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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
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Sorted by citations
Items 301–400 of 1,000
Sabbadini, S. A., & Vitiello, G. (2019) · Applied Sciences · doi:10.3390/app9153203 · Cited by 49
Neuropercolation: A Random Cellular Automata Approach to Spatio-temporal Neurodynamics
Kozma, R., Puljic, M., Balister, P., Bollobás, B., & Freeman, W. J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30479-1_45 · Cited by 49
“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).”
BackgroundCharacterizing pink and white noise in the human electroencephalogram
Barry, R. J., & Blasio, F. M. D. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abe399 · Cited by 48
Aperiodic and Hurst EEG exponents across early human brain development: A systematic review
Stanyard, R. A., Mason, D., Ellis, C. L., Dickson, H., Short, R., Batallé, D., & Arichi, T. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101402 · Cited by 52
Why should cognitive neuroscientists study the brain's resting state?
Papo, D. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00045 · Cited by 48
Circadian Rhythms in Fractal Features of EEG Signals
Croce, P., Quercia, A., Costa, S., & Zappasodi, F. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01567 · Cited by 49
“Indeed, the amplitude modulation of oscillations of neuronal pools dynamics, revealed by electrophysiological techniques as electroencephalography (EEG) or magnetoencephalography (MEG), reveal long-term spatiotemporal structure in a temporal range from few seconds to tenth of minutes in resting state conditions at bot…”
BackgroundResultInfluentialTime Irreversibility of Resting-State Activity in the Healthy Brain and Pathology
Zanin, M., Güntekin, B., Aktürk, T., Hanoğlu, L., & Papo, D. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2019.01619 · Cited by 48
Mixtures of large-scale dynamic functional brain network modes
Gohil, C., Roberts, E., Timms, R. C., Skates, A., Higgins, C., Quinn, A. J., Pervaiz, U., van Amersfoort, J., Notin, P., Gal, Y., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119595 · Cited by 55
Some Insights Into Computational Models of (Patho)physiological Brain Activity
Suffczyński, P., Wendling, F., Bellanger, J.-J., & Lopes, F. H. D. S. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.871773 · Cited by 52
Fractional Dynamics of Network Growth Constrained by Aging Node Interactions
Safdari, H., Kamali, M. Z., Shirazi, A., Khalighi, M., Jafari, G., & Ausloos, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154983 · Cited by 48
Ignaccolo, M., Latka, M., Jernajczyk, W., Paolo Grigolini, A., & West, B. J. (2009) · Journal of Biological Physics · doi:10.1007/s10867-009-9171-y · Cited by 46
“[13] consider it as the “noise” and apply DFA to the rhythm amplitude time series.”
BackgroundMethodEpilepsy as a dynamic disease: A tutorial of the past with an eye to the future
MILTON, J. G. (2010) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2010.03.002 · Cited by 46
“Several studies have shown that time series recorded from epileptic brain and tissues exhibit power laws [106,153–156].”
BackgroundCritical dynamics of endogenous fluctuations predict cognitive flexibility in the Go/NoGo task
Simola, J., Zhigalov, A., Morales‐Muñoz, I., Palva, J. M., & Palva, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-02750-9 · Cited by 47
Self-citationAlpha Power Predicts Persistence of Bistable Perception
Piantoni, G., Romeijn, N., Gómez-Herrero, G., van der Werf, Y. D., & Someren, E. J. W. V. (2017) · Scientific Reports · doi:10.1038/s41598-017-05610-8 · Cited by 49
Crevecoeur, F., Bollens, B., Detrembleur, C., & Lejeune, T. M. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.07.017 · Cited by 46
Positive Temporal Dependence of the Biological Clock Implies Hyperbolic Discounting
Ray, D., & Bossaerts, P. L. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00002 · Cited by 45
“…it may be supported by the positive autocorrelations recently discovered in human brain activity oscillations, displaying slow decreases even over thousands of cycles (Linkenkaer-Hansen et al., 2001, 2004), not unlike those generated by fractional Brownian motions (Mandelbrot and Van Ness, 1968).”
Does the brain behave like a (complex) network? I. Dynamics
Papo, D., & Buldú, J. M. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.12.006 · Cited by 44
Optimal Balance of the Striatal Medium Spiny Neuron Network
Ponzi, A. P. D., & Wickens, J. R. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002954 · Cited by 46
A Strategy to Reduce Bias of Entropy Estimates in Resting-State fMRI Signals
Yang, A. C., Tsai, S., Lin, C., & Peng, C. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00398 · Cited by 44
Multifractal Dynamic Functional Connectivity in the Resting-State Brain
Rácz, F. S., Stylianou, O., Mukli, P., & Eke, A. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01704 · Cited by 49
“, scale-free neurodynamics were suggested in numerous studies as an indication of an underlying selforganized critical state of the brain (Linkenkaer-Hansen et al., 2001; Stam and de Bruin, 2004; Stam, 2005; Kitzbichler et al., 2009; Chialvo, 2010; Van de Ville et al., 2010; Tagliazucchi et al., 2012a; Racz et al., 20…”
BackgroundA hybrid steady-state visual evoked response-based brain-computer interface with MEG and EEG
Li, X., Chen, J., Shi, N., Yang, C., Gao, P., Chen, X., Wang, Y., Gao, S., & Gao, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119736 · Cited by 43
Lombardi, F., Pepić, S. H., Shriki, O., Tkačik, G., & Martino, D. D. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00410-9 · Cited by 50
“The scaling exponent α was estimated in the n range corresponding to 2s - 60s to avoid spurious correlations induced by the signal filtering [35, 52].”
BackgroundMethodResultInfluentialTiming Deficits in ADHD: Insights From the Neuroscience of Musical Rhythm
Slater, J., & Tate, M. C. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00051 · Cited by 54
“Interestingly, individual variation in the temporal characteristics of neural activity (including longrange correlations) has been shown to predict variability inmotor timing behavior (Linkenkaer-Hansen et al., 2001; Smit et al., 2013).”
BackgroundAntiepileptic drugs induce subcritical dynamics in human cortical networks
Meisel, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1911461117 · Cited by 43
Separating scale‐free and oscillatory components of neural activity in schizophrenia
Rácz, F. S., Farkas, K., Stylianou, O., Kaposzta, Z., Czoch, A., Mukli, P., Csukly, G., & Eke, A. (2021) · Brain and Behavior · doi:10.1002/brb3.2047 · Cited by 41
Single-neuron criticality optimizes analog dendritic computation
Gollo, L. L., Kinouchi, O., & Copelli, M. (2013) · Scientific Reports · doi:10.1038/srep03222 · Cited by 41
“In the past decade, several phenomena suggestive of critical states have been observed in different systems: multielectrode data from cortical slices in vitro [3–5], anesthetized [6], awake [7] and behaving animals [8]; human electrocorticography [9], electroencephalography [10], magnetoencephalography [10, 11], as we…”
BackgroundTheoretical foundations of studying criticality in the brain
Tian, Y., Tan, Z., Hou, H., Li, G., Cheng, A., Qiu, Y., Weng, K., Chen, C., & Sun, P. (2022) · Network Neuroscience · doi:10.1162/netn_a_00269 · Cited by 49
“It remains controversial if absorbing and synchronous phase transitions robustly share specific features (see reported similarities Buendı́a et al. (2021); Di Santo et al. (2018); Fontenele et al. (2019); Girardi-Schappo et al. (2021) and differences Buendı́a et al. (2021); Fontenele et al. (2019); Girardi-Schappo et…”
BackgroundInfluentialvan Aerde, K. I., Mann, E. O., Canto, C. B., Heistek, T. S., Linkenkaer‐Hansen, K., Mulder, A. B., van der Roest, M., Paulsen, O., Brussaard, A. B., & Mansvelder, H. D. (2009) · The Journal of Physiology · doi:10.1113/jphysiol.2009.178384 · Cited by 43
“For the EEG measurements, this data set has been used in (Linkenkaer-Hansen et al. 2001).”
BackgroundMethodInfluentialSelf-citationLombardi, F., Gómez-Extremera, M., Bernaola‐Galván, P., Vetrivelan, R., Saper, C. B., Scammell, T. E., & Ivanov, P. C. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1278-19.2019 · Cited by 44
“…2008) and local field potentials in cortex slice cultures (Beggs and Plenz, 2003) and awake monkeys (Petermann et al., 2009), to the human brain (Linkenkaer-Hansen et al., 2001; Tagliazucchi et al., 2012; Palva et al., 2013; Shriki et al., 2013; Marinazzo et al., 2014), and the dynamics of…”
BackgroundChanges in EEG permutation entropy in the evening and in the transition from wake to sleep
Hou, F., Zhang, L., Qin, B., Gaggioni, G., Liu, X., & Vandewalle, G. (2020) · SLEEP · doi:10.1093/sleep/zsaa226 · Cited by 38
STXBP1 Syndrome Is Characterized by Inhibition-Dominated Dynamics of Resting-State EEG
Houtman, S. J., Lammertse, H. C. A., van Berkel, A. A., Balagura, G., Gardella, E., Ramautar, J. R., Reale, C., Møller, R. S., Zara, F., Striano, P., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.775172 · Cited by 39
Self-citationCoemergence of regularity and complexity during neural network development
Couzin-Fuchs, E., Ayali, A., Robinson, A., Hulata, E., & Ben‐Jacob, E. (2007) · Developmental Neurobiology · doi:10.1002/dneu.20557 · Cited by 43
“This was shown to be a fundamental characteristic in various studies of neuronal circuits dynamics ( Linkenkaer-Hansen et al., 2001; Ayali et al., 2004; Bertschinger and Natschlager, 2004; Hulata et al., 2004; Haldeman and Beggs, 2005) giving the nervous system the freedom to act locally while retaining global coordin…”
BackgroundColombo, M. A., Wei, Y., Ramautar, J. R., Linkenkaer‐Hansen, K., Tagliazucchi, E., & Someren, E. J. W. V. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00576 · Cited by 41
“LRTC of brain dynamics reflect a memory of the system that can span tens and even up to several hundreds of seconds (Linkenkaer-Hansen et al., 2001; Kantelhardt et al., 2015).”
BackgroundMethodInfluentialSelf-citationComputational characteristics of interictal EEG as objective markers of epileptic spasms
Smith, R. J., Hu, D. K., Shrey, D. W., Rajaraman, R. R., Hussain, S. A., & Lopour, B. A. (2021) · Epilepsy Research · doi:10.1016/j.eplepsyres.2021.106704 · Cited by 41
Dynamic primitives of brain network interaction
Schirner, M., Kong, X., Yeo, B. T. T., Deco, G., & Ritter, P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118928 · Cited by 42
“Likewise, simulations can also be simplified by not directly modlling all aspects of brain activity, but instead injecting empirical data e.g., source projected EEG or MEG, or artificially generated plausible ctivity) into the population models ( Schirner et al., 2018 ).”
InfluentialCarstens, N., Adejube, B., Strunskus, T., Faupel, F., Brown, S. A., & Vahl, A. (2022) · Nanoscale Advances · doi:10.1039/d2na00121g · Cited by 40
New Perspectives on Spontaneous Brain Activity: Dynamic Networks and Energy Matter
Tozzi, A., Zare, M., & BENASICH, A. A. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00247 · Cited by 37
Anderson, C. M., Lowen, S. B., & Renshaw, P. F. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.09.020 · Cited by 40
“By design, fMRI methods attempt to minimize the contribution of variance from low-frequency physiological 1/f-noise.”
BackgroundMethodSquarcina, L., Luca, A. D., Bellani, M., Brambilla, P., Turkheimer, F., & Bertoldo, A. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/4/1697 · Cited by 41
Altered brain dynamics index levels of arousal in complete locked-in syndrome
Zilio, F., Gómez‐Pilar, J., Chaudhary, U., Fogel, S. M., Fomina, T., Synofzik, M., Schöls, L. J., Cao, S., Zhang, J., Huang, Z., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05109-1 · Cited by 34
Wainio‐Theberge, S., Wolff, A., Gómez‐Pilar, J., Zhang, J., & Northoff, G. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119245 · Cited by 39
Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis
Matić, V., Cherian, P. J., Koolen, N., Ansari, A. H., Naulaers, G., Govaert, P. P., Huffel, S. V., Vos, M. D., & Vanhatalo, S. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00189 · Cited by 39
“Recent work has shown that LRTC in brain function is significantly affected by various clinical conditions (Linkenkaer-Hansen et al., 2001; Parish et al., 2004; Stam et al., 2005; Monto et al., 2007).”
BackgroundMethodInfluentialScarpetta, S., Morisi, N., Mutti, C., Azzi, N., Trippi, I., Ciliento, R., Apicella, I., Messuti, G., Angiolelli, M., Lombardi, F., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107840 · Cited by 44
Neurofeedback Tunes Scale-Free Dynamics in Spontaneous Brain Activity
Ros, T., Frewen, P., Théberge, J., Michela, A., Kluetsch, R., Mueller, A., Candrian, G., Jetly, R., Vuilleumier, P. O., & Lanius, R. A. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw285 · Cited by 38
“, 2011), null (middle) (Linkenkaer-Hansen, 2001), or negative (right-side) (Linkenkaer-Hansen, 2001).”
BackgroundMethodResultInfluentialWest, T. O., Farmer, S. F., Luc Berthouze, A., Jha, A., Beudel, M., Foltynie, T., Limousin, P. D., Zrinzo, L. U., Brown, P., & Vladimir Litvak, A. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00517 · Cited by 34
“This approach has been used in previously studies (e.g., Linkenkaer-Hansen et al., 2001; Botcharova et al., 2015).”
MethodNakao, T., Miyagi, M., Hiramoto, R., Wolff, A., Gómez‐Pilar, J., Miyatani, M., & Northoff, G. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116015 · Cited by 41
“temporal structure consisting in LRTC (Hardstone et al., 2012; He, 2011; He et al., 2010; Huang et al., 2017; Linkenkaer-Hansen et al., 2001; Zhang et al., 2018).”
BackgroundMethodInfluentialEEG microstate periodicity explained by rotating phase patterns of resting-state alpha oscillations
von Wegner, F., Bauer, S., Rosenow, F., Triesch, J., & Laufs, H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117372 · Cited by 36
A new perspective on the functioning of the brain and the mechanisms behind conscious processes.
Keppler, J. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00242 · Cited by 37
“The analysis of EEG and MEG background activity showed that spontaneous oscillations in the brain exhibit 1/f power-law scaling behavior, indicating that the brain operates in a scale-free state of self-organized criticality (Linkenkaer-Hansen et al., 2001; Freeman et al., 2003).”
BackgroundTewarie, P. K. B., Beernink, T. M. J., Eertman, C. J., Cornet, A. D., Beishuizen, A., van Putten, M. J. A. M., & Tjepkema‐Cloostermans, M. C. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103350 · Cited by 37
Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms
von Wegner, F., Wiemers, M., Hermann, G., Tödt, I., Tagliazucchi, E., & Laufs, H. (2023) · Brain Topography · doi:10.1007/s10548-023-01006-2 · Cited by 37
“…they can be found as complexity measures in functional MRI studies (Bullmore et al. 2009; Tagliazucchi et al. 2013; Dong et al. 2018), EEG studies (Linkenkaer-Hansen et al. 2001; Raghavendra et al. 2009; Sabeti et al. 2009; Holloway et al. 2014), but also outside brain research (Raubitzek and…”
Beyond task response—Pre-stimulus activity modulates contents of consciousness
Northoff, G., Zilio, F., & Zhang, J. (2024) · Physics of Life Reviews · doi:10.1016/j.plrev.2024.03.002 · Cited by 39
Scale-Free Cortical Planar Networks
Freeman, W. J., Kozma, R., Bollobá, B., & Riordan, O. M. (2008) · Bolyai Society mathematical studies · doi:10.1007/978-3-540-69395-6_7 · Cited by 36
“Recent studies on neocortical connectivity have emphasized conformance of large-scale connectivity to power-law distributions [115], [74], [28], [57], which opens the likelihood of self similarity of connectivity across scales, and which imposes the necessity of choosing a scale of description that…”
BackgroundBernasconi, F., Manuel, A. L., Murray, M. M., & Spierer, L. (2010) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2010.10.017 · Cited by 37
Change in the Characteristics of EEG Color Noise in Alzheimer’s Disease
Vyšata, O., Procházka, A., Mareš, J., Rusina, R., Pazdera, L., Vališ, M., & Kukal, J. (2013) · Clinical EEG and Neuroscience · doi:10.1177/1550059413491558 · Cited by 37
“Critical dynamics can allow rapid reorganization of neural networks in the brain.(4,5) Critical dynamics can only be manifested in complex systems.”
BackgroundNorthoff, G., Tsuchiya, N., & Saigo, H. (2019) · Entropy · doi:10.3390/e21121234 · Cited by 44
Ensemble of coupling forms and networks among brain rhythms as function of states and cognition
Chen, B., Ciria, L. F., Hu, C., & Ivanov, P. C. (2022) · Communications Biology · doi:10.1038/s42003-022-03017-4 · Cited by 44
Long-range correlation properties in motor timing are individual and task specific
Torre, K., Balasubramaniam, R., Rheaume, N. L., Lemoine, L., & Zelaznik, H. N. (2011) · Psychonomic Bulletin & Review · doi:10.3758/s13423-011-0049-1 · Cited by 37
“Goldberger, 1995), as well as neural activity ( Linkenkaer-Hansen, Nikouline, Palva, & Ilmoniemi, 2001 ).”
Proposed Cortical “Shutter” Mechanism in Cinematographic Perception
Freeman, W. (2007) · Springer eBooks · doi:10.1007/978-3-540-73267-9_2 · Cited by 37
“The distributions show that the neural patterns may be self-similar across multiple scales [12, 36, 37, 45 , 51].”
BackgroundThe effect of linear mixing in the EEG on Hurst exponent estimation
Blythe, D. A. J., Haufe, S., Müller, K., & Nikulin, V. V. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.041 · Cited by 36
“In the analysis of alpha range amplitude dynamics, values of H close to 1 have been found empirically to occur with significant autocorrelations persisting in the amplitude dynamics over thousands of cycles (Linkenkaer-Hansen et al., 2001; Nikulin and Brismar, 2004, 2005).”
InfluentialMultiscale temporal neural dynamics predict performance in a complex sensorimotor task
Samek, W., Blythe, D. A. J., Curio, G., Müller, K., Blankertz, B., & Nikulin, V. V. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.056 · Cited by 36
“Criticality manifests itself not only in the presence of aperiodic avalanches but also in slowly decaying autocorrelations in the amplitude dynamics of neuronal oscillations [7, 10, 11].”
BackgroundResultNorthoff, G. (2017) · Consciousness and Cognition · doi:10.1016/j.concog.2017.03.006 · Cited by 40
“Higher LRTCs, with a larger power-law exponent, are related to a higher time-lagged autocorrelation – this suggests that the past pattern of a system has a stronger influence on its future dynamics (Linkenkaer-Hansen et al., 2001).”
Emergence of local synchronization in neuronal networks with adaptive couplings
Chakravartula, S., Indic, P., Sundaram, B., & Killingback, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178975 · Cited by 36
Breakdown of long-range temporal correlations in brain oscillations during general anesthesia
Krzemiński, D., Kamiński, M., Marchewka, A., & Bola, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.047 · Cited by 34
“Robust LRTCs 80 were found in amplitude modulation of oscillatory brain activity (Linkenkaer-Hansen et al., 2001; 81 Linkenkaer-Hansen et al., 2004) and in temporal progression of EEG topographical voltage patterns (so 82 called “microstates”; van de Ville et al., 2010; Gschwind et al., 2015).”
Distinct criticality of phase and amplitude dynamics in the resting brain
Daffertshofer, A., Ton, R., Kringelbach, M. L., Woolrich, M. W., & Deco, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.002 · Cited by 38
“Spectral distributions of encephalographic signals have 1 = f -structures (Freeman and van Dijk, 1987; Dehghani et al., 2010; He et al., 2010; Fransson et al., 2013; Lei et al., 2015) and their auto-correlation structures also show power-law behavior (Linkenkaer-Hansen et al., 2001, 2007).”
BackgroundResultInfluentialI Can See Your Brain: Investigating Home-Use Electroencephalography System Security
Xiao, Y., Jia, Y., Cheng, X., Yu, J., Liang, Z., & Tian, Z. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2910115 · Cited by 37
“The philosophy behind our RCNN modeling is based on the fact that EEG data is known to possess both spatial and temporal correlations [46], [47].”
MethodBoosts in brain signal variability track liberal shifts in decision bias
Kloosterman, N. A., Kosciessa, J. Q., Lindenberger, U., Fahrenfort, J. J., & Garrett, D. D. (2020) · eLife · doi:10.7554/elife.54201 · Cited by 38
Walter, N., & Hinterberger, T. (2022) · Neuroscience of Consciousness · doi:10.1093/nc/niac008 · Cited by 39
“Despite substantial critique and controversies against the SOC theory, concerning issues such as unprecise terminology, the lack of evidence for a genuine control parameter, and the overall significance of power laws (Beggs and Timme 2012; Wilting and Priesemann 2019; Destexhe and Touboul 2021), a fair amount of resea…”
BackgroundKoolen, N., Dereymaeker, A., Räsänen, O. J., Jansen, K., Vervisch, J., Matić, V., Vos, M. D., Huffel, S. V., Naulaers, G., & Vanhatalo, S. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.01030 · Cited by 36
“…EEG classification suggests that ASI may provide an objective and quantified global biomarker for neonatal Frontiers in Human Neuroscience www.frontiersin.org December 2014 | Volume 8 | Article 1030 | 7 FIGURE 6 | ASI value is independent of postmenstrual age of the term infant (PMA >36 weeks).”
BackgroundDetecting scaling in the period dynamics of multimodal signals: Application to Parkinsonian tremor
Sapir, N., Karasik, R., Havlin, S., Simon, E. S., & Hausdorff, J. M. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.67.031903 · Cited by 36
Lombardi, F., Herrmann, H. J., Parrino, L., Plenz, D., Scarpetta, S., Vaudano, A. E., de Arcangelis, L., & Shriki, O. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113162 · Cited by 39
Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex
Mukli, P., Nagy, Z. Z., Rácz, F. S., Hermán, P., & Eke, A. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01072 · Cited by 35
Wang, J. W. J. L., Lombardi, F., Zhang, X., Anaclet, C., & Ivanov, P. C. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007268 · Cited by 36
“EEG and fMRI resting state dynamics [48, 49, 91, 92], awake monkeys [93] and resting magnetoencephalography of the human brain [46], to the dynamics of sleep-stage and arousal transitions across species [13, 26, 27, 31, 94]—where either distributions or temporal correlations of active events have been studied and disc…”
BackgroundCritical Behavior in Physics and Probabilistic Formal Languages
Lin, H. W., & Tegmark, M. (2017) · Entropy · doi:10.3390/e19070299 · Cited by 72
Sugimura, K., Iwasa, Y., Kobayashi, R., Honda, T., Hashimoto, J., Kashihara, S., Zhu, J., Yamamoto, K., Kawahara, T., Anno, M., et al. (2021) · Scientific Reports · doi:10.1038/s41598-020-79444-2 · Cited by 36
Li, F., Chao, W., Li, Y., Fu, B., Ji, Y., Wu, H., & Shi, G. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac13c0 · Cited by 36
“A previous study [34] has revealed that oscillations in the normal human brain at time scales reflect a memory of the dynamics of the system for more than a few seconds, which shows temporal correlations both in short and long terms in EEG signals.”
“Project for a Spatiotemporal Neuroscience” – Brain and Psyche Share Their Topography and Dynamic
Northoff, G., & Scalabrini, A. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.717402 · Cited by 33
Temporal order of nonlinear dynamics in human brain
Tirsch, W. S., Stude, P., Scherb, H., & Keidel, M. (2004) · Brain Research Reviews · doi:10.1016/j.brainresrev.2004.01.002 · Cited by 35
“This assumption is in line with the considerations of Linkenkaer-Hansen et al. [ 82 ] who suggested that ‘‘EEG oscillations arise from correlated activity of a large number of neurons whose interactions are generally nonlinear’’ [86,122].”
BackgroundResultNatural language processing models reveal neural dynamics of human conversation
Cai, J., Hadjinicolaou, A. E., Paulk, A. C., Soper, D. J., Xia, T., Wang, A. F., Rolston, J. D., Richardson, R. M., Williams, Z. M., & Cash, S. S. (2025) · Nature Communications · doi:10.1038/s41467-025-58620-w · Cited by 47
“EEG signal is known to possess high long-range temporal correlations and 1/f-like power distribution 74,75 .”
Ho, E. C. Y., Strüber, M., Bartos, M., Zhang, L., & Skinner, F. K. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5446-11.2012 · Cited by 32
Neural Microstates Govern Perception of Auditory Input without Rhythmic Structure
Henry, M. J., Herrmann, B., & Obleser, J. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2191-15.2016 · Cited by 37
“Neurally, 1/f activity signifies the presence of long-range temporal correlations, and as such reflects a “memory” for system dynamics (Linkenkaer-Hansen et al., 2001), which is disrupted in Alzheimer’s disease (Montez et al.”
BackgroundQuantitative evaluation of simulated functional brain networks in graph theoretical analysis
Lee, W. H., Bullmore, E. T., & Frangou, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.050 · Cited by 35
“One is based on the observation that long-range temporal correlations exhibit power-law decay in oscillatory amplitude modulation (Linkenkaer-Hansen et al., 2001).”
Sarracino, A., Arviv, O., Shriki, O., & de Arcangelis, L. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033355 · Cited by 35
Visual exploration dynamics are low-dimensional and driven by intrinsic factors
Zangrossi, A., Cona, G., Celli, M., Zorzi, M., & Corbetta, M. (2021) · Communications Biology · doi:10.1038/s42003-021-02608-x · Cited by 36
EEG biomarkers for the diagnosis and treatment of infantile spasms
Milà, B. R., Sindhu, K. R., Mytinger, J. R., Shrey, D. W., & Lopour, B. A. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.960454 · Cited by 34
“This is conceptually similar to a fractal; no single time scale can be used to accurately characterize the dynamics present in EEG (75).”
BackgroundHohlefeld, F. U., Huchzermeyer, C., Huebl, J., Schneider, G., Nolte, G., Brücke, C., Schönecker, T., Kühn, A. A., Curio, G., & Nikulin, V. V. (2013) · Neuroscience · doi:10.1016/j.neuroscience.2013.07.028 · Cited by 35
von Wegner, F., Tagliazucchi, E., Brodbeck, V., & Laufs, H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.050 · Cited by 34
“For EEG and MEG data, long-range correlations have been reported in the resting state condition (Novikov et al., 1997; Linkenkaer-Hansen et al., 2001), in response to stimuli (Linkenkaer-Hansen et al.”
Vignaud, P., Donde, C., Sadki, T., Poulet, E., & Brunelin, J. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.03.004 · Cited by 33
“At rest, LRTC may reflect the plasticity of the neural system (Linkenkaer-Hansen et al., 2001).”
BackgroundHebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery
Krueger, J., Krauth, R., Reichert, C., Perdikis, S., Vogt, S., Huchtemann, T., Dürschmid, S., Sickert, A., Lamprecht, J., Huremovic, A., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-69037-8 · Cited by 36
“14.07 amplifier (g.tec, Austria).”
InfluentialFingelkurts, A. A., Fingelkurts, A. A., & Neves, C. F. H. (2010) · Brain Research · doi:10.1016/j.brainres.2010.11.079 · Cited by 31
“…of " intermittency " , and is in fact compatible with self-organized criticality in physical systems (Bak et al., 1987) and in the brain (Novikov et al., 1997; Linkenkaer-Hansen et al., 2001; Buiatti et al., 2007; Plenz and Thiagarjan, 2007; Chialvo, 2008; Gong and van Leeuwen, 2009; Werner, 2009).”
Disturbed temporal dynamics of brain synchronization in vision loss
Bola, M., Gall, C., & Sabel, B. A. (2015) · Cortex · doi:10.1016/j.cortex.2015.03.020 · Cited by 36
“DFA has been introduced by Peng et al. (1994) and used to analyze long-range temporal correlations in non-stationary physiological time series, including EEG power envelopes and functional connectivity (Linkenkaer-Hansen et al., 2001; Stam & de Bruin, 2004).”
Griffiths phase and long-range correlations in a biologically motivated visual cortex model
Girardi‐Schappo, M., Bortolotto, G. S., Gonsalves, J. J., Pinto, L. T., & Tragtenberg, M. H. R. (2016) · Scientific Reports · doi:10.1038/srep29561 · Cited by 32
“The visual cortex also has a valuable data set available that we may use to benchmark our model results, such as the power spectrum 17,33–36 and a few avalanche experiments 13,14,16 .”
InfluentialIncreased scale-free dynamics in salience network in adult high-functioning autism
Damiani, S., Scalabrini, A., Gómez‐Pilar, J., Brondino, N., & Northoff, G. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.101634 · Cited by 33
“The slope of this line can be measured by the so-called Power-Law Exponent (PLE), in which the power spectrum follows a power-law (P ∝ 1/fβ), where β indicates the PLE (He et al., 2010; Linkenkaer-Hansen et al., 2001).”
Decoding attention control and selection in visual spatial attention
Hong, X., Bo, K., Meyyappan, S., Tong, S., & Ding, M. (2020) · Human Brain Mapping · doi:10.1002/hbm.25094 · Cited by 33
Brain response to color stimuli: an EEG study with nonlinear approach
Roy, S., Banerjee, A., Roy, C., Nag, S., Sanyal, S., Sengupta, R., & Ghosh, D. C. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09692-z · Cited by 33
Wong, C. W., Olafsson, V. T., Plank, M., Snider, J., Halgren, E., Howard Poizner, A., & Liu, T. T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109622 · Cited by 33
Modulation of scale-free properties of brain activity in MEG
Zilber, N., Ciuciu, P., Abry, P., & van Wassenhove, V. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235864 · Cited by 33
The human EEG — Physiological and clinical studies
Brismar, T. (2007) · Physiology & Behavior · doi:10.1016/j.physbeh.2007.05.047 · Cited by 33
Long-Range Temporal Correlations in the EEG Bursts of Human Preterm Babies
Hartley, C., Luc Berthouze, A., Mathieson, S., Boylan, G. B., Rennie, J. M., Marlow, N., & Farmer, S. F. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031543 · Cited by 33
Multifractal dynamics of resting-state functional connectivity in the prefrontal cortex
Rácz, F. S., Mukli, P., Nagy, Z. Z., & Eke, A. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aaa916 · Cited by 34
“As to the latter, scale-free properties in brain dynamics were demonstrated for signals acquired by different techniques (Eke and Herman 1999, Linkenkaer-Hansen et al 2001, Thurner et al 2003, Herman et al 2009, He 2011).”
MethodRhythmicity of neuronal oscillations delineates their cortical and spectral architecture
Myrov, V. O., Siebenhühner, F., Juvonen, J. J., Arnulfo, G., Palva, S., & Palva, J. M. (2024) · Communications Biology · doi:10.1038/s42003-024-06083-y · Cited by 31
“…oscillations such as amplitude ( 22 ), frequency ( 23 ), waveform ( 24 ), inter-areal phase and amplitude correlations ( 25,26 ), and long-range tem-34 poral correlations ( 27, 28 ) have been assessed in resting-state data to characterize the nature of endogenous brain dynamics ( 1, 29–31 ).”
BackgroundSelf-citation
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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