Information transfer during sleep is thought to rely on systematic interactions of the cardinal non-rapid-eye-movement (NREM) sleep rhythms (Diekelmann and Born, 2010; Klinzing et al., 2019): First, the cortical <1 Hz slow oscillation (SO) opens windows of neuronal excitability and inhibition (up- and down-states, respectively) in cortical and subcortical regions (Isomura et al., 2006; Timofeev, 2011). Triggered by SOs, the thalamus generates sleep spindles, that is, transient (0.5–2 s) oscillatory activity between 12 and 16 Hz, via thalamo-cortical loops (Timofeev and Steriade, 1996). Nested in SO up-states, spindles gate Ca²⁺ influx into dendrites and promote synaptic plasticity (Astori et al., 2013; Fernandez and Lüthi, 2020; Niethard et al., 2018; Seibt et al., 2017). Importantly, spindles have also been shown to group hippocampal sharp-wave ripples (SW-Rs) (Siapas and Wilson, 1998; Sirota et al., 2003; Staresina et al., 2015). SW-Rs are transient network oscillations in the hippocampal CA1 subfield emerging from recurrent interactions within CA3 and consist of a brief 80–200 Hz ripple-burst superimposed on a sharp wave, with the latter characterized by a peak frequency of ~3 Hz in humans (Axmacher et al., 2008; Helfrich et al., 2019; Jiang et al., 2019a; Norman et al., 2019; Staresina et al., 2015). Ripples have been linked to the reactivation of cell assemblies engaged during previous encoding (Diba and Buzsáki, 2007; Dupret et al., 2010; Wilson and McNaughton, 1994), and experimental suppression of ripples leads to an impairment of memory performance (Ego-Stengel and Wilson, 2010; Girardeau et al., 2009). Given that spindles group SW-Rs and induce neural plasticity (Jiang et al., 2019a; Niethard et al., 2018; Seibt et al., 2017; Staresina et al., 2015), they appear ideally suited to facilitate memory consolidation mechanistically. That is, spindles might synchronize the hippocampus (sender) and cortical target sites (receiver) during reactivation events and thereby induce synaptic changes in neocortex for long-term storage (Rosanova and Ulrich, 2005; Sejnowski and Destexhe, 2000). However, whether and how spindles mediate the hippocampal-neocortical dialogue still remains poorly understood.

We here examined whole-night sleep Electroencephalography (EEG) recordings from neocortex and hippocampus in 14 pre-surgical epilepsy patients (Figure 1). In a first step, we algorithmically detected spindles in neocortex (NC; scalp electrode Cz) and posterior hippocampus (HIPP; Staresina et al., 2015) as well as hippocampal sharp-wave ripples (SW-Rs). Across participants, a total of 17,174 NC spindles, 14,748 HIPP spindles and 3,748 HIPP SW-Rs were identified (Figure 1). For sleep characteristics and patient-specific event numbers and densities (events per minute), see Tables 1 and 2, respectively.

Figure 1

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Cortical and hippocampal spindles around SW-Rs

To examine whether SW-Rs co-occur not only with spindles in HIPP (Jiang et al., 2019a; Staresina et al., 2015) but also in NC, we first derived time-frequency representations (TFRs) time-locked to discrete HIPP SW-Rs (Figure 2). Results were statistically compared to TFRs obtained from control events, that is, matched ripple-free intervals randomly drawn from NREM sleep. In HIPP, we found an extended cluster of significant power increases encompassing two distinct components (p=0.001, Figure 2A): First, an increase in spindle power (12–18 Hz), peaking at ~300 ms after SW-Rs. Second, a more widespread frequency cluster with a maximum at 3 Hz, likely reflecting the sharp-wave component (Axmacher et al., 2008; Oliva et al., 2018; Staresina et al., 2015). Critically, when locking the TFR in NC to SW-Rs in HIPP (Figure 2B), we again observed significant power increases compared to ripple-free control events. A significant cluster emerged from 11 to 16 Hz (p=0.001), again peaking shortly after SW-Rs. The concurrent spindle power increase in both regions (Figure 2C) around HIPP SW-R might indicate a role of spindles in mediating cortical-hippocampal communication (see next section).

Figure 2 with 1 supplement see all

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Regarding event contingencies,~5% and 7% of all algorithmically detected sleep spindles in NC and HIPP, respectively, occurred within ±1 s of HIPP ripple maxima. Conversely, ~17% and 22% of HIPP ripples exhibited an overlap with NC and HIPP sleep spindles, respectively (see also Staresina et al., 2015). This highlights that only a fraction spindles coincides with HIPP ripples, raising the interesting question whether the remainder of spindles might serve different functions. That said, the precise numbers of event overlap depend on the detection thresholds for spindles and ripples and, for the latter, might have been reduced by the exclusion of physiological ripples coinciding with artifactual, epileptic activity. Moreover, it is likely that a certain amount of NC spindles overlap with ripples emerging in parts of HIPP not captured by our chosen contact.

While theoretical and empirical work also implicates SOs/slow-waves in the hippocampal-neocortical dialogue (Maingret et al., 2016; Sejnowski and Destexhe, 2000; Sirota et al., 2003), our ripple-locked TFR analyses revealed no significant effect at ~1 Hz. However, this might result from our control procedure, which extracts matched NREM epochs without HIPP SW-Rs. That is, the prevalence of SOs throughout NREM sleep (and thus also in control epochs) might obscure SO-related effects aligned to SW-Rs. Corroborating this notion, comparing ripple-locked and NREM control event TFRs with control events from REM sleep confirmed a significant global increase in SO power (1–1.5 Hz) during NREM both in NC and HIPP (ripple-locked vs. REM control events: t₍₃₆₁₇₎ > 11.441, p<0.001; NREM control vs. REM control events: t₍₃₉₇₄₎ > 54.177, p<0.001, Figure 2—figure supplement 1). No difference was seen between ripple-locked events and NREM control events (t₍₃₇₄₇₎ < 1.559, p>0.118). These results suggest that any clustering of SOs around hippocampal ripples might be concealed by the strong prevalence of SOs throughout NREM sleep. That said, these results might also indicate that SOs do not mediate hippocampal-cortical interactions around ripples directly, but indirectly by coordinating sleep spindles (Mölle et al., 2011; Staresina et al., 2015). Indeed, a recent human iEEG study showed that coupling between prefrontal cortex spindles and medial temporal lobe ripples was greater when prefrontal spindles were nested in the up-state of concomitant SOs (Helfrich et al., 2019). Similarly, spindle coupling between neocortex and hippocampus was found to be increased at particular SO-phases (Cox et al., 2020). This coordinating role of SOs notwithstanding, our results emphasize that spindles group around hippocampal ripples above and beyond background NREM spindle activity.

Cortical-hippocampal spindle coupling around SW-Rs

Next, we asked whether the cross-regional co-activation in the spindle band around HIPP SW-Rs may indeed reflect an increase in functional coupling between NC and HIPP. To this end, we calculated ripple-locked spectral coherence between NC and HIPP. Based on the concurrent power increases shown in Figure 2C, we determined coherence specifically for the observed spindle cluster around SW-Rs (for extended time-frequency resolved coherence analysis, see Figure 3—figure supplement 1). As shown in Figure 3A, spindle-band coherence between NC and HIPP was indeed significantly increased compared to control events (z-value = 2.12, p=0.034). Examination of time-resolved 12–16 Hz coherence confirmed a significant increase reaching its maximum shortly after the SW-R (Figure 3B). Identical results were obtained using amplitude- or phase-based connectivity measures (Figure 3—figure supplement 2A–D), that is, orthogonalized power correlation (Hipp et al., 2012) and phase-locking value (Lachaux et al., 1999), which validated the robustness of our coherence-based approach (which is impacted by both amplitude and phase) and - more importantly - ruled out any influence of volume conduction or spurious correlations due to a common referencing scheme. Note though that while power correlations and phase-locking are differentially sensitive to amplitude- and phase covariations, respectively (Siegel et al., 2012), they are not entirely independent (Palva and Palva, 2018; Siems and Siegel, 2020). In any case, these findings corroborate the notion that sleep spindles co-occurring in NC and HIPP around hippocampal ripples reflect an increase in cortical-hippocampal communication.

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Hippocampal-neocortical coupling emerges preferentially during long-duration ripples

Our study is guided by the tacit assumption that ripple-locked hippocampal-neocortical interactions are instrumental for memory consolidation. However, without an explicit pre-sleep learning and post-sleep testing component, this assumption is rather conjectural. Interestingly though, while the critical role of ripples for memory-related processes has been established in rodents (Ego-Stengel and Wilson, 2010; Girardeau et al., 2009), a recent study further demonstrated that longer-duration ripples bear greater relevance for memory-related processes than short-duration ripples (Fernández-Ruiz et al., 2019). That is, replay of newly encoded information was associated with longer duration ripples and experimental ripple prolongation or perturbation of the late ripple component enhanced or diminished memory performance, respectively. We therefore reasoned that if our results reflect hippocampal-neocortical interactions in service of memory consolidation, they should be more pronounced for longer duration compared to shorter duration hippocampal ripples.

To assess this possibility, we divided, separately for each patient to ensure matching event numbers, all ripple events into tertiles based on their duration and categorized the first tertile as short-duration and the third tertile as long-duration SW-Rs (mean ± SEM tertile thresholds = 0.046 ± 0.001 s and 0.059 ± 0.002 s for short and long-duration SW-Rs, respectively, Figure 4A). Examining the neocortical TFR locked to different-duration hippocampal ripples again revealed a significant increase in spindle power with respect to matched control events, but only for long-duration ripples (Figure 4B). In fact, directly contrasting power values in the joint NC/HIPP spindle cluster (Figure 2C) around long- vs. short-duration ripples revealed greater NC spindle power during longer ripples (t₍₂₉₃₇₎ = 1.76, p=0.039, one-sided independent samples t-test in accordance with Fernández-Ruiz et al., 2019). The same held true for the comparison of hippocampal spindle power locked to long- vs. short-duration ripples (t₍₂₉₃₇₎ = 4.68, p<0.001, one-tailed). Additionally, we correlated – for each patient separately – ripple duration with spindle power in the joint spindle cluster (Figure 2C) across events in both NC and HIPP. Resulting correlation values were Fisher z-transformed and tested against zero via a one-sample t-test across patients. Correlation values were reliably positive in both regions (NC: average Spearman r = 0.05, t₍₁₃₎ = 2.63, p=0.021; HIPP: average Spearman r = 0.19, t₍₁₃₎ = 5.75, p<0.001). Next, we repeated the PDC analyses focusing on the 12–16 Hz spindle band and found the previously observed increase in neocortical to hippocampal information transfer around the SW-Rs, but again only for long- and not short-duration ripples (Figure 4C).

Figure 4

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Memory consolidation is thought to rely on an intricate interplay between SOs, sleep spindles and SW-Rs. While the link between each of these sleep signatures and effective consolidation has been established across species (Antony et al., 2018; Ego-Stengel and Wilson, 2010; Girardeau et al., 2009; Latchoumane et al., 2017; Lustenberger et al., 2016; Maingret et al., 2016; Ngo et al., 2013), less is known about their dynamic interactions underlying the purported information transfer from hippocampus to neocortex (Diekelmann and Born, 2010). According to the ‘active systems consolidation’ framework (Born and Wilhelm, 2012; Klinzing et al., 2019), sleep spindles are thought to open fine-tuned windows of opportunity for cross-regional synchronization and plasticity (Antony et al., 2019; Born and Wilhelm, 2012; Helfrich et al., 2018; Niethard et al., 2018; Seibt et al., 2017; Sejnowski and Destexhe, 2000). Consistent with this notion, recent work has demonstrated increased functional connectivity during sleep spindles across neighboring cortical regions (Das et al., 2017; Laventure et al., 2018). Moreover, simultaneous EEG-fMRI recordings showed an increase in functional BOLD connectivity between hippocampus and neocortex during sleep spindles (Andrade et al., 2011). However, none of these studies have linked spindle-mediated connectivity to hippocampal ripples, the putative electrophysiological marker of memory reactivation (Buzsáki, 2015; Kudrimoti et al., 1999). In rodents, hippocampal ripples have been shown to coincide with spindles in prefrontal cortex (Siapas and Wilson, 1998). Similarly, we and others have shown a temporal nesting of SW-Rs within spindles observed in human hippocampus or parahippocampal areas (Clemens et al., 2007; Helfrich et al., 2019; Jiang et al., 2019a; Staresina et al., 2015). However, it has remained unclear whether hippocampal-neocortical communication indeed increases during hippocampal ripples and what the directional dynamics of such interactions might be. Here, we analyzed EEG data acquired during NREM sleep both in hippocampus (HIPP) and at scalp electrode Cz (Figure 1), the latter integrating across fronto-parietal neocortical (NC) networks. Time-locking the continuous EEG to individual HIPP SW-Rs, we first showed that spindle power (~12–16 Hz) increases not only in HIPP around SW-Rs (Jiang et al., 2019a; Jiang et al., 2019b; Staresina et al., 2015), but also in NC (Figure 2). More importantly, using (directional) coherence analyses (and thus moving beyond mere co-occurrences), we were able to show a ripple-locked increase in (directional) NC-HIPP coupling in the spindle band (Figure 3). These results dovetail with a recent iEEG study by Helfrich et al., 2019, who showed a ripple-locked, driving influence from electrode contacts in PFC to contacts in the medial temporal lobe (MTL) in the spindle band. While MTL contacts in that study also included entorhinal cortex, perirhinal cortex and parahippocampal cortex, we here pinpoint the influence from NC directly to posterior hippocampus. Together, these results support the idea that spindles serve as the mechanistic vehicle to synchronize the hippocampus with neocortical target sites at the time of hippocampal memory reactivation (Antony et al., 2019).

It deserves explicit mention though that we did not employ a pre-sleep learning task, rendering the notion of hippocampal memory reactivation during SW-Rs rather speculative. That said, recent rodent work demonstrated that specifically long-duration ripples are linked to neural replay and are particularly critical for memory consolidation (Fernández-Ruiz et al., 2019). If our observed hippocampal-cortical interactions reflect - at least in part - mnemonic information transfer, they should thus be more pronounced during longer relative to shorter ripples. Indeed, as shown in Figure 4, we found a selective increase in hippocampal-neocortical communication during long-duration compared to short-duration ripples. Nevertheless, conclusive evidence for memory reactivation would of course require well-designed memory tasks around the sleep recordings.

An important remaining question is how this hippocampal-neocortical dialogue is initiated in the sleeping brain, that is what governs the interplay between spindles and SW-Rs. One intuitive scenario is that upon spontaneous SW-Rs in HIPP, sleep spindles – projected from the thalamus to both HIPP and NC – synchronize the two regions. Indeed, rodent recordings have shown that prefrontal cortex neurons fire after HIPP neurons during slow wave sleep (Siapas and Wilson, 1998), and the stronger the hippocampal firing burst, the more likely a spindle event is to be observed in PFC (Wierzynski et al., 2009). An alternative scenario, however, is that HIPP-NC interactions are initiated in neocortex, perhaps ensuring that cortical target sites are in a state conducive to plasticity (Rothschild, 2019). Rodent recordings from NC and HIPP have shown that the NC spindles indeed trigger HIPP firing and associated ripples (with NC spindles emerging ~200 ms before HIPP ripples Sirota et al., 2003) and are prevalent in both regions thereafter (Peyrache et al., 2009). Moreover, a recent rodent study showed evidence for a cortical-hippocampal-cortical loop. Engagement of task-relevant NC sites first predicted neural activation patterns of HIPP assemblies by ~200 ms, and activation patterns in HIPP in turn predicted activation in NC (Rothschild et al., 2017). Our current results show a remarkable overlap with these studies regarding the onset of NC-HIPP coupling relative to HIPP ripples (~200 ms; Figure 3D and E), highlighting the role of spindles in synchronizing NC and HIPP prior to SW-Rs. Moreover, we were further able to show a directional influence of NC to HIPP. In particular, as shown in Figure 3D, partial directed coherence (PDC) pointed toward top-down influence from NC to HIPP in the spindle band. Corroborating the PDC analysis, we show that the prevalence of spindle onsets rises significantly in both HIPP and NC prior to ripples, but does so sooner in NC than in HIPP (Figure 3E). In sum, these results suggest that the spindle-mediated hippocampal-neocortical dialogue is initiated in NC and is sustained for an extended period after the SW-Rs, allowing for the hypothesized transfer of memory traces reactivated during HIPP SW-Rs (Figure 3F).

Interestingly, in the rodent study mentioned above (Rothschild et al., 2017), the authors were also able to bias the NC-HIPP-NC loop experimentally by playing sounds related to wake learning episodes. Specifically, presentation of the sound triggered cortical representations in relevant NC sites, which predicted hippocampal patterns during SW-Rs. The hippocampal signal in turn predicted neocortical activation patterns (although the sound was no longer present). This casts an intriguing light on some recent targeted memory reactivation (TMR) studies in humans (Cairney et al., 2018; Schreiner et al., 2018). Using multivariate decoding methods on scalp EEG data, these studies indicated two phases of memory-related reinstatement after auditory cues: An initial peak within the first second and another peak at ~2 s after cue onset, with concomitant increases in spindle power around both peaks (Cairney et al., 2018). In light of the results reported in Rothschild et al., 2017 and in conjunction with our current findings, one possibility is that hippocampal reactivation occurs between the two cortical spindle/reactivation events. Indeed, examination of NC spindle onsets around HIPP ripples hints toward a biphasic spindle increase (one before and one after the HIPP ripple; Figure 3E), although the second peak did not reach significance compared to the control events. A recent comprehensive examination of cortical spindles and their temporal alignment to hippocampal ripples showed that spindles in some cortical areas precede hippocampal ripples, whereas spindles in other areas follow them (Jiang et al., 2019a; Jiang et al., 2019b). While the pre-ripple spindles may thus reflect a first (coarse) cortical initiation of a reactivation event, post-ripple spindles in other cortical regions might reflect more detailed reinstatement following the hippocampal contribution. In particular, hippocampal reactivation during SW-Rs might enrich the cortically reactivated memory trace with spatio-temporal episodic details (Lewis and Bendor, 2019; Rothschild, 2019). This idea is also consistent with a recent TMR study that revealed a cortical spindle refractory period during which the delivery of reminder cues was relatively ineffective (Antony et al., 2018). In other words, hippocampal engagement between two cortical spindles might be dedicated to mnemonic (re)processing, resulting in limited resources for the processing of additional memory cues.

While our interpretation of results has been largely guided by the active systems consolidation framework (Klinzing et al., 2019), some of our findings are also consistent with alternative accounts. For instance, in line with the synaptic homeostasis hypothesis (Tononi and Cirelli, 2014), a recent rodent study showed that hippocampal SW-Rs trigger synaptic depression (Norimoto et al., 2018). Moreover, experimental perturbation of ripple activity not only halted synaptic down-regulation but also lead to an impairment of memory consolidation. Accordingly, rather than initiating memory transfer, hippocampal SW-Rs might shape memories, brought online via preceding NC spindles, by pruning irrelevant mnemonic elements. The refined memory trace might then be transferred back to neocortical sites via co-activation in the spindle band.

To conclude, our findings suggest that – ignited by neocortex - sleep spindles mediate cortical-hippocampal communication around SW-R. However, to what extend this interaction might underlie the reorganization of hippocampal information to neocortical structures for long-term memory or a synaptic down-scaling remains an intriguing open question for future research.

Raw EEG data (from NC and HIPP) and all results presented in all figures have been uploaded to the Open Science Framework (https://doi.org/10.17605/OSF.IO/3HPVR). Furthermore, all Matlab code used for data analysis is publicly available on a GitHub repository (https://github.com/episodicmemorylab/Ngo_et_al_eLife2020; copy archived at https://github.com/elifesciences-publications/Ngo_et_al_eLife2020).

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   Sleep spindles mediate hippocampal-neocortical coupling during long-duration ripples.

   https://doi.org/10.17605/OSF.IO/3HPVR

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Author details

1. Hong-Viet Ngo

   1. School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
   2. Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands

   Contribution

   Formal analysis, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5828-5588

2. Juergen Fell

   Department of Epileptology, University of Bonn, Bonn, Germany

   Contribution

   Conceptualization, Writing - original draft

   Competing interests

   No competing interests declared

3. Bernhard Staresina

   School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom

   Contribution

   Conceptualization, Writing - review and editing

   For correspondence

   b.staresina@bham.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0558-9745

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