nature communications

Bidirectional prefrontal-hippocampal dynamics organize information transfer during sleep in humans
Randolph F. HelfrichJanna D. LendnerBryce A. ManderHeriberto GuillenMichelle PaffLilit MnatsakanyanSumeet VaderaMatthew P. WalkerJack J. Lin & Robert T. Knight 

Nature Communications


10, Article number: 3572 (2019) Cite this article
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How are memories transferred from short-term to long-term storage? Systems-level memory consolidation is thought to be dependent on the coordinated interplay of cortical slow waves, thalamo-cortical sleep spindles and hippocampal ripple oscillations. However, it is currently unclear how the selective interaction of these cardinal sleep oscillations is organized to support information reactivation and transfer. Here, using human intracranial recordings, we demonstrate that the prefrontal cortex plays a key role in organizing the ripple-mediated information transfer during non-rapid eye movement (NREM) sleep. We reveal a temporally precise form of coupling between prefrontal slow-wave and spindle oscillations, which actively dictates the hippocampal-neocortical dialogue and information transfer. Our results suggest a model of the human sleeping brain in which rapid bidirectional interactions, triggered by the prefrontal cortex, mediate hippocampal activation to optimally time subsequent information transfer to the neocortex during NREM sleep.


Systems-level memory consolidation theory posits that initial memory encoding is supported by the hippocampus, but that overtime, memory representations become increasingly dependent upon the neocortex1. A prevailing view is that such information transfer occurs, in part, during NREM sleep1. Specifically, the hippocampus generates sharp-wave ripples (~80–120 Hz in humans), which initially facilitate the reactivation of recently learned information2,3. Ripples do no occur in isolation, but appear tightly coupled to both slow oscillations (SO; <1.25 Hz) and sleep spindles (12–16 Hz)4,5,6,7, which are thought to mediate synaptic plasticity1,8,9. In particular, cortical SOs govern the precise temporal coordination of sleep spindles and predict overnight hippocampus-dependent memory formation4,10.

Currently, the majority of evidence supporting this theory stems from invasive recordings in rodents, given the difficulty of imaging the human hippocampus with a sufficiently high temporal resolution needed to detect ripples. Furthermore, it is unclear how findings obtained in rodents relate to humans, given the different anatomical organization, in particular in the prefrontal cortex (PFC)11,12.

One influential model suggests that hippocampal ripple activity is associated with reactivation of newly acquired information13, which strengthens the mnemonic representation, before it is transferred to the neocortex14. Ripples are thought to trigger subsequent cortical SOs and spindles, which promote neuroplasticity to facilitate long-term storage15,16. Contrary to this model, several recent reports indicated that cortical activity might actually precede hippocampal ripples17,18,19, bringing into question who is the main driver of these interactions in support of memory consolidation.

While several lines of research converged on the notion that the coupling of SOs, spindles and ripples is important for memory consolidation4,5,10,16, it is unclear if coupled SO-spindles simply index preceding hippocampal processing, or instead, if they play a functional role in organizing hippocampal activity1,9. The latter finding would indicate cortical control of hippocampal reactivations, one that may advantageously insure that the neocortex receives information at an optimal time point when information transfer20,21 and plasticity dynamics22,23 are maximal.

By directly recording from the hippocampus and PFC, we demonstrate that prefrontal SO-spindle coupling initiates a bidirectional processing cascade. The quality of the SO-spindle coupling predicts hippocampal dynamics and shapes the hippocampal-neocortical information transfer in the human brain.


We combined intracranial recordings from the human medial temporal lobe (MTL) and prefrontal cortex (PFC) in epilepsy patients with scalp EEG recordings to test if coupled SO-spindle activity mediates hippocampal-neocortical information transfer during a full night of sleep (Fig. 1a, see also Supplementary Table 1). In 18 subjects, we simultaneously obtained frontal scalp EEG and MTL intracranial EEG, while a subset of 15 patients also had intracranial coverage of three PFC subregions (dlPFC, mPFC, and OFC). The majority of MTL electrode contacts were placed in the hippocampus (CA1, CA3/DG, and Subiculum), but we also assessed adjacent contacts in entorhinal cortex (ERC), perirhinal cortex (PRC), and parahippocampal gyrus (PHG) given that ripples have been observed in both the ERC24 as well as the PHG6. We collectively refer to this group as ‘MTL’ electrodes throughout the manuscript, unless we found effects that were specific to the hippocampus proper. Furthermore, we utilized bipolar referencing to minimize effects of volume spread on our connectivity analyses; hence, most contacts contained hippocampal activity after re-referencing (~70%; Supplementary Table 2).

Fig. 1

figure 1

Sleep architecture and coupled NREM oscillations. a Top: hypnogram of a single subject (MT = movement time). Bottom: corresponding multi-taper spectrogram with superimposed number of detected SO and sleep spindle events (white solid lines; 5 min averages). b Top: normalized circular histogram of detected spindle events relative to the SO phase. Note the peak around 0° (circular mean = 15.27°, i.e. after the SO peak). Bottom: peak-locked sleep spindle average across all detected events in NREM sleep (black). Low-pass filtered events (red) highlight that the sleep spindles preferentially peaked during the SO ‘up-state’. c Peak-locked spindle grand average (black; N = 18; scalp EEG) superimposed with the SO low-pass filtered signal (red). Inset: spindles were significantly nested in the SO peak (V-test: p = 0.0050; red dots depict individual subjects). d Directional SO-spindle coupling as measured by the phase-slope index (PSI; mean ± SEM). SO phases significantly predicted spindle amplitudes (one-tailed t-test: p = 0.0436). Red dots depict individual subjects

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Analysis strategy

Interictal epileptic discharges were excluded from all analyses (Supplementary Fig. 1)7,25. We focused on the most anterior available scalp electrode (typically Fz; subsequently referred to as ‘EEG’). This approach was in line with previous reports, which also utilized scalp electrodes as a surrogate of prefrontal activity6,7. We then performed similar analyses using only the intracranial PFC contacts (subsequently referred to as ‘PFC’) to investigate e.g. the spatial extent of network interactions and information transfer or directional cross-frequency coupling (CFC), which cannot be obtained from a single scalp electrode. Where applicable, we show both the EEG-MTL as well as PFC-MTL interactions to indicate that frontal EEG sensors indeed reflect a valid surrogate of the underlying population activity as measured by intracranial EEG.

To effectively combine scalp and intracranial EEG, we adopted the following analysis strategy: First, we replicated and extended our recent observation that prefrontal SOs shape spindle activity (Fig. 1b–d)10. Note that all SOs and spindle events were detected at the prefrontal EEG electrode. Second, we established the relationship between the activity at scalp EEG level and all available MTL electrodes (Fig. 2). We utilized two complementary approaches: State-dependent (based on continuous 30 s segments as defined by the hypnogram) as well as event-related (based on automatic event detection) analyses. Third, we dissected the precise relationship between cortical spindle and MTL high-frequency band (HFB; 70–150 Hz) activity (Fig. 3), which captures both non-oscillatory broadband as well as oscillatory ripple events. Subsequently, we investigated whether the coupled HFB signatures reflected ripple oscillations (Fig. 4) and how those were precisely related to distinct cortical events, such as SOs and spindles. Having established that these three cardinal NREM oscillations dynamically interact during sleep, we then investigated the overall network connectivity as a function of brain state (Fig. 5) as well as in an event-related approach (Fig. 6) to further investigate the precise role of SO-spindle coupling on inter-areal communication. Finally, we quantified interregional information transfer relative to the ripple events (Fig. 7) that have been implicated in information reactivation, replay, transfer, and consolidation2,3,15.

Fig. 2

figure 2

Intracranial electrodes and hierarchical nesting of sleep oscillations. a Schematic depiction in MNI space of the intracranial electrode placement in the medial temporal lobe (MTL; in black) and three PFC regions-of-interest (ROI) across the subset of subjects (N = 15) with simultaneous coverage of all three prefrontal ROIs (dlPFC: n = 167; OFC: n = 62; mPFC: n = 116). b Schematic depiction of MTL contacts (n = 184 electrodes in 18 subjects) on a hippocampal mesh in MNI space. Note that electrode size has been rescaled for display purposes and does not reflect the original electrode size. Precise electrode localizations were determined in native space prior to warping to MNI space. c Single subject time-frequency spectrogram from one MTL channel relative to a rescaled trough-locked cortical average SO (black). Note the circumscribed increase in high-frequency-band activity (~80–100 Hz) just prior to the SO peak. d Single subject time-frequency spectrogram from one MTL channel relative to a rescaled peak-locked cortical spindle average (black). High-frequency band activity exhibited several bursts during spindle events. e Single subject, single electrode example of simultaneous nesting of HFB power in the cortical SO peak (x-axis) and the cortical spindle trough (y-axis). f Cross-frequency coupling analysis indicated increased SO-Spindle, SO-HFB, and Spindle-HFB coupling during NREM sleep (gray dots depict individual subjects; mean ± SEM). g Full comodulogram (wake vs. NREM) depicting the effect size of the difference (Cohen’s d)

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Fig. 3

figure 3

Precise cortical SO-spindle coupling phase predicts MTL HFB activity. a Single subject average of all coupled spindles that were nested in the SO peak (in red) and trough (in blue). b All uncoupled spindles, i.e. that did not coincide with a separate SO detection. c Cortical spindle to MTL HFB phase amplitude coupling for coupled and uncoupled spindles as well as event-free random NREM intervals (normalized by the individual mean to reduce inter-subject variability). d Single subject HFB activity in the MTL as a function of the precise cortical SO-Spindle phase at the time of the spindle peak. Bars depict the average across electrodes; gray lines depict single electrodes. The red arrow indicates the SO-spindle coupling phase where the highest HFB amplitude was observed (~0°). e Different single subject example. Same conventions as in panel d. Note that HFB peaked after the SO peak (>0°). f Coupling strength: in every subject, we assessed the coupling strength by comparing the observed circular-linear correlation between SO-spindle coupling phase and HFB amplitude to a surrogate distribution. The correlation coefficient was then z-scored relative to a phase-shuffled distribution of correlation coefficients and considered significant at z = 1.96 in individual subjects (corresponding to a two-tailed p < 0.05; red solid line). The gray bar depicts the mean across subjects; black dots depict individual subjects. We observed significant HFB modulations in 18/18 subjects (mean z = 3.40, pgroup = 0.0007). g Preferred coupling phase: circular histogram of the best phase (panels d and e) where HFB peaked in individual subjects. At the group level, HFB amplitude was highest during the spindle when the spindle was nested in the SO peak (mvl = 0.44). h Preferred coupling phase across time (circular mean ± circular SEM). We repeated the analysis depicted in panel f for all time points (±1.25 s around the spindle peak). Shaded bars depict cluster-corrected significant Rayleigh tests (cluster p < 0.001) for circular non-uniformity (test for phase consistency at the group level), indicating that a preferred phase across subjects emerged around the spindle peak (cluster 1: −0.3 to 0.04 s; cluster 2: 0.12 to 0.60 s); hence, the effect was transient and not sustained. i Rayleigh Z and significant clusters as described in panel h. j Directional CFC analysis (gray dots depict individual subjects; mean ± SEM) indicating that the PFC spindle phase was predictive of hippocampal HFB, but not vice versa (rank sum test: p = 0.0005). All error bars indicate the mean ± SEM

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Fig. 4

figure 4

Physiological and artifactual ripple activity in the MTL. a Ripple-locked grand average (blue; N = 18) and the band-bass filtered signal (black; mean ± SEM). b Artifactual ripple events grand average (red) that are characterized by sharp transients that are artificially rendered sinusoidal by band-pass filtering (black). c 1/f-corrected power spectrum (mean ± SEM; irregular resampling) in the MTL during a ripple event exhibits two distinct oscillatory peaks: in the delta and spindle frequency range. Gray shaded areas indicate significant oscillatory activity (cluster test: p = 0.001). d Histogram of ripple occurrence relative to the cortical SO as detected at scalp level. Note ripple occurrence is enhanced during the cortical upstate (i.e., the SO peak, first cluster: p = 0.0010, d = 2.82; second cluster: p = 0.0040, d = 1.42), but significant more ripples occur prior to the downstate (in gray; 400 ms window around the SO peaks, paired t-test: t17 = 5.01, p < 0.0001, d = 1.88). e This relationship was not present for artifactual ripple events, which occurred at random (smallest cluster p = 0.450; no peak differences: t17 = −0.63, p = 0.5381, d = 0.27). f Physiologic ripples were preferentially nested in the trough of the cortical spindle oscillation (V-test against ± pi: v = 5.13, p = 0.0434, mvl = 0.29). g This tight temporal relationship was again not present for artifactual ripple events (v = 1.46, p = 0.3135, mvl = 0.08). h Single subject ripple-locked EEG spectrogram indicates the presence of cortical spindle oscillations during a hippocampal ripple. Note that activity in the spindle range can be observed before and after the ripple (every ~3 s; asterisks indicate elevate spindle power). i Left Spectral analysis of the spindle amplitude (green), its fractal (1/f estimate; black dashed line) as obtained from irregular resampling as well as the wake condition as a control (absence of spindle activity; cluster test: p = 0.0490, d = 0.49). Right: after subtraction of the fractal component from the NREM spectrum, we observed a distinct oscillatory peak indicating a rhythmic comodulation at ~0.37 ± 0.03 Hz (median ± SEM; sign test against zero: p < 0.0001; d = 0.70). All error bars indicate the mean ± SEM

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Fig. 5

figure 5

PFC-MTL connectivity and directionality. a Phase locking analyses revealed increased spindle phase coupling during NREM sleep (cluster tests: p = 0.0120, d = 1.37), but no elevated SO synchrony (p = 0.3087, d = 0.69). During wakefulness, we observed enhanced alpha-band coupling (p = 0.005, d = 0.83). b Power correlations (mean ± SEM) between scalp EEG and hippocampus revealed differences in amplitude couplings during NREM sleep (SO cluster: p = 0.0080, d = 1.07; spindle cluster: p = 0.0070, d = 1.20). c State-dependent directionality analysis indicates significant EEG to MTL influences (phase-slope index; mean ± SEM) in the spindle-band during NREM sleep (cluster test: p = 0.0410, d = 0.84). Inset: given the inter-subject variability in the precise spindle peak frequency, we also compared the peak-aligned (14.9 Hz ± 2.7 Hz; mean ± SD) PSI estimates to a surrogate distribution (gray), which confirmed the original observation (t-test: t17 = 2.16, p = 0.0452, d = 0.69). d Directional spindle coupling for every PFC ROI (lower panel; RM-ANOVA: F1.57,21.92 = 9.33, p = 0.002, η2 = 0.25) indicating a stronger modulatory influence from both the dlPFC and the mPFC but not the OFC. All error bars indicate the mean ± SEM

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Fig. 6

figure 6

Precise SO-spindle coupling phase modulates MTL-PFC connectivity. a Single subject example of EEG-MTL connectivity calculated separately for coupled spindles (black), uncoupled/isolated spindles (purple), and random event-free intervals (gray). Event numbers were equated by a bootstrapping procedure. Note the distinct peak in the spindle band. b We re-aligned power spectra to the individual spindle peak (14.5 ± 2.6 Hz; mean ± SD) and mean-normalized the aligned spectra to minimize inter-subject variability. c We then extracted the normalized EEG-MTL iPLV peak values for all events. A repeated measures ANOVA revealed a significant main effect (F1.49,25.27 = 7.93, p = 0.0043, η2 = 0.21; p-values from post-hoc pair-wise comparisons are depicted). We found significantly more inter-areal connectivity for coupled than for uncoupled spindles or random event-free intervals. d Directionality analysis during spindle events revealed a significant influence from prefrontal EEG spindles to the MTL (dark gray) as compared to a surrogate distribution (light gray). e Two single subject examples of event-locked spindle connectivity as a function of the precise coupling phase (mean ± SEM). Upper: the spindle band exhibits a strong peak in the spectrum and also peaks in the standard deviation spectrum (inset in red), thus, indicating that the spindle band exhibits the largest variance. f Spindle-band connectivity binned as a function of the precise coupling phase revealed strong non-uniform distributions. We highlighted the worst (lowest synchrony; red) and best (highest synchrony; blue) phase. g Anti-phasic relationship between best and worst connectivity phase (EEG-MTL: V-test against ±π: v = 7.33, p = 0.0073, mvl = 0.49). h Realigned (to the best phase) grand average spindle (12–16 Hz) iPLV histogram reveals a non-uniform relationship (mean ± SEM) between SO-Spindle coupling and EEG-MTL phase locking. Note we excluded the central aligned phase bin from subsequent testing (RM-ANOVA: F5.23,88.83 = 3.13, p = 0.011, η2 = 0.15). All error bars indicate the mean ± SEM

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Fig. 7

figure 7

Ripple-mediated information transfer. a Illustration of the information analysis strategy. A single EEG trace featuring SOs and spindles (in blue) and the corresponding MTL trace (in red) are depicted relative to a ripple event (see close-up; in black). Undirectional information was calculated by a moving time-window approach (center left), while directional information was calculated by keeping one window centered on the MTL ripple time point (center right; here fixed MTL window to moving EEG window; EEG to MTL information was calculated accordingly). b Left: time-resolved Mutual Information (MI) relative to the hippocampal ripple event (mean ± SEM). We detected enhanced MI between the hippocampus and prefrontal cortex ~1 s after the ripple event (1.3–2.0 s; cluster test: p = 0.0040, d = 1.06), but also observed two non-significant clusters (positive cluster from 0.25 to 0.35 s, p = 0.0849; negative cluster from −0.15 to −0.10 s, p = 0.1349). Right: spectrally resolved undirected mutual information. Significant (two-tailed p < 0.05) deviations from the average MI. Prior to ripple onset, mutual information was enhanced in the spindle and SO/delta range, which after 1 s dropped. c Left: time-resolved MI relative to the ripple event. We disentangled the information flow from prefrontal EEG to the MTL (purple) and vice versa (cyan). A significant cluster was observed after the ripple (cluster test: negative cluster from 0.15 to 0.50 s; p = 0.0130, d = 1.31), indicating increased information flow from the prefrontal EEG to the MTL. We found a second significant cluster where MI increased after ~1 s, which mainly reflected MTL to prefrontal EEG information flow (positive cluster from 1.1 to 1.85 s; p = 0.001, d = 1.08). Center/right: frequency- and directionality-resolved information flow. Outlined areas (black) reflect significant clusters (p < 0.05), where information flow was enhanced relative to the mean. Center: frequency-specific information flow from the MTL to neocortex was primarily increased in the SO (<2 Hz) and spindle-bands (~16 Hz). Right: information flow from the prefrontal EEG to MTL was not frequency-specific. d Upper panel: phase transfer entropy between the prefrontal EEG and the MTL (>1) is stronger than in the opposite direction (<1), in particular in the spindle-band (from −1–1.4 s; cluster test: p < 0.0010, d = 2.02). Lower left: excerpt during the ripple (t = 0) of the normalized PTE spectrum highlights the peak at ~16 Hz. Lower right: single subject observations in the spindle-band during the ripple. e Normalized spindle occurrence relative to the information peak. Spindle occurrence was significantly enhanced prior to the information peak (−2 to −0.5 s; peak time −0.91 s; cluster test: p = 0.001, d = 3.37). f Left: spindle-locked time-frequency representation of a single subject over 30 s highlights the slow (~0.4 Hz) pattern in spindle power. Superimposed is the rescaled, time-resolved undirected MI between the MTL and scalp EEG (black). Right: average spindle power (red; demeaned) and undirected MI (black; z-scored) indicating an anti-phasic relationship (gray arrows indicate MI peaks that coincide with spindle troughs). Note that the y-axis is truncated at around 0 to highlight the side lobes. Inset: group-level results revealing a statistically significant anti-phasic relationship between spindle power and MI between the MTL and frontal EEG sensors. g MI was enhanced only after a physiologic ripple as compared to an artifactual ripple event. Given that artifactual ripples were noisy and hence, variance was increased, we averaged in the time domain (cluster in panel a) prior to statistical testing. h MI was enhanced only after a physiologic ripple as compared to an artifactual ripple event (paired t-test: t17 = 2.82, p = 0.0117, d = 0.70). Note that artifactual ripples were also not significantly different from the baseline (paired t-test: t17 = −0.04, p = 0.9699, d = 0.01), thus, indicating that no information was transferred. The asterisk indicates that physiologic ripples were different from baseline as tested in panel a. i Topographical depiction of significant MI increases. All electrodes that showed a significant MI increase (z > 1.96, uncorrected p < 0.05) are color-coded according to the z-score, while electrodes without significant modulation are depicted in white. Note, the increase in MI was widespread and not restricted to a circumscribed cortical region. j MI increases per prefrontal sub-region (RM-ANOVA: F1.64, 23.01 = 9.13, p = 0.0020, η2 = 0.05). The strongest increase was observed in the dlPFC. All error bars indicate the mean ± SEM

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Cortical SO-spindle coupling shapes MTL HFB activity

We first detected SO and spindle oscillations in scalp EEG (Fig. 1) using established algorithms7,10. We utilized multi-taper spectral analyses10,26 to visualize spectral dynamics throughout the night (Fig. 1a). Event detection closely tracked spectral sleep signatures over a whole night of sleep. For every participant, we then determined the precise SO phase during the spindle peaks. We found significant (p < 0.05; Rayleigh test) non-uniform distributions in 14/18 subjects (Fig. 1b; p = 0.0154; one-tailed Binomial test; mean Rayleigh Z = 25.56 ± 6.32; resultant vector length: 0.17 ± 0.02, mean ± SEM). Spindles were preferentially nested in the SO peak (Fig. 1c; V-test against 0°: v = 7.73, p = 0.0050, mvl = 0.43; coupling phase: 1.94° ± 14.42°, circular mean ± SEM). We further quantified the directional influence using the phase-slope index27,28 (PSI) of SOs on spindles as reported previously (Fig. 1d)10. We again found that SO predicted spindle activity as indicated by a positive PSI (0.0128 ± 0.0071; mean ± SEM; one-tailed t-test against 0: t17 = 1.81, p = 0.0436, d = 0.61), which correlated negatively with age (Supplementary Fig. 2)10.

Next, we assessed how MTL activity (for electrode placement see Fig. 2a, b and Supplementary Table 2) was modulated as a function of SO and spindle activity. We found significant power modulations in the high-frequency band (HFB; 70–150 Hz), which captures both ripple oscillations as well as multi-unit spiking activity29,30, in the MTL relative to cortical SOs (Fig. 2c) and spindles (Fig. 2d). In particular, we found evidence in line with previous reports6,7 that maximal HFB activity was nested in the cortical SO peak and cortical spindle trough (Fig. 2e, see also below). Next, we calculated state-dependent cross-frequency coupling estimates using the modulation index7,31 between the three cardinal NREM frequency bands (Fig. 2f). We found significantly stronger SO-spindle (paired t-tests: t17 = −4.55, p = 0.0003), SO-HFB (t17 = −4.81, p = 0.0002) and Spindle-HFB coupling (t17 = −2.32, p = 0.0328) during NREM sleep. This finding was further corroborated by a full comodulogram, thus, replicating and confirming previous reports using different spectral estimates (Fig. 2g)7.

To determine how these three signatures dynamically interact within the human brain, we examined how hippocampal HFB activity is modulated by the precise cortical SO-spindle coupling phase. We first identified coupled (Fig. 3a) and uncoupled (Fig. 3b) spindles. Spindles were classified as coupled when a separate SO was detected in the same interval (±2.5 s; reflecting ± 2 SO cycles). We also extracted the precise SO coupling phase for every spindle (see Fig. 3a for spindles at 0° and ±180°). Next, we compared the inter-areal (EEG-MTL) spindle-HFB coupling between coupled, uncoupled and random, event-free NREM intervals (Fig. 3c). We found that inter-areal spindle-HFB coupling differed significantly (repeated measures ANOVA: F1.30,22.15 = 6.38, p = 0.013, η2 = 0.27). Post-hoc t-test indicated that coupled spindles exhibited more inter-areal CFC than uncoupled spindles (t17 = 3.82, p = 0.0013, d = 1.04) or random intervals (t17 = 2.91, p = 0.0097, d = 1.30). Uncoupled spindles and event-free random intervals did not differ significantly (t17 = 1.37, p = 0.1885, d = 0.59). Note, this result was independent of the chosen time window for co-occurrence detection (9 linearly spaced bins between 0.5 and 2.5 s; 2 × 2 RM-ANOVA with factors spindle coupling and time bin: Coupled spindles exhibited more inter-areal CFC: F1,17 = 15.42, p = 0.001, η2 = 0.39. No significant impact of factor time bin: F2.40,40.75 = 2.88, p = 0.059, η2 = 0.01; no significant interaction: F2.90,49.31 = 1.03, p = 0.384, η2 = 0.01). Furthermore, coupling estimates can be confounded by differences in oscillatory power as well as event numbers. Here we observed no differences in power between coupled and uncoupled spindles (t-test: t17 = 1.08, p = 0.2969) and stratification of trial numbers (50 repetitions) confirmed that inter-areal CFC of coupled spindles was significantly enhanced as compared to uncoupled spindles (t-test: t17 = 4.09, p = 0.0008).

Next, we sought to determine if not only the co-occurrence of SOs and spindles modulates HFB power, but if the precise temporal relationship fine-tuned HFB activity in the MTL. Therefore, we calculated the average HFB power in the MTL as a function of the precise SO-spindle coupling phase in 24 bins. Figure 3d, e highlights two single subject examples. Coupling strength was quantified in every subject as the circular-linear correlation coefficient between the coupling phase and the HFB amplitude, which was z-scored relative to a phase-shuffled surrogate distribution (1000 repetitions). We found significant coupling in 18/18 subjects (Fig. 3f; z > 1.96 corresponds to a two-tailed p < 0.05; mean z = 3.40, which corresponds to a group p = 0.0007; see also Supplementary Fig. 3). Comparable results were obtained when we utilized a RM-ANOVA with factor bins in all subject (significant at p < 0.05 in 15/18 subjects; Binomial test: p = 0.0075; η2 = 0.38 ± 0.06, mean ± SEM). Note that the precise phase bin where HFB amplitude peaked varied across subjects (red arrows in Fig. 3d, e). However, at the group level we found that HFB amplitude was significantly stronger coupled when the spindle peaked during the SO up-state (Fig. 3g; −1.00° ± 14.28°, circular mean ± SEM; Rayleigh test: p = 0.0281, Rayleigh Z = 3.49, mvl = 0.44).

Next, we investigated whether this triple coupling emerged in a temporally precise manner. Therefore, we repeated the analysis for all time points around the spindle peak (±1.25 s) and extracted the preferred phase for every subject at every time point (Fig. 3h). Using cluster-corrected Rayleigh tests, we found that a preferred coupling phase at the group level emerged around the spindle peak (1st cluster: −0.30 to 0.04 s, p < 0.001, mvl = 0.46; 2nd cluster: 0.12–0.60 s, p < 0.001, mvl = 0.51), indicating a temporal selective triple cortical-MTL coupling during the SO-spindle complex. Finally, we tested the directionality of the spindle-HFB coupling. Given that HFB cannot be reliably extracted from scalp EEG, we restricted this analysis to the 15 subjects with simultaneous intracranial PFC and MTL coverage. We calculated the inter-areal CFC between spindle phase and HFB amplitude for all PFC-to-MTL and MTL-to-PFC pairs (Fig. 3j) and observed significantly stronger directional coupling from the PFC-to-MTL than vice versa (Wilcoxon rank sum test, given the unequal variance: z = 3.48, p = 0.0005, d = 1.50).

Taken together, our analyses demonstrated that MTL HFB activity became selectively coupled to spindles, when spindles were precisely nested in the SO peak. We conclude that the quality of cortical SO-spindle coupling predicts and coordinates MTL HFB dynamics.

MTL HFB activity reflects coupled ripple oscillations

We have focused on HFB activity, which reflects both aperiodic broadband activity as well as oscillatory ripple events. Both signatures exhibit similar spectral characteristics. Therefore, we identified distinct ripple events during SO-spindle coupling in the time domain (Fig. 4a and Supplementary Fig. 4a–c)7,24. We then excluded sharp artifactual and epileptic transients, which can be mistaken as ripples in the frequency domain (Fig. 4b). For every subject, we determined the channel with the highest number of physiologic ripples (Supplementary Fig. 4d). Subsequently, we centered our ripple-locked analyses on this channel to minimize the impact of artifacts and the bipolar referencing (phase reversals as seen Supplementary Fig. 4c) in line with previous reports7.

In order to investigate if there were any other prominent rhythms during a ripple event in the MTL, we transformed ripple epochs (±2.5 s) into the frequency domain. After discounting the 1/f contribution by irregular resampling10,32, we found two distinct oscillatory peaks in the delta (~3 Hz, reflecting the hippocampal sharp wave; cluster-based permutation test: p = 0.001, d = 1.69) and spindle range (~14.5 Hz, p = 0.001, d = 0.91; Fig. 4c).

Next, we assessed the relationship of ripples to cortical SOs and spindles. We calculated the ripple occurrence relative to cortical SOs. We found that hippocampal ripple occurrence is enhanced during the SO peak (Fig. 4d; cluster-based permutation test: 1st cluster: p = 0.0010, d = 2.82; 2nd cluster: p = 0.0040, d = 1.42), particularly evident prior to the down-state (i.e. SO trough; 400 ms window around the SO peaks, paired t-test: t17 = 5.01, p < 0.0001, d = 1.88). No such relationship was observed for artifactual ripple events (Fig. 4e; smallest cluster p = 0.450; no peak differences: t17 = −0.63, p = 0.5381, d = 0.27). This is in line with the pattern that was observed for the HFB (Fig. 2c–e). Likewise, we found that ripples were preferentially nested in the trough of the cortical spindle oscillation (Fig. 4f; V-test against ±pi: v = 5.13, p = 0.0434, mvl = 0.29; see also to Fig. 2d, e). This tight temporal relationship was again not present for artifactual ripple-like events (Fig. 4g; v = 1.46, p = 0.3135, mvl = 0.08).

To investigate cortical dynamics relative to the ripple on longer time-scales, we calculated ripple-locked EEG time-frequency representations (±15 s) and observed a striking pattern in the spindle band, where spindle rhythmically re-occurred every 3–6 s (Fig. 4h). In order to quantify this effect, we calculated the spectral content of the state-dependent spindle amplitude by means of an FFT and used wakening as a control condition (absence of spindle activity). In a cluster-based permutation test, we found significantly enhanced power in the <0.5 Hz range (cluster-based permutation test: p = 0.0490, d = 0.49). We also estimated the 1/f contribution by means of irregular resampling (Fig. 4i, right panel), which further corroborated our finding that spindle amplitudes exhibit a slow, rhythmic comodulation over time (peak frequency: 0.37 ± 0.03; median ± SEM). Taken together, we identified oscillatory ripples events in the MTL, which exhibit a tight temporal relationship to cortical SOs and spindles on multiple timescales.

Brain state dependent connectivity and directionality

We established that the three cardinal spectral signatures of NREM sleep form an oscillatory hierarchy. This concept further implies that band-limited neural oscillations establish and support communication channels across distant cortical regions33,34. To address this, we determined the frequency bands in which the cortex and the MTL were coupled during the NREM sleep. We first focused on brain state dependent connectivity, which was calculated in 30 s segments as defined by the hypnogram. Undirected connectivity was calculated using metrics that suppressed effects of volume spread in the cortical tissue. We assessed two different coupling modes: Phase synchrony as measured by the imaginary phase locking value (iPLV)35 and amplitude-envelope correlations as measured by orthogonalized power correlations36.

We found that phase synchrony was only elevated in the spindle band (Fig. 5a; cluster-based permutation test: p = 0.0120, d = 1.37) but interestingly not in the SO range (p = 0.3087, d = 0.69). In addition, we found a negative cluster that indicated enhanced theta/alpha coupling during wakefulness as compared to NREM sleep (p = 0.005, d = 0.83). To further elucidate whether there is a long-range interaction in the SO range, which might not be phase-specific, we also computed power correlations between the EEG and MTL. This analysis revealed significant differences in both the SO (Fig. 5b; cluster-based permutation test: p = 0.0080, d = 1.07) as well as the spindle range (p = 0.0070, d = 1.20) as compared to wakefulness. However, from the observed pattern it is unclear whether this reflects a selective increase during NREM sleep or if the effect is driven by a relative increase of theta/alpha coupling during wakefulness. Notably, connectivity profiles as obtained from scalp EEG mimicked the pattern when intracranial PFC electrodes were used for connectivity analyses (Supplementary Fig. 5a, b). These state-dependent phase synchrony profiles exhibited a pronounced 1/f drop-off and the numerical differences were small.

An important contributing factor to this effect was that only a few spindle events occurred in a given 30 s epoch, hence, event-related synchrony effects might be underestimated when data is averaged in long epochs. Therefore, we investigated event-related connectivity profiles (see Fig. 6). An important observation that emerged from this analysis is the absence of pronounced SO phase synchrony between the EEG and MTL, indicating that SOs—in contrast to spindles—might play a negligible role for direct inter-areal information transfer. This was further corroborated by a directional connectivity analysis using the PSI (Fig. 5c). We found a spindle-band specific directional influence from the EEG to the MTL as compared to wakefulness (cluster test: p = 0.0410, d = 0.84) as well as when compared to a surrogate distribution (inset Fig. 5c). A PFC sub-region analysis (N = 15 subjects; RM-ANOVA: F1.57,21.92 = 9.33, p = 0.002, η2 = 0.25) indicated that the main drivers were the dlPFC (t14 = 2.36, p = 0.0166, d = 0.86; post-hoc one-tail t-tests against 0) and mPFC (t14 = 2.04, p = 0.0306, d = 0.74), but not the OFC (t14 = −2.56, p = 0.9887). No directional coupling effects were observed in the SO range (Fig. 5c). In summary, state-dependent directed and undirected connectivity profiles indicate that spindles play a pivotal role mediating directional influences from prefrontal to MTL regions.

Cortical SO-spindle coupling predicts PFC-MTL connectivity

These observations raise the question which role SOs play for inter-areal communication. We tested the hypothesis that the precise cortical SO-spindle coupling phase predicts the magnitude of the inter-areal phase synchrony in support of information transfer. First, we calculated phase synchrony estimates for coupled and uncoupled spindles as well as random event-free epochs (Fig. 6a, b; analogous to the analyses described in Fig. 3c). On the group level, we observed significant differences in synchrony (Fig. 6b, c; RM-ANOVA: F1.49,25.27 = 7.94, p = 0.0043, η2 = 0.21). Post-hoc t-tests revealed that coupled spindles were more synchronous than uncoupled spindles (t17 = 3.73, p = 0.0017, d = 1.00) and event-free intervals (t17 = 2.92, p = 0.0095, d = 1.05). In contrast, uncoupled spindles and random intervals did not differ (t17 = 0.63, p = 0.5369, d = 0.18). We also tested the directionality of the spindle coupling using the PSI (Fig. 6d) as compared to a surrogate distribution given the absence of spindle activity during wakefulness. We found significantly enhanced PSI values, indicating a directed influence from the prefrontal EEG to MTL electrodes (cluster test: p = 0.0130, d = 0.77), thus, replicating the effect as observed for state-dependent analyses (Fig. 5c).

Having established that coupled spindles exhibit more inter-areal synchrony, we then assessed the impact of the precise coupling phase (analogous to the analyses in Fig. 3d–g). We observed clear peaks in the connectivity spectra that were centered on spindles (Fig. 6e; ±2.5 s). Notably, peaks were also evident in spectra depicting the standard deviation across 16 phase bins (insets Fig. 6e). We assessed EEG-MTL spindle synchrony as a function of the SO-spindle coupling phase in every subject (Fig. 6f). Non-uniformity across the 16 bins was assessed using RM-ANOVAs. We found significant (p < 0.05, mean η2 = 0.32 ± 0.023; mean ± SEM) distributions in 18/18 subjects (two-tailed Binomial test: p < 0.0001). The best coupling phase was variable across subjects, but peaked on average after the SO up-state (108.85° ± 16.34°; mean ± SEM; mvl = 0.26), while the worst bin was closer to the down-state (−174.58° ± 16.89°; mvl = 0.22). Within the individual, best and worst phase were preferentially separated by ~180° (Fig. 6g; V-test: v = 7.33, p = 0.0073, mvl = 0.49). Given that we observed this variability on the best connectivity phase, we re-aligned the binned spindle synchrony to the bin centered at ~12° and mean-normalized individual distributions to reduce inter-subject variability. This bin was excluded from subsequent testing. We assessed the non-uniformity across the remaining 15 bins using a RM-ANOVA. We found significant synchrony differences as a function of the precise SO-spindle coupling phase (Fig. 6h; F5.23,88.83 = 3.13, p = 0.0109, η2 = 0.15).

Collectively, these findings establish that the precise SO-spindle coupling phase predicts the connectivity between prefrontal and MTL sites and is in line with the notion that spindles selectively establish a communication pathway between the MTL and neocortex.

Ripple-mediated information transfer

In a final step, we investigated the interplay between spindles and ripple-mediated information transfer. Ripples during hippocampal replay have been proposed to reflect information package transfer2,3. To quantify information transfer, we calculated time-resolved mutual information (MI)37 between the EEG and MTL relative to MTL ripple events (for a schematic of the analysis strategy see Fig. 7a). In particular, we utilized undirectional (time window by time window analyses) as well as directional (ripple-centered fixed time window to all other time windows) MI analyses. We first calculated the undirectional MI between the EEG and MTL (Fig. 7b left panel and Supplementary Fig. 6a). We found that MI reliably increased ~1 s after a ripple (cluster test: p = 0.0040, d = 1.06), which is in line with the idea that information is being reactivated during a ripple for further processing. Albeit non-significant (non-significant positive cluster from 0.25 to 0.35 s, p = 0.0849; non-significant negative cluster from −0.15 to −0.10 s, p = 0.1349), we also observed an additional decrement just prior to the ripple, which could potentially reflect a cortical trigger event initiating the ripple (e.g. a spindle; Supplementary Fig. 6b).

To investigate which frequency bands carried information, we next spectrally decomposed the signal. We z-normalized every frequency band separately to account for the 1/f signal drop-off and performed no baseline correction to assess information prior to the ripple. Spectrally resolved undirected MI estimates (Fig. 7b, right panel) revealed significant information away from the mean in the spindle-band (cluster test: p < 0.0010, d = 1.63) and SO range (p = 0.0060, d = 1.17) already evident prior to the ripple event. Note, these clusters also involved the delta (~3 Hz; possibly reflecting delta waves or sharp-wave activity7) and beta-activity (~32 Hz; possibly reflecting a spindle harmonic7). Spectral techniques are known to give rise to temporal smearing due to the inherent time-frequency trade-off34. However, spectral decomposition offers the advantage that the signal is not mainly dominated by strong low-frequency components, which are particularly pronounced when analyzing data in the time domain due to the 1/f signal drop-off.

In order to test if this increase reflected ripple-specific information, we calculated time-resolved MI between a fixed time-window, which was centered on the MTL ripple (±0.2 s) and all other time points (Fig. 7c, left panel). To resolve directionality, we performed this fixed window analysis twice, either centered on the MTL or the EEG. This analysis revealed that the increase that occurred ~1 s after the ripple mainly reflected information transfer from the MTL to the prefrontal EEG (Fig. 7c; cluster test: p = 0.001, d = 1.08).

Critically, we also found a prominent earlier cluster indicating increased shared information between the EEG (at the time of the ripple event) and the MTL (following a ripple; cluster test: p = 0.0130, d = 1.31), supporting rapid bidirectional interactions between the prefrontal EEG and MTL after a ripple. To further characterize this effect, we calculated time-, frequency- and directionality-resolved MI (Fig. 7c, center and right panel). We observed that shared information between the MTL ripple and the prefrontal EEG was already enhanced prior to the ripple (Fig. 7c, center panel), in particular in the low-frequency (−0.9 to −0.25 s; <2 Hz; cluster test: p = 0.0110, d = 0.87) and spindle-band (−0.85 to 0.85 s; ~16 Hz; p < 0.0010, d = 0.73). Another cluster emerged later (1.1 to 1.9 s, ~11 Hz; p = 0.0050, d = 0.59). This observation further supports the idea that cortical SOs and spindles are predictive of MTL ripple activity (c.f. Figs. 3 and 4). The control condition (Fig. 7c right panel; information between a fixed EEG window and all other MTL time points) did not reveal a frequency-specific effect, but was broadly distributed and covered all frequency bins (three clusters; all p < 0.0240; mean d = 1.62). Note, that the moving window analysis is comparable to a non-linear lagged correlation analysis, but does not actually quantify frequency-specific information flow. Hence, we calculated transfer entropy as directional information-theoretical metric of information flow (Fig. 7d).

We found that phase transfer entropy was stronger from the prefrontal EEG to the MTL than vice versa in the spindle-band (~16 Hz; from −1 to 1.4 s around the ripple; cluster test: p < 0.0010, d = 2.02). We also observed prominent lower frequency components (<3 Hz; Supplementary Fig. 6c), which resemble the low-frequency effects observed in Fig. 7b, c, but these did not exhibit a preferred directionality, mimicking the observed pattern in Fig. 5a, c.

Having established these interactions, we also determined the delay between cortical spindle activity and information transfer (Fig. 7e). In line with the spindle-ripple interactions (Fig. 4) and ripple-information dependency (Fig. 7b, c), this analysis suggested that spindles preferentially occurred 0.5–2 s prior to the information peak as extracted from single-event traces (cluster test: p = 0.001, d = 3.37). Hence, ripple-mediated information could be detected at prefrontal sites after spindle offset, i.e., in between two spindles when the neocortex is relatively desynchronized, a neurophysiological state that maximizes information-processing capacities1,9,21,37. To further highlight this relationship, we calculated spindle-locked time-frequency representations (Fig. 7f; analogous to Fig. 4h), which reveal the anti-phasic between spindle power and MI. To quantify this effect, we calculated the average phase difference at ~0.4 Hz between spindle power and MI. At the group level (inset in Fig. 7f), we found strong non-uniform distributions with a preferred phase difference of (159.6° ± 11.7°, circular mean ± SEM; Rayleigh test: Z = 7.02, p = 0.0005, mvl = 0.62). These findings were further corroborated by the observation that no reliable information transfer occurred after an artifactual ripple (Fig. 7g, h; see also Supplementary Fig. 6d).

Finally, we assessed the spatial extent of this information transfer in the subset of subjects with intracranial PFC electrodes (N = 15; Fig. 7i). We found widespread ripple-mediated information increases, which were most pronounced in the dlPFC (Fig. 7j), but also significant in all other ROIs (paired t-test against 0; dlPFC: t14 = 5.35, p = 0.0001, d = 1.95; OFC: t14 = 4.12, p = 0.0010, d = 1.51; mPFC: t14 = 4.15, p = 0.0010, d = 1.52). Taken together, these findings are consistent with the idea that ripples mediate information transfer from the MTL to the neocortex2,15,38. The data described in Fig. 7I, j indicates widespread increments in shared information following a MTL ripple, not constrained to a single PFC sub-region. Importantly, we observed a clear temporal relationship between information peaks and spindle events (Fig. 7e, f), which might reflect distinct episodes of information reactivation (triggered by spindle synchrony) and information transfer in line with previous behavioral evidence20.


Our results demonstrate that precisely coupled cortical SOs and spindles modulate hippocampal dynamics in support of hippocampal-neocortical information transfer in the human brain. In particular, the quality of this coupling predicts inter-areal connectivity, triggers hippocampal ripple activity and shapes subsequent information transfer. Our findings describe two distinct cortical states during NREM sleep, which alternate every 3–6 s. First, we observed states of high synchrony where coupled SO-spindle complexes engage the MTL and trigger ripples (Figs. 36). Second, we also found states of low synchrony, where information flow from the MTL to the neocortex was maximized, but no prominent oscillatory activity was present (Fig. 7). This two-step organization might reflect an endogenous timing mechanism, which ensures that information arrives in the neocortex when processing capacities are optimized20,22. Taken together, our results suggest that synchronized sleep oscillations provide temporal reference frames for feed-forward and feedback communication and structure hippocampal-neocortical dynamics in space and time4.

Hippocampal-prefrontal pathways and their role for memory formation have been the studied extensively over the last few decades4,7,15,16,39,40. However, the majority of evidence stems from recordings in rodents, which exhibit the same prominent network oscillations, such as SOs, spindles and ripples, despite a dramatically different anatomical structure, in particular in the PFC11,12. A major contributing factor is that imaging the human MTL with high spatiotemporal resolution is challenging when using non-invasive methods41. Here we took advantage of recordings from patients who underwent invasive monitoring for seizure localization and were implanted with electrodes in different key nodes of the MTL-PFC network. While previous human intracranial studies demonstrated the presence of hippocampal ripples24 and the nesting of sleep oscillations6,7, these studies did not address the directionality of these interactions nor did they quantify information transfer or assess the contributions of distinct PFC sub-regions.

While memory research has focused on the hippocampus-dependent encoding of novel information, how this information is transferred to long-term storage is unknown. The discovery of neuronal replay provided an elegant mechanistic explanation of how mnemonic representations are strengthened, but this concept does not directly explain how information is subsequently transferred in large-scale networks14. Furthermore, it remains unclear how the different cortical nodes ensure that the receiving area is in a favorable state to process the reactivated information40.

Systems memory consolidation suggested a two-step process: Mnemonic reactivation is associated with hippocampal ripple activity that is tightly coupled to subsequent cortical SOs and spindles, which then mediate neuroplasticity to facilitate long-term neocortical storage1,2,3,16. This model received substantial empirical support over the last two decades2,3,13,15,42,43, but has been challenged recently44,45,46. In line with several of our observations (e.g. early post-ripple PFC-to-MTL information flow; Fig. 7c), several recent studies reported early cortical contributions preceding hippocampal involvement, thus, reflecting a promising avenue for future studies investigating interactions between the hippocampus and neocortex in support of memory formation18,47,48,49.

In particular, it had been observed that SOs, spindles, and ripples are precisely coupled to one another and form an oscillatory hierarchy, where spindles are nested in SOs and ripples are nested in spindles5,7. Crucially, several groups reported that this triple coupling predicts behavior4,16. For example, ripple-triggered electrical stimulation increased subsequent prefrontal SO-spindle coupling and recall performance16. Hence, some theories assumed that the hippocampus is the driver of this interaction and subsequent SOs and spindles mainly index hippocampal processing that facilitated neocortical processing15,16. We observed several data points that contradict the classic systems memory consolidation theory: first, not all signatures of information transfer are frequency-specific (e.g. Fig. 7c). Second, we found evidence for early cortical engagement around the ripple events, which is not in-line with classic systems consolidation theory. Given the recent observation of early cortical memory representations in humans48,49, we speculate that this could reflect a uniquely human network feature, requiring further explication using combined electrophysiology and behavioral testing tracking mnemonic representations13.

Notably, several other recent reports also implied an inverse directionality compared to classic theory1,38,40: SOs predict spindles, which in turn predict ripples. Several reports suggested that cortical activity might actually precede hippocampal reactivation17,18,19, none of which were carried out in humans. The present findings provide empirical evidence for this account in the human brain. Here we demonstrate that SOs shape spindles, which in turn trigger nested ripples. Crucially, the quality of cortical SO-spindle coupling directly predicts ripple-band activity as well as inter-areal synchrony. These findings provide clear evidence that the hippocampal-neocortical dialogue relies on rapid bidirectional communication, where the neocortex can mediate hippocampal information reactivation and subsequent transfer through neocortical-hippocampal-neocortical loops.

It had long been recognized that spindle activity predicts overnight memory retention, but the underlying mechanisms remained unclear8. In particular, spindle magnitudes or densities have commonly been used to assess their role for memory consolidation50. More recently, several reports indicated that not spindle activity per se, but spindle timing relative to the SO, determines the success of information reactivation and consolidation7,10,51,52,53,54. These findings were nicely paralleled by a two-photon calcium imaging study in rodents, which revealed that excitatory neural activity is amplified when spindles coincide with the SO peak, but not when they miss a narrow ‘window-of-opportunity’22. This pattern mirrored our observation that connectivity and inter-areal coupling is increased for coupled as compared to uncoupled spindles. This observation reveals that coupled spindles do not reflect one entity, but connectivity and coupling depend on the precise SO coupling phase10,23.

Furthermore, it had recently been observed that spindles exhibit a second-order temporal structure and rhythmically reoccur every 3–6 s20. We observed a highly comparable pattern for human hippocampal ripples. This has previously observed in rodents, albeit on a slower time-scale (~12 s)5. Several lines of research converged on the notion that spindles are associated with information reactivation, but that subsequent processing actually occurs ~1–2 s after a spindle20,55. In this state, the cortex is maximally desynchronized during NREM sleep and entropy and processing capacities are maximized21. These findings are consistent with recent evidence suggesting that cued memory reactivation during the spindle refractory period, i.e., at the peak information transfer, is detrimental for memory consolidation20. Our results provide empirical evidence and offer a mechanistic explanation for this behavioral observation. The present findings reveal that cortical spindles shape hippocampal ripples and subsequent information transfer from the MTL-to-PFC, which peaks 1–2 s after the spindle, i.e., during spindle refractoriness9,20,55. We speculate that additional sensory input during the inter-spindle interval might interfere with MTL-neocortical information transfer and thereby, explain the detrimental effects on memory by cue presentation during the spindle refractoriness20.

Seminal work by Steriade et al.56,57 indicated that spindles are mainly generated in the thalamus and in a cortico-thalamic loop. In the present study, we only recorded from locations that were actively explored for clinical purposes of seizure onset localization and did not involve the thalamus41. However, a recent rare human intracranial study that simultaneously recorded from the thalamus and the PFC, but not the MTL, reported that thalamic spindles are triggered by a neocortical down-state and are then back-projected to the neocortex where the spindle coincides with the next SO up-state58. It is plausible that neocortical SOs trigger thalamic spindles, and jointly provide a messenger mechanism to trigger hippocampal reactivation and transfer. Taken together, several lines of inquiry now indicate that frequency-specific bidirectional communication in large-scale networks during NREM sleep coordinates inter-areal information flow in support of long-term memory retention.

In summary, we established that bidirectional hippocampal-neocortical interactions support hippocampal information reactivation and transfer during NREM sleep. This dynamic process ensures that the neocortex receives mnemonic data at a temporally optimal physiological moment for information transfer and neuroplasticity1,2,8,10. Given clinical time constraints in this study, we did not obtain concomitant behavior. However, the current observed physiologic patterns are in accord with recent findings on how coupled SO-spindles support hippocampus-dependent memory consolidation10. These findings are of immediate clinical relevance, given that temporal dispersion of cortical sleep networks has been suggested to constitute a novel pathway of age- as well as disease-related cognitive decline10,52,59,60,61.


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