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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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“This finding is in agreement with previous studies (Linkenkaer-Hansen et al., 2001; Nikulin and Brismar, 2004; Parish et al., 2004; Monto et al., 2007), albeit that the duration of the EEG recordings in the present study only allowed an investigation of LRTC on time scales up to 20 s ( 1/10 of the data length).”
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“In conclusion, the observation of LRTC in human electrophysiology further supports the hypothesis that criticality is a robust property of cortical activity and a relevant biomarker of E/I balance.”
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“Long-term temporal correlations have been soundly established at the population level, such as the EEG (Linkenkaer-Hansen et al., 2001) and ECoG (He et al.”
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Self-citationDiscrimination ability of individual measures used in sleep stages classification
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“Other findings include the observation of 1/f power spectra from simultaneously recorded magnetoencephalography and electroencephalography signals [22], fMRI signals [23–25] as well as from cognitive responses [26], all indicative of long range correlations in brain dynamics processes.”
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“In living animals, power-law distributions were found in LFPs of macaques [38], rhesus monkeys [39, 40], and cats [41, 42], in two-photon microscopy in mice [43, 44], in light-sheet microscopy of whole-brain GCaMP activity in zebrafish larvae [45], in LFPs from intracranial depth electrodes in humans [46], in MEG and…”
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“The statistical properties described above characterize a system that can maintain itself in a state of selforganized criticality (Bak et al. 1987; Barrie et al. 1996; Linkenkaer-Hansen et al. 2001; Freeman et al. 2000; Hwa and Ferree 2002).”
BackgroundBrain coordination dynamics: True and false faces of phase synchrony and metastability
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“For fairly constant amplitudes of the sources, scalp amplitudes simultaneously reach a minimum when incident relative phase crosses p rad and a maximum when crossing 0 rad (Fig. 7). Remarkably, amplitude fluctuation is a pervasive feature of electroencephalographic data both in the temporal (e.g., Linkenkaer-Hansen et…”
The temporal signature of self: Temporal measures of resting‐state EEG predict self‐consciousness
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BackgroundNetwork Physiology of Exercise: Vision and Perspectives
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“There is already a large body of evidence for critical brain dynamics in neurophysiological processes including spike frequency, endogenous EEG and fMRI oscillations (Maxim et al., 2005; Bullmore et al., 1994; Linkenkaer-Hansen et al., 2001), which all display characteristic power law scaling in anatomically localized…”
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Self-citationComputational and dynamic models in neuroimaging
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“Although there have been recent papers arguing for criticality and power law effects in large-scale cortical activity (e.g. Kitzbichler et al., 2009; Linkenkaer-Hansen et al., 2001; Stam and de Bruin, 2004; Freyer et al., 2009) there is also work that argues otherwise; at least at higher…”
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BackgroundDehghani, N., Bédard, C., Cash, S. S., Halgren, E., & Alain Destexhe, A. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0263-2 · Cited by 131
“First, in contrast to prior studies (such as Novikov et al. 1997; Linkenkaer-Hansen et al. 2001), we calculated the frequency scaling of all the sensors and did not confine our analysis to a specific region.”
MethodResultInfluentialLong memory of rivers from spatial aggregation
Manfred Mudelsee, A. (2007) · Water Resources Research · doi:10.1029/2006wr005721 · Cited by 127
“Examples are computer communication in an Ethernet [Beran, 1994] or neural oscillations in the human brain [Linkenkaer-Hansen et al., 2001].”
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“…highlighted the adaptive nature of self-organizing neural networks: A network that is in a state of criticality is able to quickly reorganize and swiftly adapt to new situations (Alstrøm & Stassinopoulos, 1995; cf. Linkenkaer-Hansen, 2002; Linkenkaer- Hansen, Nikouline, Palva, & Ilmoniemi, 2001).”
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“Recent studies showed an existence of longrange temporal correlations (LRTC) in the amplitude dynamics of alpha and beta oscillations (Linkenkaer-Hansen et al., 2001, 2004; Nikulin and Brismar, 2004, 2005).”
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“The mechanisms giving rise to this statistical hallmark are poorly understood, but it has been suggested to reflect self-organized activity in critical networks [Linkenkaer-Hansen et al., 2001].”
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“I T is well known in the neuroimaging community that both fMRI and EEG time series exhibit temporal autocorrelations both in the short and long range regardless of experimental stimuli [9], [10].”
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“Similar power-law scaling behavior was observed in spontaneous neural oscillations generated in the human brain 21 .”
BackgroundResting States Affect Spontaneous BOLD Oscillations in Sensory and Paralimbic Cortex
McAvoy, M. P., Larson‐Prior, L. J., Nolan, T. S., Vaishnavi, S., Raichle, M. E., & d’Avossa, G. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90426.2008 · Cited by 115
“4, A and B, in Linkenkaer-Hansen et al. 2001).”
BackgroundInterplay between functional connectivity and scale-free dynamics in intrinsic fMRI networks
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“Thus, analyzing scale-invariance in temporal dynamics may provide novel insights into brain mechanisms underlying cognition and behavior (Linkenkaer-Hansen et al., 2001; Shimizu et al., 2004; Stam and de Bruin, 2004; He et al., 2008; He et al., 2010; He, 2011; Ciuciu et al., 2012; Suckling et al., 2008; Maxim et al.,…”
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“The significant information storage in the heart and brain systems indicates the presence of structured dynamics that, at the time scales explored in this study, are probably reflecting the long-range correlations observable when looking at the temporal evolution of the heartbeat intervals [41] and of the brain waves…”
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“organized criticality in physical systems [111] and in the brain [24,57,188-192].”
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“…field potential, and spike trains recorded from network-embedded neurons in vivo and in vitro (Teich, 1989; Teich et al., 1997; Lowen et al., 1997; Linkenkaer-Hansen et al., 2001, 2005; Buzsaki, 2006; Nir et al., 2007, 2008; Monto et al., 2008; Wink et al., 2008; Esposti et al., 2009; Freyer et…”
BackgroundNeural timescales or lack thereof
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“…synchronization rate, single neuron spike counts and synaptic release in vivo and in vitro (Teich, 1989; Teich et al., 1997; Lowen et al., 1997; Linkenkaer-Hansen et al., 2001; Segev et al., 2002; Beggs and Plenz, 2003, 2004; Linkenkaer-Hansen et al., 2005; Wagenaar et al., 2006; Nir et al.,…”
Witt, A., & Malamud, B. D. (2013) · Surveys in Geophysics · doi:10.1007/s10712-012-9217-8 · Cited by 106
“• Neurosciences (Linkenkaer-Hansen et al. 2001; Bédard et al. 2006).”
Fine spatiotemporal structure of phase in human intracranial EEG
Freeman, W. J., Holmes, M., West, G. A., & Vanhatalo, S. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.03.012 · Cited by 109
Disentangling the dynamic core: a research program for a neurodynamics at the large-scale
Quyen, M. L. V. (2003) · Biological Research · doi:10.4067/s0716-97602003000100006 · Cited by 103
Tewarie, P. K. B., Liuzzi, L., O’Neill, G. C., Quinn, A. J., Griffa, A., Woolrich, M. W., Stam, C. J., Hillebrand, A., & Brookes, M. J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.006 · Cited by 109
“Since it has been argued that neuronal oscillatory dynamics, as well as fluctuations in connectivity, can be characterized by scale-freeness (Stam and De Bruin, 2004; Linkenkaer-Hansen et al., 2001), there is a need to move beyond conventional fixed sliding window analyses.”
BackgroundDistributed processing; distributed functions?
Fox, P. T., & Friston, K. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.051 · Cited by 99
“Although there have been recent papers arguing for criticality and power law effects in large-scale cortical activity (e.g. Kitzbichler et al., 2009; Linkenkaer-Hansen et al., 2001; Stam and de Bruin, 2004; Freyer et al., 2009), there is also work that argues otherwise; at least at higher…”
BackgroundFransson, P., Metsäranta, M., Blennow, M., Ådén, U., Lagercrantz, H., & Vanhatalo, S. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs047 · Cited by 106
Battaglia, D. A., Boudou, T., Hansen, E. C. A., Lombardo, D., Chettouf, S., Daffertshofer, A., McIntosh, A. R., Zimmermann, J., Ritter, P., & Jirsa, V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117156 · Cited by 98
“In neuroscience; long-term persistence has been identified in resting-state and task fluctuations of electrophysiological and imaging signals (Linkenkaer-Hansen et al., 2001; Van de Ville et al., 2010; He, 2014) and found to correlate with behavior (Palva et al.”
BackgroundInfluentialSmit, D. J. A., de Geus, E. J. C., van de Nieuwenhuijzen, M. E., van Beijsterveldt, C. E. M., van Baal, G. C. M., Mansvelder, H. D., Boomsma, D. I., & Linkenkaer‐Hansen, K. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1678-11.2011 · Cited by 102
“…we used the DFA method as in most recent papers investigating the autocorrelation structure of amplitude fluctuations in ongoing oscillations (e.g., Linkenkaer-Hansen et al., 2001; Parish et al., 2004; Nikulin and Brismar, 2005; Monto et al., 2007; Poil et al., 2008; Berthouze et al., 2010).”
BackgroundMethodInfluentialSelf-citationCortical cores in network dynamics
de Pasquale, F., Corbetta, M., Betti, V., & Penna, S. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.063 · Cited by 108
Age and cholinergic effects on hemodynamics and functional coherence of human hippocampus
Wink, A. M., Bernard, F., Salvador, R. M., Bullmore, E. T., & John Suckling, A. (2005) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2005.08.011 · Cited by 101
Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls
Lee, J., Yang, B., Lee, J.-H., Choi, J. W., Choi, I.-G., & Kim, S. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.08.001 · Cited by 100
The Nature and Perception of Fluctuations in Human Musical Rhythms
Hennig, H., Fleischmann, R., Fredebohm, A., Hagmayer, Y., Nagler, J., Witt, A., Theis, F. J., & Geisel, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026457 · Cited by 100
Brain Networks Maintain a Scale-free Organization across Consciousness, Anesthesia, and Recovery
Lee, U., Oh, G., Kim, S., Noh, G., Choi, B., & Mashour, G. A. (2010) · Anesthesiology · doi:10.1097/aln.0b013e3181f229b5 · Cited by 98
Decline of long-range temporal correlations in the human brain during sustained wakefulness
Meisel, C., Bailey, K., Achermann, P., & Plenz, D. (2017) · Scientific Reports · doi:10.1038/s41598-017-12140-w · Cited by 101
“5 to 1, which, in line with previous reports, is indicative of long-range temporal correlations in the EEG signals [26].”
BackgroundResultInfluentialThe heavy tail of the human brain
Roberts, J. A., Boonstra, T. W., & Breakspear, M. J. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.10.014 · Cited by 96
Catecholamines alter the intrinsic variability of cortical population activity and perception
Pfeffer, T. A., Avrămiea, A.-E., Nolte, G., Engel, A. K., Linkenkaer‐Hansen, K., & Donner, T. H. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2003453 · Cited by 98
Self-citationFiring statistics of a neuron model driven by long-range correlated noise
Middleton, J. W., Chacron, M. J., Lindner, B., & Longtin, A. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.68.021920 · Cited by 96
Evidence from human scalp electroencephalograms of global chaotic itinerancy
Freeman, W. (2003) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.1596553 · Cited by 96
Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals
Banerjee, A., Sanyal, S., Patranabis, A., Banerjee, K., Guhathakurta, T., Sengupta, R., Ghosh, D. C., & Ghose, P. (2015) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2015.10.030 · Cited by 95
Kolvoort, I. R., Wainio‐Theberge, S., Wolff, A., & Northoff, G. (2020) · Human Brain Mapping · doi:10.1002/hbm.25129 · Cited by 98
“…(Himberger et al., 2018; Honey et al., 2012; Murray et al., 2014) and PLE (Chialvo, 2010; Eke et al., 2000, 2002; He, 2011, 2014; He et al., 2010; Linkenkaer-Hansen et al., 2001) are analogous measures of temporal integration on the neuronal level; they assess the same property (the fractal…”
BackgroundMethodDecoding motion direction using the topography of sustained ERPs and alpha oscillations
Bae, G., & Luck, S. J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.029 · Cited by 92
“(Methodological), 289-300. Benwell CSY, Tagliabue CF, Veniero D, Cecere R, Sav azzi S, Thut G (2017) Prestimulus EEG power predicts conscious awareness but not objectiv e visual performance.”
The dual klepsydra model of internal time representation and time reproduction
Wackermann, J., & Ehm, W. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2005.08.024 · Cited by 92
“Brain electrical data may provide more direct evidence into time scales of the global brain dynamics: decay times of EEG auto-correlation functions are reported in the range ≈100 s (Linkenkaer-Hansen et al., 2001).”
BackgroundA field-theoretic approach to understanding scale-free neocortical dynamics
Freeman, W. (2005) · Biological Cybernetics · doi:10.1007/s00422-005-0563-1 · Cited by 94
“Several types of network have been proposed to model (Friston 2000; Linkenkaer–Hansen et al. 2001; Stam et al. 2003; Kozma et al. 2005) the long-distance synchronization that is readily observed in scalp EEG (Rodriguez et al.”
BackgroundThe self and its prolonged intrinsic neural timescale in schizophrenia
Northoff, G., Sandsten, K. E., Nordgaard, J., Kjaer, T. W., & Parnas, J. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa083 · Cited by 91
Lin, A., Liu, K. K. L., Bartsch, R. P., & Ivanov, P. C. (2020) · Communications Biology · doi:10.1038/s42003-020-0878-4 · Cited by 96
Kosciessa, J. Q., Kloosterman, N. A., & Garrett, D. D. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007885 · Cited by 99
“For example, long-range temporal correlations (LRTCs) of narrowband amplitudes provide an alternative window on the irregularity of temporal dynamics [107–109].”
Thuwal, K., Banerjee, A., & Roy, D. (2021) · eNeuro · doi:10.1523/eneuro.0224-21.2021 · Cited by 100
“This spontaneous 1/f aperiodic brain dynamics almost always pervasively co-exists in EEG/magnetoencephalography (MEG)/LFP signals (Lyne et al., 1992; Linkenkaer-Hansen et al., 2001; Beggs and Plenz, 2003; Bédard et al., 2006; Voytek et al., 2015) and critically influence oscillatory signatures in…”
BackgroundDissipation and spontaneous symmetry breaking in brain dynamics
Freeman, W. J., & Vitiello, G. (2008) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8113/41/30/304042 · Cited by 91
Reliability and Individual Specificity of EEG Microstate Characteristics
Liu, J., Xu, J., Zou, G., Yong He, A., Zou, Q., & Gao, J. (2020) · Brain Topography · doi:10.1007/s10548-020-00777-2 · Cited by 93
Long-range temporal correlations in epileptogenic and non-epileptogenic human hippocampus
Parish, L. M., Worrell, G. A., Cranstoun, S. D., Stead, S. M., Pennell, P. B., & Litt, B. (2004) · Neuroscience · doi:10.1016/j.neuroscience.2004.03.002 · Cited by 89
Dimensionality reduction for the analysis of brain oscillations
Haufe, S., Dähne, S., & Nikulin, V. V. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.073 · Cited by 91
“…dynamics are often studied without any reference to the external events, i. e., during resting state, where the interest is to understand dynamics of oscillations unfolding without perturbations (Linkenkaer-Hansen et al., 2001; Nikouline et al., 2001; Nikulin and Brismar, 2005; Hipp et al., 2012).”
Wu, C. W., Chen, C., Liu, P.-Y., Chao, Y., Biswal, B. B., & Lin, C. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0018 · Cited by 88
“For its task-free procedure and confirmed inter-subject stability, rs-fMRI is used not only for cognitive neuroscience investigations (Linkenkaer-Hansen et al., 2001; Weissman-Fogel et al., 2010), but also for its great potential to study functional brain changes (plasticity) under a number of…”
Smit, D. J. A., Linkenkaer‐Hansen, K., & de Geus, E. J. C. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2816-12.2013 · Cited by 88
“For example, the spectra of the amplitude modulation of neuronal oscillations in different frequency bands show power-law frequency spectra over a large frequency range (Linkenkaer-Hansen et al., 2001; Berthouze et al., 2010).”
BackgroundInfluentialSelf-citationNikulin, V. V., Linkenkaer‐Hansen, K., Huttunen, J., & Ilmoniemi, R. J. (2001) · Neuroreport · doi:10.1097/00001756-200108080-00040 · Cited by 86
Self-citationMulti-timescale neural dynamics for multisensory integration
Senkowski, D., & Engel, A. K. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00845-7 · Cited by 101
“These envelopes typically exhibit long-range temporal correlations and power-law scaling , which indicate that the ongoing neural activity fluctuates over a wide range of timescales 112,114 .”
Allegrini, P. (2010) · Frontiers in Physiology · doi:10.3389/fphys.2010.00128 · Cited by 84
“However it was later shown that this topological pattern is superimposable dIscussIon The experimental discovery of 1/f noise in the brain (e.g., Novikov et al., 1997; Linkenkaer-Hansen et al., 2001; Buiatti et al., 2007) has normally been interpreted as an indication of self-organization.”
BackgroundAviation and neurophysiology: A systematic review
van Weelden, E., Alimardani, M., Wiltshire, T. J., & Louwerse, M. M. (2022) · Applied Ergonomics · doi:10.1016/j.apergo.2022.103838 · Cited by 86
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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