Abstract
By recording brain activity in seven lizard species, humans, rats and pigeons, we demonstrate an infraslow brain rhythm during sleep in all species. This rhythm is tightly coupled with eye movements, muscle tone, heart and breathing rate in lizards, with skin brightness in chameleons and with pulsatile changes in cerebrovascular volume throughout sleep in bearded dragons and during non-rapid eye movement sleep in mice. These findings indicate that the infraslow rhythm is conserved across amniotes, questioning the evolution of sleep states.
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Data availability
The datasets used in this study are part of an ongoing research project, and additional analyses are currently being conducted. For this reason, the raw data are not yet publicly available but can be shared upon request.
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References
Le Bon, O. Relationships between REM and NREM in the NREM-REM sleep cycle: a review on competing concepts. Sleep. Med. 70, 6–16 (2020).
Vyazovskiy, V. V. & Tobler, I. The temporal structure of behaviour and sleep homeostasis. PLoS ONE 7, e50677 (2012).
Steriade, M., Contreras, D., Curro Dossi, R. & Nunez, A. The slow (< 1 Hz) oscillation in reticular thalamic and thalamocortical neurons: scenario of sleep rhythm generation in interacting thalamic and neocortical networks. J. Neurosci. 13, 3284–3299 (1993).
De Gennaro, L. & Ferrara, M. Sleep spindles: an overview. Sleep. Med. Rev. 7, 423–440 (2003).
Blumberg, M. S., Lesku, J. A., Libourel, P.-A., Schmidt, M. H. & Rattenborg, N. C. What is REM sleep?. Curr. Biol. 30, R38–R49 (2020).
Lecci, S. et al. Coordinated infraslow neural and cardiac oscillations mark fragility and offline periods in mammalian sleep. Sci. Adv. 3, e1602026 (2017).
Parrino, L., Ferri, R., Bruni, O. & Terzano, M. G. Cyclic alternating pattern (CAP): the marker of sleep instability. Sleep. Med. Rev. 16, 27–45 (2012).
Watson, B. O. Cognitive and physiologic impacts of the infraslow oscillation. Front. Syst. Neurosci. 12, 44 (2018).
Yüzgeç, Ö, Prsa, M., Zimmermann, R. & Huber, D. Pupil size coupling to cortical states protects the stability of deep sleep via parasympathetic modulation. Curr. Biol. 28, 392–400 (2018).
Osorio-Forero, A. et al. Noradrenergic circuit control of non-REM sleep substates. Curr. Biol. 31, 5009–5023 (2021).
Osorio-Forero, A. et al. Infraslow noradrenergic locus coeruleus activity fluctuations are gatekeepers of the NREM–REM sleep cycle. Nat. Neurosci. 28, 84–96 (2025).
Kjaerby, C. et al. Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine. Nat. Neurosci. 25, 1059–1070 (2022).
Helakari, H. et al. Human NREM sleep promotes brain-wide vasomotor and respiratory pulsations. J. Neurosci. 42, 2503–2515 (2022).
Bueno-Junior, L. S., Ruckstuhl, M. S., Lim, M. M. & Watson, B. O. The temporal structure of REM sleep shows minute-scale fluctuations across brain and body in mice and humans. Proc. Natl Acad. Sci. USA 120, e2213438120 (2023).
Libourel, P. A. & Herrel, A. Sleep in amphibians and reptiles: a review and a preliminary analysis of evolutionary patterns. Biol. Rev. Camb. Philos. Soc. 91, 833–866 (2016).
Shein-Idelson, M., Ondracek, J. M., Liaw, H.-P., Reiter, S. & Laurent, G. Slow waves, sharp waves, ripples, and REM in sleeping dragons. Science 352, 590–595 (2016).
Libourel, P.-A. et al. Partial homologies between sleep states in lizards, mammals, and birds suggest a complex evolution of sleep states in amniotes. PLoS Biol. 16, e2005982 (2018).
Norimoto, H. et al. A claustrum in reptiles and its role in slow-wave sleep. Nature 578, 413–418 (2020).
Albeck, N., Udi, D. I., Eyal, R., Shvartsman, A. & Shein-Idelson, M. Temperature-robust rapid eye movement and slow wave sleep in the lizard Laudakia vulgaris. Commun. Biol. 5, 1310 (2022).
Libourel, P.-A. & Barrillot, B. Is there REM sleep in reptiles? A key question, but still unanswered. Curr. Opin. Physiol. 15, 134–142 (2020).
Bergel, A., Deffieux, T., Demené, C., Tanter, M. & Cohen, I. Local hippocampal fast gamma rhythms precede brain-wide hyperemic patterns during spontaneous rodent REM sleep. Nat. Commun. 9, 5364 (2018).
Macé, E. et al. Functional ultrasound imaging of the brain. Nat. Methods 8, 662–664 (2011).
Figon, F. & Casas, J. in Encyclopedia of Life Sciences 1–11 (Wiley, 2018).
Medeiros, S. L. et al. Cyclic alternation of quiet and active sleep states in the octopus. iScience 24, 102223 (2021).
Iglesias, T. L., Boal, J. G., Frank, M. G., Zeil, J. & Hanlon, R. T. Cyclic nature of the REM sleep-like state in the cuttlefish Sepia officinalis. J. Exp. Biol. 222, jeb174862 (2019).
Parmeggiani, P. L. REM sleep related increase in brain temperature: a physiologic problem. Arch. Ital. Biol. 145, 13–21 (2007).
Halász, P., Terzano, M., Parrino, L. & Bódizs, R. The nature of arousal in sleep. J. Sleep. Res. 13, 1–23 (2004).
Rößler, D. C. et al. Regularly occurring bouts of retinal movements suggest an REM sleep–like state in jumping spiders. Proc. Natl Acad. Sci. USA 119, e2204754119 (2022).
Leung, L. C. et al. Neural signatures of sleep in zebrafish. Nature 571, 198–204 (2019).
Ungurean, G., Martinez-Gonzalez, D., Massot, B., Libourel, P.-A. & Rattenborg, N. C. Pupillary behavior during wakefulness, non-REM sleep, and REM sleep in birds is opposite that of mammals. Curr. Biol. 31, 5370–5376 (2021).
Barrillot, B. et al. Intramuscular administration of ketamine-medetomidine assures stable anaesthesia needed for long-term surgery in the Argentine tegu Salvator merianae. J. Zoo. Wildl. Med. 49, 291–296 (2018).
Massot, B. et al. ONEIROS, a new miniature standalone device for recording sleep electrophysiology, physiology, temperatures and behavior in the lab and field. J. Neurosci. Methods 316, 103–116 (2019).
Libourel, P.-A., Corneyllie, A., Luppi, P.-H., Chouvet, G. & Gervasoni, D. Unsupervised online classifier in sleep scoring for sleep deprivation studies. Sleep 38, 815–828 (2015).
Demené, C. et al. Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases Doppler and fUltrasound sensitivity. IEEE Trans. Med. Imaging 34, 2271–2285 (2015).
Mace, E. et al. Functional ultrasound imaging of the brain: theory and basic principles. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 60, 492–506 (2013).
Liu, A. A. et al. A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations. Nat. Commun. 13, 6000 (2022).
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).
Gudjonsson, S. V. The body temperature in rats on normal and deficient diets. J. Physiol. 74, 73–80 (1932).
Geneva, I. I., Cuzzo, B., Fazili, T. & Javaid, W. Normal body temperature: a systematic review. Open Forum Infect. Dis. 6, ofz032 (2019).
Phillips, N. H. & Berger, R. J. Regulation of body temperature, metabolic rate, and sleep in fasting pigeons diurnally infused with glucose or saline. J. Comp. Physiol. B 161, 311–318 (1991).
Trotts, I., Mikula, S. & Jones, E. G. Interactive visualization of multiresolution image stacks in 3D. Neuroimage 35, 1038–1043 (2007).
Hülsemann, M. J., Naumann, E. & Rasch, B. Quantification of phase-amplitude coupling in neuronal oscillations: comparison of phase-locking value, mean vector length, modulation index, and generalized-linear-modeling-cross-frequency-coupling. Front. Neurosci. 13, 573 (2019).
Berens, P. CircStat: a MATLAB toolbox for circular statistics. J. Stat. Softw. 31, 1–21 (2009).
Acknowledgements
We would like to thank O. Antonini, from Bebesaurus, for providing materials and valuable advice on the animals’ care. We also appreciate the support of the Zoo Parc de la tête d’or and all the zoo keepers and staff who took care of our animals when the animal facility was closed. We are grateful to I. Achin for assistance with data collection, and to C. Bouillot and R. Bolbos from CERMEP for enabling us to acquire high-quality CT scans and MRIs. We also thank P. Tissier for his help with the design of the ultrasound probe holder and K. Homolkova for her help in acquiring functional ultrasound data in mice. We would like to thank A. Peyrache at the Montreal Neurological Institute and Hospital, McGill University, for his assistance with computational resources. The study was funded by the Interdisciplinary mission of the CNRS (PEPS EXOMOD PHYLOREM to P.-A.L.), the French Society for Sleep Research and Medicine (SFRMS to P.-A.L. and B.B.), Rhône-Alpes Institute of Complex Sciences (IXXI, project AMPHISLEEP to P.-A.L.), Max Planck Society (to N.C.R.), National Institutes of Health (R37-HD081168 and R01-HD104616 to M.S.B.), European Sleep Foundation (to M.H.S.), Swiss National Science Foundation (to M.H.S.), AXA Research Fund under the chair New hopes in medical imaging with ultrasound (to A.B.). This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101023337 (A.B.). Chameleons were purchased using funds provided by M.S.B., M.H.S. and N.C.R.
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Contributions
P.-A.L. conceived, designed and supervised the project. A.B. and M.T. built and set up the functional ultrasound experiments. A.B., C.F., B.B. and P.-A.L. conducted the functional ultrasound experiment. A.B. verified and analyzed the functional ultrasound data. B.B., S.A., L. A., A.C. and P.-A.L. conducted the lizard electrophysiology experiments. L.A. and A.C. habituated and cared for the animals. P.-A.L., B.B. and C.C. analyzed the electrophysiological data. G.U. collected the bird data. M.H.S. and I.F. collected the human data. P.-A.L. collected the chameleon data. J.M.S. and P.-A.L. analyzed the chameleon data. B.M. and P.-A.L. conceived the electrophysiological instrumentation. A.B., J.M.S. and P.-A.L. prepared the figures. P.-A.L. wrote the first version of the paper. A.B., A.H., J.M.S., M.S.B., M.H.S., N.C.R. and P.-A.L. reviewed and completed the paper. A.B., A.H., B.B., J.M.S., M.S.B., M.H.S., N.C.R. and P.-A.L. discussed and interpreted the results.
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Extended data
Extended Data Fig. 1 The infraslow rhythms (high autocorrelation) of the beta power are sleep dependent.
Each line represents the autocorrelation map of the beta power computed over 24 h for every species. Each red dot shows the highest peak autocorrelation amplitude (higher than the mean of the autocorrelation peaks over 24 h), reflecting the stronger infraslow rhythm. The black rectangle indicates the dark phase. Above the autocorrelation map, the level of activity is represented in pink, the muscle tone is represented in green and the eye movement density in blue. On the right, the color-coded map shows the evolution of the beta power among 30-min windows. High-beta power is coded in yellow, and low-beta power is coded in blue. On the right, the percentage of time (mean value ± s.e.m.) occupied by infraslow rhythms relative to the duration of the dark and light phase shows a predominance of infraslow during the dark period for nocturnal animals. The number of individuals and nights (replicates) per species is indicated in Supplementary Table 1 (row ‘number of individual’ and ‘total number of nights’). The gray circles represent each night (replicates) from all individuals per species. We compared the light and dark phases using a two-sided Wilcoxon signed-rank test (*P<0.05, ***P<0.0001).
Extended Data Fig. 2 Spectral band powers fluctuate similarly during sleep in lizards.
Each subpanel shows 5 min of cerebral signal during sleep in lizards (black traces). Below, color-coded lines represent the normalized spectral power for the following bands: delta (0.5–4 Hz), theta (4–9 Hz), alpha (9–12 Hz), sigma (10–15 Hz), beta (10–30 Hz) and low gamma (30–40 Hz). The spectral powers are smoothed with a 30-s window sliding average and normalized over the power between 0.5 and 40 Hz.
Extended Data Fig. 3 The infraslow rhythm is a broadband phenomenon.
For each lizard species, we computed the lag (period) and the amplitude of the autocorrelation of the brain signal in the delta (1–4 Hz), theta (4–10 Hz), sigma (10–15 Hz), low-beta (15–25 Hz), high-beta (25–40 Hz) and beta (10–30 Hz) bands. We then compared the values of lag and amplitude across species using two-tailed t tests after Gaussian normalization, with a Bonferroni correction applied as a post hoc test. The number of individuals and replicates (sleep bouts) per species is indicated in Supplementary Table 1 (row ‘LFP/EEG’). The number of individuals and replicates (sleep bouts) per species is indicated in Supplementary Table 1 (row ‘LFP/EEG’). White dots indicate median value across all sleep bouts, color dots indicate all sleep bouts from all individuals and the gray vertical line indicates notch values (*P<0.05, **P<0.001, ***P<0.0001).
Extended Data Fig. 4 Coupling between heart rate and beta infraslow rhythm.
For each species, the black lines show beta power and heart rate (both z-scored over 10-min sliding windows) across 7 min. On the right, the cross-correlation maps between beta power and heart rate during sleep (NREM and N2 in mammals). The last graph illustrates the averaged ±s.e.m. cross-correlation over all analyzed periods and individuals per species. The figure shows a positive cross-correlation between the beta power and the heart rate in all species except in the bearded dragon and the rat, where the correlation is negative.
Extended Data Fig. 5 Overview of the different fields of view for the four different bearded dragons in the functional ultrasound imaging experiment.
Example ultrasound image (top), segmentation based on MRI/CT atlas registration (middle) and corresponding regions of interest (bottom) (the grey region representing the whole brain). Animal 1 was prepared for sagittal recordings, while animals 2–4 were prepared for coronal recordings.
Extended Data Fig. 6 Regional hemodynamics during a transition from wake to sleep in the bearded dragon.
a, Regional CBV dynamics in the nine regions of interest displayed in Extended Data Fig. 5 (Animal #2) (left cortex, right cortex, left DVR, right DVR, diencephalon, left mesencephalon, right mesencephalon, dorsal and ventral brainstem). b, Autocorrelation of regional CBV time series computed over 20-min periods with a 2-min sliding window. First positive peak (autocorrelation period) and first negative peaks (autocorrelation half period) are displayed in red and gray, respectively. Note the gradual stabilization of the autocorrelation period toward 100 s in all nine regions of interest.
Extended Data Fig. 7 Infraslow rhythms are specific to sleep.
a, Same as Fig. 3b,c for a wake recording in the bearded dragon. Top, LFP power in the beta band in the DVR before (black) and after (red) detrending, and the autocorrelation maps computed over a 2-min sliding window. First positive (red) and negative (gray) peaks are displayed. Right, mean auto-correlogram. Middle, same as top, for the mean cerebral blood volume of the whole brain. The absence of strong positive peaks in both auto-correlograms reveals the absence of infraslow rhythm in wake. Bottom, cross-correlogram between LFP and CBV data with the mean cross-correlation function on the right. The low cross-correlation values reveal a low coupling during wake. b, Same as Fig. 3e,f for a wake recording in the mouse. Top, LFP power in the beta band (10–30 Hz) in the DVR before (black) and after (red) detrending, and the autocorrelation maps computed over a 5 min sliding window. First positive (red) and negative (gray) peaks are displayed. Right, mean auto-correlogram. Middle, same as top, for the mean cerebral blood volume of the whole brain. The absence of strong positive peaks in both autocorrelograms reveals the absence of infraslow rhythms in wake. Bottom, cross-correlogram between LFP and CBV data with the mean cross-correlation function on the right. The low cross-correlation values reveal a low coupling during wake, with a reinstatement of high values during NREM sleep.
Extended Data Fig. 8 Infraslow rhythms are visible in all animals and are band-specific in bearded dragons and mice.
Top (bearded dragon), each row represents an animal. The two left columns display the autocorrelation function for the LFP signal filtered in the beta band (left) and for the CBV signal of the whole brain (middle left). The two right columns display the cross-correlation between the LFP signal filtered in the beta (middle right) or delta (right) bands and CBV. Data are displayed for all 5-min uninterrupted sleep and wake epochs. The distributions of the periods and amplitude of all autocorrelation functions and the x value of the positive peak (lag peak) and amplitude for all cross-correlation functions are displayed below the correlograms. Horizontal black bar—mean. Vertical black bar—mean ± s.e.m. n = 4 animals. Number of wake/sleep bouts per animal—animal 1, n = 23/29; animal 2, n = 115/347; animal 3, n = 156/268; animal 4, n = 23/231. Bottom (mouse): same as top with statistics computed on all 2-min uninterrupted wake, NREM and REM sleep bouts. n = 4 animals. Number of wake/NREM sleep/REM sleep bouts per animal—animal 1, n = 36/7/132; animal 2, n = 25/8/151; animal 3, n = 53/6/105; animal 4, n = 66/8/109. All autocorrelation or cross-correlation functions are displayed below the correlograms. Horizontal black bar—mean. Vertical black bar—mean ± s.e.m.
Extended Data Fig. 9 Tight correlation between cerebral activity, physiology and skin brightness in sleeping chameleons.
a, Ten-minute sleep recording. From the top to the bottom, electromyogram (EMG); left and right electrooculogram (EOG); heart rate; breathing movement; beta power (10–40 Hz); local field potential (LFP) recorded in the dorsal ventricular ridge (DVR) represented in the frequency (color-coded map, from low (blue) to high power (yellow)) and the corresponding raw LFP signal (lower trace). b, Correlation between the beta power and skin brightness period (from different individuals represented by color-coded dots) relative to the temperature, showing that the cerebral and skin brightness infraslow rhythms are both temperature-dependent. c–i, The heart rate (c), beta power (d), muscle tone (e), left (f) and right (g) eye movement density, left (h) and right (i) eye opening relative to the breathing rate phase of the chameleon that was recorded with electrophysiology. The orange curve shows breathing rate relative to its own phase. The colored curve and shading show the mean and s.e.m. over all sleep bouts for this chameleon. j, The skin brightness, eye movement density and eye-opening relative to the breathing rate phase on the three chameleons where skin brightness was evaluated. The blue curve shows the breathing rate variations relative to its own phase. The colored curve and shading show the mean and s.e.m. over all trials and chameleons. On subfigures a–j, the gray arrows show the mean vector for each sleep bout. The color-coded arrows represent the average of all mean vectors. The vector length has been doubled for clarification. The p values were computed using a Rayleigh test, evaluating the uniformity of the sleep bout phase distribution. c–j, The number of individuals and replicates (sleep bouts) per species is indicated in Supplementary Table 1 (row ‘LFP/EEG’, ‘ECG’, ‘Breathing’, ‘EOG’, ‘EMG’). Phase-locking statistics were computed by testing the uniformity of the circular distribution using the Rayleigh test.
Supplementary information
Supplementary Information
Supplementary Figs. 1–12.
Supplementary Table 1
Animal information for each experiment—contains data for every experiment, the species used, the animal number, sex, age, weight and the number of nights and sleep bouts used for the analysis.
Supplementary Video 1
Global infraslow rhythm in the cerebrovascular activity of a sleeping bearded dragon and a mouse during NREM sleep. Planar images of cerebral blood volume (CBV) are obtained using functional ultrasound imaging in a bearded dragon (Pogona vitticeps). The video, sped up 60 times, reveals surges in vascular activity in all brain regions, with a 100-second periodicity. The top left image shows a typical imaging plane and the corresponding segmentation for 9 regions of interest. The top center image shows the dynamics of cerebrovascular activity in the same plane. The top right image displays an infrared video of the sleeping bearded dragon equipped with the ultrasound probe. The bottom panel displays the temporal dynamics of global (black curve, top) and regional (9 regions segmented, color-coded) CBV over a 30-minute period. CBV is expressed in % of change relative to a baseline. Regions are left and right cortex (1-2), left and right dorsal ventricular ridges (3–4), diencephalon (5), left and right mesencephalon (6-7), dorsal and ventral brainstem (8–9). The second part of the video (after 0:33) shows the infraslow rhythms in cerebrovascular activity in mouse (Mus musculus) during NREM sleep. Planar images of cerebral blood volume (CBV) are obtained at 2 Hz using functional ultrasound imaging in tethered mice. The video, sped up 60 times, reveals rhythms in vascular activity in all brain regions, with a 50-second periodicity. The top left image shows a typical imaging plane and the corresponding segmentation for 8 regions of interest. The top center image shows the dynamics of cerebrovascular activity in the same plane and the top right image displays an infrared video of a sleeping mouse equipped with the ultrasound probe. The bottom panel displays the temporal dynamics of global (black curve, top) and regional (8 regions segmented, color-coded) CBV over a 10-minute period. CBV is expressed in % of change relative to a baseline, taken in the first 3-minute of behavioral sleep. Regions are respectively left and right cortex (1-2), left and right dorsal hippocampus (3-4), left and right thalamus (5-6), left and right ventral hippocampus (7–8).
Supplementary Video 2
A sleeping panther chameleon (Furcifer pardalis). The first part of the video shows a panther chameleon sleeping on a branch from 9 pm to 9 am, in low light ( < 1 lux). The video is speed up 2000 times, and shows the color slightly changing from green to yellow and back to green, variations in skin brightness and eye movements. The second part of the video is sped up 50 times. The three sub-videos on the top show the animal sleeping between 1 a.m. and 1:20 a.m.: color close-up of the skin (left); side (middle) and frontal (right) infrared view of the head showing eye state (open or closed) and eye movements. The lines below show the strong periodicity and correlation between (from the top to the bottom) the right eye state (open in red, closed in blue), skin brightness, eye movements, and breathing movements and rate.
Supplementary Video 3
Physiological and cerebral activity associated with eye movements in a sleeping panther chameleon. The video shows the global (brain and body) infraslow rhythm in a sleeping chameleon. The video is sped up 40 times and displays 500 secondes between 0:45 a.m. and 1:16 a.m. From the top to the bottom: muscle tone (μV) with a high pass filter (Fc:10 Hz, Order 10), electrocardiogram (μV), breathing movement, right and left eye movement (μV), local field potential (LFP) of the DVR (μV), the same LFP represented in time frequency (Hz) and the beta-power extracted from this same LFP. The two sub-videos show the left and right eyes.
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Bergel, A., Schmidt, J.M., Barrillot, B. et al. Sleep-dependent infraslow rhythms are evolutionarily conserved across reptiles and mammals. Nat Neurosci (2025). https://doi.org/10.1038/s41593-025-02159-y
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DOI: https://doi.org/10.1038/s41593-025-02159-y
