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Pupil size reveals arousal level fluctuations in human sleep

Carro-Domínguez, Manuel, Huwiler, Stephanie, Oberlin, Stella, Oesch, Timona Leandra, Badii, Gabriela, Lüthi, Anita, Wenderoth, Nicole, Meissner, Sarah Nadine, Lustenberger, Caroline·2025·Nature Communications

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Abstract

Recent animal research has revealed the intricate dynamics of arousal levels that are important for maintaining proper sleep resilience and memory consolidation. In humans, changes in arousal level are believed to be a determining characteristic of healthy and pathological sleep but tracking arousal level fluctuations has been methodologically challenging. Here we measured pupil size, an established indicator of arousal levels, by safely taping the right eye open during overnight sleep and tested whether pupil size affects cortical response to auditory stimulation. We show that pupil size dynamics change as a function of important sleep events across different temporal scales. In particular, our results show pupil size to be inversely related to the occurrence of sleep spindle clusters, a marker of sleep resilience. Additionally, we found pupil size prior to auditory stimulation to influence the evoked response, most notably in delta power, a marker of several restorative and regenerative functions of sleep. Recording pupil size dynamics provides insights into the interplay between arousal levels and sleep oscillations.

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Fig. 1 Experimental procedures. A Overall sleep protocol: Auditory stimulation was applied during non-rapid eye movement (NREM) sleep, whereas pupil size was measured only for up to 4 h. B Pupillometry protocol: The eye was taped open and protected with a transparent bandage while tracked with a camera. Eye features were extracted from the videos: pupil and eyelid centroids (red markers), fitted ellipse around pupil circumference (black ellipse), major and minor axis of pupil ellipse (cyan and green line), iris points connected to pupil centroid (center black lines). C–J W: Wakefulness (yellow); N1: NREM1 (gray); N2: NREM2 (green); N3: NREM3 (blue); R: REM (orange). Consent was obtained for the publication of this image. C–E Hypnogram while pupil size was recorded and accompanying time series from a representative participant. The displayed time period refers to the first 3.5 h of a recording, as shown in (A). C Pupil size fluctuations during sleep. D 30-s segments of the spectral slope without artifacts. E Heart rate (HR) in beats per minute (BPM). The gray box is the time period plotted in the bottom plot of Fig. 2A. F Average pupil size (F(4, 57.43) = 168.90, p < 0.001), G spectral slope (F(4, 68) = 41.37, p < 0.001), and H HR (F(4, 55.52) = 7.623, p < 0.001) for wake, N1, N2, N3, and REM sleep. The data of each participant is depicted in gray. Statistical differences between sleep stages are depicted (see supplementary Tables 1–3 for more detailed information). I Repeated measures correlation within sleep stages where pupil data was aggregated in 30-s segments to match the segment lengths of the spectral slope data. “All” refers to all sleep stages together (All: n = 17, W: n = 16, N1: n = 17, N2: n = 17, N3: n = 3, R: n = 12). J Repeated measures correlation within sleep stages where HR was aggregated in 30 s segments to match the segment lengths of the spectral slope data (All: n = 17, W: n = 16, N1: n = 17, N2: n = 17, N3: n = 3, R: n = 12). 30-s segments where auditory stimulation had been applied were excluded. p-values are based on a post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. Data are presented as mean values +/− 95% confidence intervals. All statistical results are based on two-sided tests.
Fig. 1 Experimental procedures. A Overall sleep protocol: Auditory stimulation was applied during non-rapid eye movement (NREM) sleep, whereas pupil size was measured only for up to 4 h. B Pupillometry protocol: The eye was taped open and protected with a transparent bandage while tracked with a camera. Eye features were extracted from the videos: pupil and eyelid centroids (red markers), fitted ellipse around pupil circumference (black ellipse), major and minor axis of pupil ellipse (cyan and green line), iris points connected to pupil centroid (center black lines). C–J W: Wakefulness (yellow); N1: NREM1 (gray); N2: NREM2 (green); N3: NREM3 (blue); R: REM (orange). Consent was obtained for the publication of this image. C–E Hypnogram while pupil size was recorded and accompanying time series from a representative participant. The displayed time period refers to the first 3.5 h of a recording, as shown in (A). C Pupil size fluctuations during sleep. D 30-s segments of the spectral slope without artifacts. E Heart rate (HR) in beats per minute (BPM). The gray box is the time period plotted in the bottom plot of Fig. 2A. F Average pupil size (F(4, 57.43) = 168.90, p < 0.001), G spectral slope (F(4, 68) = 41.37, p < 0.001), and H HR (F(4, 55.52) = 7.623, p < 0.001) for wake, N1, N2, N3, and REM sleep. The data of each participant is depicted in gray. Statistical differences between sleep stages are depicted (see supplementary Tables 1–3 for more detailed information). I Repeated measures correlation within sleep stages where pupil data was aggregated in 30-s segments to match the segment lengths of the spectral slope data. “All” refers to all sleep stages together (All: n = 17, W: n = 16, N1: n = 17, N2: n = 17, N3: n = 3, R: n = 12). J Repeated measures correlation within sleep stages where HR was aggregated in 30 s segments to match the segment lengths of the spectral slope data (All: n = 17, W: n = 16, N1: n = 17, N2: n = 17, N3: n = 3, R: n = 12). 30-s segments where auditory stimulation had been applied were excluded. p-values are based on a post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. Data are presented as mean values +/− 95% confidence intervals. All statistical results are based on two-sided tests.CC BY · PubMed Central
Fig. 2 Relationship between sigma power, pupil size, heart rate, and K-complexes. A–C Individual example of sigma power fluctuations alongside the corresponding pupil size, HR in beats per minute (BPM), and K-complexes (green dots in C), respectively. The example window corresponds to the time in the gray boxes in the sleep recording from Fig. 1C and E. D–F Sigma power (black & right axis) with pupil size (blue & left axis in D), heart rate (red & left axis in E), and K-complex likelihood (green & left axis in F). All metrics are normalized to the timing of the troughs of detected spindle infraslow fluctuations (ISF). The time between troughs was renormalized in terms of cycle percentage. Centerline plots represent the mean across participants, and shadings around center lines represent the standard error of the mean (SEM). G Average pupil size (F(3, 48.00) = 10.853, p < 0.001), H heart rate (F(3, 48.00) = 16.062, p < 0.001), and I K-complex likelihood (F(3, 48.00) = 17.602, p < 0.001) for the rise, peak, fall, and trough of the normalized sigma power fluctuations. The data of each participant is depicted in gray. Statistical differences between the rise, peak, fall, and trough of sigma power ISFs are depicted, p-values are based on post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. All statistical results are based on two-sided tests.
Fig. 2 Relationship between sigma power, pupil size, heart rate, and K-complexes. A–C Individual example of sigma power fluctuations alongside the corresponding pupil size, HR in beats per minute (BPM), and K-complexes (green dots in C), respectively. The example window corresponds to the time in the gray boxes in the sleep recording from Fig. 1C and E. D–F Sigma power (black & right axis) with pupil size (blue & left axis in D), heart rate (red & left axis in E), and K-complex likelihood (green & left axis in F). All metrics are normalized to the timing of the troughs of detected spindle infraslow fluctuations (ISF). The time between troughs was renormalized in terms of cycle percentage. Centerline plots represent the mean across participants, and shadings around center lines represent the standard error of the mean (SEM). G Average pupil size (F(3, 48.00) = 10.853, p < 0.001), H heart rate (F(3, 48.00) = 16.062, p < 0.001), and I K-complex likelihood (F(3, 48.00) = 17.602, p < 0.001) for the rise, peak, fall, and trough of the normalized sigma power fluctuations. The data of each participant is depicted in gray. Statistical differences between the rise, peak, fall, and trough of sigma power ISFs are depicted, p-values are based on post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. All statistical results are based on two-sided tests.CC BY · PubMed Central
Fig. 3 Pupil size and instantaneous heart rate dynamics of N2 events. A–C Pupil size color-coded in blue and normalized to the 5 s prior the detected event: K-complexes, sleep arousals, and spindle trains (respectively).D–F Heart rate color-coded in orange and normalized to the 5 s prior to the detected event: K-Complexes, spindle trains, and sleep arousals (respectively). C, F Green vertical shading in top panels reflects the mean ± 1 SEM of the median spindle train end across participants. The center lines represent the group mean, and shading around the center lines is the SEM. Gray lines are the mean response of each participant. Black horizontal lines mark significant differences from the 5 s baseline prior to each event (p < 0.05). p-values are based on a post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. All statistical results are based on two-sided tests.
Fig. 3 Pupil size and instantaneous heart rate dynamics of N2 events. A–C Pupil size color-coded in blue and normalized to the 5 s prior the detected event: K-complexes, sleep arousals, and spindle trains (respectively).D–F Heart rate color-coded in orange and normalized to the 5 s prior to the detected event: K-Complexes, spindle trains, and sleep arousals (respectively). C, F Green vertical shading in top panels reflects the mean ± 1 SEM of the median spindle train end across participants. The center lines represent the group mean, and shading around the center lines is the SEM. Gray lines are the mean response of each participant. Black horizontal lines mark significant differences from the 5 s baseline prior to each event (p < 0.05). p-values are based on a post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. All statistical results are based on two-sided tests.CC BY · PubMed Central
Fig. 4 Pupil and EEG responses to auditory stimulation. We applied a 10-s stimulation condition of 1 Hz rhythmic auditory stimulation at 45 dB (verum) at varying arousal levels and a control condition where no stimuli were applied (sham). A–C Pupil size, sigma power, and delta power relative to the 5 s prior to the detection of verum (blue) and sham (green) stimulation windows. Center lines represent the mean, and shading around center lines represent SEM. Time zero is the start of the stimulation window. The stimulation window start times were marked by the stimulation-algorithm independent of whether sham or verum followed. They only differ in terms of whether real tones were played or not. D–F Pupil size, sigma power, and delta power of A–C, respectively, during verum (blue) and sham (green) averaged every 5 s from time zero onwards for each participant. Black stars highlight significant differences (p < 0.05) between verum and sham for a given 5 s region. p-values are based on a post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. D Pupil in verum condition was significantly larger than sham during Stimearly (13.77 ± 4.81%, p = 0.046) and Stimlate (34.87 ± 12.90%, p = 0.046). E Sigma power in verum condition was significantly larger during Stimearly relative to sham (3.52 ± 1.30 µV2, p = 0.036) and significantly lower during Stimpost relative to sham (−2.55 ± 0.46 µV2, p = 0.001). F Delta power in verum condition increased during Stimearly (1’353 ± 271.54µV2, p = 0.002) and Stimlate (265.95 ± 78.29 µV2, p = 0.001) when compared to sham and then during Stimpost returned then back to Stimpre values (−165.23 ± 135.99 µV2, p = 0.255). See Supplementary Table 8 for a list of participants of the participants used in this analysis. All statistical results are based on two-sided tests.
Fig. 4 Pupil and EEG responses to auditory stimulation. We applied a 10-s stimulation condition of 1 Hz rhythmic auditory stimulation at 45 dB (verum) at varying arousal levels and a control condition where no stimuli were applied (sham). A–C Pupil size, sigma power, and delta power relative to the 5 s prior to the detection of verum (blue) and sham (green) stimulation windows. Center lines represent the mean, and shading around center lines represent SEM. Time zero is the start of the stimulation window. The stimulation window start times were marked by the stimulation-algorithm independent of whether sham or verum followed. They only differ in terms of whether real tones were played or not. D–F Pupil size, sigma power, and delta power of A–C, respectively, during verum (blue) and sham (green) averaged every 5 s from time zero onwards for each participant. Black stars highlight significant differences (p < 0.05) between verum and sham for a given 5 s region. p-values are based on a post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. D Pupil in verum condition was significantly larger than sham during Stimearly (13.77 ± 4.81%, p = 0.046) and Stimlate (34.87 ± 12.90%, p = 0.046). E Sigma power in verum condition was significantly larger during Stimearly relative to sham (3.52 ± 1.30 µV2, p = 0.036) and significantly lower during Stimpost relative to sham (−2.55 ± 0.46 µV2, p = 0.001). F Delta power in verum condition increased during Stimearly (1’353 ± 271.54µV2, p = 0.002) and Stimlate (265.95 ± 78.29 µV2, p = 0.001) when compared to sham and then during Stimpost returned then back to Stimpre values (−165.23 ± 135.99 µV2, p = 0.255). See Supplementary Table 8 for a list of participants of the participants used in this analysis. All statistical results are based on two-sided tests.CC BY · PubMed Central
Fig. 5 EEG responses to auditory stimulation at small and large pupil size baselines. A Sigma power 5 s before (Stimpre) and during the first 5 s (Stimearly) of verum stimulation of trials with large (highest quartile) and small pupil baselines (lowest quartile) to group into high and low arousal level trials. We found an interaction effect indicating increased sigma power from Stimpre to Stimearly in the small pupil baseline condition (5.53 ± 2.04 µV2) but not in the large pupil baseline condition (0.08 ± 2.17 µV2), FsigmaXpupilrange(1, 21) = 5.66, p = 0.027, post-hoc comparison Stimpre to Stimearly with small pupil baseline: F(1, 7) = 8.60, p = 0.044, post-hoc comparison Stimpre to Stimearly with large pupil baseline: F(1, 7) = 0.002, p = 0.97. B Delta power during Stimpre and during Stimearly of verum stimulation of trials with large (highest quartile) and small pupil baselines (lowest quartile) to group into high and low arousal level trials. We found an interaction effect indicating increased delta power from Stimpre to Stimearly in the small pupil baseline condition but not in the large pupil baseline condition FdeltaXpupilrange(1, 28) = 6.77, p = 0.015, post-hoc comparison Stimpre to Stimearly with small pupil baseline: F(1, 14) = 7.58, p = 0.031, post-hoc comparison Stimpre to Stimearly with large pupil baseline: F(1, 14) = 0.25, p = 0.623. The numbers next to the gray lines correspond to the participant number. The stimulation protocol V0 was used in 2 and 4, and V1 for the rest. The horizontal black lines connecting pupil size ranges are the interaction effects between pupil size range and Stimpre-Stimearly power values. p-values are based on a post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. All statistical results are based on two-sided tests.
Fig. 5 EEG responses to auditory stimulation at small and large pupil size baselines. A Sigma power 5 s before (Stimpre) and during the first 5 s (Stimearly) of verum stimulation of trials with large (highest quartile) and small pupil baselines (lowest quartile) to group into high and low arousal level trials. We found an interaction effect indicating increased sigma power from Stimpre to Stimearly in the small pupil baseline condition (5.53 ± 2.04 µV2) but not in the large pupil baseline condition (0.08 ± 2.17 µV2), FsigmaXpupilrange(1, 21) = 5.66, p = 0.027, post-hoc comparison Stimpre to Stimearly with small pupil baseline: F(1, 7) = 8.60, p = 0.044, post-hoc comparison Stimpre to Stimearly with large pupil baseline: F(1, 7) = 0.002, p = 0.97. B Delta power during Stimpre and during Stimearly of verum stimulation of trials with large (highest quartile) and small pupil baselines (lowest quartile) to group into high and low arousal level trials. We found an interaction effect indicating increased delta power from Stimpre to Stimearly in the small pupil baseline condition but not in the large pupil baseline condition FdeltaXpupilrange(1, 28) = 6.77, p = 0.015, post-hoc comparison Stimpre to Stimearly with small pupil baseline: F(1, 14) = 7.58, p = 0.031, post-hoc comparison Stimpre to Stimearly with large pupil baseline: F(1, 14) = 0.25, p = 0.623. The numbers next to the gray lines correspond to the participant number. The stimulation protocol V0 was used in 2 and 4, and V1 for the rest. The horizontal black lines connecting pupil size ranges are the interaction effects between pupil size range and Stimpre-Stimearly power values. p-values are based on a post hoc t-test and are adjusted for multiple comparisons using Benjamini-Hochberg correction. All statistical results are based on two-sided tests.CC BY · PubMed Central

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Research works printed but not linked: 84

  1. Poe, G. R. et al. Locus coeruleus: a new look at the blue spot. Nat. Rev. Neurosci. 21, 644–659 (2020). · DOI · Paper page
  2. Osorio-Forero, A., Cherrad, N., Banterle, L., Fernandez, L. M. J. & Lüthi, A. When the Locus Coeruleus speaks up in sleep: recent insights, emerging perspectives. Int. J. Mol. Sci. 23. https://doi.org/10.3390/ijms23095028 (2022). · DOI
  3. Aston-Jones, G. & Cohen, J. D. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 28, 403–450 (2005). · DOI · Paper page
  4. Jones, B. E. Activity, modulation and role of basal forebrain cholinergic neurons innervating the cerebral cortex. Prog. Brain Res. 145, 157–169 (2004). · DOI
  5. Jones, B. E. Arousal systems. Front. Biosci. 8, 438–451 (2003). · DOI
  6. de Lecea, L. et al. The hypocretins: hypothalamus-specific peptides with neuroexcitatory activity. Proc. Natl Acad. Sci. USA 95, 322–327 (1998). · DOI · Paper page
  7. Cazettes, F., Reato, D., Morais, J. P., Renart, A. & Mainen Z. F. Phasic activation of dorsal raphe serotonergic neurons increases pupil size. Curr. Biol. 192–197. https://doi.org/10.1016/j.cub.2020.09.090 (2020). · DOI
  8. Grujic, N., Tesmer, A., Bracey, E., Peleg-Raibstein, D. & Burdakov D. Control and coding of pupil size by hypothalamic orexin neurons. Nat. Neurosci. 26, 1160–1164 (2023). · DOI
  9. Xu, Y. L. et al. Neuropeptide S: a neuropeptide promoting arousal and anxiolytic-like effects. Neuron 43, 487–497 (2004). · DOI
  10. Jodoj, E., Chiang, C. & Aston-Jones, G. Potent excitatory influence of prefrontal cortex activity on noradrenergic locus coeruleus neurons. Neuroscience 83, 63–79 (1998). · DOI · Paper page
  11. Dorr, A. E. & Debonnel, G. Effect of vagus nerve stimulation on serotonergic and noradrenergic transmission. J. Pharmacol. Exp. Therap. 318, 890–898 (2006). · DOI · Paper page
  12. Halász, P., Terzano, M., Parrino, L. & Bódizs R. The nature of arousal in sleep. J Sleep Res. 13:1–23. https://doi.org/10.1111/J.1365-2869.2004.00388.X (2004). · DOI
  13. Halász, P. The K-complex as a special reactive sleep slow wave-a theoretical update. Sleep. Med Rev. 29, 34–40 (2016). · DOI
  14. Osorio-Forero, A. et al. Noradrenergic circuit control of non-REM sleep substates. Curr. Biol. 31, 5009–5023.e7 (2021). · DOI · Paper page
  15. Iber, C., Ancoli-Israel, S., Chesson, A. L. & Quan, S. F. The AASM Manual for the Scoring of Sleep and Associated Events (October 2012) 1–17 (American Academy of Sleep Medicine, Westchester, 2007) · DOI
  16. Kjaerby, C. et al. Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine. Nat Neurosci. 25(August). https://doi.org/10.1038/s41593-022-01102-9 (2022). · DOI · Paper page
  17. 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.e3 (2018). · DOI
  18. Lázár, Z. I., Dijk, D. J. & Lázár, A. S. Infraslow oscillations in human sleep spindle activity. J. Neurosci. Methods. 316, 22–34 (2019). · DOI · Paper page
  19. Joshi, S. & Gold, J. I. Pupil size as a window on neural substrates of cognition. Trends Cogn. Sci. 24, 466–480 (2020). · DOI
  20. Zerbi, V. et al. Rapid reconfiguration of the functional connectome after chemogenetic locus coeruleus activation article rapid reconfiguration of the functional connectome after chemogenetic locus coeruleus activation. Neuron 103, 702–718 (2019). · DOI
  21. Megemont, M., McBurney-Lin, J. & Yang, H. Pupil diameter is not an accurate real-time readout of locus coeruleus activity. Elife 11, e70510 (2022). · DOI
  22. Hayat, H. et al. Locus coeruleus norepinephrine activity mediates sensory-evoked awakenings from sleep. Sci. Adv. 6, https://doi.org/10.1126/sciadv.aaz4232 (2020). · DOI
  23. Alnæs, D. et al. Pupil size signals mental effort deployed during multiple object tracking and predicts brain activity in the dorsal attention network and the locus coeruleus. J, Vis. 14, 1 (2014). · DOI
  24. de Gee, J. W. et al. Dynamic modulation of decision biases by brainstem arousal systems. Elife 6. https://doi.org/10.7554/ELIFE.23232 (2017). · DOI
  25. Oswald, I. Falling asleep open-eyed during intense rhythmic stimulation. Br. Med J. 1, 1450–1455 (1960). · DOI
  26. McNab, A. A. The eye and sleep. Clin. Exp. Ophthalmol. 33, 117–125 (2005). · DOI
  27. Weygandt Mathis, M. & Bethge, M. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. https://doi.org/10.1038/s41593-018-0209-y. · DOI · Paper page
  28. Nath, T. et al. Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nat. Protoc. 14, 2152–2176 (2019). · DOI
  29. Lendner, J. D. et al. An electrophysiological marker of arousal level in humans. Elife 9, 1–29 (2020). · DOI
  30. Höhn, C., Hahn, M. A., Lendner, J. D. & Hoedlmoser, K. Spectral slope and Lempel-Ziv complexity as robust markers of brain states during sleep and wakefulness. eNeuro 11 (2024). · DOI
  31. Joshi, S., Li, Y., Kalwani, R. M. & Gold, J. I. Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron 89, 221–234 (2016). · DOI · Paper page
  32. Kobayashi, G., Tanaka, K. F. & Takata, N. Pupil dynamics-derived sleep stage classification of a head-fixed mouse using a recurrent neural network. Keio J. Med. 72, 44–59 (2023). · DOI
  33. Tsunematsu, T., Patel, A. A., Onken, A. & Sakata, S. State-dependent brainstem ensemble dynamics and their interactions with hippocampus across sleep states. Elife 9, 1–22 (2020). · DOI
  34. Gao. R., Peterson E. J. & Voytek, B. Inferring synaptic excitation/inhibition balance from field potentials. Published online. https://doi.org/10.1016/j.neuroimage.2017.06.078 (2017). · DOI
  35. Bódizs, R. et al. A set of composite, non-redundant EEG measures of NREM sleep based on the power law scaling of the Fourier spectrum. Sci. Rep. 11, 1–18 (2021). · DOI
  36. Miskovic, V., Macdonald, K. J., Rhodes, J. L. & Cote, K. A. Changes in EEG multiscale entropy and power-law frequency scaling during the human sleep cycle. Published online. https://doi.org/10.1002/hbm.24393 (2018). · DOI
  37. Antony, J. W. et al. Sleep spindle refractoriness segregates periods of memory reactivation. Curr. Biol. 28, 1736–1743.e4 (2018). · DOI · Paper page
  38. Bernardi, G. et al. Local and widespread slow waves in stable NREM sleep: evidence for distinct regulation mechanisms. Published online. https://doi.org/10.3389/fnhum.2018.00248 (2018). · DOI
  39. Cardis, R. et al. Cortico-autonomic local arousals and heightened somatosensory arousability during NREMS of mice in neuropathic pain. Elife 10, 1–27 (2021). · DOI
  40. Sforza, E., Jouny, C. & Ibanez, V. Cardiac activation during arousal in humans: further evidence for hierarchy in the arousal response. https://doi.org/10.1016/s1388-2457(00)00363-1 (2000). · DOI
  41. de Zambotti, M. et al. K-complexes: interaction between the central and autonomic nervous systems during sleep. Sleep 39, 1129–1137 (2016). · DOI
  42. Scoring, E. A. EEG arousals: scoring rules and examples: a preliminary report from the sleep disorders atlas task force of the American Sleep Disorders Association. Sleep 15, 174–184 (1992). · DOI
  43. Pfeffer, T. et al. Coupling of pupil-and neuronal population dynamics reveals diverse influences of arousal on cortical processing. Elife 11, e71890 (2022). · DOI
  44. Boutin A., Doyon J. A sleep spindle framework for motor memory consolidation. Philos.Trans. R. Soc. B Biol. Sci. 375. https://doi.org/10.1098/rstb.2019.0232 (2020). · DOI
  45. Ngo, H. V., Martinetz, T., Born, J. & Molle, M. Auditory closed-loop stimulation of the sleep slow oscillation enhances memory. Neuron. https://doi.org/10.1016/j.neuron.2013.03.006 (2013). · DOI · Paper page
  46. Ngo, H. V. V., Seibold, M., Boche, D. C., Mölle, M. & Born, J. Insights on auditory closed-loop stimulation targeting sleep spindles in slow oscillation up-states. J. Neurosci. Methods 316, 117–124 (2019). · DOI
  47. 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). · DOI
  48. Naji, M., Krishnan, G. P., McDevitt, E. A., Bazhenov, M. & Mednick, S. C. Coupling of autonomic and central events during sleep benefits declarative memory consolidation. Neurobiol. Learn Mem. 157, 139–150 (2019). · DOI
  49. Boutin, A. et al. Temporal cluster-based organization of sleep spindles underlies motor memory consolidation. Proc. R. Soc. B. 291, https://doi.org/10.1098/rspb.2023.1408 (2024). · DOI
  50. Huwiler, S. et al. Effects of auditory sleep modulation approaches on brain oscillatory and cardiovascular dynamics. Sleep zsac155. https://doi.org/10.1093/sleep/zsac155 (2022). · DOI
  51. Besedovsky, L. et al. Auditory closed-loop stimulation of EEG slow oscillations strengthens sleep and signs of its immune-supportive function. Nat. Commun. 8, 1984 (2017). · DOI · Paper page
  52. Krugliakova, E. et al. Boosting recovery during sleep. auditory stimulation. Front. Neurosci. 16, 755958 (2022). · DOI
  53. Leminen, M. M. et al. Enhanced memory consolidation via automatic sound stimulation during non-REM sleep. Sleep 40, zsx003 (2017). · DOI
  54. Lustenberger, C. et al. Auditory deep sleep stimulation in older adults at home: a randomized crossover trial. Commun. Med. 2, 30 (2022). · DOI
  55. Weigenand, A., Mölle, M., Werner, F., Martinetz, T. & Marshall, L. Timing matters: open-loop stimulation does not improve overnight consolidation of word pairs in humans. Eur. J. Neurosci. 44, 2357–2368 (2016). · DOI
  56. Grimaldi, D. et al. Strengthening sleep-autonomic interaction via acoustic enhancement of slow oscillations. Sleep. 1–11. https://doi.org/10.1093/sleep/zsz036 (2019). · DOI
  57. Bellesi, M., Riedner, B. A., Garcia-Molina, G. N., Cirelli, C. & Tononi, G. Enhancement of sleep slow waves: underlying mechanisms and practical consequences. Front. Syst. Neurosci. 8, 1–17 (2014). · DOI · Paper page
  58. Colrain, I. M. The K-complex: a 7-decade history review. Sleep 28, 255 (2005). · DOI
  59. Neves, R. M., van Keulen, S., Yang, M., Logothetis, N. K. & Eschenko, O. Locus coeruleus phasic discharge is essential for stimulus-induced gamma oscillations in the prefrontal cortex. J. Neurophysiol. 119, 904–920 (2017). · DOI
  60. Kokkinos, V. & Kostopoulos, G. K. Human non-rapid eye movement stage II sleep spindles are blocked upon spontaneous K-complex coincidence and resume as higher frequency spindles afterwards. J. Sleep. Res. 20, 57–72 (2011). · DOI
  61. Lustenberger, C. et al. Auditory deep sleep stimulation in older adults at home: a randomized crossover trial. Commun. Med. 2, 1–16 (2022). · DOI
  62. Van Egroo, M., Koshmanova, E., Vandewalle, G. & Jacobs, H. I. L. Importance of the locus coeruleus-norepinephrine system in sleep-wake regulation: Implications for aging and Alzheimer’s disease. Sleep. Med. Rev. 62, 101592 (2022). · DOI
  63. Van Someren, E. J. W. Brain mechanisms of insomnia: new perspectives on causes and consequences. Physiol. Rev. 101, 995–1046 (2021). · DOI
  64. McCall, J. G. et al. CRH engagement of the locus coeruleus noradrenergic system mediates stress-induced anxiety. Neuron 87, 605–620 (2015). · DOI · Paper page
  65. Wood, C. S., Valentino, R. J. & Wood, S. K. Individual differences in the locus coeruleus-norepinephrine system: relevance to stress-induced cardiovascular vulnerability. Physiol. Behav. 172, 40–48 (2017). · DOI
  66. Ben Barak-Dror, O. et al. Touchless short-wave infrared imaging for dynamic rapid pupillometry and gaze estimation in closed eyes. Commun. Med. 4, 157 (2024). · DOI
  67. Schiffman, R. M., Christianson, M. D., Jacobsen, G., Hirsch, J. D. & Reis, B. L. Reliability and validity of the Ocular Surface Disease Index. Arch. Ophthalmol. 118, 615–621 (2000). · DOI
  68. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004). · DOI · Paper page
  69. Vallat, R., Walker MP, P. & Walker, M. An open-source, high-performance tool for automated sleep staging. Elife 10, 1–24 (2021). · DOI
  70. Hill, S. L. et al. Visbrain: a multi-purpose GPU-accelerated open-source suite for multimodal brain data visualization. Front. Neuroinformat. | www.frontiersin.org. 1:14. https://doi.org/10.3389/fninf.2019.00014 (2019). · DOI
  71. Lustenberger, C., Wehrle, F., Tüshaus, L., Achermann, P. & Huber, R. The multidimensional aspects of sleep spindles and their relationship to word-pair memory consolidation. Sleep 38, 1093–1103 (2015). · DOI
  72. Prerau, M. J., Brown, R. E., Bianchi, M. T., Ellenbogen, J. M. & Purdon, P. L. Sleep neurophysiological dynamics through the lens of multitaper spectral analysis. Physiology 32, 60–92 (2017). · DOI
  73. Donoghue, T. et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat. Neurosci. 23, 1655–1665 (2020). · DOI
  74. Lacourse, K., Yetton B., Mednick S. C. & Warby S. Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data. Sci. Data 7, 190 (2020). · DOI
  75. Lacourse, K., Delfrate J., Beaudry J., Peppard P. & Warby S. C. A sleep spindle detection algorithm that emulates human expert spindle scoring. Published online. https://doi.org/10.1016/j.jneumeth.2018.08.014 (2018). · DOI
  76. Fernández-Varela I., Alvarez-Estevez D., Hernández-Pereira E. & Moret-Bonillo V. A simple and robust method for the automatic scoring of EEG arousals in polysomnographic recordings. https://doi.org/10.1016/j.compbiomed.2017.05.011 (2017). · DOI
  77. Alvarez-Estevez, D. & Fernández-Varela, I. Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases. Published online. https://doi.org/10.1016/j.sleep.2019.01.025 (2019). · DOI
  78. Kret, M. E. & Sjak-Shie, E. E. Preprocessing pupil size data: guidelines and code. Behav. Res Methods 51, 1336–1342 (2019). · DOI
  79. Behar, J. A. et al. PhysioZoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data. Front. Physiol. 9, 01390 (2018) · DOI
  80. Ferster, M. L. et al. Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep. IEEE Trans. Biomed. Eng. 69, 2916–2925 (2022). · DOI
  81. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using {lme4}. J. Stat. Softw. 67, 1–48 (2015). · DOI · Paper page
  82. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. {lmerTest} Package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017). · DOI · Paper page
  83. Bakdash, J. Z. & Marusich, L. R. Package ‘rmcorr’. Front. Psychol. 8, 456 (2017). · DOI
  84. Wickham H. Ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, New York, 2016). · DOI

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Metadata

Authors
Carro-Domínguez, M., Huwiler, S., Oberlin, S., Oesch, T. L., Badii, G., Lüthi, A., Wenderoth, N., Meissner, S. N., & Lustenberger, C.
Venue
Nature Communications
Published
2025-02-28
Volume
16
Issue
1
ISSN
2041-1723
Cited by
41
References
10
Language
en

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