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Humans can encode verbal associative information (word pairs) during slow-wave sleep
Memory was best for pairs that were encoded in sync with ongoing slow-wave peaks
Slow-wave sleep appears to entail windows of opportunity for synaptic potentiation
The hippocampus mediated retrieval, suggesting episodic memory formation during sleep
Learning while asleep is a dream of mankind, but is often deemed impossible because sleep lacks the conscious awareness and neurochemical milieu thought to be necessary for learning. Current evidence for sleep learning in humans is inconclusive. To explore conditions under which verbal learning might occur, we hypothesized that peaks of slow waves would be conducive to verbal learning because the peaks define periods of neural excitability. While in slow-wave sleep during a nap, a series of word pairs comprising pseudowords, e.g., “tofer,” and actual German words, e.g., “Haus” (house), were played to young German-speaking women and men. When the presentation of the second word of a pair (e.g., “Haus” of “tofer-house”) coincided with an ongoing slow-wave peak, the chances increased that a new semantic association between the pair had been formed and retained. Sleep-formed associations translated into awake ones, where they guided forced choices on an implicit memory test. Reactivations of sleep-formed associations were mirrored by brain activation increases measured with fMRI in cortical language areas and the hippocampus, a brain structure critical for relational binding. We infer that implicit relational binding had occurred during peaks of slow oscillations, recruiting a hippocampal-neocortical network comparable to vocabulary learning in the waking state.
] provided evidence that sleep learning is possible by the mammalian brain; studies in humans suggest that simple auditory learning may be possible, but complex verbal learning remains to be demonstrated [
]. Current sleep theories posit that teaching new vocabulary during slow-wave sleep is impractical, because this sleep stage does not provide the conditions necessary for learning. Learning requires long-term potentiation and the expression of plasticity-related genes [
] that appears incompatible with sleep learning. At the same time, the active systems consolidation theory of sleep claims that during sleep the brain actively replays and strengthens memories that have been formed during previous wakefulness [
]. Hence, sleep learning, more accurately sleep encoding, might conflict with the simultaneous replay of wake-formed memories. A problem with both claims is that they draw on physiological phenomena to infer the implausibility of sleep encoding. The only way to be certain is to have human subjects encode verbal material presented while asleep and then to test retention following waking.
Previous experiments on sleep encoding and wake retrieval yielded mixed results [
]. Here, we examine a more complex form of sleep encoding, namely rapid implicit relational binding of vocabulary, which is called paired-associates learning in the psychological literature. Paired-associates learning draws on the episodic memory system [
]. Consciousness, however, is reduced during sleep—especially during slow-wave sleep. Here, we examine encoding during slow-wave sleep to test whether implicit relational binding of vocabulary is feasible in an unconscious state.
Whereas rapid relational verbal binding in an unconscious state—such as coma, anesthesia, or slow-wave sleep—has not been reported to our knowledge, it has been reported that, in the waking state, subliminal (consciously invisible) stimulus presentations have led to unconscious rapid relational encoding [
]. In these subliminal experiments, human participants formed associations between arbitrary stimuli for long-term storage, accompanied by activation of the hippocampus and neocortex. Subliminal relational encoding was purged following hippocampal tissue damage [
], suggesting its dependence on the hippocampus. However, inferring from the awake relational processing of subliminal stimuli to the feasibility of relational binding during sleep is somewhat far-fetched, because brain states differ substantially between wake and sleep. For instance, activity in the frontoparietal neocortex, which is required for rapid relational encoding [
]. Therefore, relational encoding during sleep might only work under very narrow circumstances such as slow-wave peaks that demarcate periods of relative neuronal depolarization, thereby allowing for information processing [
To explore this possibility, we investigated the phases of slow-wave sleep for their suitability for relational encoding. We hypothesized that encoding of acoustically presented vocabulary might work during slow-wave peaks (i.e., the positive half waves of slow waves identified in the electroencephalogram), because these peaks are associated with cortical excitability along with neuronal network properties that resemble those of the waking state [
]. Whereas the primary auditory cortex processes acoustic stimuli at all times during slow-wave sleep, higher cortical areas respond only to sounds when they depolarize during the peaks of their slow waves [
We presented pairs of pseudowords representing a non-existent foreign language and real German words, e.g., “tofer”-“Haus” (house), to 41 young, native German-speaking volunteers during slow-wave sleep in a nap. The sleep-played German words designated an object that is clearly smaller or larger than a shoebox (Figure 1A). Each word pair was played four times in succession with the order of words switched (i.e., tofer-Haus, Haus-tofer, tofer-Haus, Haus-tofer) to promote a flexible binding in both directions of the pseudowords with the real German words. We played these word pairs at a steady rhythm that mimicked the frequency of slow waves (1.075-s [
] and to have them coincide with the second word of a pair. The time point when the second word of a pair was played was crucial, because this was the moment when a word-word association could be formed, and therefore when neural plasticity needs to be optimal. Following waking, subjects were presented pseudowords visually and acoustically and were asked to guess whether a previously sleep-played, but subjectively newly appearing, pseudoword designated an object smaller or larger than a shoebox. A better-than-chance guessing accuracy reflected successful relational binding during sleep. fMRI was performed during vocabulary retrieval in 15 of the 41 examined participants to infer whether the episodic memory system had supported encoding during sleep. MRI data were not collected during sleep for the reason that MRI interferes with sleep quality due to the noise of the scanner and the need to restrict body movement during scanning.
Entrainment of Slow Waves, Theta Activity, and Sleep Spindles
We played an average of 36.51 (SD 5.05) distinct word pairs with 146.05 (SD 20.20) repetitions (each word pair was presented four times) per participant during slow-wave sleep. Electroencephalogram (EEG) recordings revealed that word pairs had triggered a train of frontal slow-wave peaks. The onset of the first word of a pair triggered three positive voltage peaks in clusters of electrodes placed in frontal positions at (1) 200–600 ms (cluster-level Monte Carlo p value < 0.001 if tested against pre-stimulus baseline), (2) 900–1,500 ms (p = 0.003), and (3) 2,000–2,300 ms (p = 0.022). These peaks coincided with increased spectral power in the spindle frequency band (12–16 Hz) starting at the wave’s trough-to-peak transition in widespread frontocentral electrode clusters (Figure 2) (cluster-level Monte Carlo p < 0.01 for all three time windows). The first and second waves’ voltage peaks were followed by troughs at 500–900 ms and 1,500–2,000 ms, with associated increases in theta activity (4–8 Hz) in large electrode clusters around the vertex (both cluster-level Monte Carlo p < 0.001). It should be noted that the second entrained voltage peak coincided with the presentation of the second word of a pair (the pseudoword 2× and the real word 2×). Word presentations thus evoked frontal voltage peaks and troughs that emerged at intervals of ∼1 s representative of slow waves (∼1 Hz) and orchestrated spindle and theta activity. This finding corresponds to previous evidence of auditory stimulation entraining slow waves at ∼1 Hz [
] would potentially enable paired-associates verbal encoding during sleep (sleep encoding). However, there is no previous evidence by which an EEG-derived brain state denotes the best time for sleep encoding. In this situation, we used data-driven analyses that focused on the time point when the second word of a pair is being played, which is the point at which relational binding can occur. This is the time when the second slow-wave peak appeared (Figure 2).
First, we examined whether slow-wave peak characteristics that display in the average event-related potential (ERP) were systematically associated with correct versus incorrect responses on the memory test. We compared onset time, center time, and duration of the second entrained slow-wave peak with subsequently correctly versus incorrectly classified (retrieved) pseudowords. To identify distinct peaks and troughs in the average ERP, we analyzed how voltage was distributed across scalp electrodes at every time point in the ERP (see STAR Methods: Analysis of slow waves in the average ERP). We tested, for each time point, whether the present voltage distribution resembled the prototypical voltage distribution of a peak or a trough. Peaks usually yield a strong positivity over frontal electrodes, whereas troughs create a voltage distribution with a prominent frontal negativity (e.g., [
]; Figure S1). Consecutive time points during which the voltage distributions resembled the same prototype were considered constituents of the same peak or trough (Figures 3A and S1). This procedure established the onset times and the duration of distinct entrained slow-wave peaks and troughs, to compare to awake correctly versus incorrectly classified pseudowords. We further computed the strength of peak/trough activation—i.e., the global field power (GFP)—of the voltage distribution at each time point of the ERP. The GFP allowed us to compute the center time of peaks and troughs, i.e., the “center of gravity” of the activation as measured by the GFP. A permutation-based approach indicated whether center time, onset time, or duration of the second entrained slow-wave peak would predict significant differences between later correctly versus incorrectly classified pseudowords.
An earlier center time, earlier onset, and longer duration of the second entrained slow-wave peak predicted correct forced choices on the implicit memory test. The second slow-wave peak showed an earlier center of gravity (1,140 versus 1,397 ms, permutation-based p = 0.031), and tended to begin earlier (onset: 840 versus 1,160 ms, p = 0.056) and last longer (780 versus 480 ms, p = 0.067) for subsequently correctly versus incorrectly classified pseudowords. Visual inspection of the strength of EEG activation (GFP) suggested that the second entrained slow-wave peak boosted sleep encoding, if it reached its maximal activity just before the onset of the second word (Figure 3A). To verify this observation, we examined the voltage in the ERPs from frontal electrodes, where slow waves originate [
]. In subsequently correctly retrieved word pairs, the second peak occurred at 800–1,100 ms preceding and including the presentation of the second word (Figure 3B). Conversely, in pairs for which subsequent retrieval failed, the second peak occurred too late, namely at 1,200–1,500 ms following the onset of the second word. This dissociation concerning retrieval success was confirmed by a significant interaction of ERP latency with memory performance computed on the mean voltage of the ERP (F(1,40) = 8.23, p = 0.006, d = 0.91).
Focusing on the single-trial level, we next examined whether discrete slow-wave peaks would predict retrieval success for individual word pairs. Because the average ERP is influenced by oscillatory events of various frequencies and amplitudes, the EEG must be examined at the single-trial level to properly reveal the specific contribution of slow waves to vocabulary encoding during sleep. We again analyzed data from frontal electrodes, where slow waves are generated [
First, we assessed whether a specific phase of oscillations in the slow-wave frequency range, occurring at a specific time during the presentation of a word pair, would predict successful retrieval. To do this, we extracted the instantaneous phase of slow-wave oscillations (0.8 Hz) at each time point for each of the four presentations of all word pairs (see STAR Methods: Single-trial analysis of the slow-wave phase). For each time point, we grouped all presentations of word pairs into 12 bins encompassing all possible phase values (−180° to +180°) and then computed memory performance separately for each time-phase bin (Figure 3C). If a specific phase of slow-wave oscillations, occurring at a specific time during stimulus presentation, contributes to encoding during sleep, memory performance should be high for word pairs in the respective time-phase bin. We discovered that word pairs that were associated with a phase value resembling a slow-wave peak (0°) between 800 and 1,100 ms yielded above-chance accuracy in the subsequent memory test. A cluster-based permutation test suggested that this association between latency, phase, and memory performance was significant under the null assumption that memory performance is independent of slow-wave phase and therefore uniformly distributed across all time-phase bins (cluster-level Monte Carlo p = 0.045). Note that the instantaneous phase was analyzed at 0.8 Hz, because this is a prominent frequency of human slow waves [
]. To corroborate this finding, we obtained qualitatively similar results if phase was analyzed at 0.6, 0.7, 0.9, or 1.0 Hz. However, the association between latency, phase, and memory performance reached significance only for oscillations at frequencies ≤0.8 Hz (cluster-level Monte Carlo p < 0.045 for frequencies ≤0.8 Hz; p = 0.06 for 0.9 Hz and p = 0.10 for 1.0 Hz).
Finally, we tested whether peaks of discrete high-amplitude slow waves occurring at a specific time during word presentation would predict successful retrieval when awake. Although the above analysis of the slow-wave phase emphasized the relevance of slow-wave peaks for sleep learning, it ignored the actual amplitude of slow-wave oscillations because it included both high- and low-amplitude events. However, because slow waves are defined as high-amplitude events, we next identified discrete slow-wave peaks in the frontal EEG using a previously reported algorithm [
], one that detects slow waves based on the magnitude of the amplitude of their peaks and troughs. We computed the relative change in peak density—i.e., the change in the occurrence of peaks as a percentage of the baseline rate—across time for subsequently correctly versus incorrectly awake-retrieved pairs (Figure 3D). Peak density was significantly increased for later correctly versus incorrectly awake-classified pairs (t(40) = 3.641, p < 0.001) at the critical time window between 800 and 1,100 ms. If tested separately against baseline, peak density tended to be increased by 16.0% (SD 53.8%, t(40) = 1.908, p = 0.064) for subsequently correctly classified pairs, and tended to be reduced by 11.4% for incorrectly classified pairs (SD 38%, t(40) = −1.928, p = 0.061).
Vocabulary Encoding Is Best When Word Repetitions Consistently Target Slow-Wave Peaks
Considering our results, vocabulary encoding should be best if the second word of a word pair coincides with a slow-wave peak in each of its four repetitions. We calculated the number of times (never; once; two to four times) the second word of each pair coincided with a peak-like slow-wave phase (0°) in the critical time window (800–1,100 ms) and examined the influence of the phase consistency on retrieval performance. Note that the second word of a pair rarely coincided more than twice with a peak (see Tables S2–S5 for a detailed overview of the number of trials and mean performance in each condition). To obtain a valid estimate of subjects’ performance for pairs in which the second word repeatedly coincided with a peak, two to four co-occurrences were treated as one condition. An ANOVA revealed a significant effect of the number of peaks (never; once; two to four times) on retrieval performance (Figure 4): F(2,80) = 7.932, p < 0.001, ηp2 = 0.165. When the second word of a pair never coincided with a slow-wave peak (31.6% of word pairs) or coincided with a slow-wave peak only once (41.9% of word pairs), retrieval performance was at chance level (both p > 0.78) with 49.4% (SEM 2.2%) and 50.2% (SEM 1.5%), respectively. When the second word of a pair coincided with a slow-wave peak in 2, 3, or 4 presentations (26.5% of word pairs), retrieval performance rose to 59.9% (SEM 2.5%, t(40) = 3.92, p < 0.001, d = 0.62) (Figure 4). When retrieval performance was computed over all of the word pairs, irrespective of any coincidence with the peak-like slow-wave phase, retrieval performance was still significantly above chance level (M 52.7%, SEM 1.2%, t(40) = 2.257, p = 0.032, d = 0.35).
To ensure that slow-wave peaks rather than troughs were associated with successful vocabulary encoding during sleep, we also analyzed trough-associated retrieval performance. We therefore calculated the number of times the second word of each pair coincided with a trough-like slow-wave phase (±180°). The number of troughs (never; once; two to four times) was significantly related to retrieval performance: F(2,80) = 3.682, p = 0.030, ηp2 = 0.084 (Figure 4). When the presentation of the second word of a pair coincided twice or more often with a trough (26.3% of word pairs), retrieval performance was numerically below chance level (M 46.1%, SEM 2.7%, t(40) = −1.45, p = 0.15, d = −0.23). Conversely, retrieval performance was numerically above chance if the second word of a pair never coincided with a trough (30.8% of word pairs; M 54.0%, SEM 2.6%, t(40) = 1.54, p = 0.13, d = 0.24) or coincided with a trough only once (42.9% of word pairs; M 54.8%, SEM 1.8%, t(40) = 2.57, p = 0.014, d = 0.40). The reduced retrieval performance for pairs coinciding with two or more troughs is probably because these pairs rarely coincided with peaks. Analogously, the increased retrieval performance in the absence of slow-wave troughs reflects a slow-wave peak-associated increase in relational sleep encoding (see Tables S2 and S3).
To substantiate the claim that peaks occurring during the presentation of the second word of a pair are crucial to sleep encoding rather than the peaks occurring during the presentation of the first word of a pair, we also determined the number of times the first word of a pair coincided with a peak or trough in the time window from 275 ms before to 25 ms after the onset of the first word. Neither the number of peaks (F(2,80) = 0.165, p = 0.848) nor the number of troughs (F(2,80) = 0.650, p = 0.525) coinciding with the first word of a pair had a significant effect on memory performance (Figure 4; Tables S4 and S5).
The awake memory testing was done both in the sleep laboratory and in the MRI scanner (behavioral versus fMRI subgroup); where the testing was done did not affect retrieval performance and did not interact with the influence of the number of peaks or troughs on retrieval performance (all p > 0.195). Therefore, the reported analyses were performed without the subgroup (behavioral versus fMRI) as a factor.
Low Theta-Band Power Promoted Vocabulary Encoding during Slow-Wave Sleep
Theta bursts and fast spindles have been positively associated with memory consolidation during sleep [
], raising the question of whether theta and fast spindle bands might influence sleep encoding. To address this issue, we examined whether activity in the theta (5–7 Hz) and fast spindle (13–15 Hz) frequency bands predicted sleep encoding. Theta but not fast spindle band activity measured during slow-wave sleep predicted retrieval performance in the waking state. Theta power was lower during the presentation of successfully encoded word pairs. The difference of theta power between pairs that yielded correct versus incorrect retrieval responses reached significance in the critical time window of 800–1,100 ms (i.e., the time when an early second slow-wave peak promoted relational binding) (cluster-level Monte Carlo p < 0.001; Figure 3E).
Hippocampal Activity at Test Is Positively Associated with Retrieval Performance
fMRI data were recorded in 15 participants during the implicit memory test given following waking. We contrasted the fMRI signal between correctly versus incorrectly classified pseudowords, which had earlier been paired with real German words during sleep (Table 1). To maximize chances of comparing brain activity that truly mirrors successful versus failed retrieval, this event-related fMRI contrast was informed by a sleep-recorded EEG parameter that indicated successful sleep encoding—the mean slow-wave phase value at 800–1,100 ms when the second word of a pair was being played. The significance of clusters was evaluated at a voxel-wise p(unc.) (uncorrected p value for statistical comparisons) <0.001 outside the automated anatomical labeling (AAL) hippocampal sub-volume, and additionally at peak p(FWESV) (family-wise error corrected p value for statistical comparisons, within small brain volume of interest) <0.05 within the AAL hippocampal sub-volume, which was the primary region of interest. Correct classifications on the awake implicit memory test were associated with relative signal increases (contrast estimate: mean ± SEM, 0.26 ± 0.08), whereas incorrect classifications were associated with relative signal decreases (contrast estimate: mean ± SEM, −0.21 ± 0.09) in the right anterior hippocampus (peak at 28, −16, −10, t = 4.0; Figure 5A; 70% of this cluster was within AAL hippocampal sub-volume, significant at peak p(FWESV) = 0.039). Further signal increases resulting from this contrast were located in the left fusiform visual word-form area (−26, −58, −4, Brodmann area [BA] 20, t = 4.56, p(unc.) < 0.001; Figure 5A), presumably mediating the visual processing of written words [
], and in the right (50, −6, −26, BA 21, t = 4.09, p(unc.) < 0.001; Figure 5A) and left (−62, −60, 2, BA 21, t = 3.71, p(unc.) < 0.001) middle temporal gyrus, presumably supporting the recovery of the implicit semantic content of pseudowords [
] as encoded during sleep (see Table 1 for all significant results). There was no brain area exhibiting larger signal in incorrect versus correct trials.
Table 1Clusters of Increased fMRI Signal during the Implicit Memory Test
Cluster Size (Hippocampal)
Contrast: Slow-Wave-Phase-Informed fMRI Contrast between Correct and Incorrect Classifications on the Implicit Memory Test
R ant hipp
A cluster including R pre- and postcentral g extending into R insula and R putamen
R middle and post cingulate g
R precentral and postcentral g
L fusiform and lingual g (VWFA)
L ant insula
R lingual g
R middle temporal g
L central insula and operculum
L lateral thalamus
Bilateral ant cingulate g
L middle temporal g
R ant caudate
R intraparietal s
R frontal operculum
L precentral g
R post middle temporal g
Correlation: fMRI Contrast of Correct versus Incorrect Classifications on the Implicit Memory Test Correlated between Participants with Retrieval Accuracy on the Implicit Memory Test
R ant hipp and PRC
R post hipp
L temporal pole
L caudate, R and L thalamus
L and R cuneus and calcarine s
L post insula
L medial prefrontal c
L supplementary motor c
L post orbital g
L inferior frontal g
L middle occipital g
Coordinates are in millimeters according to Montreal Neurological Institute (MNI) standard brain space. R, right; L, left; ant, anterior; post, posterior; s, sulcus; g, gyrus; c, cortex; hipp, hippocampus; VWFA, visual word form area. p(FWESV) denotes a peak p value of a cluster within the hippocampus, family-wise error (FWE) corrected for hippocampal sub-volume. Cluster formation threshold was p < 0.001, uncorrected; clusters of 5 voxels or larger were accepted. The number of voxels in a cluster within the AAL hippocampal sub-volume is given in parentheses.
Having analyzed which brain structures exhibited within-subject fMRI signal increases to correctly versus incorrectly classified pseudowords, we then computed between-subject correlations of the contrast “correctly versus incorrectly classified pseudowords” with each participant’s total retrieval performance (Table 1). Correlations were located in the right hippocampus (30, −28, −2, t = 4.58, p(unc.) < 0.001; Figure 5B; with 64% of this cluster situated within the AAL hippocampal sub-volume at peak p(FWESV) = 0.176) and right perirhinal cortex and anterior hippocampus (34, −4, −28, t = 6.15; Figure 5B; with 42% of this cluster situated within the AAL hippocampal sub-volume, significant at peak p(FWESV) = 0.018). Furthermore, activity in the left inferior frontal gyrus (Broca’s area, BA 45; −50, 28, 10, t = 4.45, p(unc.) < 0.001; and BA 47; −30, 16, −18, t = 4.49, p(unc.) < 0.001) and in left temporal lexical-semantic storage sites (−36, 18, −36, BA 21, t = 7.21, p(unc.) < 0.001; Figure 5B) correlated with retrieval performance. There were no significant inverse correlations.
Tiredness Does Not Affect Sleep Encoding or Subsequent Wake Retrieval
Although tiredness before sleep might affect how well new information is encoded during an ensuing nap, tiredness at test might affect retrieval of sleep-encoded vocabulary. Therefore, we assessed participants’ tiredness immediately before the nap and before the implicit memory test using the Stanford Sleepiness Scale (SSS) and the Tiredness Symptoms Scale (TSS). Tiredness significantly decreased on both scales from before to after the nap (both t(40) < −2.78, p < 0.008). Hence, sleep was recuperative, even though participants successfully encoded new word pairs while in slow-wave sleep. Pre-sleep scores, pre-retrieval scores, and change scores (pre-sleep minus pre-retrieval) for the SSS and TSS were not significantly correlated with overall retrieval performance (all p > 0.426) nor with the performance for pairs that coincided with two or more ongoing slow-wave peaks (all p > 0.180). Hence, individual tiredness levels did not seem to affect sleep encoding or subsequent retrieval of sleep-played information.
Pseudoword-Inherent Size Semantics (Sound Symbolism) Do Not Account for Above-Chance Retrieval Performance
Instead of using a real foreign language, we created foreign-sounding pseudowords to avoid any influence of previous language experiences (see Stimuli in STAR Methods). Nevertheless, the sounds of pseudowords may be associated with a particular meaning [
]. For example, most people will think that “galnoog” refers to a larger object than “biktum,” even though neither has been semantically bound to a real object. Such inherent size meaning might have influenced participants’ size judgments of the presented pseudowords during the awake implicit memory test, conceivably even during sleep encoding. Although we had excluded those pseudowords that elicited common size judgments as much as possible (see STAR Methods), some inherent size semantics may still be present in the remaining pseudowords and could have affected size judgments and thus overall performance at test.
We therefore examined whether pseudowords and real German translation words were properly balanced between participants and between the categories depicting the number of second-word presentations during a second slow-wave peak. The number of times the second word of a pair coincided with a slow-wave peak varied neither systematically between pseudowords (Χ2(78) = 89.17, p = 0.182) nor between real German words (Χ2(142) = 156.19, p = 0.196), implying that all stimuli were equally often associated with slow-wave peaks. Furthermore, no specific combination of German and pseudowords occurred more often than expected by chance (Χ2(893) = 921.658, p = 0.246), nor was any pseudoword more often associated with German words of a specific size category (small or large) than expected by chance (Χ2(78) = 86.963, p = 0.228). These associations were also true for the subset of pairs that coincided with an ongoing second slow-wave peak twice or more often (all p > 0.60).
We considered whether the category of sleep-played German words predicted participants’ decisions about the corresponding pseudowords even if the influence of pseudowords’ sound-inherent semantics was taken into account. To enable us to obtain estimates of word-inherent semantics, half of the pseudowords were not played during sleep but only presented at testing, counterbalanced across participants. As a consequence, half of the participants provided estimates of word-inherent semantics to the set of pseudowords that was used for sleep encoding by the other half of the participants. Recording ratings of word-inherent semantics in the actual experiment ensured that impartial ratings and sleep-informed ratings were obtained in equal psychophysiological conditions (e.g., sleep deprivation, quality of sound and visual display, scanner noise, etc.). Inherent semantics were calculated as the percentage of “large” ratings participants gave to pseudowords that were not played during sleep. We performed mixed-effects probit regressions with random intercepts for participants and pseudowords to predict participants’ responses (whether a pseudoword designated a small or a large object) at the single-trial level (N = 1,465 trials). The following predictors were entered stepwise into an intercept-only model: (1) the category of the sleep-played real German words (small or large); (2) word-inherent semantics of pseudowords (percent of “large” answers across participants who had never heard these words before); and (3) and phase consistency (i.e., whether the second word of each pair coincided never or once versus two to four times with a peak) and the interaction between category and phase consistency. Wald Χ2 tests were used to assess changes in model fit. Wald z tests were performed to assess the significance of single predictors within a model.
As expected, the category of sleep-played real German words significantly predicted participants’ responses (Χ2(1) = 3.89, p = 0.049, z = 1.97, p = 0.049). Adding inherent semantics as a predictor improved the overall model (Χ2(1) = 4.94, p = 0.026, z = 2.210, p = 0.027) but rendered the effect of the real-word category non-significant (z = 1.93, p = 0.054). Note that adding phase consistency further improved the model (Χ2(2) = 11.28, p = 0.004). Although inherent size semantics remained a significant predictor of participants’ decisions (z = 2.24, p = 0.025), we observed a significant interaction between the real-word category and phase consistency (z = 2.96, p = 0.003). Only the category of the sleep-played real German word (z = 3.40, p < 0.001), but not inherent word semantics (z = 0.47, p = 0.637), predicted participants’ responses in those pairs where the presentation of the second word coincided with an ongoing second slow-wave peak twice or more often. The opposite pattern was observed in trials where the second word of a pair never or only once hit an ongoing slow-wave peak. Here, participants’ responses were only explained by inherent word semantics (sleep-played category: z = 0.07, p = 0.947; inherent word semantics: z = 2.33, p = 0.020). This analysis reveals that inherent word semantics, i.e., sound symbolism, drives responses but cannot explain our main memory effect—an above-chance classification accuracy in trials where the second word of a sleep-played pair coincided multiple times with a slow-wave peak. In fact, if second words were played in sync with slow-wave peaks multiple times, then semantic binding during sleep would most likely overwrite the effect of word-inherent semantics at test.
We set out to resolve the conditions under which rapid verbal learning can occur during slow-wave sleep by examining vocabulary encoding during sleep. We hypothesized that peaks of slow waves are conducive to sleep encoding because peaks demarcate periods of relative neural excitability. If the acoustic presentation of the second word of a pair during sleep repeatedly hit an ongoing slow-wave peak, classification accuracy on the memory test was ∼10% above chance level. The inherent size semantics of the pseudowords we used as the vocabulary of a non-existent foreign language did not explain this significant classification accuracy. Therefore, we conclude that this classification accuracy derived from verbal learning (relational binding) during sleep. In support of this result, when relational binding coincided with slow-wave peaks and retrieval was successful, retrieval was accompanied by increases in hippocampal activity. Hippocampal activity increases correlated with retrieval success between participants. In the following, we discuss the interpretations and implications of these results.
The timing of word presentations relative to the sleeper’s EEG was decisive for the success of sleep encoding. Previous attempts at sleep encoding yielded both positive [
], partly owing to different timing regimens. The critical relational binding process in the present vocabulary encoding task could not occur before the second word of a pair was being played, because only then can the meaning of the heard real German word be related to the sound of the pseudoword (Figure 6). This critical moment appeared to benefit from the presence of an ongoing and prolonged slow-wave peak (Figures 3A–3D). Because slow-wave peaks represent brief phases of wake-like neocortical excitability and neural connectivity [
] raises the question of whether encoding during these relatively activated phases of slow-wave sleep can be considered unconscious at all. Our participants were kept naive regarding word presentations during sleep, and when we fully informed them following the experiment, they asserted that they were unaware of any acoustic stimulation. We speculate that the cortical silence during the troughs, which tend to follow the peaks, prevented stimulus awareness [
] at the moment of sleep encoding. Hence, wake-like activity may have lingered in circumscribed, word-targeted receptive brain regions. But such temporally and spatially circumscribed activation is insufficient for a conscious experience [
]. Therefore, conscious processing of the sleep-played word pairs seems unlikely.
We collected fMRI data during memory testing in the waking state to infer the memory system supporting sleep encoding from the pattern of brain activation associated with the retrieval of sleep-learned associations. Correct answers on the memory test were associated with activation increases in the hippocampus and lexical-semantic storage sites of the brain. Hence, the presentation of the formerly sleep-played pseudowords appears to have cued the recovery of semantic associations between pseudowords and real German words by way of the hippocampus. We infer from its role in the retrieval process that the hippocampus had also assisted verbal relational binding during sleep. Hippocampus-assisted retrieval generally presupposes hippocampus-assisted encoding [
]. Nevertheless, this study lacks direct evidence of hippocampal involvement in sleep encoding. Direct evidence for hippocampus-mediated sleep encoding has only been obtained in animals using intracranial EEG [
In humans, evidence for verbal relational encoding during the unconsciousness of sleep challenges traditional notions of memory, because these notions assume a role for hippocampus-assisted relational memory in exclusively conscious encoding and retrieval [
]. But both priming and procedural learning can be excluded as potential learning mechanisms, because the words in a pair were not unitized to one single unit, which would be typical for priming, and because encoding proceeded over a few trials only but many more trials would have been necessary for procedural learning. Unitized representations hardly allow for the reinstatement of the missing item (the meaning of the real German word) based on the presentation of a remaining item (pseudoword). Also, priming provides for rigid memory representations, but our test format requires a flexible representation of word-pseudoword associations because the retrieval situation (judging the size of the object a pseudoword designates) deviates from the encoding situation (listening to two words). We had chosen to switch the order of words played in each pair during the four repetitions to foster the formation of a flexible relational rather than a unitized memory representation [
] (Figure 1B). Hence, we interpret the results as a demonstration of hippocampus-assisted verbal relational binding in an unconscious state, i.e., verbal sleep learning. This finding adds to evidence of hippocampus-assisted unconscious relational binding from subliminal images and words presented in the waking state [
Evidence for relational encoding during sleep also challenges the view that slow-wave sleep is a state of general synaptic de-potentiation. In fact, sleep-encoded vocabulary was retained for about 1 h. This relational binding must have involved late synaptic long-term potentiation including protein synthesis [
]. Consequently, slow-wave sleep seems to be the more encoding-friendly state than rapid-eye-movement sleep. Hence, de novo memory formation and synaptic potentiation are, arguably, possible during slow-wave sleep [
It is well established that slow-wave sleep serves the important function of awake-learned memory consolidation. Consolidation and encoding during sleep might interfere with each other, because both profit from the neural plasticity provided by the depolarized phases of slow-wave peaks. To our knowledge, consolidation and encoding during sleep have never been pitted against each other experimentally. Also, which types of memory representations profit from which of the sleep parameters is not completely resolved [
]. Now we find an inverse relationship, namely the association of reduced theta-band activity during successful relational binding. The decreased theta activity may represent periods of slow-wave sleep where no memory consolidation takes place. Arguably, consolidation is the biologically more pressing process than memory formation, because the privileged time for consolidation is rest and sleep, whereas the privileged time for memory formation is wakefulness [
]. The absence of consolidation, marked by reduced theta power, could provide free slots to be used for the encoding of new information. This assumption should be addressed in future studies.
This study has two limitations. Single-trial analyses of the slow-wave phase and discrete slow-wave events (Figures 3C and 3D) suggested that only an early second slow-wave peak coinciding with the presentation of the second word of a pair supported verbal relational binding during sleep. Nevertheless, peaks occurring at other times during or following stimulus presentation (e.g., the first or third induced peak; Figure 2A) might have indirectly contributed to encoding by interacting with the critical peak that coincided with the second word. Unfortunately, this study did not have the statistical power to investigate possible interactions between peaks occurring at different times across the four presentations of a word pair. Furthermore, we did not control for factors that might have affected participants’ ability to learn new vocabulary during sleep, such as proficiency in other languages, verbal IQ, hearing abilities, general memory capacity, sleep habits, the emotional state of participants during the study, or chronotype. These factors could merely contribute to individual differences in overall memory performance. But these factors cannot account for the reported accuracy on the awake implicit memory test nor for the finding that sleep encoding was mediated by slow-wave peaks, because word pairs were recombined randomly for each participant individually and all principal comparisons were within subject. An assessment of individual differences in the ability to learn during sleep, and a thorough analysis of interactions between slow-wave peaks occurring at different times during vocabulary presentation, should be the subject of future investigations.
The practical relevance of unconscious encoding during sleep for subsequent wake learning will ultimately depend on whether the first bout of sleep encoding will improve subsequent wake learning. A recent study tempered such optimism because simple noise patterns played during slow-wave sleep were harder to learn during the following wakefulness [
], suggesting a rather suppressive effect of sleep encoding on ensuing wake learning. It remains to be determined whether relational vocabulary acquisition during sleep would boost subsequent wake learning of the same vocabulary.
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Marc Alain Züst ( [email protected] ).
Experimental Model and Subject Details
Seventy-six physically and mentally healthy native German speakers with unimpaired hearing ability were recruited. Participants were kept naive about auditory stimulation during sleep. Written and verbal semi-informed consent was obtained, and participants were fully debriefed and reimbursed after the experiment. The study protocol was approved by the local ethics committee “Kantonale Ethikkommission Bern.” Thirty-five participants failed to reach sufficiently stable slow-wave sleep, which left forty-one participants in the study (age 19-32, M ± SD = 23.3 ± 3.5; 30 (73%) female). The first 26 of the 41 participants took the memory test in the sleep laboratory (the behavioral subgroup; age 19-32, M ± SD = 23.0 ± 3.4; 18 (69%) female). The remaining 15 out of the 41 participants were examined with fMRI during wake memory testing (the fMRI subgroup; age 19-31, M ± SD = 23.9 ± 3.6; 12 (80%) female).
To study unconscious, incidental vocabulary learning during sleep, we had to introduce the vocabulary learning task in a way that would prevent participants from consciously trying to attend to the sleep-played words. We decided not to mention that words would be presented during sleep; instead, we introduced the study as an investigation of “how sleep contributes to the ability to correctly guess the meaning of foreign words from a language one has never encountered before.” To implicitly prepare participants for vocabulary learning, they were shown examples of foreign words. They were told that after a nap they would be asked to guess the meaning of similar foreign words and would then have to learn the correct German translations of these words.
Participants were asked to keep a regular sleep schedule before the experimentation period. The night before the experiment, subjects were restricted to 4 h of sleep, to increase their propensity to sleep during the experiment. Sleep diaries and direct contact confirmed compliance with this protocol. On the day of the experiment, participants arrived at the sleep laboratory between noon and 1 pm, gave written informed consent and were outfitted with EEG electrodes and with in-ear headphones.
We then administered an auditory word-identification task to determine the optimal individual acoustic stimulation intensity for presentation during sleep. The identification task required participants to identify spoken German number-words “one” through “four” that were presented with varying intensity while constant Brownian noise was playing. The same Brownian noise would be playing during sleep to reduce the acoustic salience of the presented words. The intensity of played number-words varied randomly from clearly audible (signal-to-noise ratio SNR = 0.25) to clearly inaudible (SNR = 0.001). The optimal individual word identification threshold was defined as the lowest SNR at which participants could recognize at least 50% of words. This threshold was chosen as the target SNR (SNRtarget) for word presentation during sleep (median SNRtarget = 0.02). At this intensity, words in the Brownian noise were still consciously noticeable and comprehensible but were unobtrusive.
Participants were then asked to take a nap in an electrically and acoustically shielded room. Afternoon naps started between 12:40 and 14:50 (M = 13:51, SD = 39.2 min) and lasted about 90 min (M = 93.5 min, SD = 33.1; See Table S1 for an overview of the time spent in different sleep stages and see Figure S2 for a detailed overview of sleep progress and stimulus presentation for each subject). Brownian noise was quietly presented at < 50 dB(A) SPL and moved to 65-74 dB(A) SPL during late intermediate sleep (N2), i.e., as soon as slow-wave sleep was imminent. At this point, visible delta activity had not yet reached the criterion for slow-wave sleep, a peak-to-peak amplitude > 75 μV [
]. Concurrently, rhythmic presentation of randomly paired spoken numbers “one” through “four” was initiated to habituate participants to verbal stimulation during sleep. Initial stimulus intensity was SNR = 0.001 (inaudible) and was increased slowly to the previously defined target intensity (SNRtarget) within about 1 min. Once intensity was at SNRtarget, presentation of pairs of pseudowords and real German words was started. Each pair was repeated four times in sequence, changing the order of presentation each time (pseudoword – German word; German word – pseudoword; pseudoword – German word; German word – pseudoword). Stimulus onset asynchrony (SOA) for the words in a pair was 1075 ms, which corresponds to the expected peak-to-peak interval of slow-waves. Rhythmic auditory stimulation with this interval can entrain slow-waves, which has been shown to benefit memory consolidation [
]. The onset interval between word pairs was 4300 ms. Presentation of the complete set of word pairs took approximately 15 min. If the continuously monitored EEG showed signs of arousal or if no slow-waves were visible for at least 30 s, stimulus presentation was stopped. Once a participant returned to slow-wave sleep, stimulation was resumed starting with the habituation procedure as described above. Only subjects with at least 20 sleep-played word pairs were included in the data analysis.
Participants were given some time to recover from their naps before continuing with the experiment. The time interval between waking and the start of the implicit memory test was 43 ± 22 min (M ± SD, see Figure S2). In the fMRI subgroup, participants were escorted to the MR center. Participants in the behavioral subgroup took the implicit memory test in the EEG laboratory. The time between the last sleep-played word and the first trial of the implicit memory test was significantly longer for the fMRI subgroup (1 h 31 min) than the behavioral subgroup (52 min; t(39) = 5.28, p < 0.001). Differences in this time interval between the two groups were not related to overall memory performance nor to performance split by the number of times the second word of a pair coincided with a slow-wave peak (all p > 0.172).
The implicit memory test was explained as a test that “measures the degree to which people intuitively understand the meaning of unknown words in a foreign language,” because at this point subjects were still unaware that words had been presented to them while asleep. Participants were instructed that they will be presented with pseudowords, which denote objects that are physically either larger or smaller than a shoebox; their task was to decide whether each “object” would fit into a shoebox. Responses were recorded by button press: up-key for “large object, does not fit into a shoebox,” down-key for “small object, does fit into a shoebox.” Pseudowords were simultaneously presented acoustically (fMRI subgroup: headphones; behavioral subgroup: loudspeakers) and visually (written on a screen). Responses to previously sleep-played pseudowords were scored as correct if they matched the size of the sleep-played real German word.
A total of 96 pseudowords were presented at awake-testing; 48 of them had not been played during sleep. The order of the 96 pseudowords was randomized for each participant. In the fMRI subgroup, we included a baseline condition in which 48 number words were presented, either the word “one” or the word “two.” The task was to indicate, on each trial, which was smaller (“one”) or, larger (“two”). We did not use this baseline condition in our data analysis. The presentation order on-screen was: (1) fixation (250 ms) – (2) pseudoword (self-paced timing but max. 6 s response time in the behavioral subgroup; fixed and jittered 5-7 s response time in the fMRI subgroup) – (2) blank (1 s). This procedure conforms to a rapid event-related fMRI design.
At the end of the experiment, participants were asked whether they had noticed anything unusual during their naps. When participants denied this question, we followed up by asking whether they had noticed acoustic presentations of words during sleep. No participant reported having noticed words being presented during sleep. At the end of the experiment, participants were fully debriefed and financially compensated.
To assess potential influences of tiredness on encoding during sleep and retrieval of sleep-encoded vocabulary, participants rated their subjective tiredness levels on the Stanford Sleepiness Scale (SSS) [
]. Pseudowords were divided into four lists of 24; lists were matched for mean word length, pronounceability, and perceived concreteness as rated by five independent raters. Pseudowords were randomly paired with real German words, half of which designated objects that were smaller than a shoebox, and half larger than a shoebox. All words were spoken by an experienced actress in a calm, neutral voice and recorded in an acoustically shielded room. Recordings were manually edited to attenuate salient phonetic features (plosive and sibilant sounds), compressed, and normalized for peak amplitude (loudness). Pseudoword-to-German word pairs were randomly recombined for each participant to avoid any systematic association. Two lists were presented during sleep. During the awake implicit memory test, the pseudowords served as memory cues to trigger an implicit reactivation of sleep-formed semantic associations between pseudowords and German words. The other two lists were not played during sleep but only presented during implicit retrieval; this allowed us to obtain impartial apparent size ratings for all pseudowords. Sleep-played versus not-sleep-played lists were counterbalanced between participants. We used the software Presentation (v17.2, Neurobehavioral Systems, http://www.neurobs.com) for stimulus presentation and response logging.
Because half of the pseudowords played during the implicit memory test had not been played during sleep, responses to these pseudowords could not have been influenced by associations with German words. We used responses to these pseudowords to obtain impartial size ratings based on the mere sound and visual appearance. The word “biktum,” for instance, may sound like an object smaller than a shoebox, while “galnoog” may sound like an object larger than a shoebox. We refer to this phenomenon as “inherent size semantics”; in Linguistics it is an example of sound symbolism [
]. We asked half of the participants to give impartial size ratings for those pseudowords that were played during sleep to the other half of participants, and vice versa.
If a pseudoword is neutral with respect to inherent size semantics, half of the subjects are expected to rate that pseudoword as small and the other half, as large. If more than half of participants rate a pseudoword as small or large, that pseudoword may have inherent size semantics, which could bias the size judgments given on the implicit memory test. What is more, pseudowords with a strong inherent size semantics might not be relationally encoded during sleep because the size semantics of the presented real German word potentially conflicts with the stronger inherent size semantics. We, therefore, identified and excluded from all data analyses the two most consistently small-rated and the two most consistently large-rated pseudowords in each of the four word lists (i.e., four words in each of the four lists = 16 words in total). In one list, two pseudowords with identical ratings ranked second-to-largest (tied ranks) and were thus kept in the analysis. Hence, only 15 instead of 16 pseudowords were excluded in total. Ratings on these 15 most biasing pseudowords exhibited a rating-consistency (small or large) of 66.5% ± 5.6% (M ± SD) across subjects. Mean binomial probability of randomly reaching this level of agreement was 0.044 ± 0.043, indicating significant inherent size semantics. Rating-consistency on the remaining 81 pseudowords was 55.3% ± 4.1%. Mean binomial probability of randomly reaching this level of agreement was 0.271 ± 0.138, indicating no significant remaining inherent size semantics. Note that this 55.3% is not equivalent to the expected chance level in the implicit memory task because German words were randomly paired with pseudowords, resulting in unique lists for each participant. Due to this randomization process, no pseudoword was systematically associated with one category, large or small, across the group. In the Results section, we provide statistical analyses which confirm that pseudowords were equally often associated with both categories. We further provide analyses that account for a remaining bias of size judgments on the awake implicit memory test by residual inherent size semantics of pseudowords (see Results: Pseudoword-Inherent Size Semantics (Sound Symbolism) Do Not Account for Above-Chance Retrieval Performance). The chance performance on the implicit memory test remains at 50%. A remaining inherent size bias will only reduce the signal-to-noise ratio on the awake memory test. Because two lists were played during sleep, a maximum of 40 (instead of 48) sleep-played pseudowords entered data analysis.
Stimulus inclusion/exclusion criteria
We presented an average of 45.68 (SD = 5.26) word pairs to participants who were in NREM sleep. Each word pair was played four times in sequence during sleep, yielding ∼187 stimulations per participant. Sleep EEG recordings confirmed that all pairs had been presented during sleep stage N3 or late sleep stage N2 (visible delta-band activity that was below the N3 slow-wave criterion of 75 μV [
] for peak-to-peak amplitude). An average of 2.58% of word pairs per participant (SD = 6.81%) was associated with an arousal response in the EEG; these trials were excluded post hoc from the data analysis. Because we also excluded words pairs that contained a pseudoword with strong inherent size semantics, this reduced the set of statistically analyzed word pairs by an additional 17.52% (SD = 2.41%) per participant. If a participant was left with less than 20 sleep-played word pairs for the statistical analyses, then their data were not included in the statistical analyses. The final sample consisted of 41 participants. An average of 36.51 sleep-played word pairs (SD = 5.05) entered the statistical analyses.
EEG during sleep was recorded using 64 channels in a customized 10-20 montage using BrainCap MR BP-03010MR with “Fast’n Easy” electrodes by EASYCAP (http://www.easycap.de) and two BrainAmp DC, MR plus 32 channel amplifiers by Brain Products (http://www.brainproducts.com). A ground electrode was positioned at CPz and a reference electrode at Fz. Two channels were used to assess eye movements and one channel to assess muscle tone at the chin. The remaining electrodes were used to measure brain responses. The sampling rate was 500 Hz. Impedances were kept below 20 kΩ. Recordings were made with BrainVision Recorder (http://www.brainproducts.com). On-line polysomnographic visualization according to the American Academy of Sleep Medicine’s (AASM) guidelines [
]. Two trained raters, blinded to the word presentations, performed the offline sleep-scoring according to AASM guidelines using the software Polyman (http://www.edfplus.info/). If the two raters disagreed, we used the score indicating the shallower sleep stage. EEG data processing was done with the MATLAB toolbox EEGLAB (http://sccn.ucsd.edu/eeglab/) [
]. Data were band-pass filtered at 0.1 to 100 Hz and referenced to the global average for statistical analyses. Artifacts were manually rejected.
MRI data were collected with a Magnetom Trio TIM system (3 Tesla) equipped with a 12 channel head matrix coil. Anatomical reference images were obtained using a T1–weighted magnetization-prepared rapid acquisition gradient echo (MP-RAGE) sequence (176 sagittal slices, no inter-slice gaps, 256 × 256 acquisition matrix, isotropic voxel side length = 1 mm, repetition time (TR) = 2530ms, echo time (TE) = 2.2ms, flip angle (FA) = 9°). Functional volumes were acquired using a T2∗–weighted single-shot echo-planar (EPI) sequence. We recorded 580 volumes of 32 transverse slices in an interleaved sequence. Slices were longitudinally aligned with the long (A-P) axis of the hippocampal formation. Distance factor was 15%, and phase oversampling was 20%. The acquisition matrix spanned 64 × 64 voxels (isotropic voxel side length = 3 mm, TR = 2060 ms, TE = 30 ms, FA = 90°). In order to inform statistical models with a subject-wise baseline mean cerebral blood flow measure, we acquired a pseudo-continuous arterial spin-labeling (pCASL) sequence (120 volumes of 20 transverse slices oriented perpendicular to the carotid arteries, 128 × 64 acquisition matrix, voxel size = 3.6 × 1.8 × 6mm, TR = 4000 ms, TE = 18 ms, FA = 25°, post-label delay = 1250 ms). Data preprocessing and statistics were computed with SPM8 (Wellcome Trust Centre for Neuroimaging, London, UK). Functional volumes were slice-time corrected to slice 16/32, realigned to the first volume, and co-registered to the anatomical reference image. The anatomical reference image of each participant was normalized to the T1 template provided by SPM8, and the resulting deformation field was applied to the functional volumes. Functional volumes were 3-dimensionally smoothed with an isotropic Gaussian kernel with a full width at half maximum of 8 mm.
Analysis of behavioral data
The dependent variable on the implicit memory test was classification accuracy of sleep-played pseudowords. Classification accuracy is expressed as the percentage of correct judgments of the size of the associated German words played during sleep. Successful relational verbal encoding during sleep was inferred if classification accuracy exceeded chance level of 50%. Two-tailed t tests were computed to determine statistical significance. Note that it was not possible to compute classification accuracy for pseudowords that had not been played during sleep. These unpaired pseudowords were only presented on the awake implicit memory test to obtain impartial size ratings to measure pseudoword-inherent size semantics.
EEG pre-processing and analysis
To assess electroencephalographic responses to word presentations during sleep, we computed average ERPs and event-related spectral perturbations (ERSPs) for each participant, time-locked to the presentation of the first word of a pair. We segmented the raw EEG into epochs ranging from 1 s before to 3 s after the onset of the first word of a pair. Epochs containing artifacts due to transpiration sweating or electromagnetic noise (142 of a total 5860 epochs) were visually identified and eliminated. Analyses were performed with the MATLAB toolbox FieldTrip [
]. ERPs were baseline-corrected by subtracting the mean over the 1 s period preceding the presentation of the first word of a pair. ERSPs were obtained by performing Morlet wavelet transforms using three cycles in length at the lowest frequency of 2 Hz, increasing linearly in frequency to 12.7 cycles at the highest frequency of 21.5 Hz. ERSPs were averaged across trials and were baseline-corrected (dB) per participant at each frequency. To quantify the total impact of acoustic stimulation during sleep, we contrasted the average ERPs and ERSPs with the pre-stimulus-baseline. We computed the average mean voltage and mean spectral power across time-windows and across time-frequency-windows of interest. Electrodes showing significant deviations from baseline in these windows were identified with cluster-based permutation tests (5000 permutations) to correct for multiple comparisons [
]. Cluster-level Monte Carlo p values are reported.
Analysis of slow waves in the average ERP
Slow-wave peaks and troughs cast prominent, distinct voltage distributions across the electrodes of the scalp EEG: peaks are marked by a strong positivity over frontal electrodes, troughs with a prominent frontal negativity [
]. To evaluate differences in the slow-wave activity between the ERPs for word pairs that yielded correct versus incorrect responses on the implicit memory test, we, therefore, analyzed how the voltage distribution across electrodes changed over time in the average ERP. This analytic approach is derived from microstate analyses [
]. First, we low-pass filtered the raw signal at 4 Hz to focus on slow-wave activity. Next, for each participant, we averaged the filtered signal separated by accuracy (for word pairs yielding correct responses and word pairs yielding incorrect responses) on the awake memory test. We hypothesized that these slow-wave-filtered average ERPs would be dominated by two voltage distributions – those of slow-wave peaks and troughs. To test this hypothesis, we set out to identify the two most prototypical distributions in these ERPs, which should be peaks and troughs. To do this, we concatenated all time-points of all average ERPs for correct and incorrect responses for all subjects (41∗2 ERPs) into one large dataset. Next, we grouped single time-points in this concatenated dataset into clusters based on the similarity of their voltage distributions. Clustering was achieved with a k-means algorithm as implemented in the software Ragu [
]. The number of clusters to be extracted was set to two in order to obtain the two most prominent, distinct distributions. Computing the average voltage distribution within each cluster produced the two most prototypical voltage distributions. The two resulting prototypes closely resembled the typical voltage distributions of slow-wave peaks and troughs [
Next, we averaged each group’s ERPs separated for correct and incorrect trials. Each time-point of the two resulting ERPs was assigned to a slow-wave peak or trough based on the correlation between the actual voltage distribution and the two prototypical distributions. The voltage distribution of a given time-point was said to belong to the prototype with which it yielded the more positive correlation. Consecutive time-points belonging to the same prototype were considered the same slow-wave peak or trough. This procedure identified the onset time and duration of distinct slow-wave peaks and troughs.
Next, we computed the global field power (GFP) for each time point of the two averaged ERPs. Global field power is defined as the standard deviation of the voltage across all electrodes; it quantifies the strength of activity of a state (i.e., either a peak or trough) at a given sampling point. Computing the temporal center of gravity of the GFP curve, for all consecutive time-points that correspond to the same prototype, specifies the time center of activity of each peak and trough.
To assess whether time center, onset time, or duration of the second entrained slow-wave peak significantly predicted awake memory performance, we used a permutation-based approach. We created 5000 surrogate datasets with two virtual conditions by swapping the ERPs of pairs yielding correct and incorrect responses for a randomly selected subset of participants [
]. For each of these datasets, we computed the average ERPs for the two virtual conditions and then extracted the parameters of interest (time center, onset time, and duration) of the second sleep-entrained slow-wave peak. By calculating the difference between conditions for each parameter in each random dataset, we obtained the distribution of the differences under the null hypothesis for each parameter (time center, onset time, and duration). P-values for the differences in the original data were estimated as the percentage of differences in random datasets that were larger than the differences in the original data [
To further assess whether slow-wave peaks identified in the average ERP contributed to successful encoding during sleep, we contrasted mean sleep ERPs and ERSPs from frontal electrodes for word pairs that yielded correct versus incorrect responses on the awake implicit memory test. Correct responses were associated with an earlier-induced second frontal slow-wave peak during sleep (see Results and Figure 3B). We, therefore, extracted the mean ERP voltage for each participant for both the early (800-1100 ms) and the late (1200-1500 ms) induced second slow-wave peak from electrodes F1, F2, Fz, FC1, FC2, FCz. Voltage was analyzed using repeated-measures ANOVA with the two factors time-window (early versus late second slow-wave peak) and awake memory performance (correct versus incorrect response). We also extracted the mean spectral power in the theta (5-7 Hz [
]) frequency bands at the time of the early induced second slow-wave peak (800-1100 ms). Cluster-based permutation statistics (5000 permutations) were applied to identify electrodes that distinguish word pairs associated with a later correct versus incorrect response on the memory test in these time windows and frequency bands.
Single-trial analysis of the slow-wave phase
We examined whether a specific phase of slow-waves (peak, trough) occurring before, during, or following word presentation would benefit encoding. We extracted the phase of the 0.8 Hz oscillation [
] over frontal electrodes around each of the 5708 presentations of word pairs, which included all repetitions of distinct word pairs. We averaged the raw signal over electrodes F1, F2, Fz, FC1, FC2, FCz, down-sampled the signal to 100 Hz, applied a low-pass filter of 4 Hz, and finally performed continuous Morlet wavelet transformations with two cycles in length at 0.8 Hz. This analysis yielded phase values between –180° and +180°, where 0° reflects a slow-wave peak, and ± 180° reflects a slow-wave trough. The instantaneous phase was extracted at 20 ms intervals from 0.3 s before to 2.1 s after the onset of the first word of a pair during sleep. For each time point in the resulting time-phase decomposition, we grouped all trials into twelve bins (30° in width) according to their current phase. We then computed the percentage of trials in each time-phase-bin that later yielded a correct response on the memory test. If the memory of a word pair was associated with the presence of a specific slow-wave phase at a specific time during the sleep-processing of the word pair, then the percentage of trials leading to correct responses on the awake memory test should be elevated in the respective time-phase bin. We, therefore, searched for large clusters of adjacent time-phase bins, in which more than 54% (mean performance + SEM) of trials yielded a correct response on the memory test. The null hypothesis H0 assumes that the phase is not associated with memory performance. A cluster was accepted as significant if its size (i.e., the number of time-phase bins within the cluster) exceeded the size of 95% of the largest clusters found in randomized datasets. A total of 5000 random datasets were generated by shuffling the information as to whether a trial was associated with a later correct or incorrect response on the memory test.
The analysis of slow-wave phases suggested that slow-wave peaks which just preceded the presentation of the second word of a pair enhanced sleep-encoding (see Results). In order to find out how many of the four repetitions of each sleep-played word pair were associated with an early second slow-wave peak at 800-1100 ms and whether a higher number of peaks (but not troughs) would yield a better memory performance, we examined, for each repetition, whether the extracted 0.8 Hz phase reached a value close 0° (reflecting a slow-wave peak) or close to ± 180° (reflecting a trough) in this time window. A repetition was defined to coincide with a slow-wave-peak if the phase was within ± 2.5% around 0° (within ± 9°) between 800-1100 ms. Conversely, a repetition was defined to coincide with a trough if the phase was within ± 2.5% around ± 180° in this time window. For each participant, we computed the mean retrieval accuracy for sleep-played word pairs that were either never, only once, or two or more times associated with a slow-wave peak and for pairs that were never, only once, or two or more times associated with a slow-wave trough.
Single-trial analysis of discrete slow-wave events
We identified single peaks of discrete high-amplitude slow-waves based on the wave-form of the frontal EEG using a previously reported slow-wave detection algorithm [
]. To this aim, we averaged the raw signal over the frontal electrodes F3, F4, AF1, and AF2 and applied a bandpass filter at 0.15 to 2 Hz to retain only slow-wave activity. In the filtered signal, we selected all epochs between two consecutive positive-to-negative zero-crossings and identified the trough and peak of the negative and positive half-waves for each epoch. Epochs with durations between 0.9 and 2 s and trough-to-peak amplitudes of at least 2/3 of the mean trough-to-peak-amplitude of all epochs within a subject, were selected as slow-waves. The resulting time stamps of discrete slow-wave peaks allowed us to compute a moving average histogram that illustrates the relative change in peak density across time (Figure 3D). This was achieved by calculating the peak density (number of peaks per second) for subsequently correctly versus incorrectly classified pairs per participant in 250 ms moving windows at 10 ms steps from 1 s before to 3 s after the onset of the first word. Relative change (%) in peak density was calculated as the peak density at a specific window minus the average density across all windows, divided by the average density across all windows, i.e., .
To test if subsequently correctly versus incorrectly classified pairs had significantly different relative peak densities in the critical time window of interest before the onset of the second word of a pair, we calculated the average change in peak density during the critical time window at 800 ms to 1100 ms relative to the entire epoch (1 s before to 3 s after onset of the first word of a pair), i.e.,. This was done separately for pairs yielding correct versus incorrect responses. Average relative change values in peak density within the critical time window for pairs yielding correct and incorrect responses were then tested against each other (paired t test), as well as against baseline (one-sample t tests against zero).
Finally, we computed the average voltage distributions for all discrete slow-wave peaks and troughs in order to compare these distributions with the prototypical distributions extracted from the average ERPs (see STAR Methods: Analysis of slow waves in the average ERP, and Table S6 as well as Figure S1) that represented peak- and trough-activity. To this aim, we re-referenced the raw signal to the global average, applied a bandpass filter (0.3 to 35 Hz) and then computed the mean voltage for each electrode from ± 40 ms around the time of the maximal amplitude of each discrete peak or trough. We computed the average voltage distributions for peaks and troughs within each participant and then averaged these distributions across all participants. The resulting distributions were highly similar to the peak- and trough-prototypes we obtained in the analysis of voltage distributions in the average ERP (Table S6; Figure S1).
We recorded rapid event-related fMRI signals while awake participants performed the implicit memory test. The fMRI signal to correct and incorrect responses on the memory test was modeled with stick functions convolved with a canonical hemodynamic response function (HRF) provided by SPM8. Six movement parameters, which were estimated during the realignment of functional volumes, were entered as nuisance covariates to account for error variance induced by moving voxel positions. For each sleep-played word pair, the mean evoked slow-wave phase value between 700 and 1100 ms, averaged across the four repetitions of a word pair, was included in the model as a parametric modulator of truly correct and truly incorrect responses on the memory test. This was done because participants were guessing on the implicit memory test; hence, correct and incorrect answers may have occurred by chance. To measure memory-driven correct answers, the fMRI analysis was informed by the slow-wave phase, during which sleep-encoding proved feasible. Note that mean phase was significantly closer to 0° (i.e., a slow-wave peak) for correct versus incorrect responses on the memory test (t(40) = 3.32, p = 0.002). This parametric modulator was scaled such that the expected phase was 1 (i.e., a slow-wave peak for a correct response and a slow-wave trough for an incorrect response) and the inverse phase was −1 (i.e., a slow-wave trough for a correct response and a slow-wave peak for an incorrect response). Group level analyses were computed based on first-level contrasts using a within-subject one-way ANOVA. Second-level models included the individual mean global cerebral blood flow as a nuisance covariate because it can account for some error variance [
]. To identify brain regions associated with the retrieval of sleep-encoded associations, we contrasted slow-wave phase-informed signals to correct responses with those of incorrect responses on the awake memory test. This information is inherent in the interaction term of the parametric modulation analysis: (correct × slow-wave peak) > (incorrect × slow-wave trough).
Next, we computed between-subject correlations between the fMRI signals associated with the retrieval of sleep-encoded associations and the number of associations a person could retrieve. We regressed retrieval performance onto the unmodulated contrast (omitting the slow-wave phase information) of correct versus incorrect responses on awake test trials. This analysis reveals brain signals that scale linearly with memory performance.
The applied height threshold was p = 0.001 (uncorrected), and the extent threshold was five voxels. Our primary area of interest was the hippocampal formation. Therefore, we masked brain activity with the automated anatomical labeling (AAL) template [
] for the bilateral hippocampus. Voxels within the left and right hippocampus counted as belonging to a cluster at p < 0.001, uncorrected. Clusters of 5 or more voxels were accepted as significant at a peak p < 0.05, family-wise error corrected for hippocampal sub-volume (FWESV). Areas outside the AAL hippocampal template were evaluated at p < 0.001 (uncorrected).
] to pre-process and analyze the EEG data. fMRI data were processed and analyzed using the MATLAB toolbox SPM8 (Wellcome Trust Centre for Neuroimaging, London, UK). Behavioral data were analyzed with SPSS (v. 24) as well as the R package lme4 (v. 1.1.12) [
Types of statistical tests used are specified where the respective test statistics are reported. All statistical tests were two-sided. The significance threshold was p < 0.05.
The reported sample size, N = 41 (30 female), represents number of human subjects. See Experimental Model and Subject Details, as well as Method Details for subject inclusion and exclusion criteria and -procedures. Sample size was estimated using a power analysis with an expected effect size of 4%–5% and an SD of 10% (these values based on research on subliminal associative memory formation [
]) and a power of 0.8. This analysis suggested an optimal sample size of 32-49 participants.
Data and Software Availability
Data supporting the findings of this study are available upon request to the Lead Contact, Marc Alain Züst ( [email protected] ).
We would like to thank Prof. Björn Rasch for advice, and Kathrin Michel, Sandra Röthlisberger, and Angela Wüthrich for help with data collection and sleep scoring. We also wish to thank Prof. Harry A. Whitaker for assistance with editing the manuscript. This work was supported by Swiss National Science Foundation (SNSF) grant P0BEP1_148941-1 (to M.A.Z.) and the Interfaculty Research Cooperation grant “Decoding Sleep: From Neurons to Heath & Mind” of the University of Bern. Neither funding body played any role in designing, evaluating, and interpreting this work.
Conceptualization, M.A.Z., S.R., and K.H.; Methodology, M.A.Z., S.R., and K.H.; Investigation, M.A.Z. and S.R.; Formal Analysis, M.A.Z. and S.R.; Resources, M.A.Z., S.R., R.W., and K.H.; Writing – Original Draft, M.A.Z. and S.R.; Writing – Review & Editing, M.A.Z., S.R., and K.H.; Visualization, M.A.Z. and S.R.; Supervision, K.H. and R.W.; Project Administration, K.H.; Funding Acquisition, M.A.Z.