Polysomnographic Sleep Studies and Sleep Slow Waves
We obtained full night continuous polysomnographic sleep recordings in 13 neurosurgical patients, lasting 421 ± 20 min (mean ± SEM). illustrates the experimental setup and provides an overview of the data. Polysomnography included electrooculogram (EOG), electromyogram (EMG), scalp EEG, and video monitoring. Sleep-wake stages were scored as waking, NREM sleep stages N1 through N3, and REM sleep according to established guidelines (Iber et al., 2007
). Depth intracranial electrodes recorded activity in 129 medial brain regions in frontal and parietal cortices, parahippocampal gyrus, entorhinal cortex, hippocampus, and amygdala (; see Table S1A
available online). We simultaneously recorded scalp EEG, depth EEG, multiunit activity (MUA), and neuronal spiking activity () from a total of 600 units (355 single units and 245 multiunit clusters).
Measures of overnight sleep in patients resembled normal sleep in individuals without epilepsy (Figure S1
). Average (±SEM) sleep efficiency (sleep time per time in bed) was 82% ± 2%. NREM sleep, REM sleep, and wake after sleep onset (WASO) constituted 75% ± 2%, 13% ± 2%, and 12% ± 2% of sleep time, respectively. Moreover, power spectra of scalp EEG data in individual subjects (; Figure S1
) revealed robust SWA and spindle (10–16 Hz) power throughout NREM sleep, while REM sleep was characterized by power in the theta (4–8 Hz) range and in high frequencies (>20 Hz), in accordance with previous studies (Campbell, 2009
). All patients showed typical NREM-REM sleep cycles, and some showed homeostatic decline of SWA throughout sleep. These results indicate that sleep measures were in general agreement with typical findings in healthy young adults (e.g., Riedner et al., 2007
Having characterized sleep using standard noninvasive polysomnography, individual slow waves in NREM sleep were identified in the depth EEG of each brain region separately. Although sleep profiles were within the normal range, we further verified that detected waves reflected physiological sleep slow waves rather than epileptic events (Experimental Procedures). Putative slow waves were separated to those preceded (within 1 s) by an interictal spike (“paroxysmal” discharges) versus those unrelated to epilepsy (“physiological” sleep slow waves). The shape of physiological sleep slow waves was symmetrical and significantly different than that of asymmetrical paroxysmal discharges (Figure S2A
). Specifically, in paroxysmal slow waves following interictal spikes, the rise slope was 44% ± 0.07% steeper than the fall slope (n = 129 depth electrodes; p < 7.4 × 10−5
, paired t test on rise and fall slopes). In addition, paroxysmal discharges were limited to specific sites in comparison to physiological slow waves, which were detected in all brain structures in all patients. Thus, in many channels, virtually no interictal spikes were observed before slow waves (and nearly all putative slow waves were physiological), while in a few channels many events were pathological (mean, 14%; range, 0.06%–46%). By contrast, the number of physiological slow waves was consistent between electrodes, with numbers matching those found in healthy individuals (37.3 ± 0.5 slow waves per minute of NREM sleep), as in (Riedner et al., 2007
Neuronal Activity Underlying Sleep Slow Waves
Next, isolated unit discharges underlying physiological sleep slow waves were examined. provides an example of EEG and unit activities during global slow waves occurring in unison across multiple brain regions during deep NREM sleep in one individual. Negative peaks in the scalp EEG tightly corresponded to positive peaks of depth EEG in cortical and subcortical structures across different lobes and hemispheres. Locally, extracellular recordings revealed an OFF period where unit spiking activity ceased almost entirely, likely corresponding to the down state of the slow oscillations as recorded intracellularly. As in previous work (Vyazovskiy et al., 2009b
), we use the terms “ON” and “OFF” periods, instead of “up” and “down” or “depolarized” and “hyperpolarized” states (Steriade et al., 2001
), because activity and silence periods were defined here on the basis of extracellular activity rather than membrane potential fluctuations measured intracellularly. In contrast, positive peaks in scalp EEG tightly corresponded to negative peaks of depth EEG and to ON periods with rigorous spiking, in accordance with a depolarized up state.
Example of EEG and Single-Unit Activity During Global Sleep Slow Waves
We set out to examine quantitatively the relationship between sleep slow waves and the underlying spiking activity across all brain regions where units were detected (). Individual slow waves were detected automatically in the depth EEG of each brain region separately (e.g., cyan dots in ), and unit spiking activity surrounding slow waves was averaged. When focusing on the highest amplitude waves in each channel (top 20%), positive and negative peaks in depth EEG were associated with marked decreases and increases in unit discharges, respectively (; n = 600). This result should be viewed as a lower limit on the modulation strength, since timing variability across individual neurons introduced a temporal jitter, thereby smearing the average result. Therefore, the wave-triggered average of spiking activity was computed in each unit separately, searching for the minimal (maximal) rate while allowing for different time offsets around EEG peaks (n = 600, average of 10,595 waves per neuron). The minimal firing rate around EEG positivity was 39% ± 1% compared with the mean firing rate in NREM (N2+N3) sleep, and the mean latency of such OFF periods was 72 ± 9 ms before the positive EEG peak. Around EEG negativity, a maximal firing rate of 198% ± 11% was found across individual units, at 46 ± 10 ms before the negative EEG peak.
Spiking Activity Underlying EEG Slow Waves
In each subject and in each brain region, individual neurons whose activity was highly modulated by slow waves were identified (). Such neurons were found not only in neocortex, but also in limbic structures such as hippocampus and amygdala. Given the variability across individual neurons, we examined the percentage of neurons showing significant phase locking to sleep slow waves separately in each brain structure (Figure S3
; see Experimental Procedures). The results revealed considerable variability (): the lowest percentages of phase locked neurons were found in anterior cingulate (12% ± 11%, n = 84 units in 11 regions, mean and SEM across electrodes). Neocortical regions (41% ± 11%, n = 109 units in 16 regions), hippocampus (49% ± 7%, n = 100 units in 17 regions), and parahippocampal gyrus (55% ± 10%, n = 97 units in 13 regions) showed intermediate effects, while the highest percentages of phase locked neurons were found in the amygdala (87% ± 11%, n = 61 units in 9 amygdala regions), entorhinal cortex (84% ± 13%, n = 67 units in 10 regions), and posterior cingulate cortex (100% ± 0%, n = 30 units in three regions). Since slow waves were detected in the depth EEG recorded ~4 mm away from unit activity, the percentages of modulated neurons should be regarded as a lower bound. Given that slow wave amplitudes change throughout sleep with the dissipation of sleep pressure (Riedner et al., 2007
), it was of interest to check whether slow wave amplitudes were indicative of the level of unit activity modulation. To this end, unit activities were averaged around slow waves depending on the peak amplitude of the depth EEG (). The amplitude of EEG waves was parametrically related to the degree of modulation in underlying unit activity. Thus, our results demonstrate that within specific brain structures, sleep slow waves in depth EEG reliably reflect synchronous transitions between ON and OFF periods among many neurons.
Importantly, unit discharges associated with pathological waves were markedly different in that firing rate was significantly different before and after the EEG positivity, in accord with the asymmetry observed in depth EEG (Figure S2B
). The clear distinction found in spiking activity underlying physiological versus pathological waves supports the notion that sleep slow waves and epileptic events could be reliably separated.
Extent of Local Sleep Slow Waves
Next we examined whether, to what extent, and under what circumstances slow waves occur locally (i.e., out of phase between brain regions). We operationally define a local (global) slow wave as an event detected in less (more) than 50% of recording locations. Numerous incidences of regional slow waves were found (; see Figure S4
for additional examples). In such incidences, diverse measurements (depth EEG, MUA, and spiking of individual neurons) jointly indicated that one brain region was in an OFF period while another region was active.
To explore this phenomenon quantitatively we examined to what extent slow waves occurred nearly simultaneously (±400 ms) across multiple brain structures and in scalp EEG. For each wave, the underlying unit activity at concordant sites (i.e., where the same EEG wave was observed) was compared with that found in nonconcordant sites (i.e., where the “seed” wave was not observed in the “target” region). The results () revealed a clear difference in underlying spiking activity (p < 6.8 × 10−7, paired t test between concordant and nonconcordant conditions across neurons).
We quantified the number of brain structures involved in each slow wave (i.e., the number of channels in which a particular wave was detected). The distribution of involvement was skewed toward fewer regions () indicating that slow waves were typically spatially confined. Mean slow wave involvement was 27.1% ± 0.4% of monitored brain regions (n = 129 electrodes). Moreover, 85% ± 0.7% of slow waves were detected in less than half of the recording sites indicating that most slow waves were local, given the definition above. There was a strong tendency (r = 0.79; p << 1 × 10−10) of widespread waves to be of higher amplitude than spatially restricted lower-amplitude waves (). The high variability in amplitude and spatial extent of slow waves suggests a continuum rather than a categorical dichotomy between local and global waves. At one extreme, waves could be entirely local, where one region was ON and others were OFF and vice versa, and such local waves could be observed in any brain structure. At the other extreme, high-amplitude waves occurred in unison across the brain. Nearly all waves fell somewhere along this gradual continuum, with most waves being more local than global given our working definition. Finally, we examined whether specific pairs of brain structures had a strong tendency to express local slow waves concordantly and whether particular brain regions had a strong degree of involvement in slow waves (). Medial prefrontal regions, such as the anterior cingulate and orbitofrontal cortex, were typically more involved than regions in MTL. In addition, homotopic cortical regions across hemispheres tended to be concordant in prefrontal cortex (but not MTL), and there was a slight bias of regions in the left hemisphere to be more involved in slow waves.
Extent of Local Sleep Spindles
Our results thus far demonstrate that slow waves, the most prominent EEG event of NREM sleep, occur mostly locally. This finding suggests that sleep, which usually is associated with highly synchronized activity, has an important local component. We thus wondered whether sleep spindles, the other hallmark of NREM sleep EEG (Loomis et al., 1935
), also occur locally. Spindles are generated in the highly interconnected thalamic reticular nucleus, and the neocortex governs their synchronization through corticothalamic projections (McCormick and Bal, 1997
; Steriade, 2003
). Asynchronous spindles were reported in nonphysiological conditions (Contreras et al., 1996
; Gottselig et al., 2002
To examine this issue, spindles were detected automatically in each depth electrode separately (Experimental Procedures; Figure S5
), and we examined to what extent spindles occurred concurrently across frontal and parietal channels. Examination of local versus coincident spindles was performed only in cortical sites that had regular spindle occurrences, thereby excluding the possibility that local occurrence of spindles arises merely from their total absence in remote brain structures. As defined for slow waves, we operationally define a local (global) sleep spindle as an event detected in less (more) than 50% of recording locations. Numerous incidences of sleep spindles occurring in specific brain areas were found (). Regional spindles occurred without spindle activity in other regions, including homotopic regions across hemispheres and regions with equivalent signal-to-noise ratio (SNR) showing the same slow waves.
We set out to quantitatively establish to what extent local sleep spindles occur across the entire dataset. We determined for each spindle in a given region whether spindles were present or not in other brain structures (Experimental Procedures). The spectral power changes in concordant sites (34% of events where a spindle was detected in both “seed” and “target” channels; top row) were compared with those at nonconcordant sites (66% of events where a spindle was detected in the seed channel but not in the target, bottom row). A clear difference in spectral power was revealed (p < 1 × 10−41, paired t test), pointing to significant differences in underlying neuronal activity, and indicating that nonconcordant events are indeed regional spindles. Furthermore, the analysis of those cases where target channels did not exhibit any increase in spindle spectral power above the noise level (Experimental Procedures) revealed that 32% of all nonconcordant events were local in the strongest sense—that is, a full-fledged spindle occurred in the seed channel while spectral power in the target channel was not different from chance. Importantly, the occurrence of local spindles was independent of local slow waves, since spindles occurring in isolation (i.e., not associated with a slow wave within ± 1.5 s) constituted 53.7% ± 3.1% of all events and 79.8% ± 0.8% of such “isolated” spindles were detected in less than 50% of brain regions. In addition, comparing homotopic regions revealed that 40.4% ± 1.7% of spindles were observed only in one hemisphere (mean ± SEM across nine pairs), indicating that differences between anterior and posterior regions could not account for spindle locality.
Next, we quantified the involvement in spindle events by computing the number of brain structures in which each spindle was observed. The distribution of involvement in sleep spindles was skewed toward fewer regions (), indicating that spindles were typically spatially restricted. Mean involvement for sleep spindles was 45.5% ± 0.3% of brain regions (n = 50 depth electrodes). Moreover, 75.8% ± 0.9% of spindles were detected in less than 50% of regions, indicating that most spindles were local given the definition above. Finally, as was the case for slow waves, the spatial extent of spindles was significantly correlated with spindle amplitude (; r = 0.62; p < 0.0001; n = 177).
Changes in Spatial Extent of Slow Waves and Spindles between Early and Late Sleep
Increasing evidence suggests that early and late NREM sleep differ substantially in underlying cortical activity (Vyazovskiy et al., 2009b
). Hence, it was of interest to determine whether the spatial extent of slow waves and spindles changes between early and late NREM sleep. To this end, we focused on episodes of early and late NREM sleep in five individuals exhibiting a clear homeostatic decline of SWA during sleep (Figure S1
). We identified separately slow waves, spindles, and K-complexes, which are isolated high-amplitude slow waves that are triggered by external or internal stimuli on a background of lighter sleep (Colrain, 2005
). We examined for each type of sleep event separately how its spatial extent varied between early and late sleep (). Slow waves became significantly more local in late sleep as compared to early sleep (, involvement of 30.4% ± 0.57% in early sleep versus 25.0% ± 0.62% in late sleep; p < 2.6 × 10−10
, unequal variance t test). This result is in line with the finding that local waves were usually low amplitude, and low-amplitude waves typically occur in late NREM sleep when homeostatic sleep pressure has largely dissipated (Riedner et al., 2007
). By contrast, K-complexes were mostly global and stereotypical throughout the night—that is, they did not show significant changes between early and late sleep (; involvement of 54.8% ± 4.4% in early sleep versus 52.5% ± 1.9% in late sleep; p = 0.98). Interestingly, sleep spindles became slightly less local in late sleep, as sleep pressure dissipated (; involvement of 44.2% ± 0.6% in early sleep versus 47.1% ± 0.5% in late sleep; p <0.00014). This result once again supports the notion that local sleep spindles cannot be simply explained by an association with local slow waves.
Changes in Spatial Extent of Slow Waves and Spindles Between Early and Late Sleep
Sleep Slow Waves Propagate along Typical Pathways
To examine whether slow waves propagate along typical pathways, we checked for consistent temporal delays between brain regions in which the same wave was observed. provides an example of mean slow waves in depth EEG of different brain structures in one individual, revealing a propagation trend from medial frontal cortex to the MTL and hippocampus. This propagation was evident also when examining the distribution of lags for individual waves (, right). Despite variability in the timings of individual waves, some regions consistently preceded scalp EEG whereas others followed it.
Sleep Slow Waves Propagate Across Typical Paths
A systematic analysis of depth EEG established that slow waves had a strong propensity to propagate from medial frontal cortex to the MTL and hippocampus. Specifically, we identified all slow waves that were detected within ±400 ms across several brain structures (Experimental Procedures). Sorting regions according to the order in which their slow waves were detected revealed a clear tendency of slow waves to propagate from medial frontal cortex to the MTL (), which was highly significant statistically (; p < 2.3 × 10−8
, unequal variance t test). In addition, this propagation tendency was consistent across individual subjects and robust to different examinations (Figure S6
shows an example of individual slow waves propagating across multiple brain structures. As can be seen, time offsets in OFF periods in different brain regions followed a propagation from frontal cortex to the MTL (diagonal green lines). Next, slow wave propagation was quantitatively examined in unit discharges in all 11 individuals in whom unit recordings were obtained simultaneously in frontal and MTL regions. Mean spiking activities underlying slow waves in medial frontal cortex versus MTL revealed a robust time offset (, left). Across individual neurons, minimal firing in frontal neurons (n = 76) was −85 ± 22 ms relative to scalp Fz negative peak, whereas minimal firing in MTL neurons (n = 155) was +102 ± 20 ms relative to the same time reference, indicating an average difference of 187 ms (, right). The statistical significance of this time offset was confirmed via bootstrapping by computing time delays of individual neurons while randomly shuffling their anatomical labels (p < < 1 × 10−10
; Figure S6G
). In fact, across 10,000 iterations, not even one instance was found in which a random time offset between the two groups of neurons was as high as (the real) 187 ms. Next, we examined whether within the MTL, spiking activities could also reveal a preferred direction of signal propagation between cortex and hippocampus. In 11 individuals in whom unit recordings were obtained simultaneously from multiple MTL regions, slow wave-triggered averaging of spiking activity (, left) revealed that slow waves occurred first in the parahippocampal gyrus, next in entorhinal cortex, and lastly in the hippocampus, emulating the anatomical pathways (see also Discussion). Across individual neurons minimal firing in parahippocampal neurons occurred −19 ± 20 ms relative to positive peak of depth EEG slow waves in MTL, while minimal firing in hippocampal neurons occurred +103 ± 47 ms relative to the same time reference, indicating that slow waves exhibited an average time difference of 122 ms between cortex and hippocampus (, right). The statistical significance of this time offset was confirmed via bootstrapping while randomly shuffling anatomical labels (p < 0.0097; Figure S6H
Finally, we examined whether hippocampal sharp-wave/ripple (SWR) bursts may precede and drive responses in medial prefrontal cortex (mPFC), a primary projection zone of hippocampal output in primates (Cavada et al., 2000
). To this end, we focused on seven subjects in whom hippocampal ripples were recorded simultaneously along with spiking activities in hippocampus and mPFC. Hippocampal ripples were detected, and their relationship to sleep slow waves was examined (Experimental Procedures and Figure S7
). In line with previous observations (Clemens et al., 2007
; Molle et al., 2006
; Sirota et al., 2003
), hippocampal ripples were found to occur preferentially around ON periods (Figure S7D
). A fine time scale examination of spiking activities revealed that hippocampal neurons transiently elevated their firing rates around ripple occurrence (Figure S7E
). Across individual hippocampal neurons (n = 72), time offsets of peak firing were −31 ± 7 ms from detected ripples (Figure S7G
). Adjacent entorhinal neurons also elevated their firing rates transiently albeit to a lesser degree (Figure S7E
), and time offsets were −2 ± 9 ms, indicating that they followed hippocampal neurons by 29 ms on average. By contrast, individual mPFC neurons did not show a consistent transient firing rate increase (Figure S7G
). Rather, mPFC neurons only exhibited a sustained increase in firing that was significantly higher than the mean rates in NREM sleep (Figure S7E
; p < 0.05, unequal variance t test), most probably because ripples occurred preferentially during ON periods. These results suggest that ripples reflect hippocampal output that is largely confined spatially to the MTL. By contrast, slow wave-triggered averaging of spiking activity in mPFC and hippocampus once again revealed that slow waves in prefrontal cortex preceded those found in hippocampus (Figure S7F
). On the whole, our results suggest that signal propagation in slow wave sleep primarily follows a cortical-hippocampal direction.
Afferent Synaptic Input Predicts Occurrence and Timing of Activity Onsets in Individual Slow Waves
What determines whether and when a given region transitions into an active state? We hypothesized that this process is not entirely stochastic and is determined by what proportion of its afferents have just transitioned to an active state. To test this possibility, we focused on the amygdala and its afferents. This choice was guided by the fact that projections to the amygdala in primates are mostly ipsilateral and arrive from diverse sources, with a notable contribution from other limbic structures such as entorhinal cortex, cingulate cortex, and hippocampus, as well as medial prefrontal and orbitofrontal cortices serving mainly as input sources (Amaral et al., 1992
; McDonald, 1998
). We capitalized on this anatomical organization and examined whether we could predict the occurrence and timing of individual slow waves in the amygdala. Crucially, if transitions into ON periods indeed reflect cumulative drive of anatomical afferents, we expected that we could better predict the occurrence of events on the basis of ipsilateral limbic afferents than on the basis of equivalent contralateral information.
To examine this possibility, we inspected data in 17 hemispheres of nine individuals in which signals from amygdala and several other limbic structures were recorded. A linear classifier was trained with a subset of slow waves to utilize information about the occurrence, amplitude, and timing of transitions into population ON periods (positive peaks in depth EEG) in ipsilateral (or contralateral) limbic regions to predict the occurrence and timing of individual slow waves in the amygdala (Experimental Procedures). Its performance was then tested with a separate subset of waves (). In all nine individuals, the information from ipsilateral afferent regions led to significantly greater accuracy in predicting the occurrence of slow waves in the amygdala (p < 1 × 10−39 for all nine individuals). Moreover, ipsilateral prediction accuracy monotonically increased as a function of the number of afferent regions that were made available for classifier training (, red; slope = 0.04% ± 0.005% correct per neighbor). By contrast, contralateral prediction was sometimes at near-chance levels and did not depend as strongly on the number of regions (, blue; significantly smaller slopes; p < 5.4 × 10−4 via paired t test). Along the same line, the timing of individual slow waves in the amygdala (whether they occurred before or after the parietal scalp electrode) could be more accurately predicted with information from ipsilateral afferent regions (p < 1.4 × 10−7 in eight individuals and p = 0.02 in ninth individual, ). Thus, despite the probabilistic nature of activity onsets in slow waves, the underlying process is not entirely stochastic and reflects the cumulative drive of afferent synaptic input.
Afferent Information Predicts Occurrence and Timing of Activity Onsets in Individual Slow Waves