting-State Network Dynamics in fMRI
Hiltunen, T., Kantola, J., Elseoud, A. A., Lepola, P., Suominen, K., Starck, T., Nikkinen, J., Remes, J. J., Tervonen, O. A., Palva, S., et al. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0276-13.2014 · Cited by 227
“, 2011), magnetoencephalography, and electroencephalography (EEG) (Linkenkaer-Hansen et al., 2001).”
BackgroundSelf-citationFreeman, W. J., Burke, B. C., & Holmes, M. D. (2003) · Human Brain Mapping · doi:10.1002/hbm.10120 · Cited by 214
Singer, W. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.006 · Cited by 214
The Amsterdam Resting-State Questionnaire reveals multiple phenotypes of resting-state cognition
Diaz, B. A., Van Der Sluis, S., Moens, S., Benjamins, J. S., Migliorati, F., Stoffers, D., Den Braber, A., Poil, S.-S., Hardstone, R., Van't Ent, D., et al. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00446 · Cited by 194
“Also the complex temporal dynamics of ongoing activity has received a great deal of interest (Linkenkaer-Hansen et al., 2001; He, 2011; Hardstone et al., 2012) with recent studies showing that that intrinsic neuronal dynamics during rest can predict individual variability in behavior (Palva et al.,…”
BackgroundSelf-citationNorthoff, G., & Huang, Z. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.07.013 · Cited by 229
Gene expression dynamics in the macrophage exhibit criticality
Nykter, M., Price, N. D., Aldana, M., Ramsey, S. A., Kauffman, S. A., Hood, L., Yli-Harja, O. P., & Shmulevich, I. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0711525105 · Cited by 203
“For example, measurements of human brain oscillations revealed such critical dynamics of neural networks, implying their ability to effectively propagate information and rapidly reorganize (8).”
BackgroundIntegrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage
Poil, S., de Haan, W., van der Flier, W. M., Mansvelder, H. D., Scheltens, P., & Linkenkaer‐Hansen, K. (2013) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2013.00058 · Cited by 224
“Furthermore, detrended fluctuation analysis characterizing long-range temporal correlations (Linkenkaer-Hansen et al., 2001; Hardstone et al., 2012; Poil et al., 2012), multifractality spectral width (Kantelhardt et al., 2002; Ihlen, 2012) and oscillation bursts 95th percentile durations and sizes…”
Self-citationSpike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle
Ribeiro, T. L., Copelli, M., Caixeta, F. V., Belchior, H., Chialvo, D. R., Nicolelis, M. A. L., & Ribeiro, S. T. G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014129 · Cited by 200
“These include results obtained at the whole-brain scale, where functional networks compatible with a critical brain were observed via functional magnetic resonance imaging (fMRI) [14,15], magnetoencephalographic (MEG) [16] and electroencephalographic (EEG) [17] data.”
Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity
Freyer, F., Aquino, K., Robinson, P. A., Ritter, P., & Breakspear, M. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.0754-09.2009 · Cited by 189
“An important study of nontrivial temporal correlations in cortical activity is that of Linkenkaer-Hansen et al. (2001) who studied modulations in alpha (8 –13 Hz) and beta (15–25 Hz) power in both EEG and magnetoencephalographic data.”
BackgroundEroding the Boundaries of Cognition: Implications of Embodiment1
Anderson, M. L., Richardson, M. J., & Chemero, A. (2012) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2012.01211.x · Cited by 200
Inferring multi-scale neural mechanisms with brain network modelling
Schirner, M., McIntosh, A. R., Jirsa, V., Deco, G., & Ritter, P. (2018) · eLife · doi:10.7554/elife.28927 · Cited by 204
Santo, S. D., Villegas, P., Burioni, R., & Muñoz, M. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1712989115 · Cited by 192
“Moreover, a detrended fluctuation analysis (75, 76) of the time series reveals the emergence of long-range temporal correlations right at ξc (SI Appendix) as expected at a continuous phase transition.”
BackgroundHarvey, R. L., Edwards, D. J., Dunning, K. K., Fregni, F., Stein, J. M., Laine, J. T., Rogers, L. M., Vox, F., Durand-Sanchez, A., Bockbrader, M. A., et al. (2018) · Stroke · doi:10.1161/strokeaha.117.020607 · Cited by 189
Oxytocin enhances inter-brain synchrony during social coordination in male adults
Mu, Y., Guo, C., & Han, S. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw106 · Cited by 177
“After segmentation, the inter-brain phase synchrony index (phase-locking value, PLV) were quantified based on a wavelet decomposition of the signal 65between 8 and 13 Hz in 1 Hz steps, similar to previous studies (Linkenkaer-Hansen et al., 2001; Mu et al., 2008; Mu and Han, 2010; 2013).”
MethodNonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics
Freeman, W. J., III, & Vitiello, G. (2006) · eScholarship (California Digital Library) · Cited by 184
Macdonald, J. S. P., Mathan, S. A., & Yeung, N. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00082 · Cited by 186
Origin, structure, and role of background EEG activity. Part 2. Analytic phase
Freeman, W. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.02.028 · Cited by 180
Voltage imaging of waking mouse cortex reveals emergence of critical neuronal dynamics
Scott, G., Fagerholm, E. D., Mutoh, H., Leech, R., Sharp, D. J., Shew, W. L., & Knöpfel, T. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3474-14.2014 · Cited by 158
“, 2013), EEG (Linkenkaer-Hansen et al., 2001; Meisel et al., 2013), and MEG (Linkenkaer-Hansen et al.”
BackgroundOrigin, structure, and role of background EEG activity. Part 4: Neural frame simulation
Freeman, W. J. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2005.10.025 · Cited by 177
“…incorporated elements of related theories of the EEG that postulated its origin in local distributed feedback [Basar, 1998] and in noise: Gaussian [Bullock, 1969; Elul, 1972], 1/f [Linkenkaer-Hansen et al., 2001; Hwa and Ferree, 2002; Wakeling, 2004], and chaotic [Tsuda, 2001; Stam et al., 2003].”
BackgroundNonlinear synchronization in EEG and whole‐head MEG recordings of healthy subjects
Stam, C. J., Breakspear, M., van Walsum, A. V. C., & van Dijk, B. W. (2003) · Human Brain Mapping · doi:10.1002/hbm.10106 · Cited by 174
Cortical activity is more stable when sensory stimuli are consciously perceived
Schurger, A., Sarigiannidis, I., Naccache, L., Sitt, J. D., & Dehaene, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418730112 · Cited by 155
A place for time: The spatiotemporal structure of neural dynamics during natural audition
Stephens, G. J., Honey, C. J., & Hasson, U. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00268.2013 · Cited by 141
“Neural dynamics with timescales across many orders of magnitude are also observed across numerous recording methodologies, input stimuli, and organisms (Baria et al. 2011; He et al. 2010; Honey et al. 2012; Lerner et al. 2011; Linkenkaer-Hansen et al. 2001; Schneidman et al. 2006).”
BackgroundLiu, Z., Fukunaga, M., de Zwart, J. A., & Duyn, J. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.092 · Cited by 170
“Several electrophysiology studies suggest that rhythmic neuronal activity, although oscillating at high frequencies (up to 250 Hz), may exhibit a much slower fluctuation in power (or modulation amplitude) that generally coincides with the frequency range of spontaneous BOLD fluctuations (Anderson, 2008; Leopold et al.…”
BackgroundComparison of spectral analysis methods for characterizing brain oscillations
van Vugt, M. K., Sederberg, P. B., & Kahana, M. J. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.12.004 · Cited by 170
Dopaminergic drug effects on physiological connectivity in a human cortico-striato-thalamic system
Honey, G. D. (2003) · Brain · doi:10.1093/brain/awg184 · Cited by 163
“Despite the different time scales of fMRI and electrophysiological measurements, one might argue that long memory structure is typically fractal or statistically self-similar and it is, therefore, unsurprising that it should be consistently observable over a range of time scales (Linkenkaer-Hansen et al., 2001; Goldbe…”
BackgroundKozma, R., Puljic, M., Balister, P., Bollobás, B., & Freeman, W. (2005) · Biological Cybernetics · doi:10.1007/s00422-005-0565-z · Cited by 167
“The importance of long-distance correlations has been emphasized by numerous brain theorists (e.g., Ingber, 1995; Hoppenstaedt and Izhkevich, 1998; Haken, 1999; Friston, 2000; Linkenkaer-Hansen et al., 2001; Kaneko and Tsuda, 2001; Kozma et al., 2003; Stam et al., 2003).”
Background
Metadata
- Authors
- Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., & Ilmoniemi, R. J.
- Affiliations
- Helsinki University Hospital (FI)
- University of Helsinki (FI)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2001-02-15
- Volume
- 21
- Issue
- 4
- Pages
- 1370-1377
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6762238
- OpenAlex
- W2139852582
- Cited by
- 1,149
- References
- 50
- Citations by year
- 2026: 63 · 2025: 70 · 2024: 63 · 2023: 53 · 2022: 76 · 2021: 60 · 2020: 63 · 2019: 57 · 2018: 65 · 2017: 66 · 2016: 62 · 2015: 43 · 2014: 53 · 2013: 48 · 2012: 45
- FWCI
- 6.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Helsingin ja Uudenmaan Sairaanhoitopiiri
- Academy of Finland
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- long-range temporal correlations, power-law scaling, neural oscillations, spontaneous neuronal activity, self-organized criticality, neural network reorganization, scaling exponents
- MeSH terms
- Adult, Biological Clocks, Brain, Computer Simulation, Electroencephalography, Female, Humans, Male, Models, Neurological, Nerve Net, Occipital Lobe, Parietal Lobe, Signal Processing, Computer-Assisted, Stochastic Processes, Time Factors, Visual Perception, Magnetoencephalography, Fractals
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