Complementary contributions of non-REM and REM sleep to visual learning


Sleep is beneficial for learning. However, it remains unclear whether learning is facilitated by non-rapid eye movement (NREM) sleep or by REM sleep, whether it results from plasticity increases or stabilization, and whether facilitation results from learning-specific processing. Here, we trained volunteers on a visual task and measured the excitatory and inhibitory (E/I) balance in early visual areas during subsequent sleep as an index of plasticity. The E/I balance increased during NREM sleep irrespective of whether pre-sleep learning occurred, but it was associated with post-sleep performance gains relative to pre-sleep performance. In contrast, the E/I balance decreased during REM sleep but only after pre-sleep training, and the decrease was associated with stabilization of pre-sleep learning. These findings indicate that NREM sleep promotes plasticity, leading to performance gains independent of learning, while REM sleep decreases plasticity to stabilize learning in a learning-specific manner.

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Fig. 1: Procedures and results of experiment 1.
Fig. 2: Collective results of experiments 1 and 3.
Fig. 3: Experimental design and results of experiment 4.
Fig. 4: An illustration of the complementary process hypothesis.

Data availability

The datasets generated and/or analyzed in the current study are included in this published article as source data and Supplementary Table 14. Source data are provided with this paper.

Code availability

The computer code that was used to generate results central to the conclusions of this study is available from the corresponding author upon request.


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This work was supported by the NIH (R21EY028329, R01EY019466, R01EY027841, T32EY018080 and T32MH115895) and by BSF2016058. Part of this research was also supported by the Center for Vision Research, Brown University.

Author information




M.T., T.W. and Y.S. designed the research. M.T., Z.W., T.B.-D., D.G., A.V.B. and E.W. performed the experiments. M.T., E.W., Z.W. and T.B.-D. analyzed the data. M.T., Z.W., T.B.-D., D.G., A.V.B., E.W., T.W. and Y.S. wrote the manuscript.

Corresponding author

Correspondence to Yuka Sasaki.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks Penelope Lewis, Bryce Mander and Caroline Robertson for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Example structural MRS image indicating the voxel located in early visual areas.

Based on the measured anatomical structure, the voxel of interest was manually placed on the most posterior part of the occipital lobe, covering the calcarine sulci that corresponds to early visual areas bilaterally21.

Extended Data Fig. 2 Example spectra from the voxel located in early visual areas.

The measured spectrum is shown in the top row. “Fit” in the second row represents the spectrum fitted with the LCModel (see the section “MRS acquisition” in the Methods for details). “Residual” in the bottom row represents the residual remaining after the fitting. The remaining rows indicate individual fits for all metabolites that can be detected by a given acquisition. Macromolecular and lipid signals were used for the baseline correction. NAA, NAAG, GSH, Glu, Gln, and GABA represent N-acetylaspartate, N-acetylaspartylglutamate, glutathione, glutamate, glutamine, and gamma-aminobutyric acid, respectively. Glx is obtained by adding glutamine and glutamate in the LCModel. The same procedure was repeated 272 times independently with similar results.

Extended Data Fig. 3 The experimental design and results of Experiment 2.

a, Design. The first texture discrimination task (TDT) training (task A) was conducted with background A, and the second training (task B) was conducted with background B. Test sessions were conducted before the first training (pretest) and after the second training (post-test) to measure performance gains on task A. b, Boxplots for the performance change (%) for task A from the pretest session to the post-test session in the NREM + REM group (red box, n = 23 subjects) and the NREM-only group (gray box, n = 15 subjects). Significant off-line performance gains were observed among subjects who had both NREM sleep and REM sleep (**** two-sided one sample t-test against 0, t22 = 5.48, p < 0.001, d = 1.14, 95% CI [15.86, 35.17]), whereas the subjects who had only NREM sleep did not show any significant off-line performance gains (two-sided one sample t-test against 0, t14 = 0.39, p = 0.699). Furthermore, off-line performance gains were significantly different between the groups (**** two-sided independent-samples t-test, t36 = 4.02, p < 0.001, d = 1.33, 95% CI [13.49, 41.01]). For each boxplot, the bottom and top of the box correspond to the 25th and 75th percentiles (the lower and upper quartiles), respectively. The inner thick horizontal line represents the median, and the plus mark represents the mean. The whiskers show the maximum and minimum of the data. Individual data (dots) are overlaid. Grubbs’ test showed no outliers (Alpha = .05, two-sided). Source data

Extended Data Fig. 4 Mean raw spectra in experiment 1 (a, n = 19 subjects, red plots) and in experiment 3 (b, n = 19 subjects, blue plots).

The value below each plot shows the mean full-width-at-half-maximum linewidth for NAA in Hz (mean ± SEM, Experiment 1, 8.0 ± 0.07 Hz; Experiment 3, 8.0 ± 0.05 Hz). Source data

Extended Data Fig. 5 Correlation between EEG features, performance changes, and E/I balance changes during NREM and REM sleep.

We investigated whether sigma-band (13–16 Hz) and delta-band (1–4 Hz) activities during NREM sleep and theta-band (5–7 Hz) activity during REM sleep were involved in off-line performance gain or resilience to interference. We focused on these oscillatory activities because they are implicated in learning and memory43,44,45,46,47,48. First, a fast-Fourier transformation was applied to the EEG data in 5-sec epochs, and the data were smoothed with a tapered cosine window44 to compute brain activities. Second, six epochs were used to yield the mean spectral for 30 s. Third, we calculated the power for each frequency band during both sleep and wakefulness in the trained region of early visual areas using a set of O1, PO3 and PO7 EEG channels or a set of O2, PO4, and PO8 EEG channels, depending on whether the target appeared in the right or left upper visual field. Fourth, we calculated the power for each frequency band during both sleep and wakefulness in the MT region, which was considered to be a control region according to a previous paper44, using P7 and P8 EEG channels. Fifth, we normalized the power for each frequency band in both the trained region and the control region by subtracting the power for each frequency during sleep from that during wakefulness and dividing that value by that during wakefulness for each region to obtain the power of each frequency during sleep. Finally, we obtained the trained-region specific power during sleep for each frequency band by subtracting the normalized power in the control region from the normalized power in the trained region. Because NREM sleep was associated with off-line performance gains (see Fig. 1c, d), we measured the Pearson’s correlation coefficients for sigma power during NREM sleep and off-line performance gain and for delta power during NREM sleep and off-line performance gain. Analogously, because REM sleep was associated with resilience to interference (see Fig. 1e, f), we measured the Pearson’s correlation for theta-band power during REM sleep and resilience to interference. First, sigma power during NREM sleep was mildly correlated with off-line performance gains (a, n = 19 subjects, Pearson’s r17 = 0.458, two-sided t-test, p = 0.048, 95% CI [0.01, 0.76], with a Bonferroni adjusted alpha level of 0.025 (0.05/2)) and E/I balance during NREM sleep (c, n = 19 subjects, Pearson’s r17 = 0.397, two-sided t-test, p = 0.093, 95% CI [−0.070, 0.721]). Second, theta power during REM sleep was mildly correlated with resilience to interference (b, n = 10 subjects, Pearson’s r8 = 0.632, two-sided t-test, p = 0.050, with a Bonferroni adjusted alpha level of 0.025 (0.05/2), 95% CI [0.003, 0.902]) and E/I balance during REM sleep (d, n = 10 subjects, Pearson’s r8 = −0.68, two-sided t-test, p = 0.031, 95% CI [−0.916, −0.084]). Finally, delta power during NREM sleep was not significantly correlated with performance gains (n = 19 subjects, Pearson’s r17 = 0.23, two-sided t-test, p = 0.334) or E/I balance (n = 19 subjects, Pearson’s r17 = 0.38, two-sided t-test, p = 0.113) during NREM sleep. These results suggest that EEG power is only mildly correlated with off-line performance gains or with resistance to retrograde interference. However, the PSG data were obtained in a strong magnetic environment, which may not be suitable for performing a detailed power analysis. Source data

Extended Data Fig. 6 An illustration of how to co-register MRS data and PSG data.

a, Sleep stage as a function of time (min). There are 9 segments for each 10-min MRS segment. In each segment, there are 20 epochs (each 30 sec) of sleep stage scoring. Each blue dot represents the sleep stage score for 30 sec. b, Simplified sleep stage as a function of time. The sleep stage score is simplified as 3 PSG states: Wake (W), NREM sleep (N; stage N1, N2 and N3 combined), and REM sleep (R). c, Splitting of two 10-min MRS segments (original #1 and #8) into five 2-min segments. This was because there were 2 or 3 PSG states within one original 10-min segment. In a “New” segments row, a gray section indicates Wake, a pink section indicates NREM sleep, and a blue section indicates REM sleep. d, The E/I balance during NREM sleep and REM sleep for each MRS segment was obtained by normalizing them to that during Wake. e, Time course of the concentrations of Glx (red) and GABA (cyan) underlying the E/I balance. Between the transition from e to d, there were several steps, as described in “Calculation of the mean E/I balance for NREM and REM sleep” in the section “Co-registration of MRS data and sleep stages” in the Methods.

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Supplementary Table 14

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Tamaki, M., Wang, Z., Barnes-Diana, T. et al. Complementary contributions of non-REM and REM sleep to visual learning. Nat Neurosci (2020).

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