Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Sleep loss diminishes hippocampal reactivation and replay

Nature volume 630, pages 935–942 (2024)Cite this article

Subjects

Abstract

Memories benefit from sleep1, and the reactivation and replay of waking experiences during hippocampal sharp-wave ripples (SWRs) are considered to be crucial for this process2. However, little is known about how these patterns are impacted by sleep loss. Here we recorded CA1 neuronal activity over 12 h in rats across maze exploration, sleep and sleep deprivation, followed by recovery sleep. We found that SWRs showed sustained or higher rates during sleep deprivation but with lower power and higher frequency ripples. Pyramidal cells exhibited sustained firing during sleep deprivation and reduced firing during sleep, yet their firing rates were comparable during SWRs regardless of sleep state. Despite the robust firing and abundance of SWRs during sleep deprivation, we found that the reactivation and replay of neuronal firing patterns was diminished during these periods and, in some cases, completely abolished compared to ad libitum sleep. Reactivation partially rebounded after recovery sleep but failed to reach the levels found in natural sleep. These results delineate the adverse consequences of sleep loss on hippocampal function at the network level and reveal a dissociation between the many SWRs elicited during sleep deprivation and the few reactivations and replays that occur during these events.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: SD yields more SWRs but with weaker power and higher frequency ripples.
Fig. 2: Hippocampal firing rates are elevated and more dispersed during SD.
Fig. 3: Reactivation attenuates during SD and is not restored by RS.
Fig. 4: Trajectory replays deteriorate over SD and RS.

Similar content being viewed by others

Data availability

The processed group data for this study are available at https://doi.org/10.7302/73hn-m920, which includes NumPy (.npy) files used to generate most of the figures in this study. The remainder of the long-duration datasets generated during and analysed for the present study will be made available by the corresponding author on request.

Code availability

All analyses were performed using custom codes written in Python. General-purpose code is available in our laboratory’s public GitHub repository (https://github.com/diba-lab/NeuroPy, v.0.1). Code specific to this project and used for generating figures herein is located at https://github.com/diba-lab/sleep_loss_hippocampal_replay (v.0.2).

References

  1. Rasch, B. & Born, J. About sleep’s role in memory. Physiol. Rev. 93, 681–766 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Buzsaki, G. Hippocampal sharp wave-ripple: a cognitive biomarker for episodic memory and planning. Hippocampus 25, 1073–1188 (2015).

    PubMed  PubMed Central  Google Scholar 

  3. Havekes, R. & Abel, T. The tired hippocampus: the molecular impact of sleep deprivation on hippocampal function. Curr. Opin. Neurobiol. 44, 13–19 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Eschenko, O., Ramadan, W., Molle, M., Born, J. & Sara, S. J. Sustained increase in hippocampal sharp-wave ripple activity during slow-wave sleep after learning. Learn. Mem. 15, 222–228 (2008).

    PubMed  PubMed Central  Google Scholar 

  5. Girardeau, G., Benchenane, K., Wiener, S. I., Buzsaki, G. & Zugaro, M. B. Selective suppression of hippocampal ripples impairs spatial memory. Nat. Neurosci. 12, 1222–1223 (2009).

    CAS  PubMed  Google Scholar 

  6. Gridchyn, I., Schoenenberger, P., O’Neill, J. & Csicsvari, J. Assembly-specific disruption of hippocampal replay leads to selective memory deficit. Neuron 106, 291–300 (2020).

    CAS  PubMed  Google Scholar 

  7. Fernandez-Ruiz, A. et al. Long-duration hippocampal sharp wave ripples improve memory. Science 364, 1082–1086 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nitzan, N., Swanson, R., Schmitz, D. & Buzsaki, G. Brain-wide interactions during hippocampal sharp wave ripples. Proc. Natl Acad. Sci. USA 119, e2200931119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Logothetis, N. K. et al. Hippocampal–cortical interaction during periods of subcortical silence. Nature 491, 547–553 (2012).

    ADS  CAS  PubMed  Google Scholar 

  10. Karimi Abadchi, J. et al. Spatiotemporal patterns of neocortical activity around hippocampal sharp-wave ripples. eLife 9, e51972 (2020).

    PubMed  PubMed Central  Google Scholar 

  11. Rothschild, G. The transformation of multi-sensory experiences into memories during sleep. Neurobiol. Learn. Mem. 160, 58–66 (2019).

    PubMed  Google Scholar 

  12. Nere, A., Hashmi, A., Cirelli, C. & Tononi, G. Sleep-dependent synaptic down-selection (I): modeling the benefits of sleep on memory consolidation and integration. Front. Neurol. 4, 143 (2013).

    PubMed  PubMed Central  Google Scholar 

  13. Tadros, T., Krishnan, G. P., Ramyaa, R. & Bazhenov, M. Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks. Nat. Commun. 13, 7742 (2022).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. King, C., Henze, D. A., Leinekugel, X. & Buzsaki, G. Hebbian modification of a hippocampal population pattern in the rat. J. Physiol. 521, 159–167 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Sadowski, J. H., Jones, M. W. & Mellor, J. R. Sharp-wave ripples orchestrate the induction of synaptic plasticity during reactivation of place cell firing patterns in the hippocampus. Cell Rep. 14, 1916–1929 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Colgin, L. L., Kubota, D., Jia, Y., Rex, C. S. & Lynch, G. Long-term potentiation is impaired in rat hippocampal slices that produce spontaneous sharp waves. J. Physiol. 558, 953–961 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Norimoto, H. et al. Hippocampal ripples down-regulate synapses. Science 359, 1524–1527 (2018).

    ADS  CAS  PubMed  Google Scholar 

  18. Joo, H. R. & Frank, L. M. The hippocampal sharp wave-ripple in memory retrieval for immediate use and consolidation. Nat. Rev. Neurosci. 19, 744–757 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Roux, L., Hu, B., Eichler, R., Stark, E. & Buzsaki, G. Sharp wave ripples during learning stabilize the hippocampal spatial map. Nat. Neurosci. 20, 845–853 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Ognjanovski, N., Broussard, C., Zochowski, M. & Aton, S. J. Hippocampal network oscillations rescue memory consolidation deficits caused by sleep loss. Cereb. Cortex 28, 3711–3723 (2018).

    PubMed  PubMed Central  Google Scholar 

  21. Vyazovskiy, V. V. et al. Local sleep in awake rats. Nature 472, 443–447 (2011).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Friedman, L., Bergmann, B. M. & Rechtschaffen, A. Effects of sleep deprivation on sleepiness, sleep intensity and subsequent sleep in the rat. Sleep 1, 369–391 (1979).

    CAS  PubMed  Google Scholar 

  23. Thomas, C. W., Guillaumin, M. C., McKillop, L. E., Achermann, P. & Vyazovskiy, V. V. Global sleep homeostasis reflects temporally and spatially integrated local cortical neuronal activity. eLife 9, e54148 (2020).

    PubMed  PubMed Central  Google Scholar 

  24. Borbely, A. A. & Achermann, P. Sleep homeostasis and models of sleep regulation. J. Biol. Rhythms 14, 557–568 (1999).

    CAS  PubMed  Google Scholar 

  25. Miyawaki, H. & Diba, K. Regulation of hippocampal firing by network oscillations during sleep. Curr. Biol. 26, 893–902 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Saravanan, V., Berman, G. J. & Sober, S. J. Application of the hierarchical bootstrap to multi-level data in neuroscience. Neuron. Behav. Data Anal. Theory. Preprint at https://arxiv.org/abs/2007.07797 (2020).

  27. Petersen, P. C., Voroslakos, M. & Buzsaki, G. Brain temperature affects quantitative features of hippocampal sharp wave ripples. J. Neurophysiol. 127, 1417–1425 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Stark, E. et al. Pyramidal cell–interneuron interactions underlie hippocampal ripple oscillations. Neuron 83, 467–480 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Miyawaki, H., Watson, B. O. & Diba, K. Neuronal firing rates diverge during REM and homogenize during non-REM. Sci. Rep. 9, 689 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  30. Torrado Pacheco, A., Bottorff, J., Gao, Y. & Turrigiano, G. G. Sleep promotes downward firing rate homeostasis. Neuron 109, 530–544 (2021).

    CAS  PubMed  Google Scholar 

  31. Mizuseki, K. & Buzsaki, G. Preconfigured, skewed distribution of firing rates in the hippocampus and entorhinal cortex. Cell Rep. 4, 1010–1021 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Leao, R. N. et al. OLM interneurons differentially modulate CA3 and entorhinal inputs to hippocampal CA1 neurons. Nat. Neurosci. 15, 1524–1530 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Royer, S. et al. Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition. Nat. Neurosci. 15, 769–775 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Delorme, J. et al. Sleep loss drives acetylcholine- and somatostatin interneuron-mediated gating of hippocampal activity to inhibit memory consolidation. Proc. Natl Acad. Sci. USA 118, e2019318118 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Giri, B., Miyawaki, H., Mizuseki, K., Cheng, S. & Diba, K. Hippocampal reactivation extends for several hours following novel experience. J. Neurosci. 39, 866–875 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Kudrimoti, H. S., Barnes, C. A. & McNaughton, B. L. Reactivation of hippocampal cell assemblies: effects of behavioral state, experience and EEG dynamics. J. Neurosci. 19, 4090–4101 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Pennartz, C. M. et al. The ventral striatum in off-line processing: ensemble reactivation during sleep and modulation by hippocampal ripples. J. Neurosci. 24, 6446–6456 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Franken, P., Chollet, D. & Tafti, M. The homeostatic regulation of sleep need is under genetic control. J. Neurosci. 21, 2610–2621 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Lee, A., Lei, H., Zhu, L., Jiang, Z. & Ladiges, W. Resilience to acute sleep deprivation is associated with attenuation of hippocampal mediated learning impairment. Aging Pathobiol. Ther. 2, 195–202 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. van der Meer, M. A. A., Kemere, C. & Diba, K. Progress and issues in second-order analysis of hippocampal replay. Philos. Trans. R Soc. Lond B 375, 20190238 (2020).

    Google Scholar 

  41. Tingley, D. & Peyrache, A. On the methods for reactivation and replay analysis. Philos. Trans. R Soc. Lond. B 375, 20190231 (2020).

    Google Scholar 

  42. Silva, D., Feng, T. & Foster, D. J. Trajectory events across hippocampal place cells require previous experience. Nat. Neurosci. 18, 1772–1779 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Grosmark, A. D. & Buzsaki, G. Diversity in neural firing dynamics supports both rigid and learned hippocampal sequences. Science 351, 1440–1443 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  44. Farooq, U., Sibille, J., Liu, K. & Dragoi, G. Strengthened temporal coordination within pre-existing sequential cell assemblies supports trajectory replay. Neuron 103, 719–733 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Stella, F., Baracskay, P., O’Neill, J. & Csicsvari, J. Hippocampal reactivation of random trajectories resembling Brownian diffusion. Neuron 102, 450–461 (2019).

    CAS  PubMed  Google Scholar 

  46. Krause, E. L. & Drugowitsch, J. A large majority of awake hippocampal sharp-wave ripples feature spatial trajectories with momentum. Neuron 110, 722–733 (2022).

    CAS  PubMed  Google Scholar 

  47. Maboudi, K., Giri, B., Miyawaki, H., Kemere, C. & Diba, K. Retuning of hippocampal representations during sleep. Nature 629, 630–638 (2024).

  48. Csicsvari, J., Hirase, H., Mamiya, A. & Buzsaki, G. Ensemble patterns of hippocampal CA3-CA1 neurons during sharp wave-associated population events. Neuron 28, 585–594 (2000).

    CAS  PubMed  Google Scholar 

  49. Tang, W., Shin, J. D., Frank, L. M. & Jadhav, S. P. Hippocampal-prefrontal reactivation during learning is stronger in awake compared with sleep states. J. Neurosci. 37, 11789–11805 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Tononi, G. & Cirelli, C. Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81, 12–34 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Ponomarenko, A. A., Korotkova, T. M., Sergeeva, O. A. & Haas, H. L. Multiple GABAA receptor subtypes regulate hippocampal ripple oscillations. Eur. J. Neurosci. 20, 2141–2148 (2004).

    CAS  PubMed  Google Scholar 

  52. Gordon, J. A., Lacefield, C. O., Kentros, C. G. & Hen, R. State-dependent alterations in hippocampal oscillations in serotonin 1A receptor-deficient mice. J. Neurosci. 25, 6509–6519 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Oliva, A., Fernandez-Ruiz, A., Buzsaki, G. & Berenyi, A. Role of hippocampal CA2 region in triggering sharp-wave ripples. Neuron 91, 1342–1355 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Nakashiba, T., Buhl, D. L., McHugh, T. J. & Tonegawa, S. Hippocampal CA3 output is crucial for ripple-associated reactivation and consolidation of memory. Neuron 62, 781–787 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Sebastian, E. R. et al. Topological analysis of sharp-wave ripple waveforms reveals input mechanisms behind feature variations. Nat. Neurosci. 26, 2171–2181 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Wiegand, J. P. et al. Age is associated with reduced sharp-wave ripple frequency and altered patterns of neuronal variability. J. Neurosci. 36, 5650–5660 (2016).

    PubMed  PubMed Central  Google Scholar 

  57. Ponomarenko, A. A., Li, J. S., Korotkova, T. M., Huston, J. P. & Haas, H. L. Frequency of network synchronization in the hippocampus marks learning. Eur. J. Neurosci. 27, 3035–3042 (2008).

    PubMed  Google Scholar 

  58. Girardeau, G., Cei, A. & Zugaro, M. Learning-induced plasticity regulates hippocampal sharp wave-ripple drive. J. Neurosci. 34, 5176–5183 (2014).

    PubMed  PubMed Central  Google Scholar 

  59. Havekes, R. et al. Sleep deprivation causes memory deficits by negatively impacting neuronal connectivity in hippocampal area CA1. eLife 5, e13424 (2016).

    PubMed  PubMed Central  Google Scholar 

  60. Gerstner, J. R. et al. Removal of unwanted variation reveals novel patterns of gene expression linked to sleep homeostasis in murine cortex. BMC Genomics 17, 727 (2016).

    PubMed  PubMed Central  Google Scholar 

  61. Kinsky, N. R. et al. Simultaneous electrophysiology and optogenetic perturbation of the same neurons in chronically implanted animals using μLED silicon probes. STAR Protoc. 4, 102570 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Colavito, V. et al. Experimental sleep deprivation as a tool to test memory deficits in rodents. Front. Syst. Neurosci. 7, 106 (2013).

    PubMed  PubMed Central  Google Scholar 

  63. Prince, T. M. et al. Sleep deprivation during a specific 3-hour time window post-training impairs hippocampal synaptic plasticity and memory. Neurobiol. Learn. Mem. 109, 122–130 (2014).

    PubMed  Google Scholar 

  64. Siegle, J. H. et al. Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology. J. Neural Eng. 14, 045003 (2017).

    ADS  PubMed  Google Scholar 

  65. Yger, P. et al. A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. eLife 7, e34518 (2018).

    PubMed  PubMed Central  Google Scholar 

  66. Petersen, P. C., Siegle, J. H., Steinmetz, N. A., Mahallati, S. & Buzsaki, G. CellExplorer: a framework for visualizing and characterizing single neurons. Neuron 109, 3594–3608 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Bartho, P. et al. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. 92, 600–608 (2004).

    PubMed  Google Scholar 

  68. Schomburg, E. W. et al. Theta phase segregation of input-specific gamma patterns in entorhinal-hippocampal networks. Neuron 84, 470–485 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Miyawaki, H., Billeh, Y. N. & Diba, K. Low activity microstates during sleep. Sleep 40, zsx066 (2017).

    PubMed  PubMed Central  Google Scholar 

  70. Maingret, N., Girardeau, G., Todorova, R., Goutierre, M. & Zugaro, M. Hippocampo-cortical coupling mediates memory consolidation during sleep. Nat. Neurosci. 19, 959–964 (2016).

    CAS  PubMed  Google Scholar 

  71. Quirk, M. C. & Wilson, M. A. Interaction between spike waveform classification and temporal sequence detection. J. Neurosci. Methods 94, 41–52 (1999).

    CAS  PubMed  Google Scholar 

  72. Tatsuno, M., Lipa, P. & McNaughton, B. L. Methodological considerations on the use of template matching to study long-lasting memory trace replay. J. Neurosci. 26, 10727–10742 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Tenenbaum, J. B., de Silva, V. & Langford, J. C. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000).

    ADS  CAS  PubMed  Google Scholar 

  74. van der Meer, M. A. A., Carey, A. A. & Tanaka, Y. Optimizing for generalization in the decoding of internally generated activity in the hippocampus. Hippocampus 27, 580–595 (2017).

    PubMed  Google Scholar 

  75. Davidson, T. J., Kloosterman, F. & Wilson, M. A. Hippocampal replay of extended experience. Neuron 63, 497–507 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Marmelshtein, A., Eckerling, A., Hadad, B., Ben-Eliyahu, S. & Nir, Y. Sleep-like changes in neural processing emerge during sleep deprivation in early auditory cortex. Curr. Biol. 33, 2925–2940 (2023).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was funded by the US National Institute of Mental Health (R01MH117964 to K.D. and T.A.) and by the US National Institute of Neurological Disorders and Stroke (R01NS115233 to K.D.).

Author information

Author notes

  1. These authors contributed equally: Bapun Giri, Nathaniel Kinsky

Authors and Affiliations

  1. Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA

    Bapun Giri, Nathaniel Kinsky, Utku Kaya, Kourosh Maboudi & Kamran Diba

  2. Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA

    Bapun Giri & Kourosh Maboudi

  3. Department of Neuroscience and Pharmacology, Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA

    Ted Abel

  4. Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA

    Kamran Diba

Authors
  1. Bapun Giri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nathaniel Kinsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Utku Kaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kourosh Maboudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ted Abel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kamran Diba
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.D., T.A. and B.G. conceived the project. B.G. performed the experiments. B.G. and N.K. analysed the data. U.K. and K.M. contributed analytical insights. K.D. supervised the research. K.D. and B.G. wrote the manuscript with input and edits from T.A. and N.K.

Corresponding author

Correspondence to Kamran Diba.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Antoine Adamantidis, Vladyslav Vyazovskiy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Power spectra and delta for all recorded sessions.

Power spectral density of the CA1 local field potential (LFP), z-scored over 1–10 Hz for the time periods shown, with temporal evolution of delta (white) overlaid for each recorded session (similar to in Fig. 1b). Hypnograms above each panel show the brain state (active wake (AW), quiet wake (QW), rapid-eye movement (REM) sleep and non-REM sleep (NREM)). State scoring was performed at 1-s resolution but for illustration purposes is provided averaged for 30-s periods (particularly due to rapid transitions between AW and QW during SD). Animal name initial, sex and recording day are provided the left of the y-axes.

Extended Data Fig. 2 Ripple and delta features and controls across sleep and sleep deprivation sessions.

(A) Local field potential spectrogram (1–10 Hz) from a sample theta channel during recovery sleep (RS) from three rats with corresponding hypnogram indicating the scored sleep/wake state above (active wake (AW), quiet wake (QW), rapid eye movement (REM) and non-REM (NREM) sleep). The Fourier spectrogram was calculated from the whitened LFP traces using 4 s windows with 1 s overlap. Z-scored delta power (1–4 Hz, smoothed with a 12 s gaussian kernel) is overlaid in white. More detailed sleep scored sessions are available at https://github.com/diba-lab/sleep_loss_hippocampal_replay. (B) The proportion of time spent in each brain state across all sessions. Individual session values overlaid in connected dots (n = 8 NSD session and n = 8 SD sessions). We note that during sleep deprivation from ZT 0-2.5 (SD1) to ZT 2.5-5 (SD2), there was no significant change in the proportion of time in QW (P = 0.958, t(df = 7) = −0.054) or AW (P = 0.769, t(df = 7) = 0.305). (C) The rate of OFF states compared across sessions. For the non-sleep-deprived (NSD) group, OFF states were most prevalent during NS1 (ZT 0-2.5) and decreased over time, in NS2 (ZT 2.5-5) and NS3 (ZT 5-7.5). The rate of OFF states was initially lower in the SD group, but increased from SD1 to SD2, with a further large increase upon RS. (D) The rate of ripple events calculated in 5 min windows decreased over the first 5 h of NSD but remained stable during 5 h of SD. (E) Ripple rate calculated separately for NREM and WAKE states (individual sessions overlaid with connected dots). A decrease in ripple rates is observed in both NREM and WAKE in the NSD group, but there was no change in WAKE ripples from SD1 to SD2 and a decrease from SD2 to RS. Overall, NREM ripple rates were higher in NS1 vs. RS and WAKE ripple rates were higher in SD2 vs. NS2. (F) The ripple probability (solid line = mean, shaded region = s.e.m., n = 8) was modulated by delta waves. (G) However, the modulation depth of ripples by delta ((peak-trough)/mean) was not significantly different across 2.5 h blocks. (H) OFF states were frequently preceded and followed by ripples69. Modulation of OFF states by ripples did not change across NSD (n = 103,319 ripples across 8 sessions) but the probability that OFF immediately followed a ripple increased over SD, from SD1 to SD2 and further in RS, with a significant difference between RS and NS1. The inducement of OFF states by ripples is similar to the rise in OFF states following bursts induced by sensory stimulation in the cortex76. (I) Interventions needed to stop transitions to sleep during SD were tracked using piezo sensors on the sides of the home cage in 3 sessions. The number of interventions grew with time during SD. (J) Mean and 95% confidence intervals of ripple rate (left) and delta wave rate (right) relative to the onset of interventions. The rate of delta waves and concurrent ripples was higher immediately preceding interventions, consistent with signs of sleepiness that compel such interventions. (K) Ripple features (frequency, sharp wave amplitude and ripple power) evaluated separately in NREM (n = 67007 ripples from 6 NSD sessions, n = 26798 ripples from 7 SD sessions) and WAKE states (n = 74363 ripples from 6 NSD sessions and 128957 ripples from 7 SD sessions). Rightmost panels in each row provide cross-group comparisons in NS1 vs. RS strictly during NREM and NS2 vs. SD2 strictly during WAKE. These results are largely consistent with patterns in Fig. 1g–i, except that here ripple power in NS2 vs. SD2 is not significantly different during WAKE, indicating state-dependence of this effect. Additionally, we note a significant increase in ripple frequency in WAKE from PRE to POST in both NSD and SD groups, indicating an effect of the novel maze exposure. All box plots show the median and top/bottom quartiles (whiskers = 1.5 x interquartile range) of the hierarchically bootstrapped data with individual session means overlaid with connecting dots. Statistics: panels C, E, G, two-sided paired t-tests (within group) and one-sided independent groups (across groups) t-tests; panel D, Pearson correlation coefficients with two-sided p-value; panel H, χ2 tests of independence; panel K, two-sided paired within group and one-sided cross-group comparisons with hierarchical bootstrapping; ns (not significant), *P < 0.05, **P < 0.01, ***P < 0.001, with no correction for multiple comparisons. See Supplementary Tables 1 and 2 for additional details.

Extended Data Fig. 3 Firing rate changes within each state separately.

Mean firing rates calculated solely within the awake (WAKE) state (A) or solely within NREM (B) with individual sessions overlaid and connected. Differences calculated separately within wake or NREM were less pronounced than those shown in Fig. 2b,c, consistent with the noted effect of background state on hippocampal firing rates25,29. However, when estimating the metabolic cost of neuronal firing23, comparisons that overlook the state and consider temporal variations in rates, such as those depicted in Fig. 2b and c, are most appropriate. In WAKE (A), firing rates showed a trend towards decreased rates in pyramidal cells (top row) in the NSD group (n = 442 neurons from 8 sessions) but not in SD (n = 312 neurons from 8 sessions). The decrease in firing rates during brief wakings with the recovery sleep period (right panel) likewise showed a trend towards significance vs. a similar period in NSD. Interneuron firing rates (bottom row) within WAKE in recovery sleep showed a trend towards significance in comparison to the similar period in NSD (n = 48 cells from 8 NSD sessions and n = 48 cells from 8 SD sessions). In NREM (B) no significant differences were detected across groups or periods. (C) and (D) Same as (A) and (B) but for active wake (AW) and quiet wake (QW). (E) Firing rate distribution for all pyramidal cells recorded during SD sessions for AW vs. QW. Firing rates in both WAKE states remain skewed from log-normal distribution throughout SD. (F) Interquartile range (IQR) of the log firing rate of pyramidal cells reveals a trend toward a broader range of firing rates in AW vs. QW during SD. All box plots depict the median and top/bottom quartiles (whiskers = 1.5 x interquartile range) of the hierarchically bootstrapped data with individual session means overlaid with connecting dots. Statistics: A-D, F: two-sided paired within group and one-sided cross-group comparisons with hierarchical bootstrapping; E: Shapiro-Wilk tests performed on each bootstrapped log distribution, with P obtained from the proportion of bootstraps with significant skew; ns (not significant), #P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001, with no correction for multiple comparisons. See Supplementary Table 1 for additional details.

Extended Data Fig. 4 Temporal evolution of reactivation across recorded sessions.

Reactivation assessed using the explained variance (EV) metric (NSD (black), SD (red) and RS (blue)), in thirteen sessions from six different animals (3 male and 3 female, with 3 sessions from 2 animals (1 male, 1 female) excluded due to an insufficient number of stable neurons), as in Fig. 3a. Chance level (REV) is shown in maize. Solid lines show the mean and shaded regions show the standard deviation of EV/REV across all 15 min windows in POST. Each row provides session(s) from one animal, with number of putative pyramidal neurons and cell pairs used to calculate EV specified inside each panel. Hypnograms above panels depict sleep/wake history in active wake (AW), quiet wake (QW), rapid eye movement (REM) sleep and non-REM (NREM) sleep, with sleep deprivation/recovery sleep in red/blue and natural sleep in black. Animals’ tracked positions on the novel maze (purple) are depicted on the right of the panels along with the session recording day.

Extended Data Fig. 5 Accounting for the variability in reactivation during sleep deprivation.

We observed striking variability in reactivation across animals during the first block of sleep deprivation (SD1) in ZT0-2.5 (Fig. 3 and Extended Data Fig. 4). We conducted a series of analyses in an effort to account for this observation. Differences in (A) the distance run or (B) the total time spent running on the maze, did not account for the variance in EV during SD1. (C) Likewise, the variance in EV during SD1 cannot be attributed to differences in the proportion of time in active wake (left) or quiet wake (right) states during this period. (D) We next tested whether the rate of delta waves during sleep deprivation (top row, n = 7 sessions), an indicator of sleep pressure, could explain the variance in EV during SD1. Remarkably, there was a strong significant negative correlation (P = 0.006) between the rate of delta from ZT 2.5-5 (SD2) and the reactivation (EV) during SD1. If delta during SD2 thus relates to animal’s level sleepiness, consistent with the sleep homeostasis model24,38, the level of sleepiness correlates with the amount of hippocampal reactivation we observe during SD1. In contrast, we observed no correlation between EV and delta at any timepoint for NSD (bottom row, n = 6 sessions). (E) A similar relationship was not evident between delta waves and EV in NS2. (F) Reactivation (EV) during SD1 was not predictive of the reactivation during RS. Statistics: All panels, Pearson correlation coefficients with two-sided P-values, **P < 0.01, with no correction for multiple comparisons.

Extended Data Fig. 6 Comparisons across 1-hour blocks.

Changes in ripple properties, firing rates, explained variance and replays were assessed using 1-h blocks, based on the last hour of PRE, 1-h periods immediately after MAZE (ZT 0-1) and 1-h blocks immediately before and after recovery sleep (ZT 4-5 and ZT 5-6). All box plots depict the median and top/bottom quartiles (whiskers = 1.5 x interquartile range) of the hierarchically bootstrapped data with individual session means overlaid with connecting dots. Similar to our results for 2.5 h blocks in the main text, (A) ripple frequency (left) decreased over NSD (n = 143681 ripples total from 8 sessions) but increased in SD (n = 157964 ripples total from 8 sessions) relative to MAZE, with a rebound drop in RS (ZT 5-6). Rightmost panel highlights cross-group comparisons for the first block of sleep (NS1 vs. RS) and second block of SD vs. NSD. In both groups, sharp-wave amplitudes (middle) and ripple power (right) increased from MAZE to the first block of POST (ZT 0-1). Sharp-wave amplitude (middle) and ripple power (right) further increased in RS. Cross-group comparisons at ZT 4-5 showed increased ripple power in NSD compared to SD. (B) Firing rate of pyramidal neurons show decreasing firing rates during sleep but not during SD (n = 442 pyramidal neurons / 48 interneurons from 8 sessions NSD, 312 pyramidal neurons / 48 interneurons from 8 sessions SD). (C) EV was significantly lower in SD at ZT4-5 compared to NSD, with a modest but significant rebound during RS, but to lower levels than during the first hour of natural sleep. n = 20544 cell-pairs from 6 NSD sessions and n = 8114 cell-pairs from 7 SD sessions. (D) (left). The proportion of candidate ripple events that decoded continuous trajectories in different epochs (n = 65744 candidate events from 7 SD sessions and n = 56669 candidate events from 6 NSD sessions). SD sessions featured significantly fewer trajectory replays by ZT4-5. Critically, the proportion of replays in RS was significantly lower than in NS1. Similar results were observed for replay number (middle). A significant decrease was observed in mean replay event duration (right) for SD (n = 13911 replays from 7 sessions) but not NSD (n = 15866 replays from 6 sessions) from ZT0-1 to ZT4-5. Statistics: two-sided within-group comparisons and one-sided cross-group comparisons with hierarchical bootstrap, #P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001, with no correction for multiple comparisons. See Supplementary Table 1 for additional details.

Extended Data Fig. 7 Replay characterization during NREM and WAKE.

(A) Replays showed no bias in directionality. (B) The total number of candidate events decreased during POST in non sleep-deprivation (NSD, n = 64205 candidate events from 6 sessions) but remained elevated during sleep deprivation (SD, n = 72584 candidate events from 7 sessions) from the first to second block (SD1 to SD2), but dropping from SD2 to recovery sleep (RS). (C) The proportion of candidate events that scored as trajectory replays in NSD and SD groups, measured separately in WAKE (n = 30852 events from 6 NSD sessions and n = 59820 events from 7 SD sessions) and NREM (n = 32258 events from 6 NSD sessions and 11903 events from 7 SD sessions) states in each block. The rightmost panel provides comparisons between the first block of extended NREM sleep for each group (ZT 0-2.5 in the NSD group vs. ZT 5-7.5 in the SD group) and between WAKE during the second (late) block of POST (ZT 2.5-5 for both groups). There was a significantly lower proportion of trajectory replays in NREM recovery sleep (RS) compared to natural sleep (NS1) and fewer in WAKE (SD2 vs. NS2), demonstrating that these results were significant when assessed within states as well as when compared across time blocks that involved pooled states, as in Fig. 4. Note also that there was a significant increase in the proportion of trajectory replays during NREM from PRE to POST, consistent with previous studies indicating increased replay following novel MAZE exposure43,44. (D) Same as (C) but for the total number of trajectory replay events. Interestingly, the total number of trajectory replays decreased within WAKE in the NSD group, but did not change within SD, resulting in a greater total number of trajectory replays in SD2 compared to NS2. Importantly, however, there were significantly fewer trajectory replays in NREM RS vs. NS1. (E) Same as (C) but for duration of trajectory replay events (NREM: n = 8291 replays from 6 NSD sessions, n = 1869 replays from 7 SD sessions; WAKE: n = 9128 replays from 6 NSD sessions, n = 12940 replays from 7 NSD sessions). Note the decreased duration of these events during waking in SD2 vs. SD1. All box plots depict the median and top/bottom quartiles (whiskers = 1.5 x interquartile range) of the hierarchically bootstrapped data with individual session means overlaid with connecting dots. Statistics: Panel A: two-tailed, paired t-tests for within group comparisons and one-tailed Welch’s t-tests for cross-group comparisons; Panels B-E, two-sided within-group comparisons and one-sided cross-group comparisons with hierarchical bootstrap, #P < 0.01, *P < 0.05, **P < 0.01, ***P < 0.001, with no correction for multiple comparisons. See Supplementary Tables 1 and 2 for additional details.

Supplementary information

Supplementary Tables

Details of statistics for parametric and hierarchical bootstrap tests conducted for figures and Extended Data figures.

Reporting Summary

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Giri, B., Kinsky, N., Kaya, U. et al. Sleep loss diminishes hippocampal reactivation and replay. Nature 630, 935–942 (2024). https://doi.org/10.1038/s41586-024-07538-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-024-07538-2

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing
-