Cited by 425Sleep and Wakefulness ResearchNeural dynamics and brain functionSleep and related disorders

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Sleep Homeostasis and Cortical Synchronization: III. A High-Density EEG Study of Sleep Slow Waves in Humans

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Riedner, Brady A., Vyazovskiy, Vladyslav V., Huber, Reto, Massimini, Marcello, Esser, Steve, Murphy, Michael, Tononi, Giulio·2007·Sleep

DOI 10.1093/sleep/30.12.1643PMC PMC2276133

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Abstract

STUDY OBJECTIVES: The mechanisms responsible for the homeostatic decrease of slow-wave activity (SWA, defined in this study as electroencephalogram [EEG] power between 0.5 and 4.0 Hz) during sleep are unknown. In agreement with a recent hypothesis, in the first of 3 companion papers, large-scale computer simulations of the sleeping thalamocortical system showed that a decrease in cortical synaptic strength is sufficient to account for the decline in SWA. In the model, the reduction in SWA was accompanied by decreased incidence of high-amplitude slow waves, decreased wave slopes, and increased number of waves with multiple peaks. In a second companion paper in the rat, local field potential recordings during early and late sleep confirmed the predictions of the model. Here, we investigated the model's predictions in humans by using all-night high-density (hd)-EEG recordings to explore slow-wave parameters over the entire cortical mantle. DESIGN: 256-channel EEG recordings in humans over the course of an entire night's sleep. SETTING: Sound-attenuated sleep research room PATIENTS OR PARTICIPANTS: Seven healthy male subjects INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: During late sleep (non-rapid eye movement [NREM] episodes 3 and 4, toward morning), when compared with early sleep (NREM sleep episodes 1 and 2, at the beginning of the night), the analysis revealed (1) reduced SWA, (2) fewer large-amplitude slow waves, (3) decreased wave slopes, (4) more frequent multipeak waves. The decrease in slope between early and late sleep was present even when waves were directly matched by wave amplitude and slow-wave power in the background EEG. Finally, hd-EEG showed that multipeak waves have multiple cortical origins. CONCLUSIONS: In the human EEG, the decline of SWA during sleep is accompanied by changes in slow-wave parameters that were predicted by a computer model simulating a homeostatic reduction of cortical synaptic strength.

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[1]Tononi G, Cirelli C. Sleep and synaptic homeostasis: a hypothesis. Brain Res Bull. 2003;62:143–50. doi: 10.1016/j.brainresbull.2003.09.004. [DOI] [PubMed] [Google Scholar] · DOI · Paper page
[2]Tononi G, Cirelli C. Sleep function and synaptic homeostasis. Sleep Med Rev. 2006;10:49–62. doi: 10.1016/j.smrv.2005.05.002. [DOI] [PubMed] [Google Scholar] · DOI · Paper page
[3]Esser S, Hill S, Tononi G. Sleep homeostasis and cortical synchronization: I. Modeling the effects of synaptic strength on sleep slow waves. Sleep. 2007;30:1617–30. doi: 10.1093/sleep/30.12.1617. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI · Paper page
[4]Vyazovskiy VV, Riedner BA, Cirelli C, Tononi G. Sleep homeostasis and cortical synchronization: II. A local field potential study of sleep slow waves in the rat. Sleep. 2007;30:1631–42. doi: 10.1093/sleep/30.12.1631. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI · Paper page
[5]Blake H, Gerard RW. Brain potentials during sleep. Am J Physiol. 1937;119:692–703. [Google Scholar]
[6]Daan S, Beersma DG, Borbély AA. Timing of human sleep: recovery process gated by a circadian pacemaker. Am J Physiol. 1984;246:R161–83. doi: 10.1152/ajpregu.1984.246.2.R161. [DOI] [PubMed] [Google Scholar] · DOI
[7]Borbély AA. A two process model of sleep regulation. Hum Neurobiol. 1982;1:195–204. [PubMed] [Google Scholar]
[8]Borbély AA, Achermann P. Sleep homeostasis and models of sleep regulation. In: Kryger MH, Roth T, Dement WC, editors. Principles and Practice of Sleep Medicine. Philadelphia: Elsevier; 2005. pp. 405–17. [Google Scholar]
[9]Rechtschaffen A, Kales A Public Health Services-National Institutes of Health, National Institute of Neurological Diseases and Blindness, Neurological Information Network. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Bethesda, Md: U.S. Dept. of Health, Education, and Welfare, Public Health Services-National Institutes of Health, National Institute of Neurological Diseases and Blindness, Neurological Information Network; 1968. [Google Scholar]
[10]Huber R, Graf T, Cote KA, et al. Exposure to pulsed high-frequency electromagnetic field during waking affects human sleep EEG. Neuroreport. 2000;11:3321–5. doi: 10.1097/00001756-200010200-00012. [DOI] [PubMed] [Google Scholar] · DOI
[11]Aeschbach D, Borbély AA. All-night dynamics of the human sleep EEG. J Sleep Res. 1993;2:70–81. doi: 10.1111/j.1365-2869.1993.tb00065.x. [DOI] [PubMed] [Google Scholar] · DOI · Paper page
[12]Feinberg I, Floyd TC. Systematic trends across the night in human sleep cycles. Psychophysiology. 1979;16:283–91. doi: 10.1111/j.1469-8986.1979.tb02991.x. [DOI] [PubMed] [Google Scholar] · DOI
[13]Molle M, Marshall L, Gais S, Born J. Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep. J Neurosci. 2002;22:10941–7. doi: 10.1523/JNEUROSCI.22-24-10941.2002. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI · Paper page
[14]Geering BA, Achermann P, Eggimann F, Borbély AA. Period-amplitude analysis and power spectral analysis: a comparison based on all-night sleep EEG recordings. J Sleep Res. 1993;2:121–129. doi: 10.1111/j.1365-2869.1993.tb00074.x. [DOI] [PubMed] [Google Scholar] · DOI
[15]Uchida S, Feinberg I, March JD, et al. A comparison of period amplitude analysis and FFT power spectral analysis of all-night human sleep EEG. Physiol Behav. 1999;67:121–31. doi: 10.1016/s0031-9384(99)00049-9. [DOI] [PubMed] [Google Scholar] · DOI
[16]Feinberg I, March JD, Fein G, et al. Period and amplitude analysis of 0.5–3 c/sec activity in NREM sleep of young adults. Electroencephalogr Clin Neurophysiol. 1978;44:202–13. doi: 10.1016/0013-4694(78)90266-3. [DOI] [PubMed] [Google Scholar] · DOI
[17]Massimini M, Huber R, Ferrarelli F, et al. The sleep slow oscillation as a traveling wave. J Neurosci. 2004;24:6862–70. doi: 10.1523/JNEUROSCI.1318-04.2004. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI · Paper page
[18]Hamalainen MS, Ilmoniemi RJ. Interpreting magnetic fields of the brain: minimum norm estimates. Med Biol Eng Comput. 1994;32:35–42. doi: 10.1007/BF02512476. [DOI] [PubMed] [Google Scholar] · DOI · Paper page
[19]Silva C, Maltez JC, Trindade E, et al. Evaluation of L1 and L2 minimum norm performances on EEG localizations. Clin Neurophysiol. 2004;115:1657–68. doi: 10.1016/j.clinph.2004.02.009. [DOI] [PubMed] [Google Scholar] · DOI
[20]Siegel S, Castellan NJ. Nonparametric statistics for the behavioral sciences. 2nd ed. New York: McGraw-Hill; 1988. [Google Scholar]
[21]Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:1–25. doi: 10.1002/hbm.1058. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI · Paper page
[22]Landolt HP, Dijk DJ, Achermann P, Borbély AA. Effect of age on the sleep EEG: slow-wave activity and spindle frequency activity in young and middle-aged men. Brain Res. 1996;738:205–12. doi: 10.1016/s0006-8993(96)00770-6. [DOI] [PubMed] [Google Scholar] · DOI · Paper page
[23]Glazewski S, Herman C, McKenna M, et al. Long-term potentiation in vivo in layers II/III of rat barrel cortex. Neuropharmacology. 1998;37:581–92. doi: 10.1016/s0028-3908(98)00039-2. [DOI] [PubMed] [Google Scholar] · DOI
[24]O'Boyle MP, Do V, Derrick BE, Claiborne BJ. In vivo recordings of long-term potentiation and long-term depression in the dentate gyrus of the neonatal rat. J Neurophysiol. 2004;91:613–22. doi: 10.1152/jn.00307.2003. [DOI] [PubMed] [Google Scholar] · DOI
[25]Whitlock JR, Heynen AJ, Shuler MG, Bear MF. Learning induces long-term potentiation in the hippocampus. Science. 2006;313:1093–7. doi: 10.1126/science.1128134. [DOI] [PubMed] [Google Scholar] · DOI · Paper page
[26]Hill S, Tononi G. Modeling sleep and wakefulness in the thalamocortical system. J Neurophysiol. 2005;93:1671–98. doi: 10.1152/jn.00915.2004. [DOI] [PubMed] [Google Scholar] · DOI · Paper page
[27]Timofeev I, Grenier F, Bazhenov M, et al. Origin of slow cortical oscillations in deafferented cortical slabs. Cereb Cortex. 2000;10:1185–99. doi: 10.1093/cercor/10.12.1185. [DOI] [PubMed] [Google Scholar] · DOI · Paper page
[28]Nunez PL, Srinivasan R. Electric Fields of the Brain: the Neurophysics of EEG. 2nd ed. New York: Oxford; 2006. [Google Scholar]
[29]Colrain IM. The K-complex: a 7-decade history. Sleep. 2005;28:255–73. doi: 10.1093/sleep/28.2.255. [DOI] [PubMed] [Google Scholar] · DOI
[30]Finelli LA, Borbély AA, Achermann P. Functional topography of the human nonREM sleep electroencephalogram. Eur J Neurosci. 2001;13:2282–90. doi: 10.1046/j.0953-816x.2001.01597.x. [DOI] [PubMed] [Google Scholar] · DOI

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Self-citation
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Yang, F. N., Picchioni, D., de Zwart, J. A., Wang, Y., van Gelderen, P., & Duyn, J. H. (2024) · eLife · doi:10.7554/elife.98739.3 · Cited by 1
A unified framework to model synaptic dynamics during the sleep–wake cycle
Kinoshita, F. L., Yamada, R. G., Ode, K. L., & Ueda, H. R. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003198 · Cited by 3
“…durations during SWO in mice, rats, and cats are approximately 100–500 ms [9,57], while in humans, Up-state durations range from 250–1,000 ms [58], all of which fall within the ranges examined in Figs 2D and 2E. Similarly, wake-state ISI across various species typically range from 2 to 100…”
Sleep Electroencephalogram in Infants Born Moderate to Late Preterm and Association with Subsequent Neurodevelopment
Ventura, S., Ryan, M. A., Mathieson, S., Livingstone, V., O’Toole, J. M., & Boylan, G. B. (2025) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2025.114728 · Cited by 1
Spike density in children with self-limited focal epilepsies affects memory performance and slow wave-spindle coupling during sleep
Schmidt, E. C., Storz, S., Skoručak, J., et al. (2025) · Clinical Neurophysiology · doi:10.1016/j.clinph.2025.2111406 · Cited by 1
Self-citation
Post-Ictal Sleep Changes in Human Focal Epilepsy
Křemen, V., Cao, Y., Gerla, V., Mívalt, F., Sladky, V., Louis, E. K. S., Bower, M. R., Brinkmann, B. H., Miller, K. J., Gompel, J. J. V., et al. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.0303-25.2026 · Cited by 2
Neuroimaging of Brain Oscillations During Human Sleep
Salimi, A., Perrault, A. A., Zhang, V., Boucetta, S., & Dang‐Vu, T. T. (2020) · Springer eBooks · doi:10.1007/978-1-0716-0653-7_6 · Cited by 1
Mechanisms of Circadian Rhythm Regulation in Humans
Kanarskii, M. M., Nekrasova, J. Y., Kurova, N. A., & Redkin, I. V. (2022) · Human Physiology · doi:10.1134/s0362119722020098 · Cited by 1
Cerebral Gray Matter May Not Explain Sleep Slow-Wave Characteristics after Severe Brain Injury
Kalantari, N., Daneault, V., Blais, H., André, C., Sanchez, E., Lina, J., Arbour, C., Gilbert, D., Carrier, J., & Gosselin, N. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1306-23.2024 · Cited by 2
Local infrared stimulation modulates spontaneous cortical slow wave dynamics in anesthetized rats
Szabó, Á., Fiáth, R., Horváth, Á. C., Barthó, P., Ulbert, I., & Fekete, Z. (2026) · Scientific Reports · doi:10.1038/s41598-026-38781-4 · Cited by 1
The scientific study of coma and related states
Charland‐Verville, V., Vanhaudenhuyse, A., Laureys, S., & Gosseries, O. (2015) · Advances in consciousness research · doi:10.1075/aicr.92.03cha · Cited by 1
Dense array EEG measures of infant sleep: microstructure and topography as biomarkers of cognitive development
Peters, S. E. (2017) · doi:10.7282/t35142bt · Cited by 1
“There are also distinct slow oscillations (<1Hz) that originate throughout the cerebral 36 cortex via synchronization of membrane potentials of neuronal assemblies, which may be generated by the same mechanism as SWA (Esser, Hill, & Tononi, 2007; Vyazovskiy et al., 2009).”
Background
Sleep Oscillations and Aging
Sergeeva, V., Viczko, J., Owen, A. M., & Fogel, S. M. (2020) · Springer eBooks · doi:10.1007/978-1-0716-0653-7_8 · Cited by 2
Electroencephalographic and Neurophysiological Changes
Gennaro, L. D., & Ferrara, M. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-378610-4.00044-9 · Cited by 1
Analyse de l'activité en ondes lentes et des oscillations lentes chez les somnambules
Perrault, R. (2014) · doi:10.71781/18286 · Cited by 1
Automatic Sleep Stage Classification Using Electro-oculography
Virkkala, J. (2009) · Tampere University Institutional Repository (Tampere University) · Cited by 1
Probabilistic approaches for tracking physiological states in the cortex through sleep and seizures
Dadok, V. M. (2013) · eScholarship (California Digital Library) · Cited by 1
Distinct signatures of loss of consciousness during Focal Impaired Awareness versus Focal to Bilateral Tonic Clonic seizuresPreprint
Juan, E., Górska, U., Kozma, C. A., Papantonatos, C., Bugnon, T., Denis, C., Křemen, V., Worrell, G. A., Struck, A. F., Bateman, L. M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.01.462586 · Cited by 1
Self-citation
Electroencephalographic and neurophysiological changes
Gorgoni, M., Salfi, F., Gennaro, L. D., & Ferrara, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822963-7.00038-4 · Cited by 1
EEG and computational aspects of how aging affects sleep slow wavesPreprint
Kanbi, K. E., Tort‐Colet, N., Benchenane, K., & Alain Destexhe, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.28.573547 · Cited by 1
“These four measures: negative and positive amplitudes, and duration (or width) could be correlated to homeostasis processes and down states characteristics (McKillop et al., 2018; Riedner et al., 2007).”
Background
Les ondes en sommeil lent au cours du vieillissement normal : marqueurs du processus homéostatique et de la cognition
Lafortune, M. (2014) · doi:10.71781/18174
Neuroimaging of cognitive effects in obstructive sleep apnea
THOMAS, R. J., & Chan, S. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9781139088268.036
THE PUZZLE OF DECREASED HOMEOSTATIC SLOW WAVE SLEEP IN AGING
Rytkönen, K.-M. (2012) · Työväentutkimus Vuosikirja
Delta Rhythms: Models and Physiology
Traub, R. D., & Whittington, M. A. (2015) · Springer eBooks · doi:10.1007/978-1-4614-6675-8_305
Using Phase Response Curves to Understand Neuronal Synchronization and Sleep.
Fink, T. C. G. (2012) · Deep Blue (University of Michigan)
Changing Views of NREM Sleep Homeostatic Regulation
Halász, P., & Bódizs, R. (2012) · Springer eBooks · doi:10.1007/978-1-4471-4333-8_5
Enhanced stochastic resonance induced by mean field feedback in synaptic coupled networks
Wei, X., Zhang, D., Lu, M., Wang, J., Yu, H., & Deng, B. (2014) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2014.7002814
“…Feedback in Synaptic Coupled Networks Xile Wei1, Danhong Zhang1, Meili Lu2 1 School of Electrical Engineering and Automation Tianjin University Tianjin China 2 School of Informational Technology and Engineering Tianjin University of Technology and Education Tianjin China Jiang Wang1*, Haitao…”
BackgroundInfluential
LOCAL, EXPERIENCE-DEPENDENT EEG CHANGES AFTER PROLONGED WAKEFULNESS
Hung, C., Sarasso, S., Ferrarelli, F., Riedner, B. A., Cirelli, C., & Tononi, G. (2013)
Self-citation
An exploration into the link between brain rhythms and synaptic plasticity in health and infectious disease
Hartnell, I. J. (2018) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)
Cortical and subcortical hemodynamic changes during human sleep slow wavesPreprint
Betta, M., Handjaras, G., Leo, A., Federici, A., Farinelli, V., Ricciardi, E., Siclari, F., Meletti, S., Ballotta, D., Benuzzi, F., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.29.067702
“The resulting signal was baseline corrected (zero mean-centered) prior to the application of a negative-half-wave detection procedure based on the identification of consecutive signal zerocrossings (Riedner et al., 2007; Siclari et al., 2014).”
Method
Die normale Schlafphysiologie
Schmidt, M. H., Lippert, J., Adamantidis, A., & Huber, R. (2020) · Elsevier eBooks · doi:10.1016/b978-3-437-21321-2.00002-8
Self-citation
Epilepsy and Sleep, Common Bedfellows
Pavkovic, I. M., & Kothare, S. V. (2021) · Springer eBooks · doi:10.1007/978-3-030-65574-7_49
A model characterizing the coupling between slow-wave activity, instantaneous heart rate and heart rate variability during sleep
Garcia‐Molina, G. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630006
“The choice of a single EEG signal to perform this analysis was motivated by: 1) the fact that SWA manifests more prominently on frontal EEG sites, 2) SWA trends are similar across all EEG sites [21], and 3) simplicity of analysis.”
Method
Moving beyond sleep stage scoring epoch-by-epoch (other PSG/EEG characteristics and parameters)
McConnell, B. V., & Ramos, A. R. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822963-7.00247-4
The facilitation of learning and memory by sleep
Yamada, T., Watanabe, T., & Sasaki, Y. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822963-7.00293-0
Delta Rhythms: Models and Physiology
Traub, R. D., & Whittington, M. A. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_305
Delta Rhythms: Models and Physiology
Traub, R. D., & Whittington, M. A. (2014) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_305-1
Medial temporal lobe functional network architecture supports sleep-related emotional memory processing in older adultsPreprint
Chappel-Farley, M. G., Adams, J. N., Betzel, R. F., Janecek, J. C., Sattari, N. S., Berisha, D. E., Meza, N. J., Niknazar, H., Kim, S., Dave, A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.27.564260
Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRIPreprint
Yang, F. N., Picchioni, D., de Zwart, J. A., Wang, Y., van Gelderen, P., & Duyn, J. H. (2024) · medRxiv · doi:10.1101/2024.04.24.24306208
Effects of NREM sleep on cortical synapsesPreprint
Squarcio, F., Tononi, G., & Cirelli, C. (2024) · doi:10.22541/au.171628684.41660697/v1
Self-citation
eLife Assessment: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI
Schapiro, A. C. (2024) · eLife · doi:10.7554/elife.98739.1.sa2
Reviewer #1 (Public Review): Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI
Yang, F. N., Picchioni, D., de Zwart, J. A., et al. (2024) · eLife · doi:10.7554/elife.98739.1.sa1
Reviewer #2 (Public Review): Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI
Yang, F. N., Picchioni, D., de Zwart, J. A., et al. (2024) · eLife · doi:10.7554/elife.98739.1.sa0
Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRIPreprint
Yang, F. N., Picchioni, D., de Zwart, J. A., Wang, Y., van Gelderen, P., & Duyn, J. H. (2024) · eLife · doi:10.7554/elife.98739.2
Time-Resolved Neural Oscillations Across Sleep Stages: Associations with Sleep Quality and AgingPreprint
Passaro, A. D., & Poltorak, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.16.628645 · Cited by 3
“This supports the widely accepted theory of sleep homeostasis, in which slow-wave activity reflects the brain’s recovery needs and increases in response to sleep pressure (Borbély 1982); (Riedner et al. 2007).”
The Hypno-PC: Uncovering Sleep Dynamics Through Principal Component Analysis and Hidden Markov Modelling of Electrophysiological SignalsPreprint
Guendelman, M., & Shriki, O. (2025) · Qeios · doi:10.32388/vkypgf
Hemispherotomy: a cortical island of sleep-like activity in awake humansPreprint
Colombo, M. A., Favaro, J., Mikulan, E., Pigorini, A., Zauli, F. M., Sartori, I., d’Orio, P., Castana, L., Toldo, I., Sartori, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.10.637383
Self-citation
Robotic EEG sleep analysis with hybrid procedures and machine learning
Byri, S. B., Chettikrindi, M. A., Ramireedy, M., et al. (2025) · AIP conference proceedings · doi:10.1063/5.0248369
REM sleep predicts reductions in pathophysiological daytime basal ganglia-cortical circuit activity in Parkinson’s diseasePreprint
Zhang, J., Smyth, C., Cattan-Hayat, H., Anjum, M. F., Leng, Y., Krystal, A. D., Starr, P. A., & Little, S. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.07.652676
“An algorithm based on detecting consecutive signal zero-crossings (Bernardi et al. 2019; Riedner et al. 2007) was used to detect delta waves during sleep.”
iSleep: Continuous, binocular pupil tracking in sleep and reduced consciousness for physiological monitoring, predictions and interventionsPreprint
Yüzgeç, Ö., Legendre, G., Asadian, A., Gloria, Y., Bayer, L., Tourdot, Q., Pellat, S., Novozhilova, S., D'Ancona, C., Ulanova, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.13.659058
“Detection of K-complexes Detection of K-complexes was performed on the average signal of Fz and F3 re-referenced to the mastoids with a well-validated algorithm [82].”
Smartphone behavior is suppressed during bursts of wake slow wavesPreprint
Kock, R., Hof, D., Huber, R., & Ghosh, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.06.680725
“These steps follow established procedures for sleep slow wave detection [Riedner et al., 2007, Bernardi et al., 2015] and are based on an algorithm adapted for wake slow waves [Andrillon et al., 2021].”
Self-citation
Differential Nap‐To‐Nap Stability of Sleep Spindles, Slow Waves, and their Temporal Coupling: An Exploratory Study
Bergamo, D., Visalli, A., Baldassarri, A., & Cellini, N. (2025) · Journal of Sleep Research · doi:10.1111/jsr.70253 · Cited by 1
“Detection of slow waves in NREM sleep was performed, separately for each channel, using the algorithm described in Riedner et al. (2007).”
Influential
Sleep-like slow waves in wakefulness index post-stroke behavioural alterations
Pearce, D. J., Biabani, M., Brosnan, M. B., Chong, T. T. ‐., Demeyere, N., Loughnane, G. M., Mattingley, J. B., Moore, M. J., New, P. W., Rangelov, D., et al. (2026) · Journal of Neurophysiology · doi:10.1101/2025.11.24.689850
“We extracted sleep-like SWs from continuous EEG data using previously established procedures (Andrillon et al., 2021; Pinggal et al., 2022) adapted from those used to identify SWs during sleep (Riedner et al., 2007).”
Investigation of Absolute Delta Power During N3 Sleep in Patients with REM-Dependent Obstructive Sleep Apnea: A Single-Center Case-Control Study
Issı, E. S., & Ayas, S. (2025) · OSMANGAZİ JOURNAL OF MEDICINE · doi:10.20515/otd.1783673

Authors: Riedner, B. A., Vyazovskiy, V. V., Huber, R., Massimini, M., Esser, S., Murphy, M., & Tononi, G.
Affiliations: University of Wisconsin–Madison (US)
Corresponding author: Tononi, Giulio
ORCID: Riedner, Brady A.
Vyazovskiy, Vladyslav V.
Huber, Reto
Massimini, Marcello
Murphy, Michael
Tononi, Giulio
Venue: Sleep
Publisher: Oxford University Press (OUP)
Published: 2007-12
Volume: 30
Issue: 12
Pages: 1643-1657
ISSN: 0161-8105 (ISSN-L)
1550-9109
DOI: 10.1093/sleep/30.12.1643
PMC: PMC2276133
OpenAlex: W1577987636
Cited by: 424
References: 0
Citations by year: 2026: 25 · 2025: 27 · 2024: 28 · 2023: 24 · 2022: 31 · 2021: 31 · 2020: 24 · 2019: 28 · 2018: 20 · 2017: 28 · 2016: 16 · 2015: 20 · 2014: 28 · 2013: 24 · 2012: 19
FWCI: 5.00
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: SLEEP (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Open Research (University of Surrey) (submittedVersion)
Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
Funders: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (106264)
National Institutes of Health
National Institute of General Medical Sciences (T32GM007507)
National Institute of Diabetes and Digestive and Kidney Diseases (T90DK070079)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
Keywords: sleep slow waves, slow-wave activity, sleep homeostasis, cortical synchronization, thalamocortical system, synaptic efficacy, NREM sleep, high-density EEG
MeSH terms: Adult, Brain Mapping, Cerebral Cortex, Circadian Rhythm, Computer Simulation, Cortical Synchronization, Electroencephalography, Evoked Potentials, Fourier Analysis, Homeostasis, Humans, Male, Nerve Net, Reference Values, Signal Processing, Computer-Assisted, Sleep, Sleep, REM, Synapses, Thalamus

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Sleep Homeostasis and Cortical Synchronization: III. A High-Density EEG Study of Sleep Slow Waves in Humans

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