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.

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.

Figure 1

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Behavioral results: juggling performance and disentangling the learning process

Adolescents improved their juggling performance over the course of all nine blocks (Figure 2A, top; F_{3.957, 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 (F_{1, 24} = 1.002, p = 0.327, η² = 0.04), nor did they differ over the course of the juggling blocks (F_{3.957, 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; F_{4.673, 182.241} = 11.967, p < 0.001, η² = 0.24), regardless of group (F_{4.673, 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 (F_{1, 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.

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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: rho_s = 0.98, p < 0.001; performance test 2: rho_s = 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 (F_{1, 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; F_{1, 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 (F_{1, 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; rho_s = −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).

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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.

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.

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.

1. 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

1. 1. Albouy G
   2. King BR
   3. Maquet P
   4. Doyon J

   (2013) Hippocampus and striatum: dynamics and interaction during acquisition and sleep-related motor sequence memory consolidation

   Hippocampus 23:985–1004.

   https://doi.org/10.1002/hipo.22183

   • PubMed
   • Google Scholar
2. 1. Aru J
   2. Aru J
   3. Priesemann V
   4. Wibral M
   5. Lana L
   6. Pipa G
   7. Singer W
   8. Vicente R

   (2015) Untangling cross-frequency coupling in neuroscience

   Current Opinion in Neurobiology 31:51–61.

   https://doi.org/10.1016/j.conb.2014.08.002

   • PubMed
   • Google Scholar
3. 1. Barakat M
   2. Doyon J
   3. Debas K
   4. Vandewalle G
   5. Morin A
   6. Poirier G
   7. Martin N
   8. Lafortune M
   9. Karni A
   10. Ungerleider LG
   11. Benali H
   12. Carrier J

   (2011) Fast and slow spindle involvement in the consolidation of a new motor sequence

   Behavioural Brain Research 217:117–121.

   https://doi.org/10.1016/j.bbr.2010.10.019

   • PubMed
   • Google Scholar
4. 1. Berens P

   (2009) CircStat: A MATLAB Toolbox for Circular Statistics

   Journal of Statistical Software 31:1–21.

   https://doi.org/10.18637/jss.v031.i10

   • Google Scholar
5. 1. Blatter K
   2. Cajochen C

   (2007) Circadian rhythms in cognitive performance: methodological constraints, protocols, theoretical underpinnings

   Physiology & Behavior 90:196–208.

   https://doi.org/10.1016/j.physbeh.2006.09.009

   • PubMed
   • Google Scholar
6. 1. Bothe K
   2. Hirschauer F
   3. Wiesinger H-P
   4. Edfelder J
   5. Gruber G
   6. Birklbauer J
   7. Hoedlmoser K

   (2019) The impact of sleep on complex gross-motor adaptation in adolescents

   Journal of Sleep Research 28:e12797.

   https://doi.org/10.1111/jsr.12797

   • PubMed
   • Google Scholar
7. 1. Bothe K
   2. Hirschauer F
   3. Wiesinger HP
   4. Edfelder JM
   5. Gruber G
   6. Hoedlmoser K
   7. Birklbauer J

   (2020) Gross motor adaptation benefits from sleep after training

   Journal of Sleep Research 29:e12961.

   https://doi.org/10.1111/jsr.12961

   • PubMed
   • Google Scholar
8. 1. Boutin A
   2. Pinsard B
   3. Boré A
   4. Carrier J
   5. Fogel SM
   6. Doyon J

   (2018) Transient synchronization of hippocampo-striato-thalamo-cortical networks during sleep spindle oscillations induces motor memory consolidation

   NeuroImage 169:419–430.

   https://doi.org/10.1016/j.neuroimage.2017.12.066

   • PubMed
   • Google Scholar
9. 1. Boyke J
   2. Driemeyer J
   3. Gaser C
   4. Buchel C
   5. May A

   (2008) Training-Induced Brain Structure Changes in the Elderly

   Journal of Neuroscience 28:7031–7035.

   https://doi.org/10.1523/JNEUROSCI.0742-08.2008

   • PubMed
   • Google Scholar
10. 1. Brawn TP
    2. Fenn KM
    3. Nusbaum HC
    4. Margoliash D

    (2010) Consolidating the effects of waking and sleep on motor-sequence learning

    The Journal of Neuroscience 30:13977–13982.

    https://doi.org/10.1523/JNEUROSCI.3295-10.2010

    • PubMed
    • Google Scholar
11. 1. Campbell IG
    2. Feinberg I

    (2009) Longitudinal trajectories of non-rapid eye movement delta and theta EEG as indicators of adolescent brain maturation

    PNAS 106:5177–5180.

    https://doi.org/10.1073/pnas.0812947106

    • PubMed
    • Google Scholar
12. 1. Campbell IG
    2. Feinberg I

    (2016) Maturational Patterns of Sigma Frequency Power Across Childhood and Adolescence: A Longitudinal Study

    Sleep 39:193–201.

    https://doi.org/10.5665/sleep.5346

    • PubMed
    • Google Scholar
13. 1. Chauvette S
    2. Seigneur J
    3. Timofeev I

    (2012) Sleep oscillations in the thalamocortical system induce long-term neuronal plasticity

    Neuron 75:1105–1113.

    https://doi.org/10.1016/j.neuron.2012.08.034

    • PubMed
    • Google Scholar
14. 1. Clemens Z
    2. Mölle M
    3. Eross L
    4. Jakus R
    5. Rásonyi G
    6. Halász P
    7. Born J

    (2011) Fine-tuned coupling between human parahippocampal ripples and sleep spindles

    The European Journal of Neuroscience 33:511–520.

    https://doi.org/10.1111/j.1460-9568.2010.07505.x

    • PubMed
    • Google Scholar
15. 1. Cole SR
    2. Voytek B

    (2017) Brain Oscillations and the Importance of Waveform Shape

    Trends in Cognitive Sciences 21:137–149.

    https://doi.org/10.1016/j.tics.2016.12.008

    • PubMed
    • Google Scholar
16. 1. Dayan E
    2. Cohen LG

    (2011) Neuroplasticity Subserving Motor Skill Learning

    Neuron 72:443–454.

    https://doi.org/10.1016/j.neuron.2011.10.008

    • PubMed
    • Google Scholar
17. 1. Delorme A
    2. Makeig S

    (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

    Journal of Neuroscience Methods 134:9–21.

    https://doi.org/10.1016/j.jneumeth.2003.10.009

    • PubMed
    • Google Scholar
18. 1. Diekelmann S
    2. Born J

    (2010) The memory function of sleep

    Nature Reviews. Neuroscience 11:114–126.

    https://doi.org/10.1038/nrn2762

    • PubMed
    • Google Scholar
19. 1. Dinges DF
    2. Powell JW

    (1985) Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations

    Behavior Research Methods, Instruments, & Computers 17:652–655.

    https://doi.org/10.3758/BF03200977

    • Google Scholar
20. 1. Doyon J
    2. Benali H

    (2005) Reorganization and plasticity in the adult brain during learning of motor skills

    Current Opinion in Neurobiology 15:161–167.

    https://doi.org/10.1016/j.conb.2005.03.004

    • PubMed
    • Google Scholar
21. 1. Doyon J
    2. Gabitov E
    3. Vahdat S
    4. Lungu O
    5. Boutin A

    (2018) Current issues related to motor sequence learning in humans

    Current Opinion in Behavioral Sciences 20:89–97.

    https://doi.org/10.1016/j.cobeha.2017.11.012

    • Google Scholar
22. 1. Draganski B
    2. Gaser C
    3. Busch V
    4. Schuierer G
    5. Bogdahn U
    6. May A

    (2004) Neuroplasticity: changes in grey matter induced by training

    Nature 427:311–312.

    https://doi.org/10.1038/427311a

    • PubMed
    • Google Scholar
23. 1. Dvorak D
    2. Fenton AA

    (2014) Toward a proper estimation of phase-amplitude coupling in neural oscillations

    Journal of Neuroscience Methods 225:42–56.

    https://doi.org/10.1016/j.jneumeth.2014.01.002

    • PubMed
    • Google Scholar
24. 1. Flinker A
    2. Korzeniewska A
    3. Shestyuk AY
    4. Franaszczuk PJ
    5. Dronkers NF
    6. Knight RT
    7. Crone NE

    (2015) Redefining the role of Broca’s area in speech

    PNAS 112:2871–2875.

    https://doi.org/10.1073/pnas.1414491112

    • PubMed
    • Google Scholar
25. 1. Fogel S
    2. Albouy G
    3. King BR
    4. Lungu O
    5. Vien C
    6. Bore A
    7. Pinsard B
    8. Benali H
    9. Carrier J
    10. Doyon J

    (2017) Reactivation or transformation? Motor memory consolidation associated with cerebral activation time-locked to sleep spindles

    PLOS ONE 12:e0174755.

    https://doi.org/10.1371/journal.pone.0174755

    • PubMed
    • Google Scholar
26. 1. Gudberg C
    2. Johansen-Berg H

    (2015) Sleep and Motor Learning: Implications for Physical Rehabilitation After Stroke

    Frontiers in Neurology 6:241.

    https://doi.org/10.3389/fneur.2015.00241

    • PubMed
    • Google Scholar
27. 1. Hahn MA
    2. Heib D
    3. Schabus M
    4. Hoedlmoser K
    5. Helfrich RF

    (2020) Slow oscillation-spindle coupling predicts enhanced memory formation from childhood to adolescence

    eLife 9:e53730.

    https://doi.org/10.7554/eLife.53730

    • PubMed
    • Google Scholar
28. 1. Helfrich RF
    2. Mander BA
    3. Jagust WJ
    4. Knight RT
    5. Walker MP

    (2018) Old Brains Come Uncoupled in Sleep: Slow Wave-Spindle Synchrony, Brain Atrophy, and Forgetting

    Neuron 97:221–230.

    https://doi.org/10.1016/j.neuron.2017.11.020

    • PubMed
    • Google Scholar
29. 1. Helfrich RF
    2. Lendner JD
    3. Mander BA
    4. Guillen H
    5. Paff M
    6. Mnatsakanyan L
    7. Vadera S
    8. Walker MP
    9. Lin JJ
    10. Knight RT

    (2019) Bidirectional prefrontal-hippocampal dynamics organize information transfer during sleep in humans

    Nature Communications 10:3572.

    https://doi.org/10.1038/s41467-019-11444-x

    • PubMed
    • Google Scholar
30. 1. Helfrich RF
    2. Lendner JD
    3. Knight RT

    (2021) Aperiodic sleep networks promote memory consolidation

    Trends in Cognitive Sciences 25:648–659.

    https://doi.org/10.1016/j.tics.2021.04.009

    • PubMed
    • Google Scholar
31. 1. Huber R
    2. Ghilardi MF
    3. Massimini M
    4. Tononi G

    (2004) Local sleep and learning

    Nature 430:78–81.

    https://doi.org/10.1038/nature02663

    • PubMed
    • Google Scholar
32. 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.

    • Google Scholar
33. 1. Joechner AK
    2. Wehmeier S
    3. Werkle-Bergner M

    (2021) Electrophysiological indicators of sleep-associated memory consolidation in 5- to 6-year-old children

    Psychophysiology 58:e13829.

    https://doi.org/10.1111/psyp.13829

    • PubMed
    • Google Scholar
34. 1. Keisler A
    2. Ashe J
    3. Willingham DT

    (2007) Time of day accounts for overnight improvement in sequence learning

    Learning & Memory (Cold Spring Harbor, N.Y.) 14:669–672.

    https://doi.org/10.1101/lm.751807

    • PubMed
    • Google Scholar
35. 1. King BR
    2. Hoedlmoser K
    3. Hirschauer F
    4. Dolfen N
    5. Albouy G

    (2017) Sleeping on the motor engram: The multifaceted nature of sleep-related motor memory consolidation

    Neuroscience and Biobehavioral Reviews 80:1–22.

    https://doi.org/10.1016/j.neubiorev.2017.04.026

    • PubMed
    • Google Scholar
36. 1. Klinzing JG
    2. Niethard N
    3. Born J

    (2019) Mechanisms of systems memory consolidation during sleep

    Nature Neuroscience 22:1598–1610.

    https://doi.org/10.1038/s41593-019-0467-3

    • PubMed
    • Google Scholar
37. 1. Ladenbauer J
    2. Ladenbauer J
    3. Külzow N
    4. de Boor R
    5. Avramova E
    6. Grittner U
    7. Flöel A

    (2017) Promoting Sleep Oscillations and Their Functional Coupling by Transcranial Stimulation Enhances Memory Consolidation in Mild Cognitive Impairment

    The Journal of Neuroscience 37:7111–7124.

    https://doi.org/10.1523/JNEUROSCI.0260-17.2017

    • PubMed
    • Google Scholar
38. 1. Latchoumane CFV
    2. Ngo HVV
    3. Born J
    4. Shin HS

    (2017) Thalamic Spindles Promote Memory Formation during Sleep through Triple Phase-Locking of Cortical, Thalamic, and Hippocampal Rhythms

    Neuron 95:424–435.

    https://doi.org/10.1016/j.neuron.2017.06.025

    • PubMed
    • Google Scholar
39. 1. Lustenberger C
    2. Wehrle F
    3. Tüshaus L
    4. Achermann P
    5. Huber R

    (2015) The Multidimensional Aspects of Sleep Spindles and Their Relationship to Word-Pair Memory Consolidation

    Sleep 38:1093–1103.

    https://doi.org/10.5665/sleep.4820

    • PubMed
    • Google Scholar
40. 1. Mander BA
    2. Winer JR
    3. Walker MP

    (2017) Sleep and Human Aging

    Neuron 94:19–36.

    https://doi.org/10.1016/j.neuron.2017.02.004

    • PubMed
    • Google Scholar
41. 1. Maris E
    2. Oostenveld R

    (2007) Nonparametric statistical testing of EEG- and MEG-data

    Journal of Neuroscience Methods 164:177–190.

    https://doi.org/10.1016/j.jneumeth.2007.03.024

    • PubMed
    • Google Scholar
42. 1. Mikutta C
    2. Feige B
    3. Maier JG
    4. Hertenstein E
    5. Holz J
    6. Riemann D
    7. Nissen C

    (2019) Phase-amplitude coupling of sleep slow oscillatory and spindle activity correlates with overnight memory consolidation

    Journal of Sleep Research 28:e12835.

    https://doi.org/10.1111/jsr.12835

    • PubMed
    • Google Scholar
43. 1. Mölle M
    2. Bergmann TO
    3. Marshall L
    4. Born J

    (2011) Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing

    Sleep 34:1411–1421.

    https://doi.org/10.5665/SLEEP.1290

    • PubMed
    • Google Scholar
44. 1. Morita Y
    2. Ogawa K
    3. Uchida S

    (2012) The effect of a daytime 2-hour nap on complex motor skill learning

    Sleep and Biological Rhythms 10:302–309.

    https://doi.org/10.1111/j.1479-8425.2012.00576.x

    • Google Scholar
45. 1. Morita Y
    2. Ogawa K
    3. Uchida S

    (2016) Napping after complex motor learning enhances juggling performance

    Sleep Science (Sao Paulo, Brazil) 9:112–116.

    https://doi.org/10.1016/j.slsci.2016.04.002

    • PubMed
    • Google Scholar
46. 1. Muehlroth BE
    2. Sander MC
    3. Fandakova Y
    4. Grandy TH
    5. Rasch B
    6. Shing YL
    7. Werkle-Bergner M

    (2019) Precise Slow Oscillation-Spindle Coupling Promotes Memory Consolidation in Younger and Older Adults

    Scientific Reports 9:1940.

    https://doi.org/10.1038/s41598-018-36557-z

    • PubMed
    • Google Scholar
47. 1. Muehlroth BE
    2. Werkle-Bergner M

    (2020) Understanding the interplay of sleep and aging: Methodological challenges

    Psychophysiology 57:e13523.

    https://doi.org/10.1111/psyp.13523

    • PubMed
    • Google Scholar
48. 1. Ngo HV
    2. Fell J
    3. Staresina B

    (2020) Sleep spindles mediate hippocampal-neocortical coupling during long-duration ripples

    eLife 9:e57011.

    https://doi.org/10.7554/eLife.57011

    • PubMed
    • Google Scholar
49. 1. Nicolas A
    2. Petit D
    3. Rompré S
    4. Montplaisir J

    (2001) Sleep spindle characteristics in healthy subjects of different age groups

    Clinical Neurophysiology 112:521–527.

    https://doi.org/10.1016/s1388-2457(00)00556-3

    • PubMed
    • Google Scholar
50. 1. Niethard N
    2. Ngo HVV
    3. Ehrlich I
    4. Born J

    (2018) Cortical circuit activity underlying sleep slow oscillations and spindles

    PNAS 115:E9220–E9229.

    https://doi.org/10.1073/pnas.1805517115

    • PubMed
    • Google Scholar
51. 1. Nishida M
    2. Walker MP

    (2007) Daytime naps, motor memory consolidation and regionally specific sleep spindles

    PLOS ONE 2:e341.

    https://doi.org/10.1371/journal.pone.0000341

    • PubMed
    • Google Scholar
52. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:156869.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
53. 1. Pinsard B
    2. Boutin A
    3. Gabitov E
    4. Lungu O
    5. Benali H
    6. Doyon J

    (2019) Consolidation alters motor sequence-specific distributed representations

    eLife 8:e39324.

    https://doi.org/10.7554/eLife.39324

    • PubMed
    • Google Scholar
54. 1. Purcell SM
    2. Manoach DS
    3. Demanuele C
    4. Cade BE
    5. Mariani S
    6. Cox R
    7. Panagiotaropoulou G
    8. Saxena R
    9. Pan JQ
    10. Smoller JW
    11. Redline S
    12. Stickgold R

    (2017) Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource

    Nature Communications 8:15930.

    https://doi.org/10.1038/ncomms15930

    • PubMed
    • Google Scholar
55. 1. Rosanova M
    2. Ulrich D

    (2005) Pattern-specific associative long-term potentiation induced by a sleep spindle-related spike train

    The Journal of Neuroscience 25:9398–9405.

    https://doi.org/10.1523/JNEUROSCI.2149-05.2005

    • PubMed
    • Google Scholar
56. 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.

    • Google Scholar
57. 1. Sawangjit A
    2. Oyanedel CN
    3. Niethard N
    4. Salazar C
    5. Born J
    6. Inostroza M

    (2018) The hippocampus is crucial for forming non-hippocampal long-term memory during sleep

    Nature 564:109–113.

    https://doi.org/10.1038/s41586-018-0716-8

    • Google Scholar
58. 1. Schabus M
    2. Hödlmoser K
    3. Gruber G
    4. Sauter C
    5. Anderer P
    6. Klösch G
    7. Parapatics S
    8. Saletu B
    9. Klimesch W
    10. Zeitlhofer J

    (2006) Sleep spindle-related activity in the human EEG and its relation to general cognitive and learning abilities

    The European Journal of Neuroscience 23:1738–1746.

    https://doi.org/10.1111/j.1460-9568.2006.04694.x

    • PubMed
    • Google Scholar
59. 1. Schapiro AC
    2. Reid AG
    3. Morgan A
    4. Manoach DS
    5. Verfaellie M
    6. Stickgold R

    (2019) The hippocampus is necessary for the consolidation of a task that does not require the hippocampus for initial learning

    Hippocampus 29:1091–1100.

    https://doi.org/10.1002/hipo.23101

    • PubMed
    • Google Scholar
60. 1. Scheffer-Teixeira R
    2. Tort AB

    (2016) On cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus

    eLife 5:e20515.

    https://doi.org/10.7554/eLife.20515

    • PubMed
    • Google Scholar
61. 1. Schreiner T
    2. Petzka M
    3. Staudigl T
    4. Staresina BP

    (2021) Endogenous memory reactivation during sleep in humans is clocked by slow oscillation-spindle complexes

    Nature Communications 12:3112.

    https://doi.org/10.1038/s41467-021-23520-2

    • PubMed
    • Google Scholar
62. 1. Siengsukon CF
    2. Boyd LA

    (2008) Sleep enhances implicit motor skill learning in individuals poststroke

    Topics in Stroke Rehabilitation 15:1–12.

    https://doi.org/10.1310/tsr1501-1

    • PubMed
    • Google Scholar
63. 1. Silversmith DB
    2. Lemke SM
    3. Egert D
    4. Berke JD
    5. Ganguly K

    (2020) The Degree of Nesting between Spindles and Slow Oscillations Modulates Neural Synchrony

    The Journal of Neuroscience 40:4673–4684.

    https://doi.org/10.1523/JNEUROSCI.2682-19.2020

    • PubMed
    • Google Scholar
64. 1. Sobota R
    2. Hollauf M

    (2013)

    Jonglieren und Bewegungskünste [Juggling and Movement Arts DVD

    Austrian Ministry of Sports 9:002.

    • Google Scholar
65. 1. Spanò G
    2. Weber FD
    3. Pizzamiglio G
    4. McCormick C
    5. Miller TD
    6. Rosenthal CR
    7. Edgin JO
    8. Maguire EA

    (2020) Sleeping with Hippocampal Damage

    Current Biology 30:523–529.

    https://doi.org/10.1016/j.cub.2019.11.072

    • PubMed
    • Google Scholar
66. 1. Staresina BP
    2. Bergmann TO
    3. Bonnefond M
    4. van der Meij R
    5. Jensen O
    6. Deuker L
    7. Elger CE
    8. Axmacher N
    9. Fell J

    (2015) Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep

    Nature Neuroscience 18:1679–1686.

    https://doi.org/10.1038/nn.4119

    • PubMed
    • Google Scholar
67. 1. Tamaki M
    2. Matsuoka T
    3. Nittono H
    4. Hori T

    (2008) Fast sleep spindle (13-15 hz) activity correlates with sleep-dependent improvement in visuomotor performance

    Sleep 31:204–211.

    https://doi.org/10.1093/sleep/31.2.204

    • PubMed
    • Google Scholar
68. 1. Tamaki M
    2. Huang TR
    3. Yotsumoto Y
    4. Hämäläinen M
    5. Lin FH
    6. Náñez JE
    7. Watanabe T
    8. Sasaki Y

    (2013) Enhanced spontaneous oscillations in the supplementary motor area are associated with sleep-dependent offline learning of finger-tapping motor-sequence task

    The Journal of Neuroscience 33:13894–13902.

    https://doi.org/10.1523/JNEUROSCI.1198-13.2013

    • PubMed
    • Google Scholar
69. 1. Tandoc MC
    2. Bayda M
    3. Poskanzer C
    4. Cho E
    5. Cox R
    6. Stickgold R
    7. Schapiro AC

    (2021) Examining the effects of time of day and sleep on generalization

    PLOS ONE 16:e0255423.

    https://doi.org/10.1371/journal.pone.0255423

    • PubMed
    • Google Scholar
70. 1. Vahdat S
    2. Fogel S
    3. Benali H
    4. Doyon J

    (2017) Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI

    eLife 6:e24987.

    https://doi.org/10.7554/eLife.24987

    • PubMed
    • Google Scholar
71. 1. Walker MP
    2. Brakefield T
    3. Morgan A
    4. Hobson JA
    5. Stickgold R

    (2002) Practice with sleep makes perfect: sleep-dependent motor skill learning

    Neuron 35:205–211.

    https://doi.org/10.1016/s0896-6273(02)00746-8

    • PubMed
    • Google Scholar
72. 1. Wen H
    2. Liu Z

    (2016) Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal

    Brain Topography 29:13–26.

    https://doi.org/10.1007/s10548-015-0448-0

    • PubMed
    • Google Scholar
73. 1. Wilhelm I
    2. Metzkow-Mészàros M
    3. Knapp S
    4. Born J

    (2012) Sleep-dependent consolidation of procedural motor memories in children and adults: the pre-sleep level of performance matters

    Developmental Science 15:506–515.

    https://doi.org/10.1111/j.1467-7687.2012.01146.x

    • PubMed
    • Google Scholar
74. 1. Wilson MA
    2. McNaughton BL

    (1994) Reactivation of hippocampal ensemble memories during sleep

    Science (New York, N.Y.) 265:676–679.

    https://doi.org/10.1126/science.8036517

    • PubMed
    • Google Scholar
75. 1. Winer JR
    2. Mander BA
    3. Helfrich RF
    4. Maass A
    5. Harrison TM
    6. Baker SL
    7. Knight RT
    8. Jagust WJ
    9. Walker MP

    (2019) Sleep as a Potential Biomarker of Tau and β-Amyloid Burden in the Human Brain

    The Journal of Neuroscience 39:6315–6324.

    https://doi.org/10.1523/JNEUROSCI.0503-19.2019

    • PubMed
    • Google Scholar
76. 1. Xu W
    2. De Carvalho F
    3. Clarke AK
    4. Jackson A

    (2021) Communication from the cerebellum to the neocortex during sleep spindles

    Progress in Neurobiology 199:101940.

    https://doi.org/10.1016/j.pneurobio.2020.101940

    • PubMed
    • Google Scholar
77. 1. Zhang H
    2. Fell J
    3. Axmacher N

    (2018) Electrophysiological mechanisms of human memory consolidation

    Nature Communications 9:4103.

    https://doi.org/10.1038/s41467-018-06553-y

    • PubMed
    • Google Scholar

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

   "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

   "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

   "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

   "This ORCID iD identifies the author of this article:" 0000-0001-5177-4389

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