Sleep is essential for consolidating the memories that we made during the day. As we lie asleep, unconscious, our brain is busy processing the day’s memories, which travel through three parts of the brain before they are filed away. First, the hippocampus, the part of the brain that stores memories temporarily, replays the memories of the day. Then the reactivated memories pass through the thalamus, a central crossroads in the brain, so they can be embedded in the neocortex for long-term storage.

Neuroscientists can eavesdrop on the brain at work, day or night, using a technique called EEG. Short for electroencephalogram, an EEG detects brain waves like the bursts of electrical activity known as sleep spindles and slower sleep waves called slow oscillations. These two brain wave patterns represent how the brain processes memories as people sleep – and it is all about timing. If the two patterns are running in sync, then the brain’s memory systems are thought to be communicating well and memories are more likely to be stored.

But patterns of sleep spindles and slow oscillations change dramatically between childhood and adolescence. Memory consolidation also improves in those formative years. Still, it is not yet known if better synchronization between sleep spindles and slow oscillations explains how memory formation improves during this period; that is the going theory.

To test it out, Hahn et al. completed a unique study examining how well a group of 33 children could store memories, and then again when the same group were teenagers. Both times, the group was asked to memorise and then recall a set of words before and after a full night’s sleep. Hahn et al. measured how much their memory recall improved and whether their brain wave patterns were in sync, looking for any changes between childhood and adolescence. This showed that children whose sleep spindles stacked better with their slow oscillations had improved memory formation once they became teenagers.

This work highlights how communication between memory systems in the brain improves as children age, and so does memory. Moreover, it suggests that if disturbances were to be detected in patterns of sleep spindles and slow oscillations, there might be some problem with memory storage. It also points to brain stimulation as a possible treatment option for such problems in the future.

Active system memory consolidation theory proposes that sleep-dependent memory consolidation is orchestrated by three cardinal sleep oscillations (Diekelmann and Born, 2010; Helfrich et al., 2019; Klinzing et al., 2019; Mölle et al., 2011; Piantoni et al., 2013; Rasch and Born, 2013; Staresina et al., 2015): (1) Hippocampal sharp-wave ripples represent the neuronal substrate of memory reactivation (Vaz et al., 2019; Wilson and McNaughton, 1994; Zhang et al., 2018), (2) thalamo-cortical sleep spindles are thought to promote long-term potentiation (Antony et al., 2018; De Gennaro and Ferrara, 2003; Rosanova and Ulrich, 2005; Schönauer, 2018; Schönauer and Pöhlchen, 2018), while (3) neocortical SOs provide temporal reference frames where memory can be replayed, potentiated and eventually transferred from the short-term storage in the hippocampus to the long-term storage in the neocortex, rendering memories increasingly more stable (Chauvette et al., 2012; Diekelmann and Born, 2010; Frankland and Bontempi, 2005; Rasch and Born, 2013). Importantly, these three oscillations form a temporal hierarchy, where ripples and spindles are nested in SO peaks, with ripples also being locked to spindle troughs. This hierarchy likely constitutes an endogenous timing mechanism to ensure that the neocortical system is in an optimal state to consolidate new hippocampus-dependent memories (Chauvette et al., 2012; Clemens et al., 2011; Helfrich et al., 2019; Klinzing et al., 2016; Klinzing et al., 2019; Latchoumane et al., 2017; Niethard et al., 2018; Piantoni et al., 2013; Staresina et al., 2015).

Recent findings indicate that the precise temporal coordination of SO-spindle coupling is deteriorating over the lifespan, which contributes to age-related memory decline (Helfrich et al., 2018b; Muehlroth et al., 2019; Winer et al., 2019). It is currently unclear if similar principles apply to brain maturation and how the dynamic interplay of SOs and spindles is initiated. Critically, the transition from childhood to adolescence is marked by considerable changes in sleep architecture and cognitive abilities similar to the transition from young adulthood to old age (Carskadon et al., 2004; Huber and Born, 2014; Iglowstein et al., 2003; Ohayon et al., 2004; Shaw, 2007; Shaw et al., 2006). Previous research mainly focused on the individual development of SOs and sleep spindles across brain maturation, showing that these cardinal sleep oscillations undergo a substantial evolution in their defining features such as amplitude, frequency, distribution and occurrence (Campbell and Feinberg, 2009; Campbell and Feinberg, 2016; Goldstone et al., 2019; Hahn et al., 2019; Kurth et al., 2010; Nicolas et al., 2001; Purcell et al., 2017; Shinomiya et al., 1999; Tarokh and Carskadon, 2010).

Currently, two major obstacles hamper our understanding of how the precise temporal interplay between SOs and spindles predicts brain development and memory formation. First, pronounced changes in sleep oscillatory activity pose major methodological challenges for assessing and comparing SOs and sleep spindles across the age spectrum (Muehlroth and Werkle-Bergner, 2020). Second, memory performance was rarely tested in developmental sleep studies, thus, impeding our understanding of the functional significance of temporal SO-spindle coupling for memory formation.

Here, we leverage a unique longitudinal study design from childhood to adolescence to investigate how SO-spindle coupling emerges during development and infer its functional significance for developing memory networks. To account for the substantial morphological alterations of SO and spindle morphology across brain maturation, we developed a principled methodological approach to assess SO-spindle coupling. We utilized individualized cross-frequency coupling analyses, which enable a clear demonstration of SO-sleep spindles coupling during both developmental stages. Critically, over the course of brain maturation from childhood to adolescence, more spindles are tightly coupled to SOs, which directly predicts improved memory formation.

Using a longitudinal study design, we tested 33 healthy participants during childhood (age: 9.5 ± 0.8 years; mean ± SD) and during adolescence (age: 16 ± 0.9 years). At both time points participants underwent full-night ambulatory polysomnography at their home during two nights (adaptation and experimental night; Figure 1A) and performed a declarative memory task during the experimental night (Figure 1B). After encoding, participants recalled word pairs before and after a full night of sleep. As previously shown (Hahn et al., 2019), memory recall improved from childhood to adolescence (Figure 1C; F_1,32 = 38.071, p<0.001, η² = 0.54) and immediate recall was better than delayed recall (F_1,32 = 6.408, p=0.016, η² = 0.17; Maturation*Recall Time interaction: F_1,32 = 2.059, p=0.161, η² = 0.06). Next, we assessed the relationship of sleep-dependent memory consolidation (delayed recall – immediate recall) between childhood and adolescence and found no correlation between the two maturational stages (Figure 1—figure supplement 1A; for a direct comparison of sleep-dependent memory consolidation see Figure 1—figure supplement 1B). During adolescence, memory consolidation was superior after a sleep retention interval compared to a wake retention interval (Figure 1—figure supplement 1C), indicating a beneficial effect of sleep on memory.

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Oscillatory signatures of NREM sleep during childhood and adolescence

To investigate whether SO-spindle coupling accounts for enhanced memory formation from childhood to adolescence, we first assessed the oscillatory signatures of NREM (2 and 3) sleep. We compared spectral estimates during childhood and adolescence using cluster-based permutation tests (Maris and Oostenveld, 2007) across frequencies from 0.1 to 20 Hz (Figure 2A; at electrode Cz). We found that EEG power significantly decreased from childhood to adolescence between 0.1 to 13.6 Hz (cluster test: p<0.001, d = −2.74) and 14.6 to 20 Hz (cluster test: p<0.001, d = −1.60; Figure 2A). However, inspection of the underlying spectra revealed that this effect was driven by (I) an overall offset of the 1/f component of the power spectrum on the y-axis and (II) by a shift of the peak frequency in the spindle band. In order to mitigate the prominent power difference, we first z-normalized the signal in the time domain, which alleviated the differences above ~15 Hz (Figure 2B). This analysis showed increased spectral power during childhood from 0.3 to 8.4 Hz (cluster test: p=0.002, d = −084), which was broadband and not oscillatory in nature. In addition power differences between 10.6 to 12.8 Hz (cluster test: p=0.040, d = −1.07) and 13.4 and 14.8 Hz (cluster test: p=0.046, d = 1.12) directly reflected the spindle peak frequency shift from childhood to adolescence. To account for the differences in broadband 1/f and oscillatory components, we disentangled the 1/f fractal component (Figure 2C) from the oscillatory residual (Figure 2D) by means of irregular-resampling auto-spectral analysis (IRASA Helfrich et al., 2018b; Wen and Liu, 2016). We found that a significant decrease in the fractal component between 0.3 and 10.8 Hz from childhood to adolescence (Figure 2C; cluster test: p<0.013, d = −0.90), accounted for the prominent broadband power differences as observed in Figure 2A. To assess true oscillatory brain activity, we subtracted the fractal component (Figure 2C) from the normalized power spectrum (Figure 2B), to isolate SO and spindle oscillations in the frequency domain (Figure 2D).

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Based on the oscillatory residuals, we then extracted the individual peak frequency and the corresponding amplitude in the SO and sleep spindle range for each electrode in every participant during childhood and adolescence. After discounting 1/f effects, we found that spindle amplitude (Figure 2E) increased in a centro-parietal cluster (cluster test: p=0.005, d = 0.63), whereas spindle peak frequency (Figure 2F) accelerated at all channels from childhood to adolescence (cluster test: p<0.001, d = 1.57). SO amplitude and frequency decreased from childhood to adolescence (Figure 2—figure supplement 1A,B). Both, SO and spindle features have been previously related to memory formation (Gais et al., 2002; Huber et al., 2004; Lustenberger et al., 2017; Schabus et al., 2004; Schabus et al., 2006). However, neither spindle nor SO amplitude or peak frequency changes explained the behavioral differences (Figure 2—figure supplement 1C,D). Note, we also observed a peak in the theta band, which was unrelated to behavior (for theta peak frequency and amplitude correlations with behavior see Figure 2—figure supplement 1E).

Individual features of discrete SO and sleep spindle events

After having established the cardinal features of SO and spindle oscillations during childhood and adolescence, we then individually adjusted previously used SO and spindle detection algorithms (Helfrich et al., 2018b; Mölle et al., 2011; Staresina et al., 2015) according to the individual peak frequencies (Bódizs et al., 2009; Ujma et al., 2015). We considered the possibility that two distinct spindle frequency peaks exist (Anderer et al., 2001; Werth et al., 1997), but inspecting the oscillatory residuals did not indicate two clearly discernable peaks in individual electrodes of the majority of participants (for exemplary oscillatory residuals see Figure 2—figure supplement 2A). Because we observed the typical antero-posterior spindle frequency gradient (Cox et al., 2017; De Gennaro and Ferrara, 2003; Zeitlhofer et al., 1997) with slower frontal and faster posterior spindles (Figure 2—figure supplement 2B), we used the highest peak in the spindle range at every electrode as the most representative oscillatory event for the detection algorithm. Importantly, individualized SO and spindle event detections closely followed spectral sleep patterns during childhood and adolescence (Figure 3A,B; event detections are superimposed in white).

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Next, we quantified how many separate SO and spindle event detections co-occurred within a 2.5 s time window (reflecting ±2 SO cycles around the spindle peak; Helfrich et al., 2019). Note that the co-occurrence rate does not actually indicate coupled SO-spindle events but directly reflects the percentage of detected spindle events that are concomitant with detected SO events. Co-occurrence rate was higher in NREM3 than NREM2 sleep during childhood and adolescence (Figure 3C; F_1,32 = 2334.19, p<0.001, η² = 0.99). Subsequently we restricted our analyses to NREM3 sleep to avoid spurious cross-frequency coupling estimates caused by the lack of simultaneous detections during NREM2 sleep (Aru et al., 2015; for circular plots including NREM2 sleep see Figure 4—figure supplement 1D).

To ensure reliable coupling estimates, we further Z-normalized individual spindle-locked data epochs in the time domain (Figure 3D) for all subsequent analyses to avoid possible confounding amplitude differences (Aru et al., 2015; Cole and Voytek, 2017; Helfrich et al., 2018b). Differences in the grand average spindle time-lock directly reflect the enhanced SO-spindle coupling, which becomes visible in the time domain when more events are precisely coupled to the SO ‘up-state’ (positive SO-peak). This effect can also be appreciated in single subject spindle-locked data (Figure 3E,F, left). Note that we found no differences in the underlying SO-component around the spindle peak (0 s, time point of phase readout), thus, confirming that the Z-normalization alleviated possible amplitude differences (Figure 3D, inset; for non-normalized spindle-locked data see Figure 3—figure supplement 1).

To further elucidate the interaction between SO phase and spindle activity, we also assessed this effect in the time-frequency domain by calculating SO-trough-locked time-frequency representations (Figure 3G,H, left). The alternating pattern (i.e. spindle power decreases during the ‘down-state’ and increases during the ‘up-state’) within the spindle frequency range during childhood and adolescence indicated an influence of SO phase on spindle activity.

To quantify the interplay of SO and spindle oscillations, we employed event-locked cross-frequency analyses (Dvorak and Fenton, 2014; Helfrich et al., 2018b; Staresina et al., 2015). While this method is mainly equivalent to other frequently used methods to assess cross-frequency coupling (Helfrich et al., 2018a), it can be similarly impacted by their pitfalls (Aru et al., 2015). Therefore, we adopted a conservative approach by first alleviating power differences (Figure 2B and Figure 3D) and establishing the presence of oscillations in the signal (Figure 2D and Figure 3C). Next, we extracted the instantaneous SO phase during every spindle peak at every electrode and for every subject. Then we calculated the preferred phase (circular mean direction) and coupling strength (phase-locking value, plv) separately during childhood and adolescence for all events at a given electrode (see Figure 3E,F right for exemplary phase histograms). We confirmed that both markers of SO-spindle coupling were not confounded by differences in the total number of detected events using a bootstrapping procedure (Figure 4—figure supplement 1C).

Brain maturation impacts SO-spindle coupling

First, we assessed in which phase of the SO spindles preferably occur and how maturation affects the preferred coupling direction. We found that coupling direction did not change at frontal electrodes (Figure 3G,H, right), where spindles were locked to the SO peak during childhood (33.5°±44.8°; circular mean ± SD) and adolescence (34.2°±35.8°; circular mean ± SD). However, we detected differences in centro-posterior clusters (central: p<0.001, d = −0.84, parieto-occipital: p<0.001, d = 0.74; Figure 4—figure supplement 1A), which did not correlate with behavior (Figure 4—figure supplement 1B).

After showing that sleep spindles are preferably locked to the SO peaks, we subsequently quantified how precisely spindles are embedded in the preferred SO phase by assessing the respective coupling strength (1 – circular variance). Coupling strength increased from childhood to adolescence across all electrodes except P4 (cluster test: p<0.001, d = 0.74; Figure 4A), indicating that frontal sleep spindles become more tightly locked to their respective preferred SO phase as the phase on frontal sensors remained stable across maturation. To further illustrate the impact of the coupling strength increase on spindle-SO-events, we calculated the percentage of spindle events that peaked at the preferred phase (±22.5°) as compared to all detected events (Figure 4B, left). The resulting metric is directly related to the coupling strength and was solely computed to further illustrate and highlight the observed effects. Concurrent with our coupling strength analyses, percentage of spindles within the preferred phase bin increased in a fronto-parietal cluster (p<0.001 d=0.74; Figure 4B, right) however, decreased in an occipital cluster from childhood to adolescence (p=0.007, d = 0.78). These results reveal an overall increase in SO-spindle coupling precision from childhood to adolescence.

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In a longitudinal sleep and memory study, we show that SO and spindles become more precisely coupled during brain maturation from childhood to adolescence. Crucially, this increase indicated improved recall performance and sleep-dependent consolidation in a declarative memory task. Collectively, our findings suggest that the emergence of precise temporal SO-spindle coordination might index the development of hippocampal-neocortical memory systems.

Here, we employed an individualized cross-frequency coupling approach, since profound changes in the network organization during development were apparent, which potentially confound cross-frequency coupling estimates (Aru et al., 2015; Helfrich et al., 2018b): In particular, we observed (1) changes in non-oscillatory 1/f background activity (Figure 2C) and (2) low frequency power (Figure 2D and Figure 2—figure supplement 1A,B) as well as (3) a peak frequency shift in the spindle-band (Figure 2E,F). While some SO and spindle activity markers, such as amplitude and density have been previously associated with memory consolidation (Gais et al., 2002; Huber et al., 2004; Schabus et al., 2004; Schabus et al., 2006), our findings now could provide a mechanistic explanation how improved network coordination subserves memory formation. Furthermore, our results highlight the critical need to account for individual oscillatory features and to discount non-oscillatory broadband effects, which are known to confound cross-frequency coupling analyses (Figure 4—figure supplement 1D,E,G; Aru et al., 2015; Bódizs et al., 2009; Cole and Voytek, 2017; Ujma et al., 2015; Voytek et al., 2015). While our findings replicate the pattern that parietal spindles are faster than frontal spindles, we did not find reliable evidence for the presence of two distinct spindle peaks in individual electrodes (Figure 2—figure supplement 2).

Importantly, SO-spindle coupling is one of the main building blocks of the active system memory consolidation theory, which posits that sleep-dependent memory consolidation is coordinated by a temporal hierarchy of SO, sleep spindles and hippocampal ripples (Clemens et al., 2011; Diekelmann and Born, 2010; Helfrich et al., 2019; Helfrich et al., 2018b; Klinzing et al., 2016; Klinzing et al., 2019; Mölle et al., 2002; Mölle et al., 2006; Muehlroth et al., 2019; Niethard et al., 2018; Rasch and Born, 2013; Staresina et al., 2015). Recently, it has been shown, that only SO coupled spindles, not isolated spindles trigger hippocampal-neocortical information transfer, suggesting that SO-spindle coupling might be a proxy of this subcortical-cortical network communication that is measurable on the scalp level (Helfrich et al., 2019). While we did not measure hippocampal activity in the current study, our results still provide direct evidence for the behavioral relevance of the temporal hierarchy of SO and sleep spindles. We demonstrate that improved temporal coordination between prefrontal SOs and sleep spindles indexes memory network maturation. Notably, we observed that the precise coupling phase over frontal EEG sensors was already determined during childhood, but over time even more spindles became precisely locked to the preferred SO phase (~0°; SO ‘up-state’: Figure 3G,H & Figure 4—figure supplement 1A). This might indicate that SO-spindle coupling is an inherent feature of the human memory system.

Previous research has emphasized the importance of a preferred phase closer to the SO up-state (Helfrich et al., 2018b; Muehlroth et al., 2019; Winer et al., 2019). However, those studies focused on the detrimental impact of aging on memory consolidation. Now our findings reveal that mechanisms of maturation and aging are different. While maturation reduces the circular variance by increasing the coupling strength, (Figure 4A,B), aging leads to a temporal dispersion of the coupling away from the SO ‘up-state’ with almost constant circular variance (Helfrich et al., 2018b; Muehlroth et al., 2019). In both instances, only changes in frontal cross-frequency coupling were predictive of behavior, which is in line with the hypothesized origin of SOs in the medial prefrontal cortex (Massimini et al., 2004; Murphy et al., 2009). Critically, precise prefrontal SO-spindle coupling governs over hippocampal-neocortical network communication and actively initiates the information transfer within the network (Helfrich et al., 2019). Therefore, our results indicate that brain maturation leads to more fluent communication within memory networks.

A multitude of studies suggested a general positive effect of sleep on memory consolidation (Backhaus et al., 2008; Diekelmann and Born, 2010; Huber et al., 2004; Klinzing et al., 2019). Accordingly, our data provide additional support for this consideration by showing that consolidation is superior after sleep retention than after a wake period during adolescence (Figure 1—figure supplement 1C). In the present study, SO-spindle coupling strength did not predict sleep specific memory improvements (Figure 4—figure supplement 1F). Two likely explanations possibly contributed to this observation: (1) Performance in the word pair task was close to ceiling level in the adolescent group. Considering, that most evidence for a relationship between SO-spindle coupling and memory formation stems from studies comparing across relatively wide age ranges (Helfrich et al., 2018b; Muehlroth et al., 2019), an overall high performance level could lead to less variance that might mitigate detecting a relationship between coupling strength and sleep specific memory benefits. (2) Apart from memory consolidation, the coupling strength increase is also related to maturation of general cognitive abilities (Figure 1C and Figure 4C; also see Hahn et al., 2019), which further complicates disentangling sleep-specific memory benefits since both processes exhibit a substantial overlap (Hoedlmoser et al., 2014; Schabus et al., 2004; Schabus et al., 2006).

Nonetheless, several recent findings demonstrated a causal relationship between SO-spindle coupling and memory formation. For example, ripple-triggered electrical stimulation (Maingret et al., 2016) or optogenetic manipulation boosted SO-spindle coupling and memory performance the next day in rodents (Latchoumane et al., 2017). In humans, it has been shown that electrical stimulation at ~0.75 Hz induces both SO and spindle power as well as improved memory performance (Marshall et al., 2006). However, recent replication attempts provided contradictory findings (Bueno-Lopez et al., 2019; Lafon et al., 2017; Sahlem et al., 2015). Apart from electrical stimulation, slow rocking motions (Perrault et al., 2019) and auditory stimulation (Ngo et al., 2013) might be effective in entraining SOs and concomitant spindles to improve memory performance. Furthermore, targeted memory reactivation triggers SO-spindle coupling, which enabled successful decoding of mnemonic information (Antony et al., 2018; Antony et al., 2019; Cairney et al., 2018; Schönauer, 2018; Schönauer et al., 2017). Taken together, several lines of research converge on the notion that precise SO-spindle coupling constitutes to a key mechanism for memory formation.

This is of immediate relevance for future studies, since it has been shown that for example tau pathology in the medial temporal lobe, a precursor of Alzheimer’s disease, also impairs prefrontal SO-spindle coupling (Winer et al., 2019). Thus, cross-frequency coupling might potentially constitute a novel pathway to understand age- or disease-related cognitive decline, amendable to intervention. In future, it might be possible to entrain SO-spindle synchrony through electrical (Lustenberger et al., 2016) or auditory stimulation (Ngo et al., 2013) to alleviate memory deficits (for a recent review see Hanslmayr et al., 2019).

Data required to reproduce the main conclusions and all figures are available at Dryad under doi https://doi.org/10.5061/dryad.8sf7m0chn.

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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

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Writing - original draft

   For correspondence

   michael.hahn@sbg.ac.at

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3022-0552

2. 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, Writing - review and editing

   Competing interests

   No competing interests declared

3. 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

   Resources, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5899-8772

4. 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, Resources, Data curation, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing - review and editing

   Contributed equally with

   Randolph F Helfrich

   For correspondence

   kerstin.hoedlmoser@sbg.ac.at

   Competing interests

   No competing interests declared

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

5. Randolph F Helfrich

   Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Visualization, Methodology, Writing - review and editing

   Contributed equally with

   Kerstin Hoedlmoser

   For correspondence

   randolph.helfrich@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8045-3111

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