Slow oscillation–spindle coupling strength predicts real-life gross-motor learning in adolescents and adults

  1. Michael A Hahn  Is a corresponding author
  2. Kathrin Bothe
  3. Dominik Heib
  4. Manuel Schabus
  5. Randolph F Helfrich
  6. Kerstin Hoedlmoser  Is a corresponding author
  1. Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Austria
  2. Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Austria
  3. Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, Germany

Abstract

Previously, we demonstrated that precise temporal coordination between slow oscillations (SOs) and sleep spindles indexes declarative memory network development (Hahn et al., 2020). However, it is unclear whether these findings in the declarative memory domain also apply in the motor memory domain. Here, we compared adolescents and adults learning juggling, a real-life gross-motor task. Juggling performance was impacted by sleep and time of day effects. Critically, we found that improved task proficiency after sleep lead to an attenuation of the learning curve, suggesting a dynamic juggling learning process. We employed individualized cross-frequency coupling analyses to reduce inter- and intragroup variability of oscillatory features. Advancing our previous findings, we identified a more precise SO–spindle coupling in adults compared to adolescents. Importantly, coupling precision over motor areas predicted overnight changes in task proficiency and learning curve, indicating that SO–spindle coupling relates to the dynamic motor learning process. Our results provide first evidence that regionally specific, precisely coupled sleep oscillations support gross-motor learning.

Editor's evaluation

The authors used a clever design, in which adolescents and adults learned to juggle, to study the impact of sleep and associated oscillations on the consolidation of motor memory across age groups. Overall, the topic and the results of the present study are interesting and timely, and extends previous findings in the declarative memory domain to the motor memory domain.

https://doi.org/10.7554/eLife.66761.sa0

Introduction

Sleep actively supports learning (Diekelmann and Born, 2010). The influential active system consolidation theory suggests that long-term consolidation of memories during sleep is driven by a precise temporal interplay between sleep spindles and slow oscillations (SOs; Diekelmann and Born, 2010; Klinzing et al., 2019). Memories acquired during wakefulness are reactivated in the hippocampus during sharp-wave ripple events in sleep (Wilson and McNaughton, 1994; Zhang et al., 2018). These events are nested within thalamocortical sleep spindles that mediate synaptic plasticity (Niethard et al., 2018; Rosanova and Ulrich, 2005). Sleep spindles in turn are thought to be facilitated by the depolarizing phase of cortical SOs thereby forming SO–spindle complexes during which the subcortical–cortical network communication is optimal for information transfer (Chauvette et al., 2012; Clemens et al., 2011; Helfrich et al., 2019; Helfrich et al., 2018; Latchoumane et al., 2017; Mölle et al., 2011; Ngo et al., 2020; Niethard et al., 2018; Schreiner et al., 2021; Staresina et al., 2015).

Several lines of research recently demonstrated that precisely timed SO–spindle interaction mediates successful memory consolidation across the lifespan (Hahn et al., 2020; Helfrich et al., 2018; Mikutta et al., 2019; Mölle et al., 2011; Muehlroth et al., 2019). Critically, SO–spindle coupling as well as spindles and SOs in isolation are related to neural integrity of memory structures such as medial prefrontal cortex, thalamus, hippocampus, and entorhinal cortex (Helfrich et al., 2021; Helfrich et al., 2018; Ladenbauer et al., 2017; Mander et al., 2017; Muehlroth et al., 2019; Spanò et al., 2020; Winer et al., 2019). Thus, converging evidence suggests that SO–spindle coupling does not only actively transfer mnemonic information during sleep but also indexes general efficiency of memory pathways (Helfrich et al., 2021; Mander et al., 2017). In our recent longitudinal work, we found that SO–spindle coordination was not only becoming more consistent from childhood to late adolescence but also directly predicted enhancements in declarative memory formation across those formative years (Hahn et al., 2020). However, because the active system consolidation theory assumes a crucial role of hippocampal memory replay for sleep-dependent memory consolidation, most studies, including our own, focused on the effect of SO–spindle coupling on hippocampus-dependent declarative memory consolidation. Therefore, the role of SO–spindle coordination for motor learning or consolidation of procedural information remains poorly understood.

While sleep’s beneficial role for motor memory formation has been extensively investigated and frequently related to individual oscillatory activity of sleep spindles and SO (Barakat et al., 2011; Boutin et al., 2018; Fogel et al., 2017; Huber et al., 2004; King et al., 2017; Nishida and Walker, 2007; Pinsard et al., 2019; Tamaki et al., 2013; Tamaki et al., 2008; Vahdat et al., 2017; Walker et al., 2002), there is little empirical evidence for the involvement of the timed interplay between spindles and SO. In rodents, the neuronal firing pattern in the motor cortex was more coherent during spindles with close temporal proximity to SOs after engaging in a grasping motor task (Silversmith et al., 2020). In humans, stronger SO–spindle coupling related to higher accuracy during mirror tracing, a motor adaption task where subjects trace the line of a shape while looking through a mirror (Mikutta et al., 2019). So far, research focused on laboratory suitable fine-motor sequence learning or motor adaption tasks, which has hampered our understanding of memory consolidation for more ecologically valid gross-motor abilities that are crucial for our everyday life (for a review see King et al., 2017).

Only few studies have investigated the effect of sleep on complex real-life motor tasks. Overnight performance benefits for riding an inverse steering bike have been shown to be related to spindle activity in adolescents and adults (Bothe et al., 2019; Bothe et al., 2020). Similarly, juggling performance was supported by sleep and juggling training induced power increments in the spindle and SO frequency range during a nap (Morita et al., 2012; Morita et al., 2016). Remarkably, juggling has been found to induce lasting structural changes in the hippocampus and midtemporal areas outside of the motor network (Boyke et al., 2008; Draganski et al., 2004), making it a promising expedient to probe the active system consolidation framework for gross-motor memory. Importantly, this complex gross-motor skill demands accurately executed movements that are coordinated by integrating visual, sensory, and motor information. Yet, it remains unclear whether learning of these precisely coordinated movements demand an equally precise temporal interplay within memory networks during sleep.

Previously, we demonstrated that SO and spindles become more tightly coupled across brain maturation which predicts declarative memory formation enhancements (Hahn et al., 2020). Here, we expand on our initial findings by investigating early adolescents and young adults learning how to juggle as real-life complex gross-motor task. We first sought to complete the picture of SO–spindle coupling strength development across brain maturation by comparing age ranges that were not present in our initial longitudinal dataset. Second, we explicitly tested the assumption that precisely coordinated SO–spindle interaction supports learning of coordinated gross-motor skills.

By leveraging an individualized cross-frequency coupling approach, we demonstrate that adults have a more precise interplay of SO and spindles than early adolescents. Importantly, the consistency of the SO–spindle coupling dynamic tracked the dynamic learning process of a gross-motor task.

Results

Healthy adolescents (n = 28, age: 13.11 ± 0.79 years, mean ± standard deviation [SD]) and young adults (n = 41, age: 22.24 ± 2.15 years) performed a complex gross-motor learning task (juggling) before and after a full night retention interval as well as before and after a retention interval during wakefulness (Figure 1). To assess the impact of sleep on juggling performance, we divided the participants into a sleep-first group (i.e., sleep retention interval followed by a wake retention interval) and a wake-first group (i.e., wake retention interval followed by a sleep retention interval). Polysomnography (PSG) was recorded during an adaptation night and during the respective sleep retention interval (i.e., learning night) except for the adult wake-first group (for sleep architecture descriptive parameters of the adaptation night and learning night as well as for adolescents and adults see Supplementary file 1—tables 1 and 2). Participants without prior juggling experience trained to juggle for 1 hr. We measured the amount of successful three-ball cascades (i.e., three consecutive catches) during performance tests in multiple 3-min blocks (3 × 3 min for adolescents; 5 × 3 min for adults) before and after the respective retention intervals. Adolescents performed fewer blocks than adults to alleviate exhaustion from the extensive juggling training.

Study design.

Adolescents (N = 28; 23 males) and adults (N = 41; 25 males) without prior juggling experience were divided into sleep-first and wake-first groups. Participants in the sleep-first group trained to juggle for 1 hr with video instructions in the evening. Juggling performance was tested before and after a retention interval containing sleep (1), followed by a third juggling test after a retention interval containing wakefulness (2). Participants in the wake-first group followed the same protocol but in reverse order (i.e., training in the morning, first retention interval containing wakefulness and second retention interval containing sleep). Polysomnography was recorded during an adaptation night and a learning night at the respective sleep retention interval. Psychomotor vigilance tasks were conducted before each performance test. Adolescents only performed three juggling blocks per test to avoid a too excessive training load.

Behavioral results: juggling performance and disentangling the learning process

Adolescents improved their juggling performance over the course of all nine blocks (Figure 2A, top; F3.957, 94.962 = 6.948, p < 0.001, η2 = 0.23). There was neither an overall difference in performance between the sleep-first and wake-first groups (F1, 24 = 1.002, p = 0.327, η2 = 0.04), nor did they differ over the course of the juggling blocks (F3.957, 94.962 = 1.148, p = 0.339, η2 = 0.05). Similar to the adolescents, adults improved in performance across all 15 blocks (Figure 2B, top; F4.673, 182.241 = 11.967, p < 0.001, η2 = 0.24), regardless of group (F4.673, 182.241 = 0.529, p = 0.742, η2 = 0.01). Further, there was no overall difference in performance between the sleep-first and wake-first groups in adults (F1, 39 = 1.398, p = 0.244, η2 = 0.04). Collectively, these results show, that participants do not reach asymptotic level juggling performance (for single subject data of good and bad performers, see Figure 2—figure supplement 1A, B). In other words, the gross-motor skill learning process is still in progress in adolescents and adults. Therefore, we wanted to capture the progression of the learning process, rather than absolute performance metrics (i.e., mean performance) that would underestimate the dynamics of gross-motor learning.

Figure 2 with 1 supplement see all
Behavioral results and parameterizing juggling performance.

(A) The number of successful three-ball cascades (mean ± standard error of the mean [SEM]) of adolescents (circles) for the sleep-first (blue) and wake-first groups (green) per juggling block. Grand average learning curve (black lines) as computed in (C) are superimposed. Dashed lines indicate the timing of the respective retention intervals that separate the three performance tests. Note that adolescents improve their juggling performance across the blocks. (B) Same conventions as in (A) but for adults (diamonds). Similar to adolescents, adults improve their juggling performance across the blocks regardless of group. (C) Schematic representation of the juggling learning process parameterization. We used a linear fit across all juggling blocks within a performance test to estimate the learning curve (m) and the task proficiency (linear line equation solved for x = 1) for each corresponding performance test. (D) Comparison of the juggling learning curve (mean ± standard error of the mean [SEM]) between the sleep-first (blue) and wake-first groups (green) of adolescents (circles) and adults (diamonds) before and after the first retention interval to investigate the influence of sleep. Single subject data are plotted in the corresponding group color and age icon. Participants in the sleep-first group showed a steeper learning curve than the wake-first group after the first retention interval. (E) Same conventions as in (D) but for the task proficiency metric. Adolescents in the wake-first group had better overall task proficiency than adolescents in the sleep-first group. Adults in the sleep-first group displayed better overall task proficiency than adults in the wake-first group. (F) Spearman rank correlation between the overnight change in task proficiency (post–preretention interval) and the overnight change in learning curve with robust linear trend line collapsed over the whole sample. Gray-shaded area indicates 95% confidence intervals of the trend line. Adolescents are denoted as red circles and adults as black diamonds. A strong inverse relationship indicated that participants with an improved task proficiency show flatter learning curves.

Since subjects did not reach asymptotic level performance, but learning was ongoing, we parameterized the juggling learning process by estimating the learning curve for each performance test using a first-degree polynomial fit to the different blocks (Figure 2A–C, black lines). We considered the slope of the resulting trend as learning curve. The learning process of complex motor skills is thought to consist of a fast initial learning stage during skill acquisition and a much slower skill retaining learning stage (Dayan and Cohen, 2011; Doyon and Benali, 2005). In other words, within-learning session performance gains are rapid at the beginning, but taper off with increased motor skill proficiency, resembling a power-law curve. Therefore, we also estimated the task proficiency per performance test at the first time point as predicted by the model, since the learning curve is expected to be influenced by the individual juggling aptitude. Importantly, the estimated task proficiency was comparable to the observed values in the corresponding first juggling block (performance test 1: rhos = 0.98, p < 0.001; performance test 2: rhos = 0.97, p < 0.001). Besides having a more accurate picture of juggling performance, this parameterization also allowed us to compare performance of adolescents and adults on a similar scale because of the different number of juggling blocks. A mixed ANOVA with the factors performance test (pre- and postretention interval), condition group (sleep-first and wake-first) and age group (adolescents and adults) showed a significant interaction between performance test and condition group (F1, 65 = 4.868, p = 0.031, η2 = 0.07). This result indicates that regardless of age, the juggling learning curve becomes steeper after sleep than after wakefulness, thus indicating that sleep impacts motor learning (Figure 2D). No other interactions or main effects were significant (for the complete ANOVA report, see Supplementary file 1—table 3). When analyzing the task proficiency before and after the first retention interval, depending on condition and age group, we found a significant interaction between condition and age group (Figure 2E; F1, 65 = 5.210, p = 0.026, η2 = 0.07), showing that the adult sleep-first group had better overall task proficiency than the wake-first group, whereas the adolescent sleep-first group was worse than the wake-first group. The interaction (performance test × condition group) did not reach significance (F1, 65 = 1.882, p = 0.175, η2 = 0.03; also see Supplementary file 1—table 4). Collectively, these results suggest that sleep influences learning of juggling as a gross-motor task.

Figure 2A, B indicates that performance tests in the morning might be characterized by a steeper learning curve than the evening tests. We confirmed this observation using a linear mixed model (Supplementary file 1—table 5A, B). While this finding might also indicate a circadian influence on learning in our task, we did not find evidence for a circadian effect on sensitive psychomotor vigilance task reaction times. Neither when comparing sleep-first and wake-first groups (Figure 2—figure supplement 1C), nor when specifically probing evening and morning performance tests (Supplementary file 1—table 5E, F). However, these analyses cannot exclude all circadian effects. Therefore, we modeled learning curve and task proficiency with time of day (morning session, evening session) and sleep after learning as fixed effects and subjects as random effects to further disentangle circadian and sleep specific effects. Results for learning curve were inconclusive for both fixed effects (time of day: Beta = −1.008, t(202) = −1.625, p = 0.106, CI95 = [−2.231, 0.215]; sleep after learning: Beta = 0.172, t(202) = 0.268, p = 0.789, CI95 = [−1.093, 1.437]; Supplementary file 1—table 6A). Task proficiency was overall better in the evening performance tests (Beta = 5.751, t(202) = 2.252, p = 0.011, CI95 = [1.310, 10.192]) and additionally trended to benefit from sleep after learning (Beta = 3.795, t(202) = 1.672, p = 0.096, CI95 = [−0.680, 8.271]; Supplementary file 1—table 6B). These results suggest that both time of day and sleep contribute to the overall juggling performance.

Next, we further dissected the relationship between changes in the learning curve and task proficiency after the first retention interval. We hypothesized, that a stronger increase in task proficiency across sleep would lead to a flatter learning curve based on the assumption that motor skill learning involves fast and slow learning stages. Indeed, we confirmed a strong negative correlation between the change (postretention values − preretention values) in task proficiency and the change in learning curve after the retention interval (Figure 2F; rhos = −0.71, p < 0.001), which also remained strong after outlier removal (Figure 2—figure supplement 1D). This result indicates that participants who consolidate their juggling performance after a retention interval show slower gains in performance. Note, that the flattening of the learning curve does not necessarily indicate worse learning but rather mark a more progressed learning stage. These results demonstrate a highly dynamic gross-motor skill learning process. Given that sleep influences the juggling learning curve, we aimed to determine whether sleep oscillation dynamics track the dynamics of gross-motor learning.

Electrophysiological results: interindividual variability and SO–spindle coupling

To determine the nature of the timed coordination between the two cardinal sleep oscillations, we adopted the same principled individualized approach we developed earlier (Hahn et al., 2020). First, we compared oscillatory power between adolescents and adults in the frequency range between 0.1 and 20 Hz during NREM (2 and 3) sleep, using cluster-based permutation tests (Maris and Oostenveld, 2007). Spectral power was elevated in adolescents as compared to adults across the whole tested frequency range (Figure 3—figure supplement 1A left for representative electrode Cz; cluster test: p < 0.001, d = 1.88). Similar to the previously reported developmental patterns of sleep oscillations from childhood to adolescence (Hahn et al., 2020), this difference was explained by a spindle frequency peak shift and broadband decrease in the fractal or 1/f trend of the signal, thus directly replicating and extending our previous findings in a separate sample. After estimating the fractal component of the power spectrum by means of irregular-resampling autospectral analysis (Wen and Liu, 2016), we found that adolescents exhibited a higher offset of fractal component on the y-axis than adults (Figure 3—figure supplement 1A middle; cluster test: p < 0.001, d = 1.99). Next, we subtracted the fractal component from the power spectrum, which revealed clear distinct oscillatory peaks in the SO (<2 Hz) and sleep spindle range (11–16 Hz) for both adolescents and adults (Figure 3—figure supplement 1A, right). Importantly, we observed the expected spatial amplitude topography with stronger frontal SO and pronounced centroparietal spindles for both age groups (Figure 3A left).

Figure 3 with 4 supplements see all
Interindividual variability, slow oscillation (SO)–spindle coupling development, and neural correlates of gross-motor learning dynamics.

(A) Left: topographical distribution of the 1/f corrected SO and spindle amplitude as extracted from the oscillatory residual (Figure 3—figure supplement 1A, right). Note that adolescents and adults both display the expected topographical distribution of more pronounced frontal SO and centroparietal spindles. Right: single subject data of the oscillatory residual for all subjects with sleep data color coded by age (darker colors indicate older subjects). SO and spindle frequency ranges are indicated by the dashed boxes. Importantly, subjects displayed high interindividual variability in the sleep spindle range and a gradual spindle frequency increase by age that is critically underestimated by the group average of the oscillatory residuals (Figure 3—figure supplement 1A, right). (B) Spindle peak locked epoch (NREM3, co-occurrence corrected) grand averages (mean ± standard error of the mean [SEM]) for adolescents (red) and adults (black). Inset depicts the corresponding SO-filtered (2 Hz lowpass) signal. Gray-shaded areas indicate significant clusters. Note, we found no difference in amplitude after normalization. Significant differences are due to more precise SO–spindle coupling in adults. (C) Top: comparison of SO–spindle coupling strength between adolescents and adults. Adults displayed more precise coupling than adolescents in a centroparietal cluster. T-Scores are transformed to z-scores. Asterisks denote cluster-corrected two-sided p < 0.05. Bottom: Exemplary depiction of coupling strength (mean ± SEM) for adolescents (red) and adults (black) with single subject data points. Exemplary single electrode data (bottom) is shown for C4 instead of Cz to visualize the difference. (D) Cluster-corrected correlations between individual coupling strength and overnight task proficiency change (post–preretention) for adolescents (red, circle) and adults (black, diamond) of the sleep-first group (left, data at C4). Asterisks indicate cluster-corrected two-sided p < 0.05. Gray-shaded area indicates 95% confidence intervals of the trend line. Participants with a more precise SO–spindle coordination show improved task proficiency after sleep. Note that the change in task proficiency was inversely related to the change in learning curve (Figure 2F), indicating that a stronger improvement in task proficiency related to a flattening of the learning curve. Further note that the significant cluster formed over electrodes close to motor areas. (E) Cluster-corrected correlations between individual coupling strength and overnight learning curve change. Same conventions as in (D). Participants with more precise SO–spindle coupling over C4 showed attenuated learning curves after sleep.

Critically, the displayed group averages of the oscillatory residuals (Figure 3—figure supplement 1A, right) underestimate the interindividual variability of the spindle frequency peak (Figure 3A, right; oscillatory residuals for all subjects at Cz). Even though we found the expected systematic spindle frequency increase in a frontoparietal cluster from adolescence to adulthood (Figure 3—figure supplement 1B; cluster test: p = 0.002, d = −0.87), both respective age groups showed a high degree of variability of the interindividual spindle peak.

Based on these findings, we separated the oscillatory activity from the fractal activity for every subject at every electrode position to capture the individual features of SO and sleep spindle oscillations. We then used the extracted individual features from the oscillatory residuals to adjust SO and spindle detection algorithms (Hahn et al., 2020; Helfrich et al., 2018; Mölle et al., 2011; Staresina et al., 2015) to account for the spindle frequency peak shift and high interindividual variability. To ensure the simultaneous presence of the two interacting sleep oscillations in the signal, we followed a conservative approach and restricted our analyses to NREM3 sleep given the low co-occurrence rate in NREM2 sleep (Figure 3—figure supplement 1C, D) which can cause spurious coupling estimates (Hahn et al., 2020). Further, we only considered spindle events that displayed a concomitant detected SO within a 2.5-s time window.

We identified an underlying SO component (2 Hz low-pass filtered trace) in the spindle peak locked averages for adolescents and adults on single subject and group average basis (Figure 3—figure supplement 1E), indicating a temporally precise interaction between sleep spindles and SO that is clearly discernible in the time domain.

To further assess the interaction between SO and sleep spindles, we computed SO-trough-locked time–frequency representations (Figure 3—figure supplement 1F). Adolescents and adults revealed a shifting temporal pattern in spindle activity (11–16 Hz) depending on the SO phase. In more detail, spindle activity decreased during the negative peak of the SO (‘down-state’) but increased during the positive peak (‘up-state’). This temporal pattern and the underlying SO component in spindle event detection (Figure 3—figure supplement 1E) confirm the coordinated nature of the two major sleep oscillations in adolescents and adults.

Next, we determined the coordinated interplay between SO and spindles in more detail by analyzing individualized event-locked cross-frequency interactions (Dvorak and Fenton, 2014; Hahn et al., 2020; Helfrich et al., 2019). In brief, we extracted the instantaneous phase angle of the SO component (<2 Hz) corresponding to the positive spindle amplitude peak for all trials at every electrode per subject. We assessed the cross-frequency coupling based on z-normalized spindle epochs (Figure 3B) to alleviate potential power differences due to age (Figure 3—figure supplement 1A) or different EEG-amplifier systems that could potentially confound our analyses (Aru et al., 2015). Importantly, we found no amplitude differences around the spindle peak (point of SO-phase readout) between adolescents and adults using cluster-based random permutation testing (Figure 3B), indicating an unbiased analytical signal. This was also the case for the SO-filtered (<2 Hz) signal (Figure 3B, inset). Critically, the significant differences in amplitude from −1.4 to −0.8 s (p = 0.023, d = −0.73) and 0.4–1.5 s (p < 0.001, d = 1.1) are not caused by age-related differences in power or different EEG systems but instead by the increased coupling strength (i.e., higher coupling precision of spindles to SOs) in adults giving rise to a more pronounced SO-wave shape when averaging across spindle peak locked epochs. Further, we specifically focused our analyses on spindle events to account for the higher variability in the spindle frequency band than in the SO band (Figure 3A). Based on these adjusted phase values, we derived the coupling strength defined as 1 − circular variance. This metric describes the consistency of the SO–spindle coupling (i.e., higher coupling strength indicates more precise coupling) and has previously been shown to accurately track brain development and memory formation (Hahn et al., 2020). As expected, adults had a higher coupling strength in a centroparietal cluster than adolescents (Figure 3C; cluster test: p < 0.001, d = 0.88), indicating a more precise interplay between SO and spindles during adulthood.

SO–spindle coupling tracks gross-motor learning

After demonstrating that SO–spindle coupling becomes more precise from early adolescence to adulthood, we tested the hypothesis, that the dynamic interaction between the two sleep oscillations explains the dynamic process of complex gross-motor learning. When taking the behavioral analyses into account, we did not find any evidence for a difference between the two age groups on the impact of sleep on the learning curve (Figure 2D). Therefore, we did not differentiate between adolescents and adults in our correlational analyses. Furthermore, given that we only recorded PSG for the adults in the sleep-first group and that adolescents in the wake-first group showed enhanced task proficiency at the time point of the sleep retention interval due to additional training (Figure 3—figure supplement 2A), we only considered adolescents and adults of the sleep-first group to ensure a similar level of juggling experience (for summary statistics of sleep architecture and SO and spindle events of subjects that entered the correlational analyses; see Supplementary file 1—table 7). Notably, we found no differences in electrophysiological parameters (i.e., coupling strength, event detection) between the adolescents of the wake-first and sleep-first groups (Figure 3—figure supplement 2B and Supplementary file 1—table 8). To investigate whether coupling strength in the night of the first retention interval explains overnight changes of task proficiency (postretention interval 1 − preretention interval 1), we computed cluster-corrected correlation analyses. We identified a significant central cluster (Figure 3D; mean rho = 0.37, p = 0.017), indicating that participants with a more consistent SO–spindle interplay have stronger overnight improvements in task proficiency.

Given that we observed a strong negative correlation between task proficiency at a given time point and the steepness of the subsequent learning curve (Figure 2F) as subjects improve but do not reach ceiling level performance, we conversely expected a negative correlation between learning curve and coupling. Given this dependency, we observed a significant cluster-corrected correlation at C4 (Figure 3E; rhos = −0.45, p = 0.039, cluster-corrected), showing that participants with a more precise SO–spindle coupling exhibit a flatter learning curve overnight. This observation is in line with a trade-off between proficiency and learning curve, which exhibits an upper boundary (100% task proficiency). In other words, individuals with high performance exhibit a smaller gain through additional training when approaching full task proficiency.

Critically, when computing the correlational analyses separately for adolescents and adults, we identified highly similar effects at electrode C4 for task proficiency (Figure 3—figure supplement 2C) and learning curve (Figure 3—figure supplement 2D) in each group. These complementary results demonstrate that coupling strength predicts gross-motor learning dynamics in both, adolescents and adults, and further shows that this effect is not solely driven by one group. Furthermore, our results remained consistent when including coupled spindle events in NREM2 (Figure 3—figure supplement 2E) and after outlier removal (Figure 3—figure supplement 2F, G).

To rule out age as a confounding factor that could drive the relationship between coupling strength, learning curve and task proficiency in the mixed sample, we used cluster-corrected partial correlations to confirm their independence of age differences (task proficiency: mean rho = 0.40, p = 0.017; learning curve: rhos = −0.47, p = 0.049). Additionally, given that we found that juggling performance could underlie a circadian modulation we controlled for individual differences in alertness between subjects due to having just slept. We partialed out the mean PVT reaction time before the juggling performance test after sleep from the original analyses and found that our results remained unchanged (task proficiency: mean rho = 0.37, p = 0.025; learning curve: rhos = −0.49, p = 0.040). For a summary of the reported cluster-corrected partial correlations as well as analyses controlling for differences in sleep architecture, see Figure 3—figure supplement 3. Further, we also confirmed that our correlations are not influenced by individual differences in SO and spindle event parameters (Figure 3—figure supplement 4).

Finally, we investigated whether subjects with high coupling strength have a gross-motor learning advantage (i.e., trait effect) or a learning-induced enhancement of coupling strength is indicative for improved overnight memory change (i.e., state effect). First, we correlated SO–spindle coupling strength obtained from the adaptation night with the coupling strength in the learning night. We found that overall, coupling strength is highly correlated between the two measurements (mean rho across all channels = 0.55, Figure 3—figure supplement 2H), supporting the notion that coupling strength remains rather stable within the individual (i.e., trait). Second, we calculated the difference in coupling strength between the learning night and the adaptation night to investigate a possible state effect. We found no significant cluster-corrected correlations between coupling strength change and task proficiency—as well as learning curve change (Figure 3—figure supplement 2I).

Collectively, these results indicate the regionally specific SO–spindle coupling over central EEG sensors encompassing sensorimotor areas precisely indexes learning of a challenging motor task.

Discussion

By comparing adolescents and adults learning a complex juggling task, we critically advance our previous work about the intricate interplay of learning and memory formation, brain maturation, and coupled sleep oscillations: First, we demonstrated that SO–spindle interplay precision is not only enhanced from childhood to late adolescence but also progressively improves from early adolescence to young adulthood (Figure 3C). Second and more importantly, we provide first evidence that the consistency of SO–spindle coordination is a promising model to track real-life gross-motor skill learning in addition to its key role in declarative learning (Figure 3D, E). Notably, this relationship between coupling and learning occurred in a regional specific manner and was pronounced over frontal areas for declarative and over motor regions for procedural learning (Hahn et al., 2020). Collectively, our results suggest that precise SO–spindle coupling supports gross-motor memory formation by integrating information from subcortical memory structures to cortical networks.

How do SO–spindle interactions subserve motor memory formation? Motor learning is a process relying on complex spatial and temporal scales in the human brain. To acquire motor skills the brain integrates information from extracortical structures with cortical structures via cortico-striato-thalamo-cortico loops and cortico-cerebello-thalamo-cortico circuits (Dayan and Cohen, 2011; Doyon and Benali, 2005; Doyon et al., 2018; Pinsard et al., 2019). However, growing evidence also advocates for hippocampal recruitment for motor learning, especially in the context of sleep-dependent memory consolidation (Albouy et al., 2013; Boyke et al., 2008; Draganski et al., 2004; Pinsard et al., 2019; Sawangjit et al., 2018; Schapiro et al., 2019). Hippocampal memory reactivation during sleep is one cornerstone of the active systems consolidation theory, where coordinated SO–spindle activity route subcortical information to the cortex for long-term storage (Diekelmann and Born, 2010; Helfrich et al., 2019; Klinzing et al., 2019; Ngo et al., 2020). Quantitative markers of spindle and SO activity but not the quality of their interaction have been frequently related to motor memory in the past (Barakat et al., 2011; Bothe et al., 2019; Bothe et al., 2020; Huber et al., 2004; Morita et al., 2012; Nishida and Walker, 2007; Tamaki et al., 2008). Our results now complement the active systems consolidation theories’ mechanistic assumption of interacting oscillations by demonstrating that a precise SO–spindle interplay subserves gross-motor skill learning (Figure 3D, E). Of note, we did not derive direct hippocampal activity in the present study given spatial resolution of scalp EEG recordings. Nonetheless, as demonstrated recently, coupled spindles precisely capture corticohippocampal network communication as well as hippocampal ripple expression (Helfrich et al., 2019). Thus, higher SO–spindle coupling strength supporting gross-motor learning in our study points toward a more efficient information exchange between hippocampus and cortical areas.

Remarkably, hippocampal engagement is especially crucial at the earlier learning stages. Recently, it has been found that untrained motor sequences exhibit hippocampal activation that subsides for more consolidated sequences. This change was further accompanied by increased motor cortex activation, suggesting a transformative function of sleep for motor memory (Pinsard et al., 2019). In other words, hippocampal disengagement likely indexes the transition from the fast learning stage to the slower learning stage with more proficient motor skill (Dayan and Cohen, 2011; Doyon and Benali, 2005). The dynamics of the two interacting learning stages of motor skill acquisition are likely reflected by the inverse relationship between task proficiency increases and learning curve attenuation (Figure 2F). Given that our subjects did not reach asymptotic performance level (Figure 2A, B) and that SO–spindle coupling tracks gross-motor skill learning dynamics as it relates to both, learning curve attenuation and task proficiency increments, it is plausible that SO-coupling strength represents the extent of hippocampal support for integrating information to motor cortices during complex motor skill learning.

Interestingly, SO and spindles are not only implicated in hippocampal–neocortical network communication but are also indicative for activity and information exchange in subcortical areas that are more traditionally related to the shift from fast to slow motor learning stages. For example, striatal network reactivation during sleep was found to be synchronized to sleep spindles, which predicted motor memory consolidation (Fogel et al., 2017). In primates, coherence between M1 and cerebellum in the SO and spindle frequency range suggested that coupled oscillatory activity conveys information through cortico-thalamo-cerebellar networks (Xu et al., 2021). One testable hypothesis for future research is whether SO–spindle coupling represents a more general gateway for the brain to exchange subcortical and cortical information and not just hippocampal–neocortical communication.

Critically, we found that the consistency of the SO–spindle interplay identified at electrodes overlapping with motor areas such as M1 was predictive for the gross-motor learning process (Figure 3D, E). This finding corroborates the idea that SO–spindle coupling supports the information flow between task-relevant subcortical and cortical areas. Recent evidence in the rodent model demonstrated that neural firing patterns in M1 during spindles became more coherent after performing a grasping motor task. The extent of neural firing precision was further mediated by a function of temporal proximity of spindles to SOs (Silversmith et al., 2020). Through this synchronizing process and their Ca2+ influx propagating property, coupled spindles are likely to induce neural plasticity that benefits motor learning (Niethard et al., 2018).

How ‘active’ is sleep for real-life gross-motor memory consolidation? We found that sleep impacts the learning curve but did not affect task proficiency in comparison to a wake retention interval directly after learning (Figure 2D, E). Three accounts might explain the absence of a sleep effect on task proficiency. (1) Sleep rather stabilizes than improves gross-motor memory, which is in line with previous gross-motor adaption studies (Bothe et al., 2019; Bothe et al., 2020). This parallels findings in finger tapping tasks were the narrative evolved from sleep-related performance improvements (Walker et al., 2002) to stabilization (Brawn et al., 2010). (2) Presleep performance is critical for sleep to improve motor skills (Wilhelm et al., 2012). Participants commonly reach asymptotic presleep performance levels in finger tapping tasks, which is most frequently used to probe sleep effects on motor memory. Here, we found that using a complex juggling task, participants do not reach asymptotic ceiling performance levels in such a short time. Indeed, the learning progression for the sleep-first and wake-first groups followed a similar trend (Figure 2A, B), suggesting that more training and not in particular sleep drove performance gains. (3) Sleep effects are intermingled with time of day effects on juggling performance. Indeed, the steeper learning curve after the first retention interval in the sleep-first group can also be interpreted as a time of day effect. However, when modeling time of day and sleep specific effects across all performance blocks, we found a trend that sleep after learning supports task proficiency. Note, that the correlative nature of both factors in the model likely resulted in insufficient statistical power to produce independently significant results. Additionally, we did not find evidence for a circadian modulation of cognitive engagement based on objective reaction time data in our study (Figure 2—figure supplement 1C). However, a null-result does not exclude all possible circadian effects and ample evidence suggests that cognitive performance and motor learning are influenced by the time of day (Blatter and Cajochen, 2007; Keisler et al., 2007; Tandoc et al., 2021). Therefore, we cannot fully disentangle circadian and sleep effects with our study design, which should be considered a limitation to our findings.

Importantly, SO–spindle coupling still predicted learning dynamics on a single subject level advocating for a supportive function of sleep for gross-motor memory. Moreover, we found that SO–spindle coupling strength remains remarkably stable between two nights, which also explains why a learning-induced change in coupling strength did not relate to behavior (Figure 3—figure supplement 2I). Thus, our results primarily suggest that strength of SO–spindle coupling correlates with the ability to learn (trait), but does not solely convey the recently learned information. Note that state and traits effects are not mutually exclusive. The overlap of state and trait effects is a long-standing issue in spindle literature, which also seems so apply to their coordinated interplay with SOs (Lustenberger et al., 2015; Schabus et al., 2006). This set of findings is in line with recent ideas that strong coupling indexes individuals with highly efficient subcortical–cortical network communication (Helfrich et al., 2021).

This subcortical–cortical network communication is likely to be refined throughout brain development, since we discovered elevated coupling strength in adults compared to early adolescents (Figure 3C). This result compliments our earlier findings of enhanced coupling precision from childhood to adolescence (Hahn et al., 2020) and the recently demonstrated lower coupling strength in preschool children (Joechner et al., 2021). We speculate that, similar to other spindle features, the trajectory of SO-coupling strength is likely to reach a plateau during adulthood (Nicolas et al., 2001; Purcell et al., 2017). Importantly, we identified similar methodological challenges to assess valid cross-frequency coupling estimates in the current cross-sectional study to the previous longitudinal study. Age severely influences fractal dynamics in the brain (Figure 3—figure supplement 1A) and the defining features of sleep oscillations (Figure 3, Figure 3—figure supplement 1B). Remarkably, interindividual oscillatory variability was pronounced even in the adult age group (Figure 3A), highlighting the critical need to employ individualized cross-frequency coupling analyses to avoid its pitfalls (Aru et al., 2015; Muehlroth and Werkle-Bergner, 2020).

Taken together, our results provide a mechanistic understanding of how the brain forms real-life gross-motor memory during sleep. However, how time of day additionally affects and interacts with sleep to support gross-motor learning remains an open question. As sleep has been shown to support fine-motor memory consolidation in individuals after stroke (Gudberg and Johansen-Berg, 2015; Siengsukon and Boyd, 2008), SO–spindle coupling integrity could be a valuable, easy to assess predictive index for rehabilitation success.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Software, algorithmBrain Vision Analyzer 2.2Brain Products
GmbH https://www.brainproducts.com
RRID:SCR_002356
Software, algorithmCircStat 2012Berens, 2009 https://philippberens.wordpress.com/code/circstats/RRID:SCR_016651
Software, algorithmEEGLAB 13_4_4bDelorme and Makeig, 2004
https://sccn.ucsd.edu/eeglab/index.php
RRID:SCR_007292
Software, algorithmFieldTrip 20161016Oostenveld et al., 2011
http://www.fieldtriptoolbox.org/
RRID:SCR_004849
Software, algorithmIRASAWen and Liu, 2016 https://purr.purdue.edu/publications/1987/1
Software, algorithmMATLAB 2017aMathWorks IncRRID:SCR_001622
Software, algorithmRStudioRStudio TeamRRID:SCR_000432
Software, algorithmSomnolyzer 24 × 7Koninklijke Philips N.V.https://www.philips.co.in
Other‘Jonglieren und Bewegungskünste’Sobota and Hollauf, 2013 Austrian ministry of SportsJuggling video instructions

Participants

We recruited 29 adolescents (mean ± SD age, 13.17 ± 0.85 years; 5 females, 24 males) from a local boarding school and 41 young adults (mean ± SD age, 22.24 ± 2.15 years; 16 females, 25 males) from the student population of the University of Salzburg. All participants were healthy, right-handed and without prior juggling experience. However, we excluded one adolescent for all analyses post hoc for violating the prior juggling experience criteria. Two adolescents did not participate in the third performance test. We randomly divided adolescents and adults into a sleep-first (adolescents: N = 17, 12.94 ± 0.75 years; 3 females, 14 males; adults: N = 25, 21.95 ± 2.42 years; 8 females, 17 males) and a wake-first group (adolescents: N = 11, 13.36 ± 0.81 years; 2 females, 9 males; adults: N = 16, 22.69 ± 1.62 years; 8 females, 8 males). See experimental design for more detailed information about the groups. We recorded PSG during full night sleep for all participants except adults in the wake-first group. Therefore, comparison of electrophysiological data between adults and adolescents was based on the adult sleep-first group and both adolescent groups. To ensure similar juggling learning experience, we only included adults and adolescents in the sleep-first group when analyzing the relationship between electrophysiological measures and behavioral performance. All participants and the legal custodians of the adolescents provided written informed consent before participating in the study. The study protocol was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the University of Salzburg (EK-GZ:16/2014). Adults received monetary compensation or student credit for their participation. Adolescents received a set of juggling balls.

Experimental design

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Adults in the sleep-first group visited the sleep laboratory on three occasions (Figure 1). At the first day subjects slept in the sleep lab with full night PSG for adaptation purposes. On the second visit, subjects learned and practiced juggling by video instructions in the evening (8.45 pm to 9.45 pm). Juggling performance was assessed three times in total. The first performance test was conducted after the training session (10.00 pm to 10.18 pm). The second performance test (7.30 am to 7.48 am) took place after the first retention interval containing a full night of sleep with PSG (11 pm to 7 am). The third and last performance tests were executed after the second retention interval (9.00 pm to 9.18 pm) containing wakefulness. Adults in the wake-first group followed a similar protocol but with reversed order of the retention intervals (i.e., first retention interval containing wakefulness and the second interval containing sleep). Therefore, participants performed the juggling training (10.15 am to 11.15 am) and the first performance test (11.30 am to 11.48 am) in the morning, the second performance test after wakefulness (9.00 pm to 9.18 pm), and the third performance test after sleep (11.00 am to 11.18 am). We did not record PSG in the wake-first group because participants slept at home. To objectively assess attentiveness and potential circadian influences, all participants completed a psychomotor vigilance task (Dinges and Powell, 1985) before the performance tests. Actigraphy (Cambridge Neurotechnology Actiwatch, Cambridge, UK) and a sleep log (Saletu et al., 1987) verified compliance with a regular sleep schedule throughout the study.

Adolescents went through a study protocol comparable to the adults. However, we adjusted the protocol to adhere to the schedule of the boarding school and to control the training load. First, we recorded ambulatory PSG for both groups in their habitual sleep environment at the boarding school and second, we reduced the number of juggling blocks during the performance tests (for details see gross-motor task) because the study regime was already exhausting for our adult participants and we wanted to avoid a too excessive training load. The sleep-first group performed the juggling training (6.30 pm to 7.30 pm) and performance test in the evening (7.45 pm to 7.58 pm) followed by a retention interval containing sleep (21.00 pm to 6.00 am). The second performance test was conducted after sleep (7.30 am to 7.43 am) and the third performance test after wakefulness (7.30 pm to 7.43 pm). The wake-first group learned to juggle (7.30 am to 8.30 am) with a subsequent performance test (8.45 am to 8.58 am) in the morning. The second performance test was executed after wakefulness in the evening (7.30 pm to 7.43 pm) and the third performance test was completed after sleep (7.30 am to 7.43 am).

Gross-motor task

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To investigate the involvement of SO–spindle coupling in acquiring a real-life gross-motor skill, we implemented a juggling paradigm, which has been shown to induce neural plasticity (Boyke et al., 2008; Draganski et al., 2004) and to be sensitive for sleep-dependent memory consolidation (Morita et al., 2012; Morita et al., 2016). Adults and adolescents completed the same juggling training, which was based on short video clips from the ‘Juggling and Movement Arts’ DVD (‘Jonglieren und Bewegungskünste’; Sobota and Hollauf, 2013) containing step-by-step instructions from the correct stance to a full five-ball cascade (i.e., five continuous catches). We used 14 video clips demonstrating the exercises followed by a practice opportunity for the participants. The training session lasted approximately 1 hr with a short break after half an hour. During the performance tests, participants were instructed to juggle as accurately and continuously as possible. Adults juggled for five blocks a 3 min, which was always separated by a 30-s break. To alleviate the physical strain, adolescents only juggled for three blocks a 3 min during the performance tests. Training and performance tests were videotaped to evaluate the juggling performance.

Parameterizing juggling performance

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We evaluated the juggling performance by counting consecutive catches based on the video material. We used the number of three-ball cascades (i.e., three catches in a row, Figure 2A, B) as index for juggling performance by dividing the number of consecutive catches by three. We opted for three-ball cascades as a performance index because we considered three consecutive catches as the criteria for the motor task to qualify as juggling (Boyke et al., 2008; Draganski et al., 2004). Because juggling is a complex motor task where it is unlikely to reach ceiling level performance, we were interested in the progression of the learning process and how it is influenced by task proficiency. Therefore, we calculated a first-degree polynomial fit using the least-squares method to parameterize the learning curve (m, slope) per performance test block (Figure 2A, B, black lines and Figure 2C, D), using the formula:

m=i=1n(xiX´)(yiY´)i=1n(xiX´)²

Next, we calculated the intercept c according to the following formula:

c=Y´mX´

Finally, task proficiency (y1, Figure 2E) was estimated at the first time point of each performance test as

y1=m+c

PSG and sleep staging

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We recorded PSG with two systems. We conducted the ambulatory sleep recordings of the adolescents with a portable amplifier system (Alphatrace, Becker Meditec, Karlsruhe, Germany) with a sampling rate of 512 Hz. For in lab recordings of the adult participants, we utilized a 32-channel Neuroscan amplifier system (Scan 4.3.3 Software, Neuroscan Inc, Charlotte, NC) with a sampling rate of 500 Hz. Electrode placement was identical between the two recording systems and in accordance with the 10–20 system. Signals were recorded with gold cup electrodes placed at F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, and O2 on the scalp, as well as at A1 and A2 placed at the mastoids. To allow for sleep staging and to control for muscle artifacts, we recorded an electromyogram (bipolar electrodes at the musculus mentalis), a horizontal electrooculogram (EOG, above the right outer canthus and below the left outer canthus) and a vertical EOG (above and below the left eye). We used Cz as online reference and AFz as ground electrode. For sleep staging, we re-referenced the signal offline against contralateral mastoids. Sleep was semi-automatically staged in 30-s epochs using the Somnolyzer 24 × 7 algorithm (Koninklijke Philips N.V.; Eindhoven, The Netherlands) and subsequently controlled by an expert scorer according to standard sleep staging criteria (Iber et al., 2007). For all other data analyses, we demeaned and re-referenced the EEG signal to a common average.

Individualized cross-frequency coupling

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To assess the precise interplay between SO and spindles, we used the same individualized cross-frequency coupling pipeline we developed earlier in order to account for network changes induced by aging, that are known to cause spurious coupling estimates (Aru et al., 2015; Cole and Voytek, 2017; Hahn et al., 2020; Scheffer-Teixeira and Tort, 2016). In brief, our approach was based on the following principles: (1) establishing the presence of sleep oscillations, (2) individually detecting transient oscillatory events, (3) alleviating power differences, and (4) ensuring co-occurrence of SO (phase providing signal) and sleep spindles (amplitude providing signal).

Establishing sleep oscillations

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First, we z-normalized the EEG signal in the time domain to mitigate prominent power differences and computed averaged power spectra from 0.1 to 30 Hz using a Fast Fourier Transform (FFT) routine with a Hanning window on 15 s of continuous NREM sleep (i.e., NREM2 and NREM3, Figure 3—figure supplement 1A, left) with a 1-s sliding window. Data are presented in the semi-log space. Next, we sought to isolate the oscillatory activity in the normalized data by means of irregular autospectral analysis (IRASA, Wen and Liu, 2016). We first derived the 1/f fractal component (Figure 3—figure supplement 1A, middle) from 15 s NREM sleep data in 1-s sliding steps and subsequently subtracted it from the power spectrum (Figure 3—figure supplement 1A, left) to obtain an unbiased estimate of the oscillatory activity for every subject on every electrode (Figure 3—figure supplement 1A, right and Figure 3A). To separate the 1/f component from the power spectrum, we used the same parameters as specified previously (Hahn et al., 2020). In short, the signal is stretched and compressed by the same noninteger factor (e.g., stretching by a factor of 1.1 and compressing by a factor of 0.9). We repeated the resampling with factors from 1.1 to 1.9 in 0.05 steps. This pair wise stretching and compressing systematically causes frequency peak shifts in the regular oscillatory activity but leaves the more random 1/f background activity unaffected. Because the oscillatory activity becomes faster by a similar factor as it becomes slower, the oscillatory activity is averaged out by median averaging across all pair wise resampled segments thus extracting the 1/f component. We then detected individual SO (<2 Hz) and spindle peak frequencies (10–17 Hz, Figure 3—figure supplement 1B) and the corresponding 1/f corrected amplitude (Figure 3A, left) in the oscillatory residual (Figure 3—figure supplement 1A, right). We considered the highest peak within the specified SO and spindle frequency ranges above as the most representative oscillatory event in each electrode. We then utilized the individual frequency peaks to inform the algorithms for discrete SO and spindle event detection.

Individually detecting transient oscillatory events

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We employed widely used spindle and SO detection algorithms (Helfrich et al., 2018; Mölle et al., 2011; Staresina et al., 2015) and adjusted them according to the 1/f corrected SO and spindle features for a fully individualized event detection (Hahn et al., 2020).

We detected spindle events (Figure 3, Figure 3—figure supplement 1E) by band-pass filtering the continuous signal ±2 Hz around the individual spindle peak per electrode. After filtering, we computed the instantaneous amplitude via a Hilbert transform. Next, we smoothed the signal with a running average in a 200-ms window. A sleep spindle was detected, when the signal exceeded the 75-percentile amplitude criterion for a time span of 0.5–3 s. We segmented the raw data ±2.5 s centered on the positive spindle peak.

We detected SO events (Figure 3—figure supplement 1F) by first high-pass filtering the continuous EEG signal at 0.16 Hz and then low-pass filtering at 2 Hz. Based on the filtered signal, we detected the zero-crossings that fulfilled the time criterion (length 0.8–2 s). The signal between two consecutive zero-crossings was considered a valid SO if its amplitude exceeded the 75-percentile threshold. We then segmented the raw data ±2.5 s centered on the negative peak.

Alleviating power differences

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Power differences in the signal can systematically impact cross-frequency coupling measures by changing the signal-to-noise ratio, which in turn influences the precision of the phase estimation of the signal (Aru et al., 2015; Scheffer-Teixeira and Tort, 2016). Because power decreases are apparent across the lifespan (Campbell and Feinberg, 2009; Campbell and Feinberg, 2016; Hahn et al., 2020; Helfrich et al., 2018), we z-normalized all detected SO and spindle events in the time domain to alleviate this possible confound before calculating phase-amplitude coupling measures (Figure 3B).

Ensuring co-occurrence of SO and sleep spindles

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Cross-frequency coupling renders meaningful information of network communication only when the suspected interacting oscillations are present in the signal. Therefore, we only analyzed SO and sleep spindle epochs during which they co-occurred in a 2.5-s time window (±~2 SO cycles around the spindle peak). Furthermore, we restricted all our coupling analyses to sleep stage NREM3 because of general lower co-occurrence of SO and spindles in NREM2 (Figure 3—figure supplement 1C, D), which can cause spurious coupling estimates (Hahn et al., 2020).

Event-locked cross-frequency coupling

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To parameterize the timed coordination between sleep spindles and SO (Figure 3C), we computed event-locked cross-frequency coupling analyses (Dvorak and Fenton, 2014; Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Staresina et al., 2015) based on individualized and normalized spindle peak-locked segments. In short, we used a low-pass filter of 2 Hz to extract the underlying SO component (Figure 3B, inset) from the EEG signal and read out the phase angle corresponding with the sleep spindle peak after applying a Hilbert transform. We then calculated the coupling strength, which is defined as 1 − circular variance using the CircStat Toolbox function circ_r (Berens, 2009) to assess the consistency of the SO–sleep spindle interplay.

Time–frequency analyses

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We computed event-locked time–frequency representations based on −2 to 2 s epochs centered on the negative SO peak (Figure 3—figure supplement 1F). We used a 500-ms Hanning window in 50-ms steps to analyze the frequency power from 5 to 30 Hz in steps of 0.5 Hz. We subsequently baseline corrected the time–frequency representations by z-scoring the data based on the means and SDs of a bootstrapped distribution (10,000 iterations) for the –2- to −1.5-s time interval of all trials (Flinker et al., 2015; Helfrich et al., 2018).

Statistical analyses

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To compare juggling performance between the sleep-first and wake-first group and to assess the learning progression, we computed mixed ANOVAs with the between factor condition group (sleep-first, wake-first) and the repeated measure factor juggling blocks. Because number of juggling blocks differed between adolescents (9, Figure 2A) and adults (15, Figure 2B), we analyzed the juggling performance separately per age group. Influence of sleep on learning curve (Figure 2D) and task proficiency (Figure 2E) was assessed by a mixed ANOVA with the between factors condition group (sleep-first, wake-first) and age group (adolescents, adults) and the repeated factor performance test (preretention interval 1, postretention interval 1). To correct for multiple comparisons we clustered the data in the frequency (Figure 3—figure supplement 1A), time (Figure 3B), and space domain (Figure 3, Figure 3—figure supplement 1B), using cluster-based random permutation testing (Monte-Carlo method, cluster alpha 0.05, max size criterion, 1000 iterations, critical alpha level 0.05 two-sided; Maris and Oostenveld, 2007). Given our sparse sampling of only 11 scalp electrodes, we set the minimum number of neighborhood electrodes required to be included in the clustering algorithm to zero. For correlational analyses we utilized Spearman rank correlations (rhos; Figure 2F and Figure 3D, E) to mitigate the impact of possible outliers as well as cluster-corrected Spearman rank correlations by transforming the correlation coefficients to t-values (p < 0.05) and clustering in the space domain (Figure 3D, E). Linear trend lines were calculated using robust regression. To control for possible confounding factors we computed cluster-corrected partial rank correlations (Figure 3—figure supplements 3 and 4). We report partial eta squared (η2), Cohen’s d (d) and averaged Spearman correlation coefficients (mean rho) as effect sizes. Cluster effect sizes are estimated by first calculating Cohen’s d for every data point in the significant cluster and subsequently averaging across the obtained values.

Data analyses

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We used functions from the Fieldtrip toolbox (Oostenveld et al., 2011), EEGlab toolbox (Delorme and Makeig, 2004), CircStat toolbox (Berens, 2009), and custom written code implemented in MatLab 2015a (Mathworks Inc) for data analyses. IRASA (Wen and Liu, 2016) was conducted using code obtained from the original research paper.

Data availability

The behavioral and electrophysiological preprocessed data and scripts to replicate the main conclusions and figures of the paper are available at https://doi.org/10.5061/dryad.qfttdz0gh.

The following data sets were generated
    1. Hahn MA
    2. Bothe K
    3. Heib DPJ
    4. Schabus M
    5. Helfrich RF
    6. Hoedlmoser K
    (2021) Dryad Digital Repository
    Slow oscillation-spindle coupling strength predicts real-life gross-motor learning in adolescents and adults.
    https://doi.org/10.5061/dryad.qfttdz0gh

References

  1. Book
    1. Iber C
    2. Ancoli-Israel S
    3. Chesson A
    4. Quan SF
    (2007)
    The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Technical Specification
    American Academy of Sleep Medicine.
    1. Saletu B
    2. Wessely P
    3. Grünberger J
    4. Schultes M
    (1987)
    Erste klinische Erfahrungen mit einem neuen schlafanstoßenden Benzodiazepin, Cinolazepam, mittels eines Selbstbeurteilungsbogens für Schlaf- und Aufwachqualität (SSA)
    Neuropsychiatrie 1:169–176.
    1. Sobota R
    2. Hollauf M
    (2013)
    Jonglieren und Bewegungskünste [Juggling and Movement Arts DVD
    Austrian Ministry of Sports 9:002.

Decision letter

  1. Saskia Haegens
    Reviewing Editor; Columbia University College of Physicians and Surgeons, United States
  2. Laura L Colgin
    Senior Editor; University of Texas at Austin, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Slow oscillation-spindle coupling strength predicts real-life gross-motor learning in adolescents and adults" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Laura Colgin as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Overall, the topic and the results of the present study are interesting and timely, and we appreciate the use of a more ecologically valid paradigm. However, several aspects of the analyses need further clarification, and critically, a question was raised as to whether this is a sleep story or a circadian rhythm story.

Essential revisions:

1. The results may first and foremost tell a circadian (rather than sleep) story. Examining the data in Figure 2A and 2B, it appears that every AM learning period has a higher learning curve (slope) than every PM period. While this could, of course, be due to having just slept, the main story gleaned from such a result is not a sleep effect on retention, which has been the emphasis in motor memory consolidation research in the last couple of decades, but on new learning. The fact that this effect appears present in the first session (juggling blocks 1-3 in adolescents and blocks 1-5 in adults) makes this seem the more likely story here, since it has less to do with "preparing one to re-learn" and more to do with just learning and when that learning is optimal. But even if it does not reach statistical significance in the first session alone, it remains a concern and should be considered a focus in the manuscript unless the authors can devise a reason to definitively rule it out. The authors should include all sessions from all subjects into a mixed effect model, predicting the slope of the learning curve with time of day and age group as fixed effects and subjects as random effects:

learning curve slope ~ AM/PM [AM (0) or PM (1)] + age [adolescent (0) or adult (1)] + (1|subject)

…or something similar with other regressors of interest. If this is significant for AM/PM status, they should re-try the analysis using only the first session. If this is significant, then a sleep-centric story cannot be defended here.

2. Related: The sleep data of all participants (thus from both sleep first and wake first) were used to determine the features of SO-spindle coupling in adolescents and adults. Were there any differences between groups (sleep first vs. wake first)? This might be interesting in general but especially because only data of the sleep first group entered the subsequent correlational analyses.

3a. Supporting and extending previous work of the authors (Hahn et al., 2020), SO-spindle coupling over centro-parietal areas was stronger in adults as compared to adolescents. Despite these differences in the EEG results the authors collapsed the data of adults and adolescents for their correlational analyses (Figure 4a and 4b). Why would the authors think that this procedure is viable (also given the fact that different EEG systems were used to record the data)?

3b. If the authors believe it is justified to combine these groups, Figure 3 and 4 should be combined and some current figure panels in Figure 3 should be removed or moved to the supplementary information.

4. The authors might want to explicitly show that the reported correlations (with regards to both learning curve and task proficiency change) are not driven by any outliers. It would be useful to know if the relationship is significant with Pearson correlations when robust regression is applied.

5. With only a single night of recording data, it is impossible to disentangle possible trait-based sleep characteristics (e.g., Subject 1 has high SO-spindle coupling in general and retains motor memories well, but these are independent of each other) from a specific, state-based account (e.g., Subject 1's high SO-spindle coupling on night 1 specifically led to their improved retention or change in learning, etc., and this is unrelated to their general SO-spindle coupling or motor performance abilities). Clearly, many studies face this limitation, but this should be acknowledged.

6. The authors used a partial correlation analysis to rule out that age drove the relationship between coupling strength, learning curve and task proficiency. It seems like this analysis was done specifically for electrode C4, after having already established that coupling strength at electrode C4 correlates in general with changes in the learning curve and task proficiency. The claim that results were not driven by age as confounding factor would be stronger if the authors used a cluster-corrected partial correlation in the first place (just as in the main analysis).

7. To allow a more comprehensive assessment of the underlying data information with regards to general sleep descriptives (minutes, per cent of time spent in different sleep stages, overall sleep time etc.) as well as related to SOs, spindles and coupled events (e.g. number, density etc.) would be needed.

8. The authors state that "To ensure the simultaneous presence of the two interacting sleep oscillations in the signal, we restricted our analyses to NREM3 sleep given the higher co-occurrence rate." We do not understand this reasoning. The utilized procedure of specifically isolating sleep spindles that are followed or preceded by slow oscillations already ensures the presence of SOs and sleep spindles in the data. Hence, why not take coupled events from sleep stage N2 into account? Or do the authors think that light sleep SO-spindle events are qualitatively different from SWS SO-spindle complexes (and if so does the present data support such a notion)?

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Slow oscillation-spindle coupling strength predicts real-life gross-motor learning in adolescents and adults" for further consideration by eLife. Your revised article has been evaluated by Laura Colgin (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

The authors have done an impressive job with this revision. It is meticulously organized, thorough, and clearly stated. That is all to their major credit. However, I still cannot come to agree that their data supports much of the story they are telling.

First, part of the issue may be the change from their original story and the new one following the revision. Making major revisions can obviously be tricky, especially when a revision requires as many changes as theirs did (and I again commend them on the overhaul). But there is still something unclear in their primary claims. They say in their cover letter, "Collectively, our results suggest that SO-spindle coupling indexes the integrity of memory pathways (as reviewed in detail recently: Helfrich et al., 2021); thus, reflects a trait-specific (in contrast to a state-specific) correlate of learning capacity." However, this story does not come through clearly in the new paper. In fact, reading the new paper, it seems this is nodded to only here in the Discussion – "Thus, our results primarily suggest that strength of SO-spindle coupling correlates with the ability to learn (trait), but does not solely convey 534 the recently learned information. This set of findings is in line with recent ideas that strong coupling indexes individuals with highly efficient subcortical-cortical network communication (Helfrich et al., 2021)." Much of the paper instead talks about active systems consolidation theory, which I believe is not supported in their data, and the authors do seem to agree. If the authors indeed want to make this more of a memory pathway integrity story, it seems more unpacking of the ideas in their recent review is warranted, as does perhaps some evidence in the literature linking sleep measures to integrity in some neural pathways (e.g., Mander et al., 2017).

Second, they concede in various locations that the circadian story cannot be ruled out, which I also commend, but then the paper still largely revolves around active sleep consolidation theory. I invite the authors to imagine convincing a hypothetical researcher who thought the brain just shuts off entirely during sleep ("sleep does nothing") and that people have different abilities based on the time of day. (Believe it or not, this is not my belief.) How would the authors convince this person based on these behavioral data that sleep is actually doing something? I do not know whether they could, given the mixed-effects model findings.

Of course, they could point to the prior literature. The prior literature on sleep and motor learning has shown, in the case of the Morita juggling studies cited, that there should be better overall performance after sleep (vs equivalent wake periods). And in the case of countless finger-tapping studies, even though the major story has changed from one of absolute improvement (e.g., Walker et al., 2002) to stabilization (e.g., Brawn et al., 2010) after sleep, there seem to be sleep (vs wake) benefits on overall performance (analogous to task proficiency here). This, however, is not what the authors find with their learning curve findings here, as performance seems, if anything, worse on the first few trials after sleep (though this may not be significant) and then catches up more quickly. So, it is hard to know whether the prior literature would necessarily help them convince this researcher about their own findings.

This researcher may also say that the inclusion of a PVT is great, and the null results across sessions is more helpful than not to their story. But this researcher may add that a null PVT difference does not exclude all possible circadian effects. There are certainly circadian effects on cognition – including the very recent publication of Tandoc et al., (2021) and even on motor learning (Keisler et al., 2007) – and indeed the authors do find such an effect here in their mixed-effects model analysis. Therefore, the null PVT results are not conclusive, especially in counteracting an effect that they actually found in their paper.

One could then point this researcher to the SO-spindle coupling results as evidence that sleep is playing a strong role here. However, given that these are trait- vs. state-based results, it is unclear why stronger SO-spindle coupling for some individuals – which may be having an impact on neural integrity over a long timescale – would prime their nervous systems for more learning right after sleep than at some other time during the day. The researcher may say, okay, SO-spindle coupling results do not prove sleep does anything, they merely correlate with the observed behavioral result, and moreover, they constitute a trait (vs post-learning sleep state) effect. They may add that it is also unclear why, if stronger SO-spindle coupling is doing something, it could not alternatively reflect some other individual trait that could even lead to the observed circadian effects that learning curves are higher in the morning.

One analysis that could possibly work to disentangle circadian vs. active sleep effects would be to include a different factor in the mixed-effects model that could tease apart time of day from sleep-after-learning effects. In addition to including “Time of day”, where all mornings = 1 and all evenings = 0, the authors could include the conjunction of “Time of day + after learning”, where mornings on the 2nd and 3rd sessions = 1 and mornings on the 1st session and all evenings = 0. This would capture the idea that post-learning mornings show differential improvement because post-learning sleep sort of “prepared” the networks to re-learn within a short time span, and this preparation was not operative before the 1st session. I say it could “possibly” work above because the two factors would still be quite correlated (identical except for the first morning session), which could hurt their statistical power to independently produce effects. Nevertheless, if BOTH factors end up being significant, I think the authors could make the claim that both are contributing (that is, time of day + after learning is actually independently contributing above and beyond what time of day could do alone). If only one is significant, then the story is clean, but may have to change. If neither are significant, then it may be difficult to know what to do, and the authors may have to fall back on the original time-of-day analysis and keep things closer to as is but acknowledge more of the uncertainty surrounding the effects. If nothing changes upon a second revision in this regard, I do expect the authors to incorporate circadian possibilities more thoroughly in their paper, such as in their abstract and with more citations of this literature.

I realize this may seem a lot for a second round of revisions, and the authors have clearly done an impressive amount of work on the paper, but I feel that the authors can still strengthen it, either with this last analysis or by refocusing on the stories that can and cannot be supported here. There is something here that lacks clarity in translating from the data to the story about them, and, as a result, it remains difficult to confidently find the main takeaway from the manuscript.

https://doi.org/10.7554/eLife.66761.sa1

Author response

Essential revisions:

1. The results may first and foremost tell a circadian (rather than sleep) story. Examining the data in Figure 2A and 2B, it appears that every AM learning period has a higher learning curve (slope) than every PM period. While this could, of course, be due to having just slept, the main story gleaned from such a result is not a sleep effect on retention, which has been the emphasis in motor memory consolidation research in the last couple of decades, but on new learning. The fact that this effect appears present in the first session (juggling blocks 1-3 in adolescents and blocks 1-5 in adults) makes this seem the more likely story here, since it has less to do with “preparing one to re-learn” and more to do with just learning and when that learning is optimal. But even if it does not reach statistical significance in the first session alone, it remains a concern and should be considered a focus in the manuscript unless the authors can devise a reason to definitively rule it out. The authors should include all sessions from all subjects into a mixed effect model, predicting the slope of the learning curve with time of day and age group as fixed effects and subjects as random effects:

learning curve slope ~ AM/PM [AM (0) or PM (1)] + age [adolescent (0) or adult (1)] + (1|subject)

…or something similar with other regressors of interest. If this is significant for AM/PM status, they should re-try the analysis using only the first session. If this is significant, then a sleep-centric story cannot be defended here.

We thank the reviewer for the insightful comment and the detailed suggestion for how to further capture the temporal dynamics of learning in the juggling task.

The reviewer raises an important issue, which often pertains studies that examine sleep-dependent memory formation and it is inherent to our task design that sleep (state-specific) and circadian effects cannot fully be disentangled.

Therefore, we actually performed a control task in order to circumvent this issue and to assess the overall cognitive engagement as a function of time of day. Note, we did not report this data in the initial submission, but it is now included (see below). Furthermore, we also conducted the suggested linear mixed model analyses to obtain an additional statistical metric to quantify the temporal dynamics of learning.

Importantly, we would like to re-emphasize that our key results primarily suggest that the strength of SO-spindle coupling correlates with the ability to learn, but does not solely convey the recently learned information.

Collectively, our results suggest that SO-spindle coupling indexes the integrity of memory pathways (as reviewed in detail recently: Helfrich et al., 2021); thus, reflects a trait-specific (in contrast to a state-specific) correlate of learning capacity. For the full statistical analyses and all results, please see our response to issue #5 below. In the following, we separately address the reviewers’ comments based on our (1) control analyses and (2) the additional linear mixed models.

1. Control analyses from an independent task

To assess whether juggling performance is impacted by circadian rhythmicity (i.e. influenced by time of day; differences in tiredness and alertness; sleep inertia), we analyzed the reaction times in a psychomotor-vigilance-task (PVT; Dinges and Powell, 1985). The PVT is a reaction time task that is considered to be the gold standard to assess alertness and vigilance due to its high sensitivity to sleep loss and circadian influences (Dinges et al., 1997; Killgore, 2010; Van Dongen et al., 2003). In our experiment, we conducted a PVT before each juggling performance test. Thus, if a circadian effect indeed confounded our analysis, we artid expect a significant interaction between condition groups (sleep first vs wake first) and performance test (pre vs post retention interval 1) in this objective reaction time task.

We found no significant difference between the groups (Figure 2 – figure supplement 1C A, F(1,67) = 1.87, p = 0.18, partial eta² = 0.03) nor between the performance tests (F(1,67) = 1.06, p = 0.31, partial eta² = 0.02). Critically, we found no significant interaction (F(1,67) = 0.35, p = 0.55, partial eta² = 0.01) indicating that participants’ cognitive engagement did not differ in the juggling performance tests due to the preceding sleep or wake intervals. Taken together, these results rule out a strong circadian influence on the juggling performance.

Next, we computed cluster-corrected partial correlations to control whether our initially reported correlations between coupling strength and task performance are influenced by individual differences in cognitive engagement due to having just slept. When controlling for mean PVT reaction time in the morning our results remained unchanged, showing that subjects with higher coupling strength have better task proficiency after sleep (mean rho = 0.37, p = 0.025) and flatter learning curves (rho = -0.47, p = 0.049). These control analyses as well as other cluster-corrected partial correlations for various possible confounding factors (see our answer to issue #7) are now fully reported in the revised version of the manuscript as Figure 3 —figure supplement 3.

Taken together these analyses show that first, we found no evidence for circadian rhythmicity as a confounding factor between the groups and second that our correlational analyses were not impacted by differences in individual cognitive engagement.

2. Linear mixed model analyses

2.1. Learning curve

We followed the reviewer’s suggestion and predicted the learning curve using a linear mixed model with age group and time of day (i.e. performance test in the morning or evening) as fixed effects and subjects as random effects (Learning curve ~ Age group + Time of day + (1|Subjects)). The following linear mixed models were computed with the fitlme.m matlab function using maximum likelihood estimation.

We found that the learning curve was flatter in the performance tests in the evening than in the morning (Β = -1.129, t(202) = -2.885, p = 0.004, CI95 = [-1.901, -0.357], for the full report see Table 5 in Supplementary file 1). Next, we predicted the learning curve only in the first performance test using an identically structured linear mixed model. As expected, the learning curve was flatter in the evening than in the morning (Β = -1.294, t(66) = -2.885, p = 0.028, CI95 = [-2.442, -0.146], for the full report see Table 5 in Supplementary file 1).

Combined, these results could in principle indicate a circadian modulation of the juggling learning process. However, one cannot reject the hypothesis that sleep might have an additional impact. We fully acknowledge this shortcoming and address it in detail in the discussion.

2.2. Task proficiency

Another possible explanation for the difference in learning curves between morning and evening is a systematic difference in task proficiency, given that we found that both parameters are inversely correlated (Figure 2F). This is in line with the notion that task performance determines the magnitude of subsequent performance gains (Dayan and Cohen, 2011).

Using an identically structured linear mixed model as above, we found that task proficiency was generally better in the evening performance tests (Β = 2.7467, t(202) = 0.951, p = 0.047, CI95 = [0.037, 5.456], for the full report see Table 5 in Supplementary file 1). However, as illustrated in Figure 2AB, it is likely that this effect is mainly driven by the adult sleep first group as initially reported in the mixed ANOVA (Figure 2E, F(1,65) = 5.210, p = 0.026, partial eta² = 0.074).

In summary, differences in learning curve depending on time of day are explained by differences in task proficiency (i.e. the better one already performs in a task the harder it is to improve). Even though it is impossible to completely rule out circadian influences in the current task-design, we could not find any evidence for a differences in alertness (Figure 2 – figure supplement 1C A). Further, while we do interpret our result based on the mechanistic assumption of the active systems consolidation theory of interacting sleep oscillations supporting memory formation, our results rather suggest a trait- than a state-effect (please refer to our response to issue #5 for more details). We also discuss this issue in the revised discussion.

2.3. PVT reaction time

Finally, we wanted to specifically probe whether we can find evidence for a circadian modulation using a higher statistically powered linear mixed model (as compared to the general linear model in Figure 2 – figure supplement 1C). Therefore, we also predicted the PVT reaction time with age group and time of day as fixed effects and allowed for random intercepts for subjects.

We found no significant difference in reaction times in the PVT between performance tests in the morning and in the evening (Β = -1.107, t(202) = -0.238, p = 0.812, CI95 = [-10.29, 8.077], for the full report see Table 6). Likewise, there were no differences in reaction time when only considering the reaction times for the first performance test (Β = 9.622, t(66) = 0.951, p = 0.345, CI95 = [-10.586, 29.831], for the full report see Table 6).

Together, this suggests that there were no differences in alertness and vigilance as a function of time of day in the performance tests. This observation further supports our initial control analysis (Figure 2 – figure supplement 1C). Notably, a simple reaction time task is not fully comparable to the complex juggling learning process, which is again addressed in the discussion. In summary, we did not find evidence for a circadian modulation in our sample based on the objective reaction time data.

Table note: we used reference dummy coding, where the coefficient of the first category is set to 0 (i.e. fixed effect of age group is referenced to adolescents whereas the Time of day fixed effect is referenced to performance tests in the morning).

3. Conclusion

Taken together, our behavioral results might be compatible with a sleep or circadian effect. The analysis from the control task does not indicate the presence of strong behavioral effects. Furthermore, it is in fact likely that sleep- and circadian-effects are simultaneously present. However, given that we observed robust electrophysiological correlates that indicate a trait- and not a state-like effect, we are convinced that a circadian effect does not compromise our results and the conclusions.

4. Changes in the manuscript

All of these important control analyses are now included in the revised version of the manuscript.

We now refer to these analyses in the Results section (page 9, lines 174 – 181):

“Figure 2A and 2B indicate that performance tests in the morning might be characterized by a steeper learning curve than the evening tests. We confirmed this observation using a linear mixed model (Supplementary file – table 5AB). While this finding might also indicate a circadian influence on learning in our task, we did not find evidence for an effect on circadian sensitive psychomotor vigilance task reaction time. Neither when comparing sleep first and wake first groups (Figure 2 —figure supplement 1C), nor when specifically probing evening and morning performance tests (Supplementary file – table 5EF).”

And (page 16, lines 351 – 360):

“Additionally, given that we found that juggling performance could underlie a circadian modulation we controlled for individual differences in alertness between subjects due to having just slept. We partialed out the mean PVT reaction time before the juggling performance test after sleep from the original analyses and found that our results remained unchanged (task proficiency: mean rho = 0.37, p = 0.025; learning curve: rhos = -0.49, p = 0.040). For a summary of the reported cluster-corrected partial correlations as well as analyses controlling for differences in sleep architecture see Figure 3 —figure supplement 3.”

In the light of these results, we now discuss the sleep effect on gross-motor learning and a potential circadian influence in detail in the Discussion section (page 22 – 23, lines 502 – 518):

“How relevant is sleep for real-life gross-motor memory consolidation? We found that sleep impacts the learning curve but did not affect task proficiency in comparison to a wake retention interval (Figure 2DE). […] Here we found that using a complex juggling task, participants do not reach asymptotic ceiling performance levels in such a short time. Indeed, the learning progression for the sleep-first and wake-first groups followed a similar trend (Figure 2AB), suggesting that more training and not in particular sleep drove performance gains. We note that juggling performance in our study could have been influenced by the timing of when learning is optimal in the circadian cycle. However, we did not find evidence for a circadian modulation of cognitive engagement based on objective reaction time data (Figure 2 —figure supplement 1C).”

We further clearly state that the possibility of simultaneously present sleep- and circadian effect is a limitation to our study (page 23, lines 518 – 519):

“Nonetheless, we cannot fully disentangle circadian and sleep effects with our study design.”

Additionally, we discuss that our correlations rather reflect a trait-effect (page 23, lines 521 – 528):

“Moreover, we found that SO-spindle coupling strength remains remarkably stable between two nights, which also explains why a learning-induced change in coupling strength did not relate to behavior (Figure 3 —figure supplement 2I). Thus, our results primarily suggest that strength of SO-spindle coupling correlates with the ability to learn (trait), but does not solely convey the recently learned information. This set of findings is in line with recent ideas that strong coupling indexes individuals with highly efficient subcortical-cortical network communication (Helfrich et al., 2021).”

2. Related: The sleep data of all participants (thus from both sleep first and wake first) were used to determine the features of SO-spindle coupling in adolescents and adults. Were there any differences between groups (sleep first vs. wake first)? This might be interesting in general but especially because only data of the sleep first group entered the subsequent correlational analyses.

We thank the reviewers for their remark. We agree that adding additional information about possible differences between the sleep first and wake first groups would allow for a more comprehensive assessment of the reported data. We did not explain our reasoning to include only the sleep first groups for the correlation analyses clearly enough in the original manuscript. Unfortunately, we can only report data for the adolescents in our sample, because we did not record polysomnography (PSG) for the adult wake first group. This is also one of the two reasons why we focused on the sleep first groups for our correlational analyses.

Adolescents in the sleep first group did not differ from adolescents in the wake first group in terms of sleep architecture (except REM (%), which did not correlate with behavior [task proficiency: rho = -0.17, p = 0.28; learning curve: -0.02, p = 0.90]) as well as SO and sleep spindle event descriptive measures (see Table 7 in Supplementary file 1). Importantly, we found no differences in coupling strength between the two groups (Figure 3 – figure supplement 2AB).

The second reason why we focused our analyses on sleep first was that adolescents in the wake first group had higher task proficiency after the sleep retention interval than the sleep first group (Figure 3 – figure supplement 2A; t(23) = -2.24, p = 0.034). This difference in performance is directly explained by the additional juggling test that the wake first group performed at the time point of their learning night, which should be considered as additional training. Therefore, we excluded the wake first group from our correlational analyses because sleep and wake first group are not comparable in terms of juggling training during the night when we assessed SO-spindle coupling strength.

These additional analyses and the summary statistics of sleep architecture and SO/spindle event descriptives of adolescents in the sleep first and wake first group, are now reported in the revised version of the manuscript as Figure 3 —figure supplement 2AB and Supplementary file – table 7.

We now explicitly explain our rationale of why we only considered participants in the sleep first group for our correlational analyses in the Results section (page 6, lines 101 – 105):

“Polysomnography (PSG) was recorded during an adaptation night and during the respective sleep retention interval (i.e. learning night) except for the adult wake-first group (for sleep architecture descriptive parameters of the adaptation night and learning night as well as for adolescents and adults see Supplementary file – table 1 and 2).”

And (page 15, lines 311 – 320):

“Furthermore, given that we only recorded polysomnography for the adults in the sleep first group and that adolescents in the wake first group showed enhanced task proficiency at the time point of the sleep retention interval due to additional training (Figure 3 —figure supplement 2A), we only considered adolescents and adults of the sleep-first group to ensure a similar level of juggling experience adolescents and adults of the sleep-first group to ensure a similar level of juggling experience (for summary statistics of sleep architecture and SO and spindle events of subjects that entered the correlational analyses see Supplementary file – table 6). Notably, we found no differences in electrophysiological parameters (i.e. coupling strength, event detection) between the adolescents of the wake first and sleep first group (Figure 3 —figure supplement 2B and Supplementary file – table 7).”

3a. Supporting and extending previous work of the authors (Hahn et al., 2020), SO-spindle coupling over centro-parietal areas was stronger in adults as compared to adolescents. Despite these differences in the EEG results the authors collapsed the data of adults and adolescents for their correlational analyses (Figure 4a and 4b). Why would the authors think that this procedure is viable (also given the fact that different EEG systems were used to record the data)?

We thank the reviewers for the opportunity to clarify why we think it is viable to collapse the data of adolescents and adults for our correlational analyses. In the following we split our answers based on the two points raised by the reviewers: (1) electrophysiological differences (i.e. coupling strength) between the groups and (2) potential signal differences due to different EEG systems.

1. Electrophysiological differences

Upon inspecting the original Figure 4, it is apparent that the coupling strength of the combined sample does not form isolated clusters for each age group. In other words, while adult coupling strength is on the higher and adolescent coupling on the lower end due to the developmental increase in coupling strength we reported in the original Figure 3F, both samples overlap forming a linear trend. Second, when running the correlational analyses between coupling strength and task proficiency as well as learning curve separately for each age group, we found that they follow the same direction. Adolescents with higher coupling strength show better task proficiency (rhos = 0.66, p = 0.005). This effect was also present when using robust regression (b = 109.97, t(15)=3.13, rho = 0.63, p = 0.007). Like adolescents, adults with higher coupling strength at C4 displayed better task proficiency after sleep (rhos = 0.39, p = 0.053). This relationship was stronger when using robust regression (b = 151.36, t(23)=3.17, rho = 0.56, p = 0.004). For learning curves, we found the expected negative correlation at C4 for adolescents (rhos = -0.57, p = 0.020) and adults (rhos = -0.44, p = 0.031). Results were comparable when using robust regression (adolescents: b = -59.58, t(15) = -2.94, rho = -0.60, p = 0.010; adults: b = -21.99, t(23 ) = -1.71, rho = -0.37, p = 0.101).

Taken together, these results demonstrate that adolescents and adults show the effects and the same direction at the same electrode, thus, making it highly unlikely that our results are just by chance and that our initial correlation analyses are just driven by one group.

Additionally, we already controlled for age in our original analyses using partial correlations (also refer to our answer to issue #6). Hence, our additional analyses provide additional support that it is viable to collapse the analyses across both age groups even though they differ in coupling strength.

2. Different EEG-systems

The reviewers also raise the question whether our analyses might be impacted by the different EEG systems we used to record our data. This is an important concern especially when considering that cross-frequency coupling analyses can be severely confounded by differences in signal properties (Aru et al., 2015). In our sample, the strongest impact factor on signal properties is most likely age, given the broadband power differences in the power spectrum we found between the groups (original Figure 3A). Importantly, we also found a similar systematic power difference in our longitudinal study using the same ambulatory EEG system for both data recordings (Hahn et al., 2020). This is in line with numerous other studies demonstrating age related EEG power changes in broadband- as well as SO and sleep spindle frequency ranges (Campbell and Feinberg, 2016; Feinberg and Campbell, 2013; Helfrich et al., 2018; Kurth et al., 2010; Muehlroth et al., 2019; Muehlroth and Werkle-Bergner, 2020; Purcell et al., 2017). Therefore, we already had to take differences in signal property into account for our cross-frequency analyses. Regardless whether the underlying cause is an age difference or different signal-to-noise ratios of different EEG systems.

To mitigate confounds in the signal, we used a data-driven and individualized approach detecting SO and sleep spindle events based on individualized frequency bands and a 75-percentile amplitude criterion relative to the underlying signal. Additionally we z-normalized all spindle events prior to the cross-frequency coupling analyses. We found no amplitude differences around the spindle peak (point of SO-phase readout) between adolescents that were recorded with an ambulatory amplifier system (alphatrace) and adults that were recorded with a stationary amplifier system (neuroscan) using cluster-based random permutation testing. This was also the case for the SO-filtered (< 2 Hz) signal. Critically, the significant differences in amplitude from -1.4 to -0.8 s (p = 0.023, d = -0.73) and 0.4 to 1.5 s (p < 0.001, d = 1.1) are not caused by age related differences in power or different EEG-systems but instead by the increased coupling strength (i.e. higher coupling precision of spindles to SOs) in adults giving rise to a more pronounced SO-wave shape when averaging across spindle peak locked epochs.

Consequently, our analysis pipeline already controlled for possible differences in signal property introduced through different amplifier systems. Nonetheless, we also wanted to directly compare the signal-to-noise ratio of the ambulatory and stationary amplifier systems. However, we only obtained data from both amplifier systems in the adult sleep first group, because we recorded EEG during the juggling learning phase with the ambulatory system in addition to the PSG with the stationary system. First, we computed the power spectra in the 1 to 49 Hz frequency range during the juggling learning phase (ambulatory) and during quiet wakefulness (stationary) for every subject in the adult sleep first group in 10-seconds segments. Next, we computed the signal-to-noise ratio (mean/standard deviation) of the power spectra per frequency across all segments. We only found a small negative cluster from 21.9 to 22.5 Hz (p = 0.042, d = 0.53), which did not pertain our frequency-bands of interest. Critically, the signal-to-noise ratio of both amplifiers converged in the upper frequency bands approaching the noise floor, therefore, strongly supporting the notion that both systems in fact provided highly comparable estimates.

In conclusion, both age groups display highly similar effects and direction when correlating coupling strength with behavior. Further, after individualization and normalization the analytical signal, we found no differences in signal properties that would confound the cross-frequency analysis. Lastly, we did not find systematic differences in signal-to-noise ratio between the different EEG-systems. Thus, we believe it is justified to collapse the data across all participants for the correlational analyses, as it combines both, the developmental aspect of enhanced coupling precision from adolescence to adulthood and the behavioral relevance for motor learning which we deem a critical research advance from our previous study.

We have now added Figure 3B to the revised version of the manuscript to demonstrate that there were no systematic differences between the two age groups in the analytical signal due to the expected age related power differences or EEG-systems.

Specifically, we now state in the Results section (page 13 – 14, lines 282 – 294):

“We assessed the cross frequency coupling based on z-normalized spindle epochs (Figure 3B) to alleviate potential power differences due to age (Figure 3 —figure supplement 1A) or different EEG-amplifier systems that could potentially confound our analyses (Aru et al., 2015). Importantly, we found no amplitude differences around the spindle peak (point of SO-phase readout) between adolescents and adults using cluster-based random permutation testing (Figure 3B), indicating an unbiased analytical signal. This was also the case for the SO-filtered (< 2 Hz) signal (Figure 3B, inset). Critically, the significant differences in amplitude from -1.4 to -0.8 s (p = 0.023, d = -0.73) and 0.4 to 1.5 s (p < 0.001, d = 1.1) are not caused by age related differences in power or different EEG-systems but instead by the increased coupling strength (i.e. higher coupling precision of spindles to SOs) in adults giving rise to a more pronounced SO-wave shape when averaging across spindle peak locked epochs.”

Further, we added the correlational analyses that we computed separately for the age groups to the revised manuscript (Figure 3 —figure supplement 2CD) as they further substantiate our claims about the relationship between SO-spindle coupling and gross-motor learning.

We now refer to these analyses in the Results section (page 16, lines 338 – 343):

Critically, when computing the correlational analyses separately for adolescents and adults, we identified highly similar effects at electrode C4 for task proficiency (Figure 3 —figure supplement 2C) and learning curve (Figure 3 —figure supplement 2D) in each group. These complementary results demonstrate that coupling strength predicts gross-motor learning dynamics in both, adolescents as well as adults, and further show that this effect is not solely driven by one group.

3b. If the authors believe it is justified to combine these groups, Figure 3 and 4 should be combined and some current figure panels in Figure 3 should be removed or moved to the supplementary information.

We thank the reviewers for their suggestion and we agree that the figures of our manuscript would benefit from more focus. Therefore, we combined Figure 3 and 4 from the original manuscript into a revised Figure 3 in the updated version of the manuscript. In more detail, subpanels that explain our methodological approach can now be found in Figure 3 —figure supplement 1, while the updated Figure 3 now focuses on developmental changes in oscillatory dynamics and SO-spindle coupling strength as well as their relationship to gross-motor learning.

4. The authors might want to explicitly show that the reported correlations (with regards to both learning curve and task proficiency change) are not driven by any outliers. It would be useful to know if the relationship is significant with Pearson correlations when robust regression is applied.

We thank the reviewers for their suggestion. We agree that when inspecting the scatter plots it looks like that the correlations could be severely influenced by two outliers in the adult group. Because this is an important matter, we recalculated all previously reported correlations without the two outliers (Author response image 1, left column) and followed the reviewer’s suggestion to also compute robust regression (Author response image 1, right column) and found no substantial deviation from our original results.

Author response image 1
(A) Spearman rank correlation between task proficiency change and learning curve change collapsed across adolescents (red dot) and adults (black diamonds) after removing two outlier subjects in the adult age group.

Grey-shaded area indicates 95% confidence intervals of the robust trend line. (B) Robust regression of task proficiency change and learning curve change of the original sample. (C) Cluster-corrected correlations (right) between individual coupling strength and overnight task proficiency change (post – pre retention) after outlier removal (left, spearman correlation at C4, uncorrected). Asterisks indicate cluster-corrected two-sided p < 0.05. (D) Robust regression of coupling strength at C4 and task proficiency of the original sample. (E) Same conventions as in (C) but for overnight learning curve change. (F) Same conventions as in (D) but for overnight learning curve change.

In more detail, increase in task proficiency resulted in flattening of the learning curve when removing outliers (Author response image 1, rhos = -0.70, p < 0.001) and when applying robust regression analysis (Author response image 1, b = -0.30, t(67) = -10.89, rho = -0.80, p < 0.001). Likewise, higher coupling strength still predicted better task proficiency (mean rho = 0.35, p = 0.029, cluster-corrected) and flatter learning curves after sleep (rho = -0.44, p = 0.047, cluster-corrected) when removing the outliers (Author response image 1) and when calculating robust regression (Author response image 1), task proficiency: (b = 82.32, t(40) = 3.12, rho = 0.45, p = 0.003; learning curve: b = -26.84, t(40) = -2.96, rho = -0.43, p = 0.005). Furthermore, we calculated spearman rank correlations and cluster-corrected spearman rank correlations in our original manuscript, to mitigate the impact of outliers, even though Pearson correlations are more widely used in the field. Therefore, we still report spearman rank correlations for single electrodes instead of robust correlations as it is more consistent with the cluster-correlation analyses.

We now use robust trend lines instead of linear trend lines in our scatter plots. Further, we added the correlations without outliers (Author response image 1) to the supplements as Figure 2 —figure supplement 1D and Figure 3 —figure supplement 2 FG. These additional analyses are now reported in the Results section of the revised manuscript (page 9, lines 186 – 191):

“we confirmed a strong negative correlation between the change (post retention values – pre retention values) in task proficiency and the change in learning curve after the retention interval (Figure 2F; rhos = -0.71, p < 0.001), which also remained strong after outlier removal (Figure 2 —figure supplement 1D). This result indicates that participants who consolidate their juggling performance after a retention interval show slower gains in performance.”

And (page 16, lines 343 – 346):

“Furthermore, our results remained consistent when including coupled spindle events in NREM2 (Figure 3 —figure supplement 2E) and after outlier removal (Figure 3 —figure supplement 2FG).”

Furthermore, we now state that we specifically utilized spearman rank correlations to mitigate the impact of outliers in our analyses in the method section (page 35, lines 808 – 813):

“For correlational analyses we utilized spearman rank correlations (rhos; Figure 2F and Figure 3DE) to mitigate the impact of possible outliers as well as cluster-corrected spearman rank correlations by transforming the correlation coefficients to t-values (p < 0.05) and clustering in the space domain (Figure 3DE). Linear trend lines were calculated using robust regression.”

5. With only a single night of recording data, it is impossible to disentangle possible trait-based sleep characteristics (e.g., Subject 1 has high SO-spindle coupling in general and retains motor memories well, but these are independent of each other) from a specific, state-based account (e.g., Sub’ect 1's high SO-spindle coupling on night 1 specifically led to their improved retention or change in learning, etc., and this is unrelated to their general SO-spindle coupling or motor performance abilities). Clearly, many studies face this limitation, but this should be acknowledged.

We thank the reviewers for their important remark. We agree that it is impossible to make a sound statement about whether our reported correlations represent trait- or state-based aspects of the sleep and learning relationship with the data that we have reported in the manuscript. However, while we are lacking a proper baseline condition without any task engagement, we still recorded polysomnography for all subjects during an adaptation night. Given the expected pronounced differences in sleep architecture between the adaptation nights and learning nights (see Supplementary file 1 – table 1 for an overview collapsed across both age groups), we initially refrained from entering data from the adaptation nights into our original analyses, but we now fully report the data below. Note that the differences are driven by the adaptation night, where subjects first have to adjust to sleeping with attached EEG electrodes in a sleep laboratory.

To further clarify whether subjects with high coupling strength have a motor learning advantage (i.e. trait-effect) or a learning induced enhancement of coupling strength is indicative for improved overnight memory change (i.e. state-effect), we ran additional analyses using the data from the adaptation night. Note that the coupling strength metric was not impacted by differences in event number and our correlations with behavior were not influenced by sleep architecture (please refer to our answer of issue #7 for the results).Therefore, we considered it appropriate to also utilize data from the adaptation night.

First, we correlated SO-spindle coupling strength obtained from the adaptation night with the coupling strength in the learning night. We found that overall, coupling strength is highly correlated between the two measurements (mean rho across all channels = 0.55), supporting the notion that coupling strength remains rather stable within the individual (i.e. trait), similar to what has been reported about the stable nature of sleep spindles as a “neural finger-print” (De Gennaro and Ferrara, 2003; De Gennaro et al., 2005; Purcell et al., 2017).

To investigate a possible state-effect for coupling strength and motor learning, we calculated the difference in coupling strength between the two nights (learning night – adaptation night) and correlated these values with the overnight change in task proficiency and learning curve. We identified no significant correlations with a learning induced coupling strength change; neither for task proficiency nor learning curve change. Note that there was a positive correlation of coupling strength change with overnight task proficiency change at Cz, however it did not survive cluster-corrected correlational analysis (rhos = 0.34, p = 0.15). Combined, these results favor the conclusion that our correlations between coupling strength and learning rather reflect a trait-like relationship than a state-like relationship. This is in line with the interpretation of our previous studies that SO-spindle coupling strength reflects the efficiency and integrity of the neuronal pathway between neocortex and hippocampus that is paramount for memory networks and the information transfer during sleep (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Winer et al., 2019). For a comprehensive review please see Helfrich et al. (2021), which argued that SO-spindle coupling predicts the integrity of memory pathways and therefore correlates with various metrics of behavioral performance or structural integrity.

We have now added the additional state-trait analyses to the updated manuscript as Figure 3 —figure supplement 2HI and report them in the Results section (page 17, lines 361 – 375):

“Finally, we investigated whether subjects with high coupling strength have a gross-motor learning advantage (i.e. trait-effect) or a learning induced enhancement of coupling strength is indicative for improved overnight memory change (i.e. state-effect). First, we correlated SO-spindle coupling strength obtained from the adaptation night with the coupling strength in the learning night. We found that overall, coupling strength is highly correlated between the two measurements (mean rho across all channels = 0.55, Figure 3 —figure supplement 2H), supporting the notion that coupling strength remains rather stable within the individual (i.e. trait). Second, we calculated the difference in coupling strength between the learning night and the adaptation night to investigate a possible state-effect. We found no significant cluster-corrected correlations between coupling strength change and task proficiency- as well as learning curve change (Figure 3 —figure supplement 2I).”

Collectively, these results indicate the regionally specific SO-spindle coupling over central EEG sensors encompassing sensorimotor areas precisely indexes learning of a challenging motor task.

We further refer to these new results in the Discussion section (page 23, lines 521 – 528):

“Moreover, we found that SO-spindle coupling strength remains remarkably stable between two nights, which also explains why a learning-induced change in coupling strength did not relate to behavior (Figure 3 —figure supplement 2I). Thus, our results primarily suggest that strength of SO-spindle coupling correlates with the ability to learn (trait), but does not solely convey the recently learned information. This set of findings is in line with recent ideas that strong coupling indexes individuals with highly efficient subcortical-cortical network communication (Helfrich et al., 2021).”

Additionally, we now provide descriptive data of the adaptation and learning night in the Supplementary file – table 1 and explicitly mention the adaptation night in the Results section, which was previously only mentioned in the method section (page 6, lines 101 – 105):

“Polysomnography (PSG) was recorded during an adaptation night and during the respective sleep retention interval (i.e. learning night) except for the adult wake-first group (for sleep architecture descriptive parameters of the adaptation night and learning night as well as for adolescents and adults see Supplementary file – table 1 and 2).”

6. The authors used a partial correlation analysis to rule out that age drove the relationship between coupling strength, learning curve and task proficiency. It seems like this analysis was done specifically for electrode C4, after having already established that coupling strength at electrode C4 correlates in general with changes in the learning curve and task proficiency. The claim that results were not driven by age as confounding factor would be stronger if the authors used a cluster-corrected partial correlation in the first place (just as in the main analysis).

The reviewers are correct that initially we only conducted the partial correlation for electrode C4. Following the reviewers suggestion we now additionally computed cluster-corrected partial correlations similar to our main analysis. Like in our original analyses, we found a significant positive central cluster (mean rho = 0.40, p = 0.017) showing that higher coupling strength related to better task proficiency after sleep and a negative cluster-corrected correlation at C4 showing that higher coupling strength was related to flatter learning curves after sleep (rho = -0.47, p = 0.049) also when controlling for age.

We now always report cluster-corrected partial correlations when controlling for possible confounding variables in the updated version of the manuscript (also see answer to issue #7). A summary of all computed partial correlations can now be found as Figure 3 —figure supplement 3 and 4 in the revised manuscript.

Specifically we now state in the Results section (page 16 – 17, lines 347 – 360):

“To rule out age as a confounding factor that could drive the relationship between coupling strength, learning curve and task proficiency in the mixed sample, we used cluster-corrected partial correlations to confirm their independence of age differences (task proficiency: mean rho = 0.40, p = 0.017; learning curve: rhos = -0.47, p = 0.049). Additionally, given that we found that juggling performance could underlie a circadian modulation we controlled for individual differences in alertness between subjects due to having just slept. We partialed out the mean PVT reaction time before the juggling performance test after sleep from the original analyses and found that our results remained stable (task proficiency: mean rho = 0.37, p = 0.025; learning curve: rhos = -0.49, p = 0.040). For a summary of the reported cluster-corrected partial correlations as well as analyses controlling for differences in sleep architecture see Figure 3 —figure supplement 3. Further, we also confirmed that our correlations are not influenced by individual differences in SO and spindle event parameters (Figure 3 —figure supplement 4).”

And in the methods section (page 35, lines 813 – 814):

“To control for possible confounding factors we computed cluster-corrected partial rank correlations (Figure 3 —figure supplement 3 and 4).”

7. To allow a more comprehensive assessment of the underlying data information with regards to general sleep descriptives (minutes, per cent of time spent in different sleep stages, overall sleep time etc.) as well as related to SOs, spindles and coupled events (e.g. number, density etc.) would be needed.

We agree with the reviewers that additional information about sleep architecture and SO as well as sleep spindle characteristics are needed for a more comprehensive assessment of our data. We now added summary tables for sleep architecture and SO/spindle event descriptive measures for the whole sample and for the sleep first groups that we used for our correlational analyses to the supplementary material in the updated manuscript. It is important to note, that due to the longer sleep opportunity of adolescents that we provided to accommodate the overall higher sleep need in younger participants, adolescents and adults differed in most general sleep architecture markers and SO as well as sleep spindle descriptive measures. In addition, changes in sleep architecture are prominent during the maturational phase from adolescence to adulthood, which might introduce additional variance between the two age groups.

We now provide general sleep descriptives in the revised version of the manuscript as Supplementary file – table 2 and table 7. These data are referred to in the Results section (page 6, lines 101 – 105):

“Polysomnography (PSG) was recorded during an adaptation night and during the respective sleep retention interval (i.e. learning night) except for the adult wake-first group (for sleep architecture descriptive parameters of the adaptation night and learning night as well as for adolescents and adults see Supplementary file – table 1 and 2).”

And (page 15, lines 311 – 318):

“Furthermore, given that we only recorded polysomnography for the adults in the sleep first group and that adolescents in the wake first group showed enhanced task proficiency at the time point of the sleep retention interval due to additional training (Figure 3 —figure supplement 2A), we only considered adolescents and adults of the sleep-first group to ensure a similar level of juggling experience (for summary statistics of sleep architecture and SO and spindle events of subjects that entered the correlational analyses see Supplementary file – table 7).”

The additional control analyses are also now added to the revised manuscript as Figure 3 —figure supplement 3 and 4 in the Results section (page 16, lines 356 – 360):

“For a summary of the reported cluster-corrected partial correlations as well as analyses controlling for differences in sleep architecture see Figure 3 —figure supplement 3. Further, we also confirmed that our correlations are not influenced by individual differences in SO and spindle event parameters (Figure 3 —figure supplement 4).”

8. The authors state “that "To ensure the simultaneous presence of the two interacting sleep oscillations in the signal, we restricted our analyses to NREM3 sleep given the higher co-occurrence” rate." We do not understand this reasoning. The utilized procedure of specifically isolating sleep spindles that are followed or preceded by slow oscillations already ensures the presence of SOs and sleep spindles in the data. Hence, why not take coupled events from sleep stage N2 into account? Or do the authors think that light sleep SO-spindle events are qualitatively different from SWS SO-spindle complexes (and if so does the present data support such a notion)?

We thank the reviewers for bringing this issue to our attention and apologize for not explaining our rationale about only including coupled NREM3 events in our analyses clearly enough. First, to our knowledge, there is no consensus yet about whether SO-spindle complexes differ between light and deep sleep. Instead evidence rather points to a distinction between coupled and uncoupled spindle events due to their differences in underlying cortical circuitry dynamics (Niethard et al., 2018) and hippocampal-neocortical communication (Helfrich et al., 2019).

Accordingly, the reviewers are correct, that given that we control for co-occurrence to ensure concomitant SO and spindle oscillations, a perfectly viable approach would be to analyze all coupled events instead of just events in NREM3. However, we decided against this approach in our initial analyses because of two reasons. First, similar to our previous longitudinal study (Hahn et al., 2020), we found that overall SO-spindle co-occurrence (%) is extremely low in NREM2 sleep compared to NREM3 sleep (Figure R9A, sleep stage main effect: F(1, 51) = 1209.09, p < 0.001, partial eta² = 0.96, age main effect: F(1, 51) = 11.35, p = 0.001, partial eta² = 0.18, interaction: F(1, 51) = 0.02, p = 0.89, partial eta² < 0.001). Further, some subjects did not even show co-occurring SO-spindle events in a handful of electrodes during NREM2 (Figure R9B, data collapsed across all subjects and electrodes). Importantly, we showed earlier that the low co-occurrence rate, or in other words, the high amount of isolated spindles in NREM2 can introduce a serious amount of noise for the preferred SO-phase estimation (Figure R9C) which eventually can mask a potential behavioral correlate (Figure R9D). Therefore, we opted for a conservative approach for our analyses by only computing coupling strength for co-occurring events in NREM3. The second reason why we did not include coupled events during NREM2 sleep is that we wanted to apply the exact same analysis pipeline we developed in the previous study to the current research advance study. We are convinced that demonstrating the behavioral relevance of SO-spindle coupling in a different age sample performing an ecologically valid motor task by employing the same signal processing pipeline is one key strength of the present research advance manuscript.

Regardless, we recalculated SO-spindle coupling strength for co-occurrence controlled events across both sleep stages and correlated the results with task proficiency change and learning curve change after sleep. Similar to our original results higher coupling strength indicated better task proficiency (Figure R9E, left, rho = 0.45, p = 0.047, cluster-corrected) and flatter learning curves after sleep (Figure R9E, right, rho = -0.46, p = 0.037, cluster-corrected). However, given the low co-occurrence rate in NREM2 (cf. Figure R9AB) a reliable estimation of coupling strength is compromised and therefore we cannot give a sound assessment of whether coupled in events in NREM2 differ from coupled events in NREM3.

We added Figure R9AB to the revised manuscript as Figure 3 —figure supplement 1CD and further added a clarifying statement about our rationale of only analyzing events in NREM3 to the Results section (page 12 – 13, lines 258 – 264):

“To ensure the simultaneous presence of the two interacting sleep oscillations in the signal, we followed a conservative approach and restricted our analyses to NREM3 sleep given the low co-occurrence rate in NREM2 sleep (Figure 3 —figure supplement 1CD) which can cause spurious coupling estimates (Hahn et al., 2020). Further, we only considered spindle events that displayed a concomitant detected SO within a 2.5 s time window.”

And in the methods section (page 34, lines 763 – 770):

“Ensuring co-occurrence of SO and sleep spindles

Cross-frequency coupling renders meaningful information of network communication only when the suspected interacting oscillations are present in the signal. Therefore, we only analyzed SO and sleep spindle epochs during which they co-occurred in a 2.5s time window (± ~2 SO cycles around the spindle peak). Furthermore, we restricted all our coupling analyses to sleep stage NREM3 because of general lower co-occurrence of SO and spindles in NREM2 (Figure 3 —figure supplement 1CD), which can cause spurious coupling estimates (Hahn et al., 2020).”

The additional correlational analyses that include co-occurrence corrected events in NREM2 (Figure R9E) can now be found in the Results section as Figure 3 —figure supplement 2FG in the Results section (page 16, lines 343 – 346):

“Furthermore, our results remained consistent when including coupled spindle events in NREM2 (Figure 3 —figure supplement 2E) and after outlier removal (Figure 3 —figure supplement 2FG).”

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

The authors have done an impressive job with this revision. It is meticulously organized, thorough, and clearly stated. That is all to their major credit. However, I still cannot come to agree that their data supports much of the story they are telling.

We thank the reviewers for their positive assessment of our previous work. We appreciate the thoughtful and detailed feedback on how to improve the main message of our manuscript.

First, part of the issue may be the change from their original story and the new one following the revision. Making major revisions can obviously be tricky, especially when a revision requires as many changes as theirs did (and I again commend them on the overhaul). But there is still something unclear in their primary claims. They say in their cover letter, "Collectively, our results suggest that SO-spindle coupling indexes the integrity of memory pathways (as reviewed in detail recently: Helfrich et al., 2021); thus, reflects a trait-specific (in contrast to a state-specific) correlate of learning capacity." However, this story does not come through clearly in the new paper. In fact, reading the new paper, it seems this is nodded to only here in the Discussion“ – "Thus, our results primarily suggest that strength of SO-spindle coupling correlates with the ability to learn (trait), but does not solely convey 534 the recently learned information. This set of findings is in line with recent ideas that strong coupling indexes individuals with highly efficient subcortical-cortical network communication (Helfrich et al., ”021)." Much of the paper instead talks about active systems consolidation theory, which I believe is not supported in their data, and the authors do seem to agree. If the authors indeed want to make this more of a memory pathway integrity story, it seems more unpacking of the ideas in their recent review is warranted, as does perhaps some evidence in the literature linking sleep measures to integrity in some neural pathways (e.g., Mander et al., 2017).

We thank the reviewer for making us aware of this issue. We agree that the neural efficiency literature needs to be more prominently featured in our manuscript. Thus, we incorporated references and extended discussion in multiple instances in the manuscript. To highlight this consideration from the very beginning, we added a new paragraph to the introduction in the revised version of the manuscript (pages 3 -4, lines 40 – 50):

“Several lines of research recently demonstrated that precisely timed SO-spindle interaction mediates successful memory consolidation across the lifespan (Hahn et al., 2020; Helfrich et al., 2018; Mikutta et al., 2019; Molle et al., 2011; Muehlroth et al., 2019). Critically, SO-spindle coupling as well as spindles and SOs in isolation are related to neural integrity of memory structures such as medial prefrontal cortex, thalamus, hippocampus and entorhinal cortex (Helfrich et al., 2021; Helfrich et al., 2018; Ladenbauer et al., 2017; Mander et al., 2017; Muehlroth et al., 2019; Spano et al., 2020; Winer et al., 2019). Thus converging evidence suggests that SO-spindle coupling does not only actively transfer mnemonic information during sleep but also indexes general efficiency of memory pathways (Helfrich et al., 2021; Mander et al., 2017).”

Furthermore, we now critically discuss the active contribution of sleep to gross-motor memory learning in the discussion (pages 23 – 24, lines 522 – 536):

“How ‘active’ is sleep for real-life gross-motor memory consolidation? We found that sleep impacts the learning curve but did not affect task proficiency in comparison to a wake retention interval directly after learning (Figure 2DE). Three accounts might explain the absence of a sleep effect on task proficiency. (1) Sleep rather stabilizes than improves gross-motor memory, which is in line with previous gross-motor adaption studies (Bothe et al., 2019; Bothe et al., 2020). This parallels findings in finger tapping tasks were the narrative evolved from sleep-related performance improvements (Walker et al., 2002) to stabilization (Brawn et al., 2010). (2) Pre-sleep performance is critical for sleep to improve motor skills (Wilhelm et al., 2012). Participants commonly reach asymptotic pre-sleep performance levels in finger tapping tasks, which is most frequently used to probe sleep effects on motor memory. Here we found that using a complex juggling tasks, participants do not reach asymptotic ceiling performance levels in such a short time. Indeed, the learning progression for the sleep-first and wake-first groups followed a similar trend (Figure 2AB), suggesting that more training and not in particular sleep drove performance gains.”

Additionally, we would like to emphasize that state and trait effects are not mutually exclusive. In fact, the overlaps of state and trait effects have been previously identified in sleep spindle literature (Lustenberger et al., 2015; Schabus et al., 2006). With SO-spindle coupling we do indeed investigate the mechanistic assumption of the active systems memory consolidation theory. However, like spindles, SO-spindle coupling relates to memory reactivation (cf. Cairney et al., 2018; Schreiner et al., 2021) and also to neural integrity/efficiency (Helfrich et al., 2018; Muehlroth et al., 2019). Thus, memory consolidation does not only need an information transfer between memory structures in the brain (i.e. Cortex and Hippocampus) but it also needs an efficient neural pathway to exchange said information.

We now also elaborate on this issue in the discussion (page 25, lines 550 – 561):

“Importantly, SO-spindle coupling still predicted learning dynamics on a single subject level advocating for a supportive function of sleep for gross-motor memory. Moreover, we found that SO-spindle coupling strength remains remarkably stable between two nights, which also explains why a learning-induced change in coupling strength did not relate to behavior (Figure 3 —figure supplement 2I). Thus, our results primarily suggest that strength of SO-spindle coupling correlates with the ability to learn (trait), but does not solely convey the recently learned information. Note that state and traits effects are not mutually exclusive. The overlap of state and trait effects is a long-standing issue in spindle literature, which also seems so apply to their coordinated interplay with SOs (Lustenberger et al., 2015; Schabus et al., 2006). This set of findings is in line with recent ideas that strong coupling indexes individuals with highly efficient subcortical-cortical network communication (Helfrich et al., 2021).”

Second, they concede in various locations that the circadian story cannot be ruled out, which I also commend, but then the paper still largely revolves around active sleep consolidation theory. I invite the authors to imagine convincing a hypothetical researcher who thought the brain just shuts off entirely during sleep ("sleep does nothing") and that people have different abilities based on the time of day. (Believe it or not, this is not my belief.) How would the authors convince this person based on these behavioral data that sleep is actually doing something? I do not know whether they could, given the mixed-effects model findings.

Of course, they could point to the prior literature. The prior literature on sleep and motor learning has shown, in the case of the Morita juggling studies cited, that there should be better overall performance after sleep (vs equivalent wake periods). And in the case of countless finger-tapping studies, even though the major story has changed from one of absolute improvement (e.g., Walker et al., 2002) to stabilization (e.g., Brawn et al., 2010) after sleep, there seem to be sleep (vs wake) benefits on overall performance (analogous to task proficiency here). This, however, is not what the authors find with their learning curve findings here, as performance seems, if anything, worse on the first few trials after sleep (though this may not be significant) and then catches up more quickly. So, it is hard to know whether the prior literature would necessarily help them convince this researcher about their own findings.

This researcher may also say that the inclusion of a PVT is great, and the null results across sessions is more helpful than not to their story. But this researcher may add that a null PVT difference does not exclude all possible circadian effects. There are certainly circadian effects on cognition – including the very recent publication of Tandoc et al., (2021) and even on motor learning (Keisler et al., 2007) – and indeed the authors do find such an effect here in their mixed-effects model analysis. Therefore, the null PVT results are not conclusive, especially in counteracting an effect that they actually found in their paper.

One could then point this researcher to the SO-spindle coupling results as evidence that sleep is playing a strong role here. However, given that these are trait- vs. state-based results, it is unclear why stronger SO-spindle coupling for some individuals – which may be having an impact on neural integrity over a long timescale – would prime their nervous systems for more learning right after sleep than at some other time during the day. The researcher may say, okay, SO-spindle coupling results do not prove sleep does anything, they merely correlate with the observed behavioral result, and moreover, they constitute a trait (vs post-learning sleep state) effect. They may add that it is also unclear why, if stronger SO-spindle coupling is doing something, it could not alternatively reflect some other individual trait that could even lead to the observed circadian effects that learning curves are higher in the morning.

One analysis that could possibly work to disentangle circadian vs. active sleep effects would be to include a different factor in the mixed-effects model that could tease apart time of day from sleep-after-learning effects. In addition to including "Time of day", where all mornings = 1 and all evenings = 0, the authors could include the conjunction of "Time of day + after learning", where mornings on the 2nd and 3rd sessions = 1 and mornings on the 1st session and all evenings = 0. This would capture the idea that post-learning mornings show differential improvement because post-learning sleep sort of "prepared" the networks to re-learn within a short time span, and this preparation was not operative before the 1st session. I say it could "possibly" work above because the two factors would still be quite correlated (identical except for the first morning session), which could hurt their statistical power to independently produce effects. Nevertheless, if BOTH factors end up being significant, I think the authors could make the claim that both are contributing (that is, time of day + after learning is actually independently contributing above and beyond what time of day could do alone). If only one is significant, then the story is clean, but may have to change. If neither are significant, then it may be difficult to know what to do, and the authors may have to fall back on the original time-of-day analysis and keep things closer to as is but acknowledge more of the uncertainty surrounding the effects. If nothing changes upon a second revision in this regard, I do expect the authors to incorporate circadian possibilities more thoroughly in their paper, such as in their abstract and with more citations of this literature.

I realize this may seem a lot for a second round of revisions, and the authors have clearly done an impressive amount of work on the paper, but I feel that the authors can still strengthen it, either with this last analysis or by refocusing on the stories that can and cannot be supported here. There is something here that lacks clarity in translating from the data to the story about them, and, as a result, it remains difficult to confidently find the main takeaway from the manuscript.

We thank the reviewer for the detailed feedback and the valuable input on subsequent analyses and interpretations. We followed the reviewers’ suggestion and calculated additional mixed-models to further disentangle sleep and time of day effect. We modeled learning curve and task proficiency separately across all testing blocks with the fixed effects “time of day” and “sleep after learning” (i.e. performance test 2 for the sleep first group and performance test 3 for the wake first group) and subjects as random effects (see Table 6 for the full report).

For learning curve, we found that the fixed effect “time of day” approached conventional significance levels (time of day: Β = -1.008, t(202) = -1.625, p = 0.106, CI95 = [-2.231, 0.215]) using the suggested mixed-effect model. Sleep after learning had no effect on the learning curve (Β = 0.172, t(202) = 0.268, p = 0.789, CI95 = [-1.093, 1.437]). Task proficiency however, was overall better in the evening performance tests (Β = 5.751, t(202) = 2.252, p = 0.011, CI95 = [1.310, 10.192]) and additionally benefited from sleep after learning (Β = 3.795, t(202) = 1.672, p = 0.096, CI95 = [-0.680, 8.271]).

The reviewer correctly noted that the correlative nature of the fixed effects might dilute statistical power to prevent the identification of independent effects. However, given the lower end of the confidence interval close to zero for the fixed effect “sleep after learning” there is not enough evidence to accept the null hypotheses that sleep has no effect at all. Consequently, it suggests that both time of day and sleep seem to contribute to the overall juggling performance.

We have now added table 6 to the supplementary file 1 and updated the Results section with these important additional analyses (pages 9 – 10, lines 189 – 200):

“However, these analyses cannot exclude all circadian effects. Therefore, we modeled learning curve and task proficiency with time of day (morning session, evening session) and sleep after learning as fixed effects and subjects as random effects to further disentangle circadian and sleep specific effects. Results for learning curve were inconclusive for both fixed effects (time of day: Β = -1.008, t(202) = -1.625, p = 0.106, CI95 = [-2.231, 0.215]; Sleep after learning: Β = 0.172, t(202) = 0.268, p = 0.789, CI95 = [-1.093, 1.437]; Supplementary file 1 – table 6A). Task proficiency was overall better in the evening performance tests (Β = 5.751, t(202) = 2.252, p = 0.011, CI95 = [1.310, 10.192]) and additionally trended to benefit from sleep after learning (Β = 3.795, t(202) = 1.672, p = 0.096, CI95 = [-0.680, 8.271]; Supplementary file 1 – table 6B). These results suggest that both, time of day and sleep contribute to the overall juggling performance.”

In the light of these additional analyses and feedback from the reviewer, we incorporated possible time of day effects more thoroughly in the updated version of the manuscript.

We now report time of day effects in the abstract (page 2, lines 1 – 16):

“Previously, we demonstrated that precise temporal coordination between slow oscillations (SO) and sleep spindles indexes declarative memory network development (Hahn et al., 2020). However, it is unclear whether these findings in the declarative memory domain also apply in the motor memory domain. Here, we compared adolescents and adults learning juggling, a real-life gross-motor task. Juggling performance was impacted by sleep and time of day effects. Critically, we found that improved task proficiency after sleep lead to an attenuation of the learning curve, suggesting a dynamic juggling learning process. We employed individualized cross-frequency coupling analyses to reduce inter and intra-group variability of oscillatory features. Advancing our previous findings, we identified a more precise SO-spindle coupling in adults compared to adolescents. Importantly, coupling precision over motor areas predicted overnight changes in task proficiency and learning curve, indicating that SO-spindle coupling is relates to the dynamic motor learning process. Our results provide first evidence that regionally specific, precisely coupled sleep oscillations support gross-motor learning.”

The new results are also further elaborated in the Discussion section. Additionally, we now fully acknowledge that the null-effect of the PVT does not exclude all possible circadian effects (pages 24 – 25, lines 536 – 549):

“How ‘active’ is sleep for real-life gross-motor memory consolidation? […] (3) Sleep effects are intermingled with time of day effects on juggling performance. Indeed, the steeper learning curve after the first retention interval in the sleep first group can also be interpreted as a time of day effect. However, when modeling time of day and sleep specific effects across all performance blocks, we found a trend that sleep after learning supports task proficiency. Note, that the correlative nature of both factors in the model likely resulted in insufficient statistical power to produce independently significant results. Additionally, we did not find evidence for a circadian modulation of cognitive engagement based on objective reaction time data in our study (Figure 2 —figure supplement 1C). However, a null-result does not exclude all possible circadian effects and ample evidence suggests that cognitive performance and motor learning are influenced by the time of day (Blatter and Cajochen, 2007; Keisler et al., 2007; Tandoc et al., 2021). Specifically, implicit learning seems to be affected by time of day rather than sleep (Keisler et al., 2007). Therefore, we cannot fully disentangle circadian and sleep effects with our study design, which should be considered a limitation to our findings.”

We now also reference the time of day effects in the conclusion (page 26, lines 577 – 583):

“Taken together, our results provide a mechanistic understanding of how the brain forms real-life gross-motor memory during sleep. However, how time of day additionally affects and interacts with sleep to support gross-motor learning remains an open question. As sleep has been shown to support fine-motor memory consolidation in individuals after stroke (Gudberg and Johansen-Berg, 2015; Siengsuhon and Boyd, 2008), SO-spindle coupling integrity could be a valuable, easy to assess predictive index for rehabilitation success.”

https://doi.org/10.7554/eLife.66761.sa2

Article and author information

Author details

  1. Michael A Hahn

    1. Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria
    2. Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
    3. Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing
    For correspondence
    michael.andreas.hahn@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3022-0552
  2. Kathrin Bothe

    1. Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria
    2. Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Dominik Heib

    1. Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria
    2. Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
    Contribution
    Data curation, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Manuel Schabus

    1. Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria
    2. Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
    Contribution
    Methodology, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5899-8772
  5. Randolph F Helfrich

    Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen, Germany
    Contribution
    Conceptualization, Methodology, Software, Supervision, Validation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8045-3111
  6. Kerstin Hoedlmoser

    1. Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria
    2. Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
    Contribution
    Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review and editing
    For correspondence
    kerstin.hoedlmoser@plus.ac.at
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5177-4389

Funding

Austrian Science Fund (W1233-G17)

  • Michael A Hahn

Austrian Science Fund (P25000-B24)

  • Kerstin Hoedlmoser

Deutsche Forschungsgemeinschaft (HE 8329/2-1)

  • Randolph F Helfrich

Hertie Network of Excellence in Clinical Neuroscience

  • Randolph F Helfrich

Jung Foundation for Science and Research (Ernst Jung Career Advancement Award)

  • Randolph F Helfrich

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

This research was supported by Austrian Science Fund (P25000-B24) and the Centre for Cognitive Neuroscience Salzburg (CCNS). MAH was additionally supported by the Doctoral College ‘Imaging the Mind’ (FWF, Austrian Science Fund W1233-G17). RFH is supported by the German Research Foundation (DFG, HE 8329/2-1), the Hertie Foundation (Hertie Network of Excellence in Clinical Neurosciences), and the Jung Foundation for Science and Research (Ernst Jung Career Advancement Award).

Ethics

The study protocol was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the University of Salzburg (EK-435 GZ:16/2014). Participants and their legal custodian provided written informed consent before entering the study.

Senior Editor

  1. Laura L Colgin, University of Texas at Austin, United States

Reviewing Editor

  1. Saskia Haegens, Columbia University College of Physicians and Surgeons, United States

Publication history

  1. Preprint posted: January 21, 2021 (view preprint)
  2. Received: March 9, 2021
  3. Accepted: February 4, 2022
  4. Version of Record published: February 21, 2022 (version 1)

Copyright

© 2022, Hahn et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Michael A Hahn
  2. Kathrin Bothe
  3. Dominik Heib
  4. Manuel Schabus
  5. Randolph F Helfrich
  6. Kerstin Hoedlmoser
(2022)
Slow oscillation–spindle coupling strength predicts real-life gross-motor learning in adolescents and adults
eLife 11:e66761.
https://doi.org/10.7554/eLife.66761

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