Roles of multiscale brain activity fluctuations in shaping the variability and dynamics of psychophysical performance
Abstract
Spontaneous brain activity across many time scales influences sensory perception and human cognitive performance. Empirical insight into the underlying systems-level mechanisms has, however, remained fragmented. We review here recent studies on how wideband scale-free and scale-specific neuronal activity fluctuations together bias sensory processing and perceptual performance. We posit that these fluctuations constitute the neurophysiological foundation for both the trial-to-trial behavioral variability and the scaling laws governing psychophysical performance.
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Electrophysiological infraslow fluctuations are believed to coordinate large-scale intrinsic connectivity network organization and long-range information exchange.
“and are believed to coordinate large-scale ICN organization and long-range information exchange [68, 83–91].”
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Waking state: rapid variations modulate neural and behavioral responses
McGinley, M. J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C. R., Tolias, A. S., Cardin, J. A., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.012 · Cited by 801 · see citing works
“…the marked impact of apparent spontaneous variations in the human waking state on both cortical responses and perceptual abilities has begun to be more widely appreciated (Boly et al., 2007; Fox and Raichle, 2007; He, 2013; Hesselmann et al., 2008a; Hesselmann et al., 2008b; Palva and Palva, 2011).”
Kucyi, A., & Davis, K. D. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2014.11.006 · Cited by 587 · see citing works
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 511 · see citing works
Intrinsic 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 · see citing works
Functional Roles of Alpha-Band Phase Synchronization in Local and Large-Scale Cortical Networks
Palva, S., & Palva, J. M. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00204 · Cited by 477 · see citing works
Self-citationPalva, 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 453 · see citing works
Self-citationMind wandering away from pain dynamically engages antinociceptive and default mode brain networks
Kucyi, A., Salomons, T. V., & Davis, K. D. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312902110 · Cited by 402 · see citing works
Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs
Palva, S., & Palva, J. M. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.02.004 · Cited by 375 · see citing works
Self-citationMechanisms underlying gain modulation in the cortex
Ferguson, K. A., & Cardin, J. A. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0253-y · Cited by 327 · see citing works
Outline of a general theory of behavior and brain coordination
Kelso, J. A. S., Dumas, G., & Tognoli, E. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.09.003 · Cited by 162 · see citing works
Palva, J. M., & Palva, S. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.060 · Cited by 163 · see citing works
Self-citationOscillatory multiplexing of neural population codes for interval timing and working memory
Gu, B.-M., van Rijn, H., & Meck, W. H. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.10.008 · Cited by 155 · see citing works
Switching between internal and external modes: A multiscale learning principle
Honey, C. J., Newman, E. L., & Schapiro, A. C. (2017) · Network Neuroscience · doi:10.1162/netn_a_00024 · Cited by 110 · see citing works
Cognitive fluctuations in Lewy body dementia: towards a pathophysiological framework
Matar, E., Shine, J. M., Halliday, G. M., & Lewis, S. J. G. (2019) · Brain · doi:10.1093/brain/awz311 · Cited by 119 · see citing works
Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity
Kucyi, A., Tambini, A., Sadaghiani, S., Keilholz, S. D., & Cohen, J. R. (2018) · Network Neuroscience · doi:10.1162/netn_a_00037 · Cited by 90 · see citing works
Dimitriadis, S., Laskaris, N., Bitzidou, M., Tarnanas, I., & Tsolaki, M. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00350 · Cited by 88 · see citing works
Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury
Antonakakis, M., Dimitriadis, S. I., Zervakis, M. E., Micheloyannis, S., Rezaie, R., Abbas Babajani-feremi, A., Zouridakis, G., & Papanicolaou, A. C. (2016) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2016.02.002 · Cited by 70 · see citing works
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 · see citing works
Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior
Benisty, H., Barson, D., Moberly, A. H., Lohani, S., Tang, L., Coifman, R. R., Crair, M. C., Mishne, G., Cardin, J. A., & Higley, M. J. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01498-y · Cited by 65 · see citing works
Tokariev, A., Videman, M., Palva, J. M., & Vanhatalo, S. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv219 · Cited by 62 · see citing works
Self-citationNorthoff, G. (2017) · Psychological Inquiry · doi:10.1080/1047840x.2017.1337396 · Cited by 57 · see citing works
Slow brain oscillations of sleep, resting state, and vigilance
Someren, E. J. W. V., van der Werf, Y. D., Roelfsema, P. R., Mansvelder, H. D., & Lopes, F. H. D. S. (2011) · Progress in brain research · doi:10.1016/b978-0-444-53839-0.00001-6 · Cited by 56 · see citing works
Bönstrup, M., Schulz, R., Feldheim, J., Hummel, F. C., & Gerloff, C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.052 · Cited by 56 · see citing works
Preterm Birth Changes Networks of Newborn Cortical Activity
Tokariev, A., Stjerna, S., Lano, A., Metsäranta, M., Palva, J. M., & Vanhatalo, S. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy012 · Cited by 53 · see citing works
Self-citationCircadian 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 · see citing works
Cai, L., Wei, X., Wang, J., Yu, H., Deng, B., & Wang, R. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.07.019 · Cited by 47 · see citing works
Dimitriadis, S. I., Laskaris, N., Simos, P. G., Fletcher, J. M., & Papanicolaou, A. C. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00163 · Cited by 39 · see citing works
Dimitriadis, S. I., López, M. E., Bruña, R., Cuesta, P., Marcos, A., Maestú, F., & Pereda, E. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00306 · Cited by 41 · see citing works
Cortical localization of phase and amplitude dynamics predicting access to somatosensory awareness
Hirvonen, J., & Palva, S. (2015) · Human Brain Mapping · doi:10.1002/hbm.23033 · Cited by 36 · see citing works
Self-citationSensory and cross-network contributions to response inhibition in patients with schizophrenia
Hoptman, M. J., Parker, E. M., Nair-Collins, S., Dias, E. C., Ross, M. E., DiCostanzo, J., Sehatpour, P., & Javitt, D. C. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.01.001 · Cited by 35 · see citing works
Multiscale Network Organization in the Human Brain
Bassett, D. S., & Siebenhühner, F. (2013) · Wiley eBooks · doi:10.1002/9783527671632.ch07 · Cited by 35 · see citing works
Northoff, G. (2017) · Consciousness and Cognition · doi:10.1016/j.concog.2017.03.006 · Cited by 36 · see citing works
Rhythmicity 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 · see citing works
Self-citationArnett, A. B., Peisch, V. D., & Levin, A. R. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00295.2022 · Cited by 29 · see citing works
Antonakakis, M., Dimitriadis, S. I., Zervakis, M., Papanicolaou, A. C., & Zouridakis, G. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.05.032 · Cited by 25 · see citing works
Sensory processing in humans and mice fluctuates between external and internal modes
Weilnhammer, V. A., Stuke, H., Standvoss, K., & Sterzer, P. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002410 · Cited by 24 · see citing works
Antonakakis, M., Dimitriadis, S. I., Zervakis, M. E., Papanicolaou, A. C., & Zouridakis, G. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00416 · Cited by 22 · see citing works
Scale-Free Amplitude Modulation of Neuronal Oscillations Tracks Comprehension of Accelerated Speech
Borges, A. F. T., Giraud, A., Mansvelder, H. D., & Linkenkaer‐Hansen, K. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1515-17.2017 · Cited by 25 · see citing works
Nested synchrony—a novel cross-scale interaction among neuronal oscillations
Monto, S. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00384 · Cited by 17 · see citing works
Bistable perception alternates between internal and external modes of sensory processing
Weilnhammer, V. A., Chikermane, M., & Sterzer, P. (2021) · iScience · doi:10.1016/j.isci.2021.102234 · Cited by 14 · see citing works
Scale-free amplitude modulation of low-frequency fluctuations in episodic migraine
Hodkinson, D. J., Lee, D. H., Becerra, L. R., & Borsook, D. (2019) · Pain · doi:10.1097/j.pain.0000000000001619 · Cited by 13 · see citing works
Networks of cortical activity show graded responses to perinatal asphyxia
Syvälahti, T., Tuiskula, A., Nevalainen, P., Metsäranta, M., Haataja, L. M., Vanhatalo, S., & Tokariev, A. (2023) · Pediatric Research · doi:10.1038/s41390-023-02978-4 · Cited by 13 · see citing works
Peisch, V. D., & Arnett, A. B. (2022) · Development and Psychopathology · doi:10.1017/s095457942200116x · Cited by 11 · see citing works
The impact of EEG electrode density on the mapping of cortical activity networks in infants
Asayesh, A., Vanhatalo, S., & Tokariev, A. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120932 · Cited by 10 · see citing works
Perez, T. M., Glue, P., Adhia, D. B., Navid, M. S., Zeng, J., Dillingham, P., Smith, M., Niazi, I. K., Young, C. K., & De Ridder, D. (2022) · Trials · doi:10.1186/s13063-022-06863-z · Cited by 8 · see citing works
Comparison of brain network models using cross-frequency coupling and attack strategies
Antonakakis, M., Dimitriadis, S. I., Zervakis, M. E., Rezaie, R., Abbas Babajani-feremi, A., Micheloyannis, S., Zouridakis, G., & Papanicolaou, A. C. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320108 · Cited by 9 · see citing works
Palva, J. M., & Palva, S. (2014) · Wiley eBooks · doi:10.1002/9783527651009.ch5 · Cited by 9 · see citing works
Self-citationAttentional brain rhythms during prolonged cognitive activityPreprint
Gaillard, C., Sousa, C. D., Amengual, J. L., Loriette, C., Ziane, C., Hamed, S. B., Bello, F. D., & Hamed, S. B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.26.445730 · Cited by 9 · see citing works
Rapid fluctuations in functional connectivity of cortical networks encode spontaneousPreprint
Benisty, H., Barson, D. G., Moberly, A. H., Lohani, S., Coifman, R. R., Mishne, G., Crair, M. C., Cardin, J. A., & Higley, M. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.15.456390 · Cited by 6 · see citing works
Northoff, G. (2018) · Springer eBooks · doi:10.1007/978-3-319-94700-6_5 · Cited by 4 · see citing works
Increased insula activity precedes the formation of subjective illusory Gestalt
Wilding, M., Körner, C., Ischebeck, A. K., & Zaretskaya, N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119289 · Cited by 4 · see citing works
On brain criticality in the context of resonance
Leisman, G. (2023) · Journal of Multiscale Neuroscience · doi:10.56280/1579669390 · Cited by 5 · see citing works
Ge, S., He, J., Lin, P., Wang, R., Iramina, K., Leng, Y., Wang, H., Zheng, W., Xiao, C., & Li, Z. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3201547 · Cited by 3 · see citing works
Bimodal inference in humans and micePreprint
Weilnhammer, V. A., Stuke, H., Standvoss, K., & Sterzer, P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.20.457079 · Cited by 3 · see citing works
Real-Time Brain-State-Coupled Corticocortical Paired Associative Stimulation of Cognitive Networks
Jovellar, D. B., Turrini, S., Belardinelli, P., Roy, O., Santarnecchi, E., & Ziemann, U. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.2386-24.2026 · Cited by 1 · see citing works
The Case for Quantifying Artificial General Intelligence with Entropy Semifields
Valverde-Albacete, F. J., & Peláez-Moreno, C. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-72711-6_5 · Cited by 1 · see citing works
Networks of cortical activity in infants with epilepsy
Auno, S., Jonsson, H., Linnankivi, T., Tokariev, A., & Vanhatalo, S. (2022) · Brain Communications · doi:10.1093/braincomms/fcac295 · Cited by 1 · see citing works
Studying connectivity in the neonatal EEG
Tokariev, A. (2015) · Työväentutkimus Vuosikirja · Cited by 1 · see citing works
Kucyi, A. (2014) · TSpace (University of Toronto)
Scale-free dynamic shapes perception and cognition
Northoff, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-821935-5.00036-3
Prefrontal markers of spatial attention
Ferreira, C. D. S. (2021) · doi:10.70675/fb26d059zc23dz413eza6f4z830427c1478c
Causal modulation of cortical amplitude coupling through dual-site amplitude-modulated tACSPreprint
Fiene, M., Siems, M., Kammerer, T., Schneider, T. R., & Engel, A. K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.14.718451
- Authors
- Palva, J. M., & Palva, S.
- Affiliations
- University of Helsinki (FI)
- Corresponding author
- Palva, J. Matias
- Venue
- Progress in Brain Research
- Publisher
- Elsevier
- Published
- 2011
- Volume
- 193
- Pages
- 335-350
- ISSN
- 0079-6123 (ISSN-L)
- OpenAlex
- W1551536281
- Cited by
- 63
- References
- 105
- Citations by year
- 2026: 2 · 2024: 3 · 2023: 5 · 2022: 6 · 2021: 6 · 2020: 1 · 2019: 2 · 2018: 8 · 2017: 7 · 2016: 2 · 2015: 7 · 2014: 4 · 2013: 4 · 2012: 4
- FWCI
- 9.54
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Progress in brain research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Academy of Finland
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- spontaneous brain activity, sensory perception, perceptual performance, trial-to-trial variability, scaling laws, psychophysical performance, cognitive performance
- MeSH terms
- Animals, Behavior, Brain, Brain Mapping, Cognition, Electroencephalography, Humans, Magnetic Resonance Imaging, Models, Neurological, Neurons, Psychomotor Performance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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