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Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine

Nature Neuroscience volume 25, pages 1059–1070 (2022)Cite this article

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Matters Arising to this article was published on 20 April 2023

Abstract

Sleep has a complex micro-architecture, encompassing micro-arousals, sleep spindles and transitions between sleep stages. Fragmented sleep impairs memory consolidation, whereas spindle-rich and delta-rich non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep promote it. However, the relationship between micro-arousals and memory-promoting aspects of sleep remains unclear. In this study, we used fiber photometry in mice to examine how release of the arousal mediator norepinephrine (NE) shapes sleep micro-architecture. Here we show that micro-arousals are generated in a periodic pattern during NREM sleep, riding on the peak of locus-coeruleus-generated infraslow oscillations of extracellular NE, whereas descending phases of NE oscillations drive spindles. The amplitude of NE oscillations is crucial for shaping sleep micro-architecture related to memory performance: prolonged descent of NE promotes spindle-enriched intermediate state and REM sleep but also associates with awakenings, whereas shorter NE descents uphold NREM sleep and micro-arousals. Thus, the NE oscillatory amplitude may be a target for improving sleep in sleep disorders.

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Fig. 1: Increased NE descent amplitude primes awakenings over micro-arousals.
Fig. 2: Prolonged NE descent promotes spindle-rich IS and REM sleep transitions.
Fig. 3: Optogenetic suppression of LC induces IS-REM sleep sequences and improves memory.
Fig. 4: Optogenetic reduction of NE amplitude reduces sigma activity and disrupts memory.
Fig. 5: Pharmacological reduction of NE amplitude promotes micro-arousal and compromises memory.
Fig. 6: Model diagram: NE amplitude defines the memory restorative properties of sleep.

Data availability

The datasets generated during and/or analyzed in this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

The custom-made MATLAB code used in this study is available from GitHub: https://github.com/MieAndersen/NE-oscillations.

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Acknowledgements

Funding: Lundbeck Foundation, R386–2021–165; Independent Research Council Denmark, 7016–00324A; Augustinus Foundation, 16–3735.; Novo Nordisk Foundation, NNF20OC0066419; US Department of Health & Human Services, National Institutes of Health (NIH), R01AT011439; US Department of Defense, Army Research Office, W911NF1910280; The Simons Foundation, 811237; Adelson Foundation; National Key R&D Program of China, 2021YFF0502904; National Natural Science Foundation of China, 31925017 and 31871087; NIH BRAIN Initiative, 1U01NS113358 and 1U01NS120824; and the FENG Foundation. We thank Myles Billard for technical support and Dan Xue for graphical assistance.

Author information

Author notes

  1. These authors contributed equally: Celia Kjaerby, Mie Andersen.

Authors and Affiliations

  1. Division of Glial Disease and Therapeutics, Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark

    Celia Kjaerby, Mie Andersen, Natalie Hauglund, Verena Untiet, Camilla Dall, Björn Sigurdsson, Pia Weikop, Hajime Hirase & Maiken Nedergaard

  2. Division of Glial Disease and Therapeutics, Center for Translational Neuromedicine, Department of Neurosurgery, University of Rochester Medical Center, Rochester, NY, USA

    Fengfei Ding & Maiken Nedergaard

  3. Department of Pharmacology, Shanghai Medical College, Fudan University, Shanghai, China

    Fengfei Ding

  4. State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing, China

    Jiesi Feng & Yulong Li

  5. PKU-IDG/McGovern Institute for Brain Research, Beijing, China

    Yulong Li

  6. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Yulong Li

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Contributions

Conceptualization: C.K., M.A., H.H., M.N. and P.W. Fiber photometry and EEG methodology and investigation: C.K., M.A., N.H., C.D. and V.U. Viral constructs: Y.L., J.F. and F.D. HPLC methodology: P.W. Analysis: C.K., M.A. and B.S. Visualization: C.K. and M.A. Supervision: H.H. and M.N. Writing—original draft: C.K. and M.A. Writing—review and editing: C.K., M.A., H.H. and M.N.

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Correspondence to Celia Kjaerby or Maiken Nedergaard.

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Extended data

Extended Data Fig. 1 All ascending stages of NE oscillations are associated with EEG-defined micro-arousals or awakenings.

a, Mean power traces for delta, theta, sigma and beta frequency bands aligned to NE rise associated with EEG/EMG-based transitions from NREM sleep to continued NREM sleep (blue, MANE), micro-arousals (MAEEG/EMG, orange) and wake (grey, wakeEEG/EMG). b, Summary plot showing the reduction in band power across the different transitions. There was no difference between MANE and MAEEG/EMG for theta, sigma and beta band power, which are the frequencies used to assess micro-arousals. Significance was calculated by means of two-way repeated measures ANOVA with Šídák’s post hoc test (only MANE and MAEEG/EMG post hoc comparisons shown in graph for simplicity, P = 0.0003, delta; P = 0.13, theta; P = 0.11, sigma; P = 0.65, beta). c, Slope of linear regression on 5 initial seconds of NE rise was used as estimate for rise time. d, NE slope across the different type of transitions (repeated measures one-way ANOVA with Tukey’s multiple comparisons test, P = 0.0059, MANE vs wake; P = 0.041, MAEEG/EMG vs wake). e, Multi-taper power spectral analysis showed increased power for slower frequencies for NREM sleep compared to wake and REM sleep with no defined peak frequency likely due to the discrete nature of NE oscillations. n = 7. Data is shown as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001.

Source data

Extended Data Fig. 2 Decrease in sigma power marks tone-evoked arousal.

Based on the local decline in sigma power in response to tone (> 0.1 log(μV2)), the tone outcome was divided into arousal and otherwise maintained sleep. a, Mean sigma power traces in response to tone leading to either arousal or persistent sleep. b, The maximum value of sigma power maximum prior to tone (‘pre’) and the minimum sigma power after the tone (‘post’) for the defined arousal and sleep conditions. c, The difference in sigma power occurring in response to the tone. Significance was calculated by means of two-way repeated measures ANOVA with Šídák’s post hoc test (b, P = 0.035, pre; P = 0.023, post) or two-tailed paired t-test (c, P = 0.0034). n = 6. Data is shown as mean ± SEM. *P < 0.05, **P < 0.01.

Source data

Extended Data Fig. 3 Relationship between norepinephrine, sigma power, spindles and delta power.

a, Representative trace showing sigma power of surface EEG recordings with corresponding detection of spindles based on S1 cortical LFP recordings. b, Correlation between spindle occurrences and mean sigma power across 10 s bins over a 5 min NREM sleep episode. c, Mean Pearson r values (0.63 ± 0.02). d, Mean sigma power traces normalized to baseline showing the amount of sigma power increase associated with NE descents preceeding microarousals (MANE or MAEEG/EMG) or awakenings (wakeEEG/EMG). Calculated area under the curve (AUC) for sigma power is largest during the NE descents associated with awakenings transitions (two-tailed paired t-test; P = 0.60, MANE vs MAEEG/EMG; P = 0.0057, MANE vs wake; P = 0.044, MAEEG/EMG vs wake). e, Mean correlation coefficient between delta power and NE level (5 min NREM sleep episodes). f, Three different example traces showing how optogenetic activation of locus coeruleus (LC, 2 s 20 Hz 10 ms pulses) during periods of NE descend leads to NE ascend followed by a delayed change in spindle occurrences and amplitude reduction of 7–15 bandpass filtered EEG that is not represented by sigma power (window = 5 s, overlap: 2.5 s). n = 4 (c); n = 7 (d, e). Data is shown as mean ± SEM. *P < 0.05, **P < 0.01.

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Extended Data Fig. 4 Optogenetic suppression of LC.

a, Arch expression in LC was verified by co-staining for TH and GFP. Arrowheads indicate the tip location of the optic fiber (scale bar = 800 µm). b, Close up of a (scale bar = 400 µm). c, Example traces showing norepinephrine level, sigma power, sleep spindles, bandpass LFP in sigma range, and EMG raw data. d, Mean sigma power aligned to onset of NE drop (NREM and IS-REM) or onset of laser stimulation (Arch). e, Time spent in sleep/wake stages during 2-h recording between memory encoding and recall (2-way repeated measures ANOVA with Šídák’s multiple comparison post hoc test). f, Number of laser stimulation (% of total number) in each sleep/wake stage (2-way repeated measures ANOVA with Šídák’s multiple comparison post hoc test). g, Mean NE trace at laser onset during wakefulness. h, NE descent amplitude induced by laser onset during NREM and wakefulness (2-way repeated measures ANOVA with Šídák’s multiple comparison post hoc test, P = 0.0046, NRREM). i, Percentage of laser stimulations during wakefulness resulting in transition to NREM sleep (unpaired t-test). j, Distance moved and ratio between approaches during the recall phase of the novel object recognition (NOR) (two-tailed unpaired t-test, P = 0.049, one-sample t-test P = 0.49, YFP; P = 0.026, Arch). k, Distance moved and object approaches during the acquisition phase of the NOR (unpaired t-test). l, Linear regression between object exploration ratio and time spent in REM sleep. m, Linear regression between object exploration ratio and number of REM bouts/h. n, Linear regression between object exploration ratio and mean theta power during REM sleep. o, Linear regression between object exploration ratio and time spent in NREM sleep. p, Linear regression between object exploration ratio and theta amplitude in response to laser. q, Linear regression between object exploration ratio and delta amplitude in response to laser. n = 9 Arch, 5 YFP. Data is shown as mean ± SEM. *P < 0.05, **P < 0.01.

Source data

Extended Data Fig. 5 Optogenetic activation of LC.

a, Distance moved and ratio between novel and familiar object approaches during recall phase of novel object recognition (ratio: two-tailed unpaired t-test, P = 0.052; one-sample t-test, P = 0.035, YFP; P = 0.64, ChR2). b, Distance moved and object exploration during memory acquisition (right). n = 7 ChR2, 7 YFP. Data is shown as mean ± SEM. *P < 0.05.

Source data

Extended Data Fig. 6 The effect of NE reuptake inhibition on EEG and arousability.

a, Representative traces of norepinephrine, raw EEG and EMG before and after administration of the NE reuptake inhibitor, desipramine (des, 10 mg/kg). b, Example EEG and EMG traces from NREM sleep prior to desipramine administration (top), during undefinable phases following desipramine administration (middle) and from defined NREM sleep following administration with desipramine (bottom). c, Sigma power traces aligned to the onset of awakenings (wakeEEG/EMG) following either treatment with saline or desipramine. d, Mean sigma power before and after NREM-wakeEEG/EMG transition (2-way repeated measures ANOVA with Šídák’s multiple comparison post hoc test, P = 0.0024, pre; P = 0.031, post). e, Reduction in sigma power amplitude (two-tailed paired t-test, P = 0.0007). f, Distance moved and ratio between novel and familiar object approaches during the recall phase of the novel object recognition (NOR) as well as distance moved and number of object approaches during the acquisition phase (unpaired t-test and one-sample t-test). n = 7 (c–e), n = 7 des, 9 sal (f). Data is shown as mean ± SEM. **P < 0.01, ***P < 0.001.

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Kjaerby, C., Andersen, M., Hauglund, N. et al. Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine. Nat Neurosci 25, 1059–1070 (2022). https://doi.org/10.1038/s41593-022-01102-9

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