Motor memory is the capacity that affords the development of a repertoire of motor skills essential for daily life activities such as typing on a keyboard or buttoning a shirt. After initial acquisition, a motor memory undergoes consolidation, which is the offline (i.e. without further practice) process by which the acquired memory trace becomes stable and long-lasting (Maquet, 2001; Robertson et al., 2004). Sleep, and non-rapid eye movement sleep (NREM) in particular (Albouy et al., 2008; Albouy et al., 2013), is thought to offer a privileged window for the consolidation process to occur (King et al., 2017). The specific electrophysiological events characterizing NREM sleep, such as slow waves (SW - high amplitude waves in the 0.5–2 Hz frequency band) (Ngo et al., 2013b), thalamo-cortical spindles (short burst of oscillatory activity in the 12–16 Hz sigma band) (Barakat et al., 2013; Ngo et al., 2019) and hippocampal ripples (80–100 Hz oscillations in humans) (Axmacher et al., 2008), as well as their precise synchrony, have been described to support neuroplasticity processes underlying consolidation (Muehlroth et al., 2019).

In recent years, there has been increasing evidence in both the declarative and motor memory domains that consolidation processes can be augmented by experimental interventions such as targeted memory reactivation (TMR) applied during post-learning sleep (Rudoy et al., 2009; Cousins et al., 2016; Schönauer et al., 2014; Hu et al., 2020). In TMR protocols, sensory stimuli (e.g. sounds) that are associated to the learned material during the learning episode are presented offline, during the consolidation interval, in order to reactivate the encoded memory trace (Ngo et al., 2013b). This memory reinstatement is thought to be supported by a TMR-mediated reinforcement of the endogenous brain reactivation patterns that occur spontaneously during the consolidation process. Such reactivations are thought to support the transfer of memory traces to the neocortex (Born and Wilhelm, 2012). While the beneficial effect of TMR on motor performance has been highlighted in previous research (e.g. Antony et al., 2012 ; Faul et al., 2007; Schönauer et al., 2014; Cousins et al., 2016), the neurophysiological processes supporting these effects have been scarcely studied. Therefore, the goal of the present study was to elucidate the neurophysiological processes supporting memory reactivation during sleep which underlie TMR-induced enhancement in motor memory consolidation.

We designed a within-participant experiment (Figure 1) pre-registered in the Open Science Framework (available at https://osf.io/y48wq). Young healthy participants were trained on a Serial Reaction Time task (Nissen and Bullemer, 1987 ) during which they learned two different motor sequences, each associated to a particular sound. Participants were then offered a 90-min nap that was monitored with polysomnography. During NREM 2–3 sleep stages, the sound associated to one of the two trained sequences (‘Associated’ sound to the ‘Reactivated’ sequence) as well as a control sound (‘Unassociated’) that was not associated to the learned material were played. The sound associated to the other learned sequence was not presented during the nap, thus serving as a no-reactivation control condition (‘Non-reactivated’). The time course of the TMR-induced consolidation process was assessed with retests after the nap episode as well as after a night of sleep spent at home. At the behavioral level, results demonstrated the expected TMR benefit. At the brain level, they indicate a TMR-mediated enhancement of SWs and SW-sigma coupling after the peak of the SW such that the higher the coupling, the greater the effect of TMR on motor performance. Intriguingly, unassociated sounds also strengthened SW-sigma coupling but at a different phase of the SW (trough) and the increase in sigma power nested in the trough of the potential evoked by the unassociated sounds was related to the TMR benefit. Altogether, our findings suggest that sigma oscillations may play a dual role in the consolidation process depending on both the nature of the information to be processed and the phase of the slow oscillation in which they occur. We propose that sigma oscillations protect or reinstate motor memory depending on their temporal coordination with slow oscillations during post-learning sleep.

Figure 1

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The analyses presented in the current paper that were not pre-registered are referred to as exploratory.

Behavioral data

As per our pre-registration, behavioral analyses focused on performance speed (i.e. response time (RT) on correct key presses) on the motor sequence learning task measured at three time points: pre-nap, post-nap, and post-night (Figure 1a). Results of the analyses on performance accuracy are presented in Figure 2—figure supplement 1.

Analyses of the pre-nap training data indicated that participants learned the two sequence conditions (reactivated and non-reactivated sequences) to a similar extent during initial learning (16 blocks of training; main effect of Block: F(15, 345)=34.82; p-value = 2.04e-26; η²=0.6; main effect of Condition: F(1, 23)=0.16; p-value = 0.69; Block by Condition interaction: F(15, 345)=1.09; p-value = 0.37; Figure 2a). After initial training, participants were offered a short break (~5 min) and were then tested again on the learned motor sequences. This short pre-nap test session was designed to offer a fatigue-free measure of the end-of-training, asymptotic performance to be used as baseline for the computation of subsequent offline changes in performance (see description below) (Pan and Rickard, 2015). Before computing offline changes in performance, we first assessed whether participants reached stable and similar performance levels between conditions during the pre-nap test session. Results showed that while performance reached similar levels between conditions (4 blocks; main effect of Condition: F(1,23) = 3.39e-5; p-value = 0.99; Block by Condition interaction: F(3,69) = 1.21; p-value = 0.31), asymptotic performance levels were not reached as shown by a significant Block effect (F(3,69) = 6.67; p-value = 0.001; η²=0.22). To meet the performance plateau pre-requisite to compute offline changes in performance, the first block of the pre-nap test session driving this effect was removed from further analyses. Performance on remaining blocks was stable as indicated by a non-significant Block effect (F(2,46) = 1.56; p-value = 0.22). Similar to above, the main effect of Condition (F(1,23) = 0.04; p-value = 0.85) and the Block by Condition interaction (F(2,46) = 1.81; p-value = 0.18) were not significant. Altogether, these results indicate that a performance plateau was ultimately reached and both sequence conditions were learned similarly (Figure 2a).

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Post-nap and post-night offline changes in performance were then computed for both conditions as the relative change in speed between the three plateau blocks of the pre-nap test and the first four blocks of the post-nap and post-night sessions, respectively. As such, improvement in performance from training to retest (i.e. faster performance at retest compared to training) was reflected by positive offline changes in performance. A repeated measures analysis of variance (rmANOVA) performed on offline changes in performance with Time-point (post-nap vs. post-night) and Condition (reactivated vs. non-reactivated) as within-subject factors showed a significant Time-point effect, whereby changes in performance were significantly higher at the post-night as compared to the post-nap retest (F(1,23) = 46.53; p-value = 5.89e-7; η²=0.67; Figure 2b). Critically, offline changes in performance for the reactivated sequence were significantly higher than for the non-reactivated sequence (Condition effect: F(1,23) = 4.75; p-value = 0.0397; η²=0.17). The Condition by Time-point interaction was not significant (F(1,23) = 7.42e-4; p-value = 0.98). In conclusion, our behavioral results indicate a TMR-induced enhancement in performance that did not differ across nap and night intervals.

Electrophysiological data

Participants’ sleep was recorded using a 6-channel EEG montage during a 90-min episode following learning. Sleep was monitored online and sounds were presented during NREM sleep stages. Sleep characteristics resulting from the offline sleep scoring as well as the distribution of auditory cues across sleep stages are shown in in Supplementary file 1. Briefly, results indicate that all the participants slept during the nap (average total sleep time: 67 min; average sleep efficiency: 74.9%) and that cues were accurately presented in NREM sleep (average stimulation accuracy: 88.4%).

Event-related analyses

Event-related analyses assessed the effect of the sound condition (i.e. associated vs. unassociated) on both the potentials (ERP) but also the oscillatory activity (time-frequency analyses) evoked by the auditory cues.

For the analyses of the auditory evoked potentials, we first computed ERPs on each EEG channel (see Figure 3—figure supplement 1 for channel level data) separately for associated and unassociated auditory cues presented during NREM2-3 stages and subsequently averaged ERPs across channels (Figure 3a). ERP amplitude was extracted for the 2 conditions from the temporal window highlighted in Figure 3a in which the amplitude of the auditory responses across conditions was significantly lower (trough) than zero (from 0.44 to 0.63 s relative to cue onset, see Figure 3—figure supplement 2 and Figure 3—figure supplement 3 for across- and within-channel level data, respectively). Between-condition comparisons using Wilcoxon signed-rank test showed that the amplitude of the ERP trough was significantly deeper (V=75, p-value = 0.016) following associated as compared to unassociated cues (Figure 3b).

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For the analyses of the auditory evoked oscillatory activity, we investigated whether EEG sigma oscillation power (12–16 Hz) evoked by the auditory cues on each channel was modulated by the different stimulation conditions in the 2.5 s following the cue onset. Note that, for completeness, time-frequency analyses were performed on a wider frequency range (5–30 Hz) and that analyses outside the sigma band were considered as exploratory. Cluster Based Permutations (Maris and Oostenveld, 2007) (CBP) tests computed on power averaged across all channels did not highlight any significant clusters between the two auditory cues.

Sleep event detection

Slow waves (SWs) and spindles were detected automatically (Vallat, 2020) on all EEG channels in all NREM2-3 sleep epochs (thus including associated and unassociated sound stimulation intervals as well as non-stimulation intervals, see Figure 1b). The detection tool identified on average 424.8 [95% CI 328–521.6] slow waves and 98 [95% CI: 82.8–113.2] spindles averaged across channels during the nap episode (see methods for details on the detection algorithms and in Supplementary file 2 for the number of events detected on each channel and each condition).

Concerning the detected SWs (Figure 4a), both peak-to-peak (PTP) amplitude and density (averaged across all EEG channels) were greater for the associated as compared to the unassociated stimulation intervals (amplitude: t=2.7; df = 21; p-value = 0.009; Cohen’s d=0.55; and density V=197; p-value = 0.01; r=0.58). Exploratory analyses including the detected SWs in the non-stimulation (rest) intervals did not highlight PTP amplitude differences between the rest intervals and the two types of stimulation intervals (rest vs. associated: t=0.82; df = 21; p-value = 0.42; rest vs. unassociated: t=–0.92; df = 21; p-value = 0.42; Figure 4b–c). However, SW density was significantly lower during the rest as compared to the stimulation intervals, regardless of the cue type (rest vs. associated: V=232; p-uncorrected=0.0002; p-FDR=0.0004; r=0.66; rest vs. unassociated: V=224; p- uncorrected = 0.0008; p-FDR=0.00081; r=0.6; Figure 4d). Altogether, these results indicate that auditory stimulation induced an overall increase in SW density, and, more importantly, that the associated sounds resulted in an increase in SW amplitude and density as compared to the unassociated sounds.

Figure 4

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Sleep spindle density averaged across all channels did not differ between associated and unassociated stimulation intervals (V=98, p-value = 0.89). Similarly, exploratory analyses on additional spindle features including amplitude and frequency did not yield any significant differences between stimulation conditions (all p-values > 0.2). As no effect of stimulation was observed on spindle characteristics, the two conditions were pooled together in exploratory analyses including spindles detected during rest intervals (Figure 5). Results show that spindle density did not differ between stimulation and rest intervals (V=97, p-value = 0.22). Interestingly, the difference in spindle amplitude was marginally significant with higher amplitudes during the auditory stimulation intervals as compared to the rest intervals (V=199; p-value = 0.065; r=0.73), whereas spindle frequency showed the opposite pattern (t=–3.42; df = 22; p-value = 0.005; Cohen’s d=0.71). In summary, these results indicate that while auditory stimulation altered spindle features (frequency and amplitude to a lesser extent) as compared to rest, the two sound conditions did not differently influence these characteristics.

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Phase-amplitude coupling

We investigated whether the phase of the slow oscillations in the 0.5–2 Hz frequency band was coupled to the amplitude of sigma (12–16 Hz) oscillations following either the auditory cue or the negative peak of the detected (i.e. spontaneous) SWs. The analyses presented below focus on the comparison between conditions but see Figure 6—figure supplement 1 for coupling analyses performed within each stimulation condition and at rest.

Event-related phase-amplitude coupling (ERPAC) analyses were performed across channels on a wider frequency range (7–30 Hz) for completeness; thus, analyses outside the pre-registered sigma band (see red frame in Figure 6) are considered exploratory. The ERPAC values locked to the auditory cues were compared between the two stimulation conditions. The CBP test did not highlight any significant clusters (alpha threshold = 0.025, cluster p-value = 0.44). The preferred coupling phase, which represents the phase at which the maximum amplitude is observed, did not significantly differ between conditions (F(1,46) = 0.3, p-value = 0.9). These results suggest that the stimulation conditions did not influence the coupling between the phase of the slow oscillations and the amplitude of sigma oscillations at the auditory cue.

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Comparison of the ERPAC locked to the negative peak of the SWs (Figure 6 and Figure 6—figure supplement 2 for channel level data) between stimulation conditions revealed a significant cluster (alpha threshold = 0.025, cluster p-value = 0.024; Cohen’s d=–0.56). Specifically, the coupling between the phase of the signal in the 0.5–2 Hz frequency band and the amplitude of the signal in the 14–18 Hz frequency band was significantly stronger around the negative SW peak (from –0.8 to 0.2 s relative to negative peak) during unassociated as compared to associated stimulation intervals (Figure 6b). The exploratory comparison between rest and associated stimulation intervals did not reveal any significant clusters (alpha threshold = 0.025, all cluster p-values > 0.6) but a significant cluster was observed between unassociated stimulation and rest (alpha threshold = 0.025, cluster p-value = 0.001; Cohen’s d=0.53; Figure 6c). This cluster was observed between 13.5 and 20 Hz and –1–0.5 s around the negative peak of the SW. The preferred phases in each of the conditions were not significantly different (associated vs. unassociated: F(1,42) = 0;007, p-value = 0.94; associated vs. rest: F(1,42) = 0.01, p-value = 0.91; unassociated vs. rest: F(1,42) = 0.3, p-value = 0.87; see Figure 6—figure supplement 1). Altogether, these results suggest that slowand sigma oscillation coupling observed just before the onset of the SW was stronger during unassociated as compared to associated and rest intervals but that the preferred coupling phase was not modulated by the experimental conditions.

Correlational analyses

Correlation analyses between the TMR index (i.e. the difference in offline changes in performance – averaged across time points – between the reactivated and the non-reactivated sequence) and the density of SW and spindles as well as with the amplitude of the ERP did not yield any significant results (density of spontaneous SW: S=2486, p-value = 0.97) density of spontaneous spindles S=1412, p-value = 0.24; amplitude of the negative peak of the ERP S=2282, p-value = 0.73. However, the correlational CBP analysis between the TMR index and the difference in TF power elicited by the different auditory cues highlighted one significant cluster (alpha threshold = 0.025, cluster centered on 0.5 s post-cue p-value = 0.022, rho = - 0.46; Figure 7a and Figure 7—figure supplement 1 for channel level data). For illustration purposes, we extracted the difference in TF power within the significant cluster included in the pre-registered frequency band (12–16 Hz) and from 0.35 to 1 s post-cue onset (see Figure 3). The resulting scatter plot presented in Figure 7b indicates that higher TMR index (i.e. greater behavioral benefit of TMR) was related to higher sigma oscillation power for the unassociated compared to the associated sound condition.

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Finally, with respect to ERPAC-TMR index correlation analyses, no significant correlation was observed between the auditory-locked ERPAC metrics and the TMR index (alpha threshold = 0.025, cluster p-values >0.09). In contrast, cluster-based permutation correlational tests performed between the 12 and 16 Hz TFR SW-locked ERPAC difference between the two conditions and the TMR index revealed a significant cluster. Results show that the associated vs. unassociated difference in coupling strength between the phase of the signal in the 0.5–2 Hz frequency band and the amplitude of the signal in the 14.5–17 Hz frequency band, just after the SW peak (0.5 and 1 s), was positively correlated with the TMR index (alpha threshold = 0.025, cluster p-value = 0.0499, rho = 0.55; Figure 8a and Figure 8—figure supplement 1 for channel level data). For illustration purposes, we extracted the difference in ERPAC in the significant cluster included in the pre-registered frequency band (between 14.5 and 16 Hz) from 0.55 to 1.05 s. The resulting scatter plot (Figure 8b) indicates that the stronger the phase-amplitude coupling during associated as compared to the unassociated stimulation intervals, the higher the TMR index.

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In the present study, we examined the impact of auditory TMR on motor memory consolidation as well as the neurophysiological processes supporting reactivation during sleep. Our results demonstrate a TMR-induced behavioral advantage such that offline changes in performance were larger on the reactivated as compared to the non-reactivated sequence. These behavioral results are in line with earlier motor learning studies showing improvement in performance after auditory (Schönauer et al., 2014; Antony et al., 2012; Cousins et al., 2016) or olfactory (Laventure et al., 2016) TMR during sleep. As opposed to earlier TMR research though, the current results suggest that TMR-induced consolidation is not a protracted process that needs additional time and/or sleep to develop (Cairney et al., 2018), as a behavioral advantage could already be observed immediately after the TMR episode. Also, in contrast to earlier work showing that TMR effects can be transient (Cousins et al., 2016), the current data indicate that the effect of TMR on motor performance was sustained overnight. It remains unclear whether these discrepancies are related to the nature of the task (e.g. declarative vs. motor), the sensory stimulus used for reactivation (words vs. sound) or the duration of the reactivation / sleeping episode (nap vs. night). Nevertheless, our findings suggest that the TMR episode during a nap immediately following learning set the reactivated memory trace on a distinct yet parallel trajectory as compared to the non-reactivated memory trace.

TMR effects were also observed at the brain level such that electrophysiological responses differed according to whether they were evoked by associated or unassociated cues. Specifically, the amplitude of the negative component of the auditory ERP was higher for the sounds associated to the motor memory task as compared to the unassociated sounds. These results are in line with findings from earlier associative learning studies performed during wakefulness showing that auditory cues evoke larger responses after conditioning (i.e. after they are associated to another stimulus) and that ERP amplitude is restored to pre-association levels after extinction (see Christoffersen and Schachtman, 2016 for a review). The current findings also extend prior observation of a modulation of auditory-TMR-evoked responses during sleep (Rudoy et al., 2009). This earlier study showed that auditory cues presented during post-learning sleep evoked larger ERPs when they were associated to items better remembered at subsequent recall as compared to cues associated to less remembered items. Our findings not only concur with this post-hoc analysis, but also provide the first direct evidence of an ERP modulation based on the memory content of the cue during post-learning sleep. This difference in brain potentials during sleep might be seen as the neural signature of the plasticity processes that occurred during learning. Not exclusive to the previous speculation, such effects might also be attributed to the (re)processing of the memory trace during post-learning sleep. Importantly, one could argue that the difference in ERP amplitude observed in the present study might be due to familiarity effects, as the unassociated sound might have been perceived as novel as compared to the associated sound. We argue that this is unlikely as new or rare auditory stimuli usually present larger negative amplitudes as compared to old or frequent sounds during both sleep and wakefulness (e.g. FN 400 [Rugg and Curran, 2007; Paller et al., 2012]) for old/new paradigms during wake and mismatch negativity components for oddball paradigms during both wake and sleep (Näätänen et al., 2007; Ruby et al., 2008). Instead, we propose that the auditory evoked brain responses observed in the current study reflect the (re)processing of the motor memory trace that was encoded during initial learning. It is worth noting that the negative peak of the potential evoked by the unassociated cues was not only lower (i.e. less negative) than for the associated cues but was also sometimes even absent on some channels (see Figure 3—figure supplement 1). These observations are partly in line with earlier research (Cairney et al., 2018) but contradict other findings. For example, Weigenand et al., 2016 as well as Schreiner and Rasch, 2015 observed significant negative responses evoked by control sounds or by sounds that were associated to later forgotten items. These discrepancies remain unclear but might in part be explained by methodological differences that are known to influence the amplitude of ERPs (e.g. sleep cycle stimulated, electrode(s) examined, number of stimulation repetitions provided).

In addition to the modulation of auditory-evoked responses described above, the properties of the detected (spontaneous) SWs were influenced by sound presentation and sound condition. Specifically, SW density was higher during sound presentation as compared to rest and the density and amplitude of the SWs were greater during intervals of associated – as compared to unassociated – cue stimulation. The effect of sound presentation on SW characteristics is in line with previous work showing sound-related entrainment of SW trains and increase in SW amplitude during sleep (Ngo et al., 2013a; Ngo et al., 2013b; Ngo et al., 2015; Ngo et al., 2019). More importantly, in line with the ERP results, our data show that the memory content of the cue modulated SW physiology above and beyond the mere effect of sound presentation. This is the first evidence, to the best of our knowledge, of a modulation of SW physiology based on the relevance of the sensory cues presented during sleep. We speculate that the processing of the memory content associated to the cue resulted in enhanced SW activity. Specifically, the greater amplitude of the SWs during associated sound intervals might reflect neural synchronization (Carrier et al., 2011; Esser et al., 2007) known to promote sleep-dependent plasticity processes for example (Born and Wilhelm, 2012; Ngo et al., 2013b). We thus propose that the TMR-effect observed in this study might therefore be mediated by SWs likely in relation with spindle activity.

While the characteristics of spontaneous spindles (amplitude and frequency) were only modulated by sound presentation and not sound condition, the properties of sigma oscillations (i.e. its amplitude and its coupling with the SO phase) were differently affected by the cue type and related to the TMR-induced behavioral advantage. The observation of a modulation of spindle characteristics irrespective of the sound condition suggests that spindle activity (in term of events) during reactivation is not related to motor memory processing per se. This stands in contrast with earlier reports of spindle-mediated effect of TMR on the consolidation of both declarative (Cairney et al., 2018) and motor (Laventure et al., 2016; Cousins et al., 2016) memory tasks. It is worth noting, however, that this earlier work did not compare different stimulus conditions as in the present study. Nonetheless, this previous research demonstrated that reactivation was related to an increase in spindle features (amplitude and frequency) that were linked to the TMR-induced motor performance advantage (Laventure et al., 2016).

Importantly, it is worth explicitly stating that our results do not rule out the involvement of spindle activity in TMR-related motor memory consolidation processes. Recent evidence has brought forward the idea that spindle event detection in general is less sensitive than the study of the sigma rhythm as a whole (Dimitrov et al., 2021). In line with these observations, our results show that sigma oscillation properties – as opposed to spindle events – were modulated by the sound condition and that such modulation was related to the TMR-induced behavioral advantage. Specifically, higher coupling between sigma oscillation amplitude and the SO phase, for associated as compared to unassociated sounds, on the descending phase following the peak of the SW was correlated with the TMR index. To the best of our knowledge, this is the first time that the strength of the coupling between the SO phase and the amplitude of the sigma oscillations nested within the peak of the SW is directly related to a TMR-related behavioral advantage. Earlier studies comparing different age groups provided convincing, yet indirect, evidence that the precise temporal coordination of SO and sleep spindles represents a critical mechanism for sleep-dependent memory consolidation (Muehlroth et al., 2019; Helfrich et al., 2018). The timing reported in this earlier work is consistent with the current data showing increased SW-sigma coupling on the descending phase following the peak of the SW. Our results are also in line with previous frameworks proposing that sigma oscillation (Cairney et al., 2018) / spindles (Antony et al., 2019) offer a privileged time window for relevant memories to be reinstated during sleep. Together with evidence that TMR boosted SW features, the current data suggest that both SWs and sigma oscillations play a critical role in the reactivation of motor memories.

In addition to the modulation of neurophysiological responses described above and triggered by the associated sounds, we report an intriguing pattern of brain results for the unassociated sounds. Specifically, the coupling between sigma amplitude and the SO phase was strengthened for unassociated sounds just before the onset of the SW negative peak. Furthermore, we observed that the increase in sigma power nested in the trough of the auditory evoked potential for unassociated (as compared to associated) sounds was related to higher TMR-induced performance enhancement. It is tempting to speculate that sigma oscillations might prevent the processing of unassociated/irrelevant sounds during post-learning sleep which might in turn be reflected by a decrease in the amplitude of the slow electrophysiological responses (i.e. smaller ERP and SWs) during non-associated sound intervals. We argue that sigma oscillations might play the role of a gatekeeper for the consolidation process and protect the motor memory trace against potential interfering effects induced by the unassociated sounds which might in turn potentiate the effect of TMR at the behavioral level. In order to further examine this possibility, we performed additional exploratory analyses testing for potential relationships between the SW-sigma coupling observed during unassociated stimulation intervals and slow electrophysiological responses (see Figure 6—figure supplement 3). Results showed a negative correlation between SW-sigma coupling and SW features such that higher coupling was related to lower SW amplitude and density during unassociated stimulation intervals. These results provide further support for the protective effect of sigma oscillations (nested in the trough of slow oscillations) against potential interfering effects induced by the unassociated sounds. These assumptions are also in line with a growing body of literature pointing towards a sensory gating role of spindle activity / sigma oscillations (Dang-Vu et al., 2011; Schabus et al., 2012) that might be critical to facilitate the memory consolidation process during sleep (Schreiner and Staudigl, 2020; Antony et al., 2019). Specifically, it has been proposed that a function of the thalamus is to suppress distraction and gate information processing via alpha/beta oscillations during wakefulness (Jensen and Mazaheri, 2010) and sigma oscillations during sleep (Chen et al., 2015). Further support for the gating hypothesis comes from observations of both increased arousal threshold as well as decreased amplitude of auditory ERP when sounds are presented simultaneously to a spindle event (Yamadori, 1971; Cote et al., 2000). Along the same lines, previous studies using simultaneous EEG-fMRI recordings showed that the BOLD responses in relation to sound processing are inconsistent or even absent when sounds occur during sleep spindles or before the negative peak of the SW (Dang-Vu et al., 2011; Schabus et al., 2012). The present data therefore suggest that the precise SO-sigma coordination does not only play a role in the reinstatement of relevant memories, but is also critical to prevent the processing of irrelevant information during post-learning sleep. These observations are remarkably in line with recent theoretical views putting forward the concept that temporally precise SO-spindle coupling might not convey only memory-specific information (Helfrich et al., 2021). It is argued that while synchronized states might trigger memory reactivation, the underlying neural activity might offer limited opportunities for information processing. Our data concur with this theory as they suggest that SO-sigma coupling, depending on its temporal characteristics, either prevents the processing of irrelevant information or supports memory reactivation during post-learning sleep.

In conclusion, our results depict a complex organization of the different physiological processes supporting motor memory consolidation during post-learning sleep. While associated sounds appeared to boost SW features and SO-sigma coupling at the peak of the SW, unassociated sounds predominantly modulated the properties of the sigma oscillations at the trough of the slow oscillation. Our findings suggest a dual role of sigma oscillations whereby, depending on their temporal coordination with SWs, they either protect memories against irrelevant material processing or promote the reactivation of relevant motor memories; two processes that critically contribute to the motor memory consolidation process.

This study was pre-registered in the Open Science Framework (https://osf.io/). Our pre-registration document outlined our hypotheses and intended analysis plan as well as the statistical models used to test our a priori hypotheses (available at https://osf.io/y48wq). Whenever an analysis presented in the current paper was not pre-registered, it is referred to as exploratory. Additionally, to increase transparency, any deviation from the pre-registration is marked with a (#) symbol and listed in Supplementary file 3 together with a justification for the change.

All data can be found at https://zenodo.org/record/6642860#.YqoI46hBzD5. The source code is available at https://github.com/judithnicolas/MotorMemory_OpenLoop_TMR, copy archived at swh:1:rev:1300ddefdec0c9980058d378fd06eeb8119971c4.

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Author details

1. Judith Nicolas

   1. Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, Leuven, Belgium
   2. LBI - KU Leuven Brain Institute, KU Leuven, Leuven, Belgium

   Contribution

   Methodology, Validation, Visualization, Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Writing - original draft, Writing - review and editing

   For correspondence

   nicolasjdh@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7142-1449

2. Bradley R King

   Department of Health and Kinesiology, College of Health, University of Utah, Salt Lake City, United States

   Contribution

   Methodology, Conceptualization, Data curation, Formal analysis, Investigation, Project administration, Resources, Software, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3010-8755

3. David Levesque

   Center for Advanced Research in Sleep Medicine, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Ile de Montréal, Montreal, Canada

   Contribution

   Project administration, Software, Supervision

   Competing interests

   No competing interests declared

4. Latifa Lazzouni

   McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada

   Contribution

   Formal analysis, Software

   Competing interests

   No competing interests declared

5. Emily Coffey

   Department of Psychology, Concordia University, Montréal, Canada

   Contribution

   Formal analysis, Software, Supervision

   Competing interests

   No competing interests declared

6. Stephan Swinnen

   1. Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, Leuven, Belgium
   2. LBI - KU Leuven Brain Institute, KU Leuven, Leuven, Belgium

   Contribution

   Methodology, Conceptualization, Software

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7173-435X

7. Julien Doyon

   McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada

   Contribution

   Methodology, Conceptualization, Formal analysis, Investigation, Software

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3788-4271

8. Julie Carrier

   1. Center for Advanced Research in Sleep Medicine, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Ile de Montréal, Montreal, Canada
   2. Department of Psychology, Université de Montréal, Montreal, Canada

   Contribution

   Methodology, Conceptualization, Formal analysis, Investigation, Software

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5311-2370

9. Genevieve Albouy

   1. Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, Leuven, Belgium
   2. LBI - KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
   3. Department of Health and Kinesiology, College of Health, University of Utah, Salt Lake City, United States

   Contribution

   Methodology, Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Software, Supervision, Writing - review and editing

   For correspondence

   genevieve.albouy@kuleuven.be

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5437-023X

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