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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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“Correlated amplitude fluctuations reveal information about the temporal structure of the EEG (Linkenkaer-Hansen et al., 2001).”
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BackgroundThe fractal brain: scale-invariance in structure and dynamics
Grosu, G. F., Hopp, A. V., Moca, V. V., Bârzan, H., Ciuparu, A., Ercsey-Ravasz, M., Winkel, M., Linde, H., & Muresan, R. C. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac363 · Cited by 87
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BackgroundInfluentialEEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease
Simpraga, S., Álvarez-Jiménez, R., Mansvelder, H. D., van Gerven, J., Groeneveld, G. J., Poil, S., & Linkenkaer‐Hansen, K. (2017) · Scientific Reports · doi:10.1038/s41598-017-06165-4 · Cited by 78
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InfluentialComparison of connectivity analyses for resting state EEG data
Olejarczyk, E., Marzetti, L., Pizzella, V., & Zappasodi, F. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa6401 · Cited by 82
A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram
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Plenz, D. (2012) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2012-01575-5 · Cited by 72
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“(8) A final possibility to be considered is that the brain as a system might function in a state of self-organized criticality (Schroeder, 1991; Bak, 1997; Jensen, 1998; Linkenkaer-Hansen et al., 2001; Freeman, 2005).”
A Neural “Tuning Curve” for Multisensory Experience and Cognitive-Perceptual Schizotypy
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Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks
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Scale-Free Bursting in Human Cortex following Hypoxia at Birth
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“First, in the timescales of seconds to tens of minutes, powerlaw long-range temporal correlations (LRTCs) characterize the amplitude envelopes of neuronal oscillations in human MEG and EEG (Linkenkaer-Hansen et al., 2001) as well as in intracranial recordings (Monto et al.”
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“The power spectral densities in both time and space of ECoGs from surface arrays conform to power-law distributions [13, 14, 35, 36, 37], which suggests that the activity patterns generated by neocortical neuropil might be scale-free [38, 39] with self-similarity in ECoGs patterns over distances ranging from hypercolu…”
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“It should be pointed out that, since the organizational dynamics of individual proteins, cells, organs, organisms, and economies display longrange correlations in time [24-29], the structure and dynamics of any living system - be it a protein, a cell, an organism, an organization, or an economy - carry within them the…”
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Connecting brain and behavior in clinical neuroscience: A network approach
Blanken, T. F., Bathelt, J., Deserno, M. K., Voge, L., Borsboom, D., & Douw, L. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.07.027 · Cited by 75
“Although this method is commonly viewed as the gold standard for anatomical connectivity estimation, it is important to note that tractography always yields merely an estimate of the actual anatomical connectivity between brain regions, and is subject to several methodological pitfalls (Maier-Hein et al., 2017), such…”
Controlling the Temporal Structure of Brain Oscillations by Focused Attention Meditation
Irrmischer, M., Houtman, S. J., Mansvelder, H. D., Tremmel, M., Ott, U., & Linkenkaer‐Hansen, K. (2018) · Human Brain Mapping · doi:10.1002/hbm.23971 · Cited by 69
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MethodSelf-citationNeurofeedback Tunes Scale-Free Dynamics in Spontaneous Brain Activity
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From pre-processing to advanced dynamic modeling of pupil data
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“and scale-free temporal patterns (Kello et al., 2010), mean-”
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“LRTC have been suggested to reflect the degree to which the brain remains capable of quick reorganization (Deco et al., 2013; Linkenkaer-Hansen et al., 2001; Singer, 2013; Tognoli & Kelso, 2014) and, thus, is responsive to different processing demands.”
BackgroundSelf-citationSoft-assembly of sensorimotor function.
Kello, C. T., & Orden, G. C. V. (2009) · PubMed · Cited by 68
Pinging the brain to reveal the hidden attentional priority map using encephalography
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“…matrix, as well as decoding without boosting as per preregistration). at the high probability location ( t (12)= 6.751, p < 0.001, d Z = 1.9, 95% CI [25.27, 52.76]) suggesting that our observed results were not solely driven by a subset of aware participants (see Supplementary Fig.”
ResultInfluentialIs criticality a unified setpoint of brain function?
Hengen, K. B., & Shew, W. L. (2025) · Neuron · doi:10.1016/j.neuron.2025.05.020 · Cited by 80
Distributed Dynamical Computation in Neural Circuits with Propagating Coherent Activity Patterns
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“…and temporal correlations have been very well documented by analyzing brain activity from several different perspectives, including the temporal fluctuations of power of brain oscillations [39,40], the distribution of size of neural avalanches [41], and the intervals of synchronized activity [42].”
BackgroundModular co-organization of functional connectivity and scale-free dynamics in the human brain
Zhigalov, A., Arnulfo, G., Nobili, L., Palva, S., & Palva, J. M. (2017) · Network Neuroscience · doi:10.1162/netn_a_00008 · Cited by 61
“We used detrended fluctuation analysis (DFA) to assess the scaling exponents of the longrange temporal correlations (LRTCs; Linkenkaer-Hansen et al., 2001; J. M. Palva et al., 2013; Peng, Havlin, Stanley, & Goldberger, 1995).”
BackgroundMethodInfluentialSelf-citationLong-range temporal correlations in scale-free neuromorphic networks
Shirai, S., Acharya, S. K., Bose, S. K., Mallinson, J. B., Galli, E., Pike, M. D., Arnold, M. D., & Brown, S. A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00128 · Cited by 68
“…measurements such as temporal patterns of heartbeats (Goldberger et al., 2002) have been interpreted as a consequence of an underlying fractal physical structure (Di Ieva et al., 2014; Werner, 2010) and are a signature of correlations on many temporal scales (Linkenkaer-Hansen et al., 2001).”
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“In neurophysiology, similar long-range correlation effects have been identified from microscopic to macroscopic domains, ranging from the spike patterns of maintained discharges of retinal ganglion cells, lateral geniculate nucleus (Teich et al., 1997) and medullary sympathetic neurons (Lewis et al., 2001 )t o global…”
BackgroundInfluentialA pseudo-equilibrium thermodynamic model of information processing in nonlinear brain dynamics
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“This neural mechanism depends on robust maintenance by cortex approximating scale-free dynamics at criticality (Linkenkaer-Hansen et al, 2001; Freeman, 2007a ), where all frequencies and wavelengths of activity may simultaneously be expressed, as revealed in the power-law distributions of spectra…”
Quasi-periodic Fluctuations in Default Mode Network Electrophysiology
Ko, A. L., Darvas, F., Poliakov, A. V., Ojemann, J. G., & Sorensen, L. B. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.5730-10.2011 · Cited by 62
“Previous electrophysiological studies have measured verylow-frequency fluctuations in power within the traditional frequency bands from EEG recordings, reporting large power variations with 1/f-like power spectra (Linkenkaer-Hansen et al., 2001).”
BackgroundCritical branching neural networks.
Kello, C. T. (2013) · Psychological Review · doi:10.1037/a0030970 · Cited by 63
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“Future studies are needed to resolve the role of these mechanisms in ISFs in vivo as well as the contributions that the slow amplitude dynamics of fast >1 Hz neuronal activities (Deco et al., 2011; Linkenkaer-Hansen et al., 2001) have in shaping the ISFs and ICN fluctuations.”
BackgroundInfluentialSelf-citationMetastability demystified — the foundational past, the pragmatic present and the promising future
Hancock, F., Rosas, F. E., Luppi, A. I., Zhang, M., Mediano, P. A. M., Cabral, J., Deco, G., Kringelbach, M. L., Breakspear, M. J., Kelso, J. A. S., et al. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00883-1 · Cited by 92
Effects of Accuracy Feedback on Fractal Characteristics of Time Estimation
Kuznetsov, N. A., & Wallot, S. (2011) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2011.00062 · Cited by 65
“At the neural level, there is coordination across multiple time scales of the neural firing patterns ranging from relatively slow alpha rhythms to very high frequency theta oscillations (Linkenkaer-Hansen et al., 2001; cf. Buzsaki, 2006).”
BackgroundRácz, F. S., Stylianou, O., Mukli, P., & Eke, A. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00049 · Cited by 66
“A view shared by many is that scale-free fluctuations are the result of an underlying self-organized critical state of the brain that gives rise for its ability to perform large-scale reorganizations quickly in response to external/internal stimuli (Linkenkaer-Hansen et al., 2001; Bullmore et al., 2009; Chialvo, 2010;…”
BackgroundAge-Related Changes in the Energy and Spectral Composition of EEG
Vyšata, O., Kukal, J., Procházka, A., Pazdera, L., & Vališ, M. (2012) · Neurophysiology · doi:10.1007/s11062-012-9268-y · Cited by 61
How spontaneous brain activity and narcissistic features shape social interaction
Scalabrini, A., Huang, Z., Mucci, C., Perrucci, M. G., Ferretti, A., Fossati, A., Romani, G. L., Northoff, G., & Ebisch, S. J. H. (2017) · Scientific Reports · doi:10.1038/s41598-017-10389-9 · Cited by 64
Can a time varying external drive give rise to apparent criticality in neural systems?
Priesemann, V., & Shriki, O. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006081 · Cited by 61
Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity
Kida, T., Tanaka, E., & Kakigi, R. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00713 · Cited by 67
Neural Mechanisms of Cognitive Dissonance (Revised): An EEG Study
Colosio, M., Шестакова, А. Н., Nikulin, V. V., Blagovechtchenski, E., & Klucharev, V. A. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3209-16.2017 · Cited by 67
“Importantly, LRTCs indicate the presence of a scale-free structure of neuronal activation on multiple time scales that is important for optimal neuronal processing in the human brain (Linkenkaer-Hansen et al., 2001; Hardstone et al., 2012; Palva et al., 2013).”
BackgroundFuscà, M., Siebenhühner, F., Wang, S. H., Myrov, V. O., Arnulfo, G., Nobili, L., Palva, J. M., & Palva, S. (2023) · Nature Communications · doi:10.1038/s41467-023-40056-9 · Cited by 65
“We used Detrended Fluctuation Analysis (DFA) to estimate mono-fractal scaling exponents of neuronal LRTCs that typically vary between 0.5 and 1 30,52,55,64 .”
InfluentialSelf-citationMeisel, C., Klaus, A., Vyazovskiy, V. V., & Plenz, D. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0448-17.2017 · Cited by 59
“The observation of long network timescales during wake is in agreement with this criticality hypothesis (Linkenkaer-Hansen et al., 2001) and indicates that the ability to maintain memory of past activity is preserved in neural systems across different spatial scales (Meisel et al., 2015).”
ResultSpontaneous Brain Activity Predicts Task-Evoked Activity During Animate Versus Inanimate Touch
Scalabrini, A., Ebisch, S. J. H., Huang, Z., Plinio, S. D., Perrucci, M. G., Romani, G. L., Mucci, C., & Northoff, G. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy340 · Cited by 58
Northoff, G. (2017) · Psychological Inquiry · doi:10.1080/1047840x.2017.1337396 · Cited by 59
Nikulin, V. V., Fedele, T., Mehnert, J., Lipp, A., Noack, C., Steinbrink, J. M., & Curio, G. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.008 · Cited by 58
Multifractal formalism by enforcing the universal behavior of scaling functions
Mukli, P., Nagy, Z. Z., & Eke, A. (2014) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2014.09.002 · Cited by 57
“A line of research into diverse directions found them ubiquitous in nature [2–17].”
BackgroundTurkheimer, F., Leech, R., Expert, P., Lord, L.-D., & Vernon, A. C. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.04.014 · Cited by 55
Midfrontal theta tracks action monitoring over multiple interactive time scales
Cohen, M. X. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.054 · Cited by 56
Ongoing Brain Activity and Its Role in Cognition: Dual versus Baseline Models
Northoff, G., Vatansever, D., Scalabrini, A., & Stamatakis, E. A. (2022) · The Neuroscientist · doi:10.1177/10738584221081752 · Cited by 56
Markers of criticality in phase synchronization
Botcharova, M., Farmer, S. F., & Luc Berthouze, A. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00176 · Cited by 53
Graph theoretical analysis of brain connectivity in phantom sound perception
Mohan, A., Ridder, D. D., & Vanneste, S. (2016) · Scientific Reports · doi:10.1038/srep19683 · Cited by 56
Manjusha, M. K. S. R., & Rajaguru, H. (2016) · International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet.2016.7566575 · Cited by 55
Tagliazucchi, E. (2017) · Consciousness and Cognition · doi:10.1016/j.concog.2017.08.008 · Cited by 55
França, L. G. S., Miranda, J. G. V., Leite, M., Sharma, N. K., Walker, M. C., Lemieux, L., & Wang, Y. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01767 · Cited by 55
“Fractal geometry has been applied to the study of temporal dynamics, such as human brain dynamics in health (Lutzenberger et al., 1995; Pereda et al., 1998; Eke et al., 2000, 2002; Linkenkaer-Hansen et al., 2001; Bullmore et al., 2003, 2009; Gong et al., 2003; Acharya et al., 2005; Bassett et al., 2006, 2010; Hsu et a…”
MethodPapo, D. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00112 · Cited by 45
“The brain generates fluctuations with complex scaling properties (Novikov et al., 1997; Linkenkaer-Hansen et al., 2001; Gong et al., 2002, 2007; Freeman et al., 2003; Bianco et al., 2007; Suckling et al., 2009; Freyer et al., 2009), even in the absence of exogenous perturbations or changes in…”
BackgroundLow-Dimensional Dynamics of Resting-State Cortical Activity
Mehrkanoon, S., Breakspear, M. J., & Boonstra, T. W. (2013) · Brain Topography · doi:10.1007/s10548-013-0319-5 · Cited by 55
Time scales in cognitive neuroscience
Papo, D. (2013) · Frontiers in Physiology · doi:10.3389/fphys.2013.00086 · Cited by 51
“…∼ exp [−(t/τ)α] at microscopic scales (t < τ), and asymptotical convergence to an inverse power law ∼ [−(t/τ)α] for t τ, 0 < α < 1 (Novikov et al., 1997; Linkenkaer-Hansen et al., 2001; Gong et al., 2002; Freeman et al., 2003; Stam and de Bruin, 2004; Buiatti et al., 2007; van de Ville et…”
BackgroundNeuromodulatory control of complex adaptive dynamics in the brain
Shine, J. M. (2023) · Interface Focus · doi:10.1098/rsfs.2022.0079 · Cited by 58
“org/journal/rsfs Inrface Fcus 13:(2)0220079 2 methods have looked at temporal activity and used detrended fluctuation analysis [11,12], the Hurst exponent [13,14], or the power spectrum [15] to provide further evidence for criticality in neural recordings.”
MethodLong‐range temporal correlations in the subthalamic nucleus of patients with Parkinson’s disease
Hohlefeld, F. U., Huebl, J., Huchzermeyer, C., Schneider, G., Schönecker, T., Kühn, A. A., Curio, G., & Nikulin, V. V. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08198.x · Cited by 54
“…studies have demonstrated that LRTC in cortical alpha and beta oscillations were not very sensitive to whether eyes were open or closed (known to represent different levels of arousal: Niedermeyer & Lopes da Silva, 2004) in healthy subjects (Linkenkaer-Hansen et al., 2001; Nikulin & Brismar, 2005).”
BackgroundMethodInfluentialNon-linear EEG analyses predict non-response to rTMS treatment in major depressive disorder
Arns, M., Cerquera, A., Gutiérrez, R. M., Hasselman, F., & Freund, J. A. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.11.022 · Cited by 54
“Given that specifically the slow APF values have yielded predictive power (for overview see Arns and Olbrich, 2013) and LRCTs indicate power laws in which slow frequencies dominate the signal (Babloyantz, 1991; Linkenkaer-Hansen et al., 2005; Linkenkaer-Hansen et al., 2001), we used the above frequency band.”
MethodHoldsworth, A. M., & Myers, P. G. (2015) · Journal of Climate · doi:10.1175/jcli-d-14-00564.1 · Cited by 53
“The open-source toolbox is freely available and is well tested (Linkenkaer-Hansen et al. 2001; Moy et al. 2002; Holdsworth et al. 2012).”
MethodBieberich, E. (2002) · Biosystems · doi:10.1016/s0303-2647(02)00040-0 · Cited by 53
Buiatti, M., Papo, D., Baudonnière, P.-M., & van Vreeswijk, C. (2007) · Neuroscience · doi:10.1016/j.neuroscience.2007.02.048 · Cited by 53
Abnormal Dynamics of EEG Oscillations in Schizophrenia Patients on Multiple Time Scales
Sun, J., Tang, Y., Lim, K. O., Wang, J., Tong, S., Li, H., & He, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2306424 · Cited by 52
Rocca, D. L., Zilber, N., Abry, P., van Wassenhove, V., & Ciuciu, P. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.09.010 · Cited by 51
Brain dynamics across levels of organization
Werner, G. (2007) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2007.12.001 · Cited by 52
Wang, Z., Tian, C., Dhamala, M., & Liu, Z. (2017) · Scientific Reports · doi:10.1038/s41598-017-00697-5 · Cited by 51
Long-Range Amplitude Coupling Is Optimized for Brain Networks That Function at Criticality
Avrămiea, A.-E., Masood, A., Mansvelder, H. D., & Linkenkaer‐Hansen, K. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1095-21.2022 · Cited by 51
“The DFA was introduced as a method to quantify correlations in complex data with less strict assumptions about the stationarity of the signal than the classical autocorrelation function or power spectral density (Peng et al., 1995; Linkenkaer-Hansen et al., 2001; Hardstone et al., 2012).”
BackgroundMethodSelf-citationCritical Fluctuations in Cortical Models Near Instability
Aburn, M. J., Holmes, C. A., Roberts, J. A., Boonstra, T. W., & Breakspear, M. J. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00331 · Cited by 47
“These findings are consistent with prior reports of intermittent non-linear structure within (Stam et al., 1999) and between (Breakspear and Terry, 2002) surface EEG channels.”
BackgroundInfluentialComparison of fractal and power spectral EEG features: Effects of topography and sleep stages
Weiss, B., Clemens, Z., Bódizs, R., & Halász, P. (2010) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2010.12.005 · Cited by 49
“Unlike deterministic approaches aimed at finding low-dimensional chaos, the SOC framework allows for describing the high-dimensional character of the dynamics and the presence of stochastic effects [58].”
InfluentialInvestigating long-range correlation properties in EEG during complex cognitive tasks
Karkare, S., Saha, G., & Bhattacharya, J. (2009) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2009.03.148 · Cited by 50
Díaz, H., Córdova, F. M., Cañete, L. R., Palominos, F., Cifuentes, F., Sánchez, C., & Herrera, M. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.07.135 · Cited by 50
Homeostatic plasticity and emergence of functional networks in a whole-brain model at criticality
Rocha, R. P., Koçillari, L., Suweis, S., Corbetta, M., & Maritan, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-33923-9 · Cited by 50
Tamura, Y., Hoshiyama, M., Nakata, H., Hiroe, N., Inui, K., Kaneoke, Y., Inoue, K., & Kakigi, R. (2005) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2005.04096.x · Cited by 53
“frequency ranges, suggesting the distinct significance of neuronal oscillations in human behaviour (Linkenkaer-Hansen et al., 2001; Nikouline et al., 2001).”
BackgroundLewis, C., Bosman, C. A., Womelsdorf, T., & Fries, P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1513773113 · Cited by 51
“Such passive states would therefore exhibit a characteristic 1/f spectrum (Freeman, 2004; He, 2014; Linkenkaer-Hansen et al., 2001).”
BackgroundAlpha Oscillations Reduce Temporal Long-Range Dependence in Spontaneous Human Brain Activity
Becker, R., Ville, D. V. D., & Kleinschmidt, A. K. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0831-17.2017 · Cited by 49
“…1/f brain activity during aging (Bak et al., 1987; Voytek et al., 2015), of slope-modulations during sleep versus awake states (Gilden et al., 1995; Linkenkaer-Hansen et al., 2001; He et al., 2010; Van de Ville et al., 2010; Zempel et al., 2012), of slope-modulations during task activation in…”
BackgroundMethodInfluential
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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