Deep sleep maintains learning efficiency of the human brain

Sara Fattinger, Toon T de Beukelaar, Kathy L Ruddy, Carina Volk, Natalie C Heyse, Joshua A Herbst, Richard H R Hahnloser, Nicole Wenderoth, Reto Huber, Sara Fattinger, Toon T de Beukelaar, Kathy L Ruddy, Carina Volk, Natalie C Heyse, Joshua A Herbst, Richard H R Hahnloser, Nicole Wenderoth, Reto Huber

Abstract

It is hypothesized that deep sleep is essential for restoring the brain's capacity to learn efficiently, especially in regions heavily activated during the day. However, causal evidence in humans has been lacking due to the inability to sleep deprive one target area while keeping the natural sleep pattern intact. Here we introduce a novel approach to focally perturb deep sleep in motor cortex, and investigate the consequences on behavioural and neurophysiological markers of neuroplasticity arising from dedicated motor practice. We show that the capacity to undergo neuroplastic changes is reduced by wakefulness but restored during unperturbed sleep. This restorative process is markedly attenuated when slow waves are selectively perturbed in motor cortex, demonstrating that deep sleep is a requirement for maintaining sustainable learning efficiency.

Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1. Overview of the experimental protocol.
Figure 1. Overview of the experimental protocol.
(a) After a familiarization session, volunteers participated in two experimental sessions. These were separated by 1 week, with both sessions including three separate learning assessments, the first in the morning of day 1 (Mor D1), the second in the evening on the same day (Eve D1), and a third in the morning the next day (Mor D2). During the night, sleep was recorded using high density EEG. In a cross-over counter balanced design, in one experimental session, slow waves were localy perturbed over left primary motor cortex by acoustic stimulation delivered time-locked to the donw-phase of sleep slow waves (STIM, yellow) while in the other experimental session no stimulation was applied (NOSTIM, green, b). During each learning assessment participatns were subjected to a new motor sequence training (finger taping task) and corticomotor excitability was measured via an IO curve before and after performing the motor sequence training, in order to determine changes indicative of motor plasticity as a consequence of training (c). (b) In the STIM sessions slow waves were perturbed using acoustic stimulation precisely time-locked to the down-phase of sleep slow waves (yellow arrows) detected in the electrode located closest to the sensorimotor representation of the trained hand (red electrode and signal). (c) During each learning assessment subjects acquired a new six-element motor sequence during 12 training trials (that is, 30 s tapping followed by 30 s rest) lasting 12 min in total. Changes in corticomotor excitability of the right first dorsal interosseus (FDI, red dot) were measured before motor training (TMS-PRE) and after (TMS-POST) via an input-output curve (IO curve).
Figure 2. Topographical distribution of low-slow wave…
Figure 2. Topographical distribution of low-slow wave activity.
Comparisons of low slow wave activity (low-SWA, 1–2 Hz) between NOSTIM and STIM sessions. (a) Topographical map of low-SWA of the two experimental nights scaled to maximum (red) and minimum (blue) power values (μV2/Hz). Red circles indicate the position of the TMS hotspot, the white circle indicates the position of the selected electrode for slow wave detection. Note, in the main experiment the TMS hotspot-electrodes and the selected electrodes for slow wave detection were the same. (b) Statistical comparison (t-values) of low-SWA between the STIM and NOSTIM sessions (paired t-test; n=13). Blue colours indicate a decrease and red colours an increase in low-SWA in the STIM compared to NOSTIM session. During STIM session sleep a reduction of low-SWA of 12.00±3.92% (P=0.009) in a local cluster of 9 electrodes over left sensory-motor area (white dots, P<0.05, after nonparametric cluster-based statistical testing) was found. See Methods section for further details.
Figure 3. Stimulation evoked effects on slow…
Figure 3. Stimulation evoked effects on slow waves.
Stimulation evoked modulation and induced changes on general slow wave characteristics in the hotspot-electrode. (a,b) Stimulation evoked modulation recorded at the hotspot-electrode during the NOSTIM and STIM sessions. The immediate up-slope (red) and duration (blue) of the initial single slow wave (see schema) are shown (mean values of the NOSTIM and STIM session for each subject). (c,d) Induced changes in slow wave characteristics during sleep. The down-slope of slow waves (green, mean down-slope) and the probability of slow waves (gray, defined as all waves with a peak-to-peak amplitude larger than 75 μV) are shown (mean values of the NOSTIM and STIM session of each subject). *P<0.05, **P<0.001, paired t-test; n=13. See Methods section for further details. (e,f) Topographical distribution of the comparison (t-values) for changes in general slow wave characteristics (similar to c,d). (e) Down-slope of slow waves (mean down slope) between STIM and NOSTIM sessions. (f) Probability of slow waves (defined as all waves with a peak-to-peak amplitude larger than 75 μV) between STIM and NOSTIM night. Blue colours indicate a decrease and red colours an increase in the STIM compared to NOSTIM session (paired t-test; n=13, white dots, P<0.05, after nonparametric cluster-based statistical testing).
Figure 4. Behavioural and neurophysiological markers.
Figure 4. Behavioural and neurophysiological markers.
Behavioural and neurophysiological markers of neuroplastic changes in response to motor training. Data (mean±s.e.m.) obtained for each learning assessment (Mor D1, Eve D1, Mor D2) are shown for both the NOSTIM session (green) and the STIM session (yellow) as separate learning curves (a) for Performance Scores (% correct sequences divided by inter-tap interval in s, n=11) and (b) for Variability (average s.d. of inter-tap intervals in completed sequencesn, n=11). (c,d) Input-output curves for the first dorsal interosseous muscle, n=10) (see Supplementary Fig. 4 for other intrinsic hand muscles). See Methods for further details.
Figure 5. Correlation analysis between low-slow wave…
Figure 5. Correlation analysis between low-slow wave activity changes and neoplastic changes in response to motor training.
(a) Hotspot-electrode locations for each subject depicted on top of the topographical map showing low-SWA differences between the STIM and the NOSTIM sessions (blue colours indicate less SWA during STIM sleep; n=13). (b) Plateau performance of Tapping Variability (n=11) for each learning assessment in both the STIM and NOSTIM session. (c) Changes in corticomotor excitability (n=10) for each learning assessment were summarized by a Facilitation Index. A FacIndex>1 indicates an increase in corticomotor excitability from PRE to POST (see Fig. 4c,d), while a FacIndex<1 indicates a decrease. (d) Rank ordered SWA ratio (SWA STIM/SWA NOSTIM) calculated for the hotspot-electrode of all subjects included in the correlation analysis. Note that small SWA ratios (<1) indicate that less SWA was observed in the STIM than in the NOSTIM session. (e) Overnight change in Variability was calculated for all subjects included in the correlation analysis. Note that a Variability Δ ratio<0 indicates an overnight increase in Variability in the STIM compared to NOSTIM session. (f) The perturbation related effect on the overnight change in FacIndex was calculated for all subjects included in the correlation analysis. Note that a higher FacIndex Δ ratio indicates a loss in the capacity to exhibit a training-induced increase of corticomotor excitability in the STIM session compared to NOSTIM session. (g) There was no significant correlation between Variability Δ ratio and FacIndex Δ ratio (n=7). (h) The SWA ratio and Variability Δ ratio exhibited a significant positive correlation indicating that a large reduction of SWA during the STIM night was associated with a larger overnight increase in variability when compared to the NOSTIM session (n=10). (i) The SWA ratio and FacIndex Δ ratio exhibited a significant negative correlation indicating that a large reduction of SWA during the STIM night was associated with smaller increases in corticomotor excitability in response to motor learning (n=9). Spearman’s rho coefficient, #post hoc analysis STIM vs NOSTIM (P<0.03—adjusted alpha level); vertical bars denote s.e.m.s. See Methods section for further details.
Figure 6. Control experiment and comparison to…
Figure 6. Control experiment and comparison to the main experiment.
Control experiment targeting right temporo-parietal cortex (white circle) during STIM nights (n=7). (a) Topographical map of low-SWA (1–2 Hz) of the two experimental nights scaled to maximum (red) and minimum (blue) power values (μV2 per Hz). The red circle indicates the location of the individuals’ TMS hotspot which clearly differed from the electrodes selected for local slow wave perturbation (white circle). (b) Statistical comparison (t values) of low-SWA between the STIM and NOSTIM sessions (paired t-test; n=7, white dots, P<0.05, uncorrected). Blue colors indicate a decrease and red colors indicate an increase in low-SWA in the STIM compared to the NOSTIM session. (c) SWA ratio over the hotspot-electrode (red circle in a,b) for the main and control experiment. Comparison of the Variability Δ ratio (d) and the FacIndex Δ ratio (e) between the main experiment and control experiment, using Wilcoxon Rank Sum tests (P values above brackets). Box-plots: middle line indicates the median, the bottom and top of the box indicate the first and third quartiles, whiskers extend to 1.5 × the IQR. Stars indicate significant deviations from the value representing ‘no change’ (1 for c and 0 for d,e). See Methods section for further details.

References

    1. Xie L. et al.. Sleep drives metabolite clearance from the adult brain. Science 342, 373–377 (2013).
    1. Vyazovskiy V. V. & Harris K. D. Sleep and the single neuron: the role of global slow oscillations in individual cell rest. Nat. Rev. Neurosci. 14, 443–451 (2013).
    1. 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).
    1. Bellina V. et al.. Cortical excitability and sleep homeostasis in humans: a TMS/hd-EEG study. J. Sleep Res. 17, 39–39 (2008).
    1. Vyazovskiy V. V., Cirelli C., Pfister-Genskow M., Faraguna U. & Tononi G. Molecular and electrophysiological evidence for net synaptic potentiation in wake and depression in sleep. Nat. Neurosci. 11, 200–208 (2008).
    1. Huber R. et al.. Human cortical excitability increases with time awake. Cereb. Cortex 23, 332–338 (2013).
    1. Turrigiano G. G. The self-tuning neuron: synaptic scaling of excitatory synapses. Cell 135, 422–435 (2008).
    1. Steriade M. The corticothalamic system in sleep. Front. Biosci. 8, d878–d899 (2003).
    1. Vyazovskiy V. V. et al.. Cortical firing and sleep homeostasis. Neuron 63, 865–878 (2009).
    1. Borbély A. A. & Achermann P. Principles and Practice of Sleep medicine 6th edn (eds Kryger M. H., Roth T., Dement W. C. 405–417 ISBN 0323377521 Elsevier Saunders (2015).
    1. Huber R., Ghilardi M. F., Massimini M. & Tononi G. Local sleep and learning. Nature 430, 78–81 (2004).
    1. Hanlon E. C., Vyazovskiy V. V., Faraguna U., Tononi G. & Cirelli C. Synaptic potentiation and sleep need: clues from molecular and electrophysiological studies. Curr. Top Med. Chem. 11, 2472–2482 (2011).
    1. Hung C. S. et al.. Local experience-dependent changes in the wake EEG after prolonged wakefulness. Sleep 36, 59–72 (2013).
    1. Rosenkranz K., Kacar A. & Rothwell J. C. Differential modulation of motor cortical plasticity and excitability in early and late phases of human motor learning. J. Neurosci. 27, 12058–12066 (2007).
    1. Zhang X. et al.. Movement observation improves early consolidation of motor memory. J. Neurosci. 31, 11515–11520 (2011).
    1. Classen J., Liepert J., Wise S. P., Hallett M. & Cohen L. G. Rapid plasticity of human cortical movement representation induced by practice. J. Neurophysiol. 79, 1117–1123 (1998).
    1. Perez M. A., Wise S. P., Willingham D. T. & Cohen L. G. Neurophysiological mechanisms involved in transfer of procedural knowledge. J. Neurosci. 27, 1045–1053 (2007).
    1. Rosenkranz K., Williamon A. & Rothwell J. C. Motorcortical excitability and synaptic plasticity is enhanced in professional musicians. J. Neurosci. 27, 5200–5206 (2007).
    1. Liepert J., Terborg C. & Weiller C. Motor plasticity induced by synchronized thumb and foot movements. Exp. Brain Res. 125, 435–439 (1999).
    1. Ruddy K. L. et al.. Neural Adaptations Associated with Interlimb Transfer in a Ballistic Wrist Flexion Task. Front. Hum. Neurosci. 10, 1318 (2016).
    1. Carson R. G., Ruddy K. L. & McNickle E. Progress in Motor Control; Theories and Translations (eds Laczko J., Latash M. L. ISBN 978-3-319-47312-3 Springer International Publishing AG.
    1. Bestmann S. & Krakauer J. W. The uses and interpretations of the motor-evoked potential for understanding behaviour. Exp. Brain Res. 233, 679–689 (2015).
    1. Massimini M., Huber R., Ferrarelli F., Hill S. & Tononi G. The sleep slow oscillation as a traveling wave. J. Neurosci. 24, 6862–6870 (2004).
    1. Mensen A., Riedner B. & Tononi G. Optimizing detection and analysis of slow waves in sleep EEG. J. Neurosci. Methods 274, 1–12 (2016).
    1. Ngo H. V. V., Martinetz T., Born J. & Mölle M. Auditory closed-loop stimulation of the sleep slow oscillation enhances memory. Neuron 78, 545–553 (2013).
    1. de Beukelaar T. T., Van Soom J., Huber R. & Wenderoth N. A day awake attenuates motor learning-induced increases in corticomotor excitability. Front. Hum. Neurosci. 10, 1080 (2016).
    1. Lustenberger C. et al.. Feedback-controlled transcranial alternating current stimulation reveals a functional role of sleep spindles in motor memory consolidation. Curr. Biol. 26, 2127–2136 (2016).
    1. Todorov E. & Jordan M. I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002).
    1. Hill S., Tononi G. & Ghilardi M. F. Sleep improves the variability of motor performance. Brain Res. Bull. 76, 605–611 (2008).
    1. Shmuelof L., Krakauer J. W. & Mazzoni P. How is a motor skill learned? Change and invariance at the levels of task success and trajectory control. J. Neurophysiol. 108, 578–594 (2012).
    1. Rioult-Pedotti M. S., Friedman D. & Donoghue J. P. Learning-induced LTP in neocortex. Science 290, 533–536 (2000).
    1. Bütefisch C. M. et al.. Mechanisms of use-dependent plasticity in the human motor cortex. Proc. Natl Acad. Sci. USA 97, 3661–3665 (2000).
    1. Rioult-Pedotti M. S., Friedman D., Hess G. & Donoghue J. P. Strengthening of horizontal cortical connections following skill learning. Nat. Neurosci. 1, 230–234 (1998).
    1. Stefan K. et al.. Temporary occlusion of associative motor cortical plasticity by prior dynamic motor training. Cereb. Cortex 16, 376–385 (2006).
    1. Vyazovskiy V. V., Olcese U., Cirelli C. & Tononi G. Prolonged wakefulness alters neuronal responsiveness to local electrical stimulation of the neocortex in awake rats. J. Sleep Res. 22, 239–250 (2013).
    1. Oldfield R. C. The assessment and analysis of handedness: The Edinburgh Inventory. Neuropsychologia 9, 97–113 (1971).
    1. Karni A. et al.. The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. Proc. Natl Acad. Sci. USA 95, 861–868 (1998).
    1. Pascual-Leone A., Manoach D. S., Birnbaum R. & Goff D. C. Motor cortical excitability in schizophrenia. Biol. Psychiatry 52, 24–31 (2002).
    1. Rossini P. M. et al.. Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee. Electroencephalogr. Clin. Neurophysiol. 91, 79–92 (1994).
    1. Driver H. S., Dijk D. J., Werth E., Biedermann K. & Borbély A. A. Sleep and the sleep electroencephalogram across the menstrual cycle in young healthy women. J. Clin. Endocrinol. Metab. 81, 728–735 (1996).
    1. Kirkwood A., Rioult M. C. & Bear M. F. Experience-dependent modification of synaptic plasticity in visual cortex. Nature 381, 526–528 (1996).
    1. Gueorguieva R. & Krystal J. H. Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry. Arch. Gen. Psychiatry 61, 310–317 (2004).
    1. Gelman A. & Hill J. Data Analysis Using Regression and Multilevel/Hierarchical Models Cambridge University Press (2007).
    1. de Beukelaar T. T., Woolley D. G. & Wenderoth N. Gone for 60sec: Reactivation length determines motor memory degradation during reconsolidation. Cortex 59, 138–145 (2014).
    1. Hess C. W., Mills K. R., Murray N. M. & Schriefer T. N. Magnetic brain stimulation: central motor conduction studies in multiple sclerosis. Ann. Neurol. 22, 744–752 (1987).
    1. Devanne H., Lavoie B. A. & Capaday C. Input-output properties and gain changes in the human corticospinal pathway. Exp. Brain Res. 114, 329–338 (1997).
    1. Carson R. G. et al.. Characterizing changes in the excitability of corticospinal projections to proximal muscles of the upper limb. Brain Stimul. 6, 760–768 (2013).
    1. Iber C., Ancoli-Israel S., Chesson A. L. & Quan. S. F. The AASM Manual for the Scoring of Sleep and Associated Events: rules, terminology and technical specifications 1st ed. American Academy of Sleep Medicine (2007).
    1. Huber R. et al.. Exposure to pulsed high-frequency electromagnetic field during waking affects human sleep EEG. NeuroReport 11, 3321–3325 (2000).
    1. Riedner B. A. et al.. Sleep homeostasis and cortical synchronization: III. A high-density EEG study of sleep slow waves in humans. Sleep 30, 1643–1657 (2007).
    1. Nichols T. E. & Holmes A. P. Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum. Brain Mapp. 15, 1–25 (2002).
    1. Ringli M. et al.. Topography of sleep slow wave activity in children with attention-deficit/hyperactivity disorder. Cortex. 49, 340–347 (2013).
    1. Keppel G. & Wickens T. D. Design and Analysis: A Researcher’s Handbook Prentice Hall (1991).

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