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Sleep restores an optimal computational regime in cortical networks

Nature Neuroscience volume 27, pages 328–338 (2024)Cite this article

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Abstract

Sleep is assumed to subserve homeostatic processes in the brain; however, the set point around which sleep tunes circuit computations is unknown. Slow-wave activity (SWA) is commonly used to reflect the homeostatic aspect of sleep; although it can indicate sleep pressure, it does not explain why animals need sleep. This study aimed to assess whether criticality may be the computational set point of sleep. By recording cortical neuron activity continuously for 10–14 d in freely behaving rats, we show that normal waking experience progressively disrupts criticality and that sleep functions to restore critical dynamics. Criticality is perturbed in a context-dependent manner, and waking experience is causal in driving these effects. The degree of deviation from criticality predicts future sleep/wake behavior more accurately than SWA, behavioral history or other neural measures. Our results demonstrate that perturbation and recovery of criticality is a network homeostatic mechanism consistent with the core, restorative function of sleep.

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Fig. 1: Visual cortical dynamics fluctuate around criticality over long time scales during natural sleep and wake states.
Fig. 2: DCC predicts future sleep- and wake-dense epochs better than prior behavior, time of day, SWA and other neural measures.
Fig. 3: Circuit use during waking undermines criticality, which is restored during sleep.
Fig. 4: Experimentally extended waking drives increased DCC consistent with naturally occurring waking.
Fig. 5: Prior behavior influences DCC within sleep.
Fig. 6: Sustained network movements toward and away from criticality predominantly correlate with sleep and wake, respectively.

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Data availability

The raw datasets generated in this study constitute >10 terabytes of raw neural broadband. The data are stored in a cost-efficient manner not immediately accessible to the Internet. Data are available upon request (khengen@wustl.edu). Source data are provided with this paper.

Code availability

Code is available on GitHub (https://github.com/hengenlab/Sleep_restores_criticality).

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Acknowledgements

We would like to thank V. Priesemann and M. Frank for helpful discussions. This project was supported by National Institutes of Health BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative award R01NS118442 (K.B.H.) and the Incubator for Transdisciplinary Futures, an Arts & Sciences signature initiative at Washington University in St. Louis (K.B.H.).

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Authors and Affiliations

  1. Department of Biology, Washington University in St. Louis, St. Louis, MO, USA

    Yifan Xu, Aidan Schneider & Keith B. Hengen

  2. Department of Physics, Washington University in St. Louis, St. Louis, MO, USA

    Ralf Wessel

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Contributions

Y.X. developed code, ran analyses, performed surgeries, collected data and contributed to the writing and figure production. A.S. built the model for sleep/wake prediction. R.W. provided mentorship and intellectual and technical consultation. K.B.H. led, directed and envisioned the project, edited figures and wrote the paper.

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Correspondence to Keith B. Hengen.

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Extended data

Extended Data Fig. 1 Variability within animals over days in the distribution of wake and sleep substates.

a. Example of sleep scoring shows local field potential recorded across cortical layers (green to red heat map) as well as 15 Hz measurement of motor output (gray). Scoring (semi-supervised) is shown along the top in colored blocks. Black arrows denote microarousals. In this preparation (V1, array of microelectrodes), NREM sleep is characterized by high delta power (0.1–4 Hz), low theta (6–8 Hz), and small muscle movements. REM sleep is characterized by low delta, and obvious theta. Wake is consistently demarcated by low delta and increased motor signal. b. Young rats spend significantly more time asleep in the light than the dark, although this is highly variable (n = 8 animals). All box-and-whisker plots in b-f show the median, the first and third quartiles, minimum and maximum, and outliers of the data. c-f. 5 d of data from a single animal showing the percentage of each hour spent in each of four states: NREM (c, blue), REM (d, orange), active wake (e, green), and quiet wake (f, pink). The total time (across 8 animals) in each state as a function of light/dark is shown to the right of each panel. Statistics for b-f, linear mixed effects: % time sleep ~ condition + (1|animal). g. Example raw data traces from a single tetrode (4 channels). Spikes with high signal-to-noise ratio are readily apparent. h. Normalized single unit firing rate considered by state. Each unit is normalized to its own mean rate during REM. Linear mixed effects: FR ~ Behavior + (1|animal). i. Mean single unit coefficient of variation by state. Data in h and i are presented as mean ± s.e.m. across 8 animals.

Source data

Extended Data Fig. 2 Avalanche statistics, and DCC by ZT and light/dark.

a. Two seconds of raw voltage data during wake from four channels show clear action potentials (top). Binarized spike counts are extracted (middle), and the integrated network activity (bottom) shows fluctuations. Neuronal ‘avalanches’ start when network activity crosses above a threshold (dashed pink) and stop when it drops below. Avalanches are measured in terms of their size (total number of spiking neurons) and duration. b. Avalanches can include activity from a variable number of recorded neurons. Plotted is a kernel density estimate of the number of neurons activated in an avalanche. c. A given neuron may contribute to many or few avalanches. Plotted is a histogram of the proportion of avalanches that individual neurons contribute to. d. An example of an animal’s avalanche rate (Hz) across 5 d shows stability when binned at 1 h. e. Avalanche rate as a function of behavioral state and light/dark condition. f. Variability in DCC is not explained by time of day. DCC data from 8 animals shown in 2 h bins across the 24 h cycle. g. DCC is not significantly different in light than in dark (n = 8 animals). P = 0.165, Linear mixed effects: DCC ~ Condition + (1|animal). All box-and-whisker plots in e-g show the median, the first and third quartiles, minimum and maximum, and outliers of the data.

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Extended Data Fig. 3 Effects of behavior and environmental conditions on neural dynamics.

a. In both light and dark, there is a significant negative correlation between time spent asleep and DCC (measured across the entire 4 h window). Note that this differs from the data in Fig. 3d by virtue of examining DCC across the entire 4 h window, inclusive of both sleep and wake. In this approach, each point is 4 h of data, but contains mixed states. In Fig. 3d, DCC is calculated only during the subset of the window that is spent asleep, and thus has variable time but constant state. The remaining measurements (b-f) are calculated only within a given state, consistent with Fig. 3. b. Time spent in active wake (locomotion) in the dark significantly correlates with DCC. Time spent in quiet waking in the dark has no correlation with DCC. c. Time spent asleep has no correlation with normalized firing rates during sleep in either light or dark. d. Similarly, time spent awake has no correlation with normalized firing rates during waking in light or dark. e, f. Same as c and d but coefficient of variation (CV) of interspike intervals. Statistics of a-f are derived from the default linear regression fit in the Scipy Package (Python).

Source data

Extended Data Fig. 4 Extended waking data.

a. Process S is traditionally quantified by changes in slow-wave activity (SWA), especially in the context of sleep deprivation8 (Franken et al.). To confirm that our extended waking protocol is consistent with prior work, we quantified SWA (absolute power) throughout an extended waking epoch and the recovery period. 90 min of extended wake (teal) is sufficient to drive elevated SWA at the onset of NREM (blue) during recovery sleep. SWA progressively declines during NREM sleep. SWA was calculated in 5 mins bins (with a median filter of 4 s sliding windows in each epoch, similar to Franken et al.8). b. Normalized firing rate (FR) does not vary as a function of time spent in extended waking. FR is significantly lower in the recovery phase, the majority of which is NREM sleep (consistent with Extended Data Fig. 1h). Data in b–f are presented as mean ± s.e.m. c. FR at the start and end of extended waking, divided by light and dark phases. d. The interspike interval coefficient of variation (CV) does not vary significantly across the extended waking protocol or recovery period. e. Same as in c but for CV. f. The impact of extended waking on DCC is a significant increase between the start and end of the protocol. The magnitude of this change does not differ when comparing the first half of the 24 h period (light blue) to the second half (dark blue). P = 2.4e−16 for the first half; P = 6.7e−4 for the second half; Linear mixed effects: DCC ~ stage of extended wake + (1|animal). g. Quantification of data in f (P = 0.549, Linear mixed effects). The box-and-whisker plots show the median, the first and third quartiles, minimum and maximum, and outliers of the data. Data in b-f are from 8 animals. *** P < 0.001.

Source data

Extended Data Fig. 5 Prior behavior influences DCC within wake.

a. 1 h wake-dense epochs are identified (window B, red) in which features of neural activity are calculated. The amount of sleep in the preceding 2 h window (A, black) is calculated as the recent behavioral history. b. Despite the ongoing effects of wake on DCC within each wake-dense block, there is a significant negative correlation in DCC during 1 h of wake and time spent asleep in the prior 2 h. c. In the same data, no relationship is observed between sleep in the 2 h prior and normalized single unit firing rate (left) or coefficient of variation (right) in current wake-dense window. Statistics are derived from the default linear regression fit in the Scipy Package (Python).

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Xu, Y., Schneider, A., Wessel, R. et al. Sleep restores an optimal computational regime in cortical networks. Nat Neurosci 27, 328–338 (2024). https://doi.org/10.1038/s41593-023-01536-9

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