Polysomnographic sleep studies
Polysomnographic sleep studies were conducted in 13 neurosurgical patients with intractable epilepsy. Continuous overnight recordings lasted 421 ± 20 minutes (mean ± SEM). Polysomnography included electrooculogram (EOG), electromyogram (EMG), scalp EEG, and video monitoring. summarizes the experimental recording setup during sleep. Sleep-wake stages were scored following established guidelines as waking, NREM sleep stages 1 through 3 and REM sleep (Iber et al., 2007
). Intracranial depth EEG was recorded in 129 medial brain regions bilaterally in frontal and parietal cortices as well as multiple limbic structures in the medial temporal lobe (). While it should be noted that our sampling was mostly limited to medial brain areas, scalp topography suggests that human spindle activity is maximal at midline regions (Ferrarelli et al., 2007
), thereby making our data particularly well suited to examine sleep spindles. We simultaneously recorded scalp EEG, depth EEG, and spiking activity from a total of 600 units (355 putative single units, 245 multiunit clusters).
Measures of overnight sleep in patients were in general agreement with typical findings in healthy young adults (Riedner et al., 2007
). Sleep efficiency, time spent in different sleep stages, NREM-REM sleep cycles, and power spectra of scalp EEG resembled normal sleep characteristics. Moreover, in every subject, power spectra of scalp EEG data in NREM sleep () revealed robust slow wave activity (<4Hz) and spindle (9-16Hz) power. These results indicate that sleep measures were similar to those of normal sleep in individuals without epilepsy.
Spindle occurrence across multiple regions in the human brain
Having characterized sleep using standard non-invasive polysomnography, we proceeded to identify individual spindles in NREM sleep in the scalp EEG and in the depth EEG of each brain region separately using an automatic algorithm (see Methods and ). Channels with robust spindle activity in NREM sleep () in which power increases of detected events were specific to the spindle range rather than broadband () were identified. provides the results of spindle detection across multiple brain regions. In the scalp EEG, spindles were robustly observed (90 ±4.7% of electrodes showed spindle detections; mean ± SEM across individuals) and their density (1.9 ±0.13 spindles/min) was consistent with densities reported in healthy individuals (Wei et al., 1999
; Ferrarelli et al., 2010
). Spindle density was significantly higher during sleep stage N2 than N3 (1.9 ±0.14 vs. 1.6 ±0.14 spindles/min respectively, p < 10E-3 via paired t-test, n = 46 channels). In the depth EEG, frontal and parietal cortices exhibited robust spindle detections (82% and 89% respectively) with densities comparable to those observed in scalp EEG (1.8 ±0.16 and 1.3 ±0.29 spindles/min, respectively).
Table 1 Spindle occurrence across multiple regions in the human brain Columns (left to right) show the brain region, the number of channels with spindle detections (over the total number of electrodes in that region), and the mean density of spindles per minute (more ...)
Spindle occurrence was further examined in the medial temporal lobe (MTL), where the presence of physiological spindles is still debated (Malow et al., 1999
; Nakabayashi et al., 2001
; Pare et al., 2002
). The current results indicate reliable spindle occurrence in the parahippocampal gyrus (PHG, 69% of channels; 1.3 ±0.25 spindles/min) and hippocampus (44% of channels; 1.1 ±0.13 spindles/min), while spindle occurrence was lower in entorhinal cortex and amygdala (31% and 21% of channels; 0.74 ±0.11 and 0.89 ±0.18 spindles/min, respectively). Given the variability in spindle occurrence in the MTL that could be due in part to epileptogenicity, we analyzed spindles in MTL separately from spindles in frontal and parietal cortices, and present data from the MTL in the final section of the results.
Fast centroparietal spindles differ from slow frontal spindles
Since scalp EEG studies have long suspected a distinction between slow (11-13Hz) and fast (13-16Hz) spindles (Gibbs, 1950
; Anderer et al., 2001
; De Gennaro and Ferrara, 2003
; Schabus et al., 2007
), we computed the distribution of frequencies across all spindles in each channel separately across multiple brain regions (see Methods). In scalp EEG, centroparietal electrodes (C3, C4, Pz) showed a significant albeit diffuse dominance of fast spindles, whereas slow spindles prevailed in frontal derivations (Fz). Average spindle frequencies in Pz and Fz were 12.6 ±0.23 Hz vs. 11.3 ±0.30 Hz, respectively (mean ± SEM across individuals) and these differences were statistically significant (p < 0.01, non-parametric Mann-Whitney U-test across 24 channels). Next, the mean frequency of spindles was mapped across individual depth electrodes (). In contrast to the small, yet statistically significant, differences in frequency observed with scalp EEG, the intracranial results reveal a topographical organization with a clear difference between fast (>12.5Hz) centroparietal spindles and slow (<12.5Hz) frontal spindles. “Centroparietal” was defined as brain areas from the SMA proper back to posterior cingulate and parietal cortex, although some of these locations are in the frontal lobe. The distributions of spindle frequencies were averaged within and compared between the following five regions: orbitofrontal cortex (OF), anterior cingulate cortex (AC), pSMA, SMA, and posterior cingulate cortex (PC). The results () confirm the distinction between slow and fast spindles captured in part by scalp EEG. Furthermore, a difference between adjacent pSMA and SMA was observed in the same patient, and it nearly reached statistical significance across patients despite the limited sample (10.9 ±0.72Hz vs. 12.6 ±0.19Hz respectively, p = 0.057, non-parametric Mann-Whitney U-test, n=7 channels) supporting the notion of two spindle groups rather than a continuous cortical gradient (see also Discussion). In addition, while frontal spindles were exclusively slow, centroparietal spindles had a bimodal distribution with a majority of fast spindles and fewer slow spindles, and this was also evident in the data of individual channels.
We also found that in the same brain region, frequency decreases during individual spindles. shows the average frequency dynamics in a representative SMA electrode (fast spindles, left) and an OF electrode (slow spindles, right). As can be seen, the instantaneous frequency of fast SMA spindles dropped on average from 13.8 to 12.4Hz, and slow OF spindles showed a similar deceleration (from 11.5 to 10.4Hz on average). A quantitative analysis across the entire dataset (2,851 fast and 10,607 slow spindles in 50 channels) revealed a significant decrease of −0.8Hz/s in instantaneous frequency between spindle start and end times (-0.8 ±0.04Hz/s and −0.8 ±0.02Hz/s for fast and slow spindles, respectively, p < 10E-12 between start and end times via two-tail t-test).
Fast centroparietal spindles precede slow frontal spindles
To further understand the distinction between fast centroparietal and slow frontal spindles, we examined whether these occurred independently or with consistent temporal relations. shows examples of individual spindles recorded simultaneously across several brain regions. As can be seen, fast centroparietal spindles occurred before slow frontal spindles, and the successive temporal occurrence across channels was accompanied by a decrease in spindle frequency. Next, a quantitative analysis of time offsets across all pairs of brain regions was conducted and the order in which spindles appeared was computed (). The results indicate that centroparietal spindles (in SMA and PC) occurred before slow frontal spindles (pSMA, AC, and OF) with a time difference of 203 ±16ms (mean ± SEM across 2,024 pairs of spindles, p < 10E-12 via one-tail t-test). Sorting regions according to the order in which their spindles were detected revealed that centroparietal regions typically preceded frontal locations (). In contrast, within centroparietal regions and within frontal regions no significant time differences were found, supporting the notion of a discontinuous transition in timing and frequency between fast centroparietal and slow frontal spindles (see Discussion). provides an example of the average timing relation between all spindle pairs (n=139) detected concomitantly in the PC and AC in one individual. As can be seen, timing and frequency differences appear closely linked. Indeed, across the entire dataset timing and frequency of spindles were found to be tightly related in pairs of regions such that larger frequency deceleration was associated with longer time delays (, r = −0.90, n = 9,741 spindles). Overall, the reduction in spectral frequency with respect to time delay between spindles was −1.3 ±0.5Hz/s, and this negative rate was comparable to that found for spindles within the same regions (-0.8 ±0.02Hz/s, see previous section).
Association of spindles with slow wave up-states
Since spindles are often associated with slow wave up-states (Steriade et al., 1993a
; Steriade and Amzica, 1998
; Molle et al., 2002
), we examined how this association may vary for different spindles detected across different brain structures. We have recently characterized in detail slow waves and underlying unit activities in the same dataset (Nir et al., 2011
). Here we used the same automatic detection of large slow waves in each individual channel and examined the occurrence of spindles around positive and negative peaks in depth EEG, corresponding to down and up states, respectively. depicts an example of an individual slow wave detected in the anterior cingulate followed by a spindle, whereas presents the quantitative analysis of spindle detections in relation to slow waves across the entire dataset. Higher spindle occurrence (, right
) was found in the 0-500ms interval following negative peaks in depth EEG (up states), in line with previous reports (Steriade et al., 1993a
; Molle et al., 2002
Next, association of spindles with slow wave up-states was compared between different brain regions in the 1s interval immediately following the transition to up-states when spindle occurrence was maximal (). Fast centroparietal spindles were found to be more tightly associated with up states compared with slow frontal spindles. Spindles exhibited the shortest delay with up-states in the SMA, the region where spindles were also detected earliest. More generally, typical time differences between regions were maintained for spindles associated with slow wave up-states. Specifically, the average delays from negative peaks in the depth EEG to maximal spindle density in the SMA and PC were 267 ±11ms and 363 ±11ms, respectively, while frontal regions exhibited longer delays (AC, 460 ±8ms; OF, 425 ±6ms; pSMA, 449 ±14ms). The mean difference in delays between centroparietal and frontal sites was statistically significant (p < 10E-12, unpaired two-tailed t-test). Thus, the tighter association between centroparietal spindles and slow wave up-states is consistent with their earlier occurrence compared with slow frontal spindles (see also Discussion).
Local sleep spindles
Although concomitant spindles were observed across multiple recording sites (above), we recently found that both sleep spindles and slow waves often occur independently in separate brain regions (Nir et al., 2011
). We present these findings here as well to place them in the context of a comprehensive examination of sleep spindles in the human brain. Examination of local vs. simultaneous spindles was carried out 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. Numerous examples of local sleep spindles occurring in specific brain regions were found (). Local spindles occur without spindle activity in other regions, including homotopic regions across hemispheres and regions with equivalent signal-to-noise ratio (SNR) showing global 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 (Methods). The spectral power changes in concordant sites (45% of cases where a spindle was detected in both “seed” and “target” channels, top row) were compared with those at non-concordant sites (55% of cases where a spindle was detected in the seed channel but not in the target, bottom row). The results revealed a clear difference between peak spectral power values of concordant and non-concordant conditions across spindles in target channels (p < 10E-48, paired t-test), indicating significant differences in underlying neuronal activity, and that non-concordant cases are indeed local spindles. Furthermore, the analysis of those cases where target channels did not exhibit any increase in spindle spectral power above the noise level (Methods) revealed that 32% of all non-concordant cases were local in the strongest sense, i.e. a full-fledged spindle was detected 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 “isolated” spindles without a slow wave within ± 1.5s (35% of all spindles) were likewise mostly local (). In addition, comparing homotopic regions revealed that 36 ±2% of spindles were observed only in one hemisphere (mean ± SEM across 12 pairs), indicating that differences between anterior and posterior regions could not account for spindle locality.
Next, we quantified the involvement of multiple regions in spindles by computing the number of brain areas in which spindles were observed. The majority of sleep spindles involved a limited number of brain regions (), indicating that local spindles were more frequent than global spindles. Mean involvement in individual spindles was 48 ±0.7% of monitored brain regions (mean ± SEM across 49 depth electrodes), indicating that most spindles were predominantly local. Finally, the spatial extent of spindles correlated with spindle amplitude (, r = 0.52, p < 0.0001, n = 212).
Deep sleep is associated with lower spindle frequencies
The variability in spindle spectral frequency between brain structures and during individual spindles described in the preceding paragraphs could be due to changes in levels of thalamocortical polarization. Since the degree of thalamic hyperpolarization dictates the period of spindle oscillations (McCormick and Bal, 1997
; Steriade, 2003
), we hypothesized that spindles during deep sleep (early in the night or in the middle of NREM cycles when SWA is highest), would contain lower spectral frequencies than spindles during lighter sleep (late in the night or closer to REM transitions when SWA is lower).
To examine this possibility, we first compared spindle frequencies in five individuals showing a typical homeostatic decline of SWA, i.e. a progression from deep early sleep with powerful slow waves to lighter sleep late in the night. Spindle frequency was significantly lower in early deep sleep when SWA was highest (, reduction of 0.63 ± 0.12Hz, mean ± SEM across 16 channels, p = 0.0004, non-parametric Mann-Whitney U-test). The modulation of spindle frequency in early vs. late sleep was specific to slow spindles (p = 0.002 for slow frontal spindles vs. p = 0.62 for fast spindles, Mann-Whitney U-test).
Along the same line, spindle frequency changed within NREM cycles in accord with changes in SWA. shows an example of dynamics in spindle frequency and SWA across sleep in one individual, illustrating that in the middle of each NREM cycle when SWA was highest, spindle frequency was lowest. Next, this relation was examined in all NREM cycles followed by REM epochs (16 cycles in 29 depth EEG channels and 8 individuals, see Methods). Each NREM cycle was divided into 10 intervals with equal duration and SWA, spindle frequency and spindle density were computed separately for each interval (). Both spindle frequency and spindle density were negatively correlated with SWA during NREM episodes (r= −0.81, p = 0.005 and r = −0.73 p = 0.02). These results indicate that deeper sleep and higher SWA are associated with lower spindle frequencies and fewer events.
Neuronal discharges during sleep spindles
Given that spiking activity across the human brain is tightly locked to EEG slow waves (Nir et al., 2011
) we examined whether cortical neurons consistently modulated their firing rate during spindles. Examination of firing rates in putative single-units from individual subjects revealed little evidence for consistent modulations in relation to spindles. On average across all 207 cortical units, we found that spindles occurring within 500ms of transitions from inactive to active (UP) periods (8.5% of events) were accompanied by robust firing rate modulations (, left), as expected (Nir et al., 2011
). In contrast, when considering all spindles (n=63,724) firing rate modulations were largely absent (, right), although a small (~4%) yet significant reduction in neuronal discharges was observed 100-400ms following the middle point of spindles, likely reflecting a diffuse tendency for an inactive (DOWN) state to occur at that time.
Next, we evaluated the phase-locking of unit discharges during spindles to determine if units preferably fire at a specific phase of spindle oscillations (). presents an example unit in which spikes preferably occurred at a particular phase (-30 degrees) during spindles. Overall, 19.5% of neurons showed significant phase locking (p < 0.05, see Methods) with a tendency to fire more at the ascending phase of the spindle oscillation (preferred phase = −35 ±8.5 degrees, SEM across units) as measured with depth EEG ().
On average, phase-locked units had a higher firing rate than the average firing rate (5.9 ±1.2Hz vs. 3.1 ±0.3Hz, p = 0.004, Mann-Whitney U-test for putative single units). It could be argued that the firing of many neurons is phase-locked during spindles, but only high firing rate neurons allow enough statistical power to reveal this phenomenon. However, 46% of phase locked neurons had firing rates that were below the mean rate across the entire population. Alternatively, higher firing rates in phase-locked units may reflect a bias towards specific cell types such as interneurons (see also Discussion).
Spindles in the Medial Temporal Lobe (MTL)
Spindle occurrence in the MTL and its possible relation to pathology in epilepsy patients is a matter of continued debate (Malow et al., 1999
; Nakabayashi et al., 2001
; Pare et al., 2002
). Our results () revealed reliable spindle occurrence in the PHG, a dependable albeit lower occurrence in the hippocampus, and lower spindle occurrence in the entorhinal cortex and amygdala. Could such events reflect epileptiform activity? To address this, spindle detections in MTL were further analyzed in relation to the seizure onset zone (SOZ) based on the medical records of the patients. We examined whether inclusion in the epileptic focus (25 of 64 MTL channels) could predict spindle detection and spindle density in each region separately. If spindles recorded in MTL solely reflect abnormal activity, then it might be expected that the number of channels and rates of spindles would be higher inside than outside the SOZ, spindles in hippocampus would occur more regularly with spindles in adjacent MTL than remote neocortical sites, or unit firing would be more strongly correlated with MTL than neocortical spindles. Analysis found that in PHG and hippocampus, the majority of channels where spindles were recorded were outside
the SOZ (64% and 63% respectively), and there was a trend for higher rates of hippocampal spindles outside than inside the SOZ (1.1 vs. 0.8 spindles/min outside and inside the SOZ, respectively). In the entorhinal cortex and amygdala, spindle occurrence was comparable within and outside the SOZ. In addition, we found that detected events in hippocampus (Methods) co-occur more often with frontal/parietal spindles (remote from the SOZ) than with events detected in amygdala or entorhinal cortex (p < 0.05, Mann-Whitney U-test). Finally, analysis did not find a stronger correlation between unit discharges and spindles in MTL than neocortical sites. It should be noted that due to possible volume conduction and lack of clear firing rate modulations our results cannot resolve the long-lasting controversy of potential epileptiform nature of hippocampal spindles. Nevertheless, these results indicate that although MTL spindles are less frequent and more variable, PHG and hippocampus can exhibit physiological spindles that may transcend possible links to pathology.
Sleep spindles and epilepsy
Our data were recorded in medicated epilepsy patients in whom epileptiform events during seizure-free periods (i.e. interictal episodes) may affect sleep, and sleep spindles in particular (Dinner and Lüders, 2001
; Steriade, 2005
). Therefore, it was imperative to confirm that our results could be generalized to the healthy population, and multiple observations strongly suggest that this is the case. First, overnight recordings were carried out prior to routine tapering of anti-epileptic drugs to ensure a less significant contribution of epileptiform activities. Second, sleep measures were within the expected normal range, including distribution of sleep stages, NREM-REM cycles, and EEG power spectra of each sleep stage. Third, we specifically detected paroxysmal discharges and separated them from physiological sleep spindles, and we also confirmed that pathological events were not falsely detected as spindles through extensive visual inspection of each individual’s data (Methods). Fourth, the occurrence rate of paroxysmal discharges was highly variable across channels, limited in its spatial extent, and entirely absent in some channels. By contrast, all the results reported here could be observed in every individual despite highly idiosyncratic clinical profiles. Fifth and most importantly, previous analysis revealed significant firing rate modulations in the same neurons during paroxysmal discharges (Nir et al., 2011
), whereas unit discharges were not robustly modulated during sleep spindles, attesting to a good separation between pathological and physiological spindles. Since interictal EEG spikes and sleep spindles may be confounded in a more complex manner in the MTL, we adopted a conservative approach and analyzed those spindles separately from data obtained in frontal and parietal cortices based on a visual detection. Overall, we are confident that the current results can be generalized to individuals without epilepsy.