1 Laboratoire de Sommeil et de Neurophysiologie, Hôpitaux Universitaires de Genève, Belle Idée, 1225 Chêne-Bourg, Geneva, Switzerland and 2 CERN European Organisation for Nuclear Research, 1211 Geneva 23, Switzerland
Address correspondence to Helli Merica, Hôpitaux Universitaires de Genève, Belle Idée, Laboratoire de Sommeil et de Neurophysiologie, 2 Chemin du Petit Bel-Air, 1225 Chêne-Bourg, Geneva, Switzerland. Email: helli.merica{at}hcuge.ch.
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Abstract |
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Key Words: beta band limits brainstemthalamic activating neurons neuronal transition probability model spectral analysis spindles
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Introduction |
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Materials and Methods |
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The study uses data from 18 healthy paid volunteers aged between 20 and 30 years (24.6 ± 2.8 years) from whom informed consent was obtained in accordance with local Ethical Committee requirements. The data were selected randomly from our data bank of all-night drug-free sleep recordings carried out under controlled environmental conditions. Bedtimes were scheduled from 21:30 to 07:00 h. All subjects were screened for good health on the basis of their history and clinical examination and the absence of any sleep disturbances determined on the basis of a first night polysomnography that also served as a habituation night. Subjects were asked to refrain from drinking more than two cups of coffee or more than two glasses of wine per day in the week preceding nocturnal recording and to abstain from the consumption of both beverages over the recording period.
EEG recording, Data Extraction and Spectral Analysis
All-night sleep was recorded using three bipolar EEG derivations (F4Cz, C4T4 and PzO2), one horizontal electrooculogram, one submental electromyogram, an electrocardiogram and respiration (monitored by thermistors under the nostrils). Sleep stages were visually scored every 20 s using Rechtschaffen and Kales rules (Rechtschaffen and Kales, 1968). Signals from the F4Cz derivation used in this study were high-pass and low-pass filtered (0.5 and 70 Hz) and digitized at a sampling rate of 256 Hz with 12-bit resolution. Prior to analyses, the signals were subjected to an automatic one-second-resolution artefact detection routine using a background-dependent filter based on the root mean square amplitude of the signals. After visual validation, all epochs containing artefacts were coded as missing data so as to preserve time continuity. Power spectra in units of µV2 were computed by fast Fourier transform with a Hanning window for consecutive 4 s epochs, giving a 0.25 Hz resolution over a frequency range 1430 Hz, adequate to cover the traditional beta range. Four consecutive elementary bins of width 0.25 Hz were integrated to give the 1 Hz resolution interval used in this study. All frequency ranges are defined here in the same way: 1518 Hz, for example, signifies that the first constituting interval starts at 15 Hz and the last ends at 18 Hz. Sleep staging and signal analysis, as well as the preliminary artefact rejection, were done using the PRANA package from PHITOOLS Grenoble, France.
REM and NREM episodes were separated using the 15 min combining rule for defining the end of a REM episode (Feinberg and Floyd, 1979; Merica and Gaillard, 1991
). No minimum duration of REM sleep was required in order to define the start of a REM episode. The start of the first NREM episode was set at sleep onset defined as the first appearance of stage 2 sleep. The epoch that immediately follows the end of a REM episode gave the start for the following NREM episode. We have retained the first four NREMREM cycles for study.
Data Analysis
Time-courses of our own averaged data for 1 Hz bins in the region of the 18 Hz point of discontinuity were plotted to confirm that there effectively exists a fundamental change in comportment in this region, as suggested by the data in Figure 1. We then looked in greater detail at this by examining individual-subject real-time data for each of the four NREMREM cycles as we moved upward in 1Hz steps through the frequency range 1430 Hz. For each cycle, three criteria were used to locate the discontinuity. These criteria are based on the character of the discontinuity as seen in Figure 1 and on cellular level results (Steriade et al., 1990) indicating that brainstem-induced activation processes in thalamic and cortical systems should be U-shaped in NREM, with a subsequent high plateau in REM:
Principle component analysis (PCA) and hierarchical cluster analysis were then applied as an additional check. PCA used in the past to assign band limits (e.g. Corsi-Cabrera et al., 2000; De Gennaro et al., 2001
, 2002
) was carried out across the four cycles on 1 Hz power values averaged over 18 subjects in the traditionally defined beta range to explore the homogeneity in this band. This statistical technique is used to reduce a large number of variables to a small set of derived variables (components or factors) with a minimum loss of information, so as to detect any underlying structure in the relationships between the variables. This reduction is achieved by expressing inter-correlated variables as single factors. Hierarchical cluster analysis was carried out on the power values within 1 Hz bins extending over the sigma (1015Hz) and the traditionally defined beta (1525 Hz) ranges in order to explore any partiality in the bin assignment of the 1518 Hz range to either sigma or beta. Average power for consecutive 1 Hz bins was computed over the four cycles and the distance (degree of dissimilarity) between all bin pairs assessed using the R2 metric computed as 1 minus the squared Pearson product moment correlation coefficient between each pair of objects (1 Hz bins). The bins were then joined using the Ward minimum variance algorithm, giving rise to a hierarchical tree. Statistical analyses were carried out using SYSTAT software.
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Results |
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Figure 2 gives for the first NREMREM cycle a clear confirmation of the discontinuity in time-course shape at 18 Hz originally indicated by the data in Figure 1. The time-courses for the 1 Hz bins in the interval 1420 Hz, for data averaged over 18 subjects, show an abrupt change within NREM from M-shaped below 18 Hz to U-shaped above 18 Hz, and an accompanying change in the REM plateau level from low to high. It is remarkable that this phenomenon is totally invisible in the power-frequency spectrum where no discontinuity occurs in the region of 18 Hz. The spectrum over the range 1420 Hz can be obtained by integrating the power over the time-courses in Figure 2. We obtain the mean values 6.5, 3.8, 2.4, 1.9, 1.7 and 1.5 µV2/Hz respectively for the six 1 Hz bins. These show a smooth
1/f decrease with no discontinuity, despite the sharp discontinuity in time-course shape at
18 Hz.
Figure 3 shows examples of 1 Hz bin time-courses for two typical subjects to illustrate the data used to determine more precisely the point of time-course discontinuity and to show that this point varies considerably from one subject to another. In straightforward cases like these, which constitute 80% of the total measured over the four NREMREM cycles, the U to M discontinuity is evident from one 1 Hz bin to the next showing that discontinuity takes place in less than 1 Hz. In these cases the discontinuity frequency is taken as the integer frequency separating the two bins. In 12% of the cases, M- and U-type bins are separated by one bin of indeterminate time-course shape and in 8% by two. For these the discontinuity frequencies are calculated as the centre of the indeterminate interval. In 36% of the cases the three criteria agree exactly on the assignment of the discontinuity frequency. In 50%, two of the three agree, and in all cases the three agree within 1 Hz. Although the discontinuity frequency varies considerably between subjects, repeated measures ANOVA results show that within subjects there is no significant variation either overnight over the four NREMREM cycles [F(3,51) = 1.48, P = 0.24] or over the three different criteria [F(2,34) = 1.85, P = 0.18], so that the results can be combined to give an overall mean and standard deviation of 18.1 ± 1.5 Hz. The histogram of discontinuity frequencies for all 18 subjects is shown in Figure 4.
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Discussion |
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It is clear from Figure 5 that zones of opposite slopes at the start and end of the NREM episode render the two curve-types immiscible for the purpose of time-course analysis. Mixing the two in a single band, as done for the traditional beta band, can lead to ambiguous results which may render difficult, in particular, the study of sleep state transitions (sleep onset, REM onset, and REM offset). Since the lower frequencies of the beta band carry greater power than the higher (Fig. 2) they tend to contribute more to the overall shape of this band. It is this that led to the incongruous results of De Gennaro et al. (2002), where beta power rises at the start of NREM and falls at the end a result which is contrary to cellular neurophysiological findings reporting that the faster frequencies are enhanced during states of brain activation (REM sleep and wakefulness) and that this activation occurs minutes before entry into REM sleep (Steriade et al., 1990
, 1997
).
The NTP model, fully described elsewhere (Merica and Fortune, 1997, 2000
), places the observed discontinuity in a physiological context. Briefly, this model was conceived on the basis of findings on the generating mechanisms giving rise to the various sleep EEG rhythms, in particular the existence of different modes of oscillation of TC neurons under the modulatory control of brainstemthalamic activating neurons (Steriade et al., 1993a
; McCormick and Bal, 1997
). It postulates that the relationship observed between the time-courses of power in the different frequency bands of human sleep EEG, describing the progression of sleep states within the ultradian cycle is the result of stochastic transitions of the firing-rate frequencies of the brainstemthalamic activating neurons: first a cascade of transitions towards and then a cascade away from deep sleep. The time-courses of the firing rates corresponding to each sleep state form a template that modulates both thalamic and cortical output. Consequently, at the thalamus the modulation results in identical cascades of frequency transitions of TC neurons: from beta to sigma to clock-like delta oscillatory mode in the sleep-deepening phase followed by the inverse cascade delta to sigma to beta in the sleep-lightening phase (Merica and Fortune, 2003
). The cortically generated slow oscillation (<1 Hz) triggers shapes and synchronizes these thalamically generated cascades, forming at the cortex the complex wave sequences observed on the EEG (Steriade et al., 1993b
) from which the time-courses of power are extracted. The discontinuity of time-course shape with frequency is thus inherent in the model: the population of neurons of high firing-rate frequency present during brain activated states at NREM onset is the source of a cascade of frequency transitions to the lower frequencies present only in NREM. The depleting source implies a decreasing high-beta power and therefore a negative slope, while for any lower-frequency band power is increasing from zero and therefore has a positive slope. This discontinuity at the start of the NREM episode is accompanied by a similar but inverse discontinuity at the end of it. The model reproduces with some precision the very particular relationship between power in the major frequency bands as seen in the human EEG, and also seen in cat data at both the thalamus and cortex (Lancel et al., 1992
). The neurophysiological findings on which the model is based are derived mainly from cat data, which comforts us in the belief that the model holds for all mammals from cats to humans. Similar time-course relations have not yet been confirmed for much smaller animals such as the rat (Bjorvatn et al., 1998
), where a detailed time-course analysis is difficult because of the polyphasic nature of rat sleep.
While our present results provoke a fundamental reassessment of the way in which the progression of power in the different frequency bands should be measured, they also raise the question of what the 1518 Hz range represents. Is it an extension of the traditional sigma band (1115 Hz), with which it has strong affinity in shape, or is it a low beta band specific to NREM sleep? To address this question, we turn again to evidence acquired at the cellular level, where it has been shown (Steriade et al. 1996; Steriade 2001
) that fast frequency rhythms are voltage (depolarization)-dependent and therefore appear superimposed on the depolarizing phase of the cortically generated slow oscillation that is present only during NREM sleep. This grouping or coalescence of both the beta and spindle rhythms by the slow oscillation has also been confirmed in human sleep EEG (Mölle et al. 2002
). Moreover, at the EEG we have direct measurement and therefore irrefutable evidence of the coexistence of all frequency bands at all times, albeit at different power levels, during the NREM episode (e.g. Fig. 1). These findings allow the view that the 1518 Hz range may be a NREM-specific low beta band: it starts low at the onset of NREM and falls off before entry into REM.
There is, on the other hand, also support in favour of the sigma extension hypothesis. Two recent studies report the presence of fast spindles in healthy subjects: Anderer et al. (2001) in the frequency range 9.617.6 Hz and Nader et al. (2003)
in the range 1619 Hz. Further work is required to substantiate these findings since the majority of studies on spindle activity (Jobert et al., 1992
; Werth et al., 1997
; Zygierewicz et al., 1999
; Blinowska and Durka, 2001
; Himanen et al., 2002
) suggest that spindles are relatively rare above 15 Hz. The shape of the 1518 Hz range may also be a pointer to its identity. The two-peak shape of the sigma power time-course within NREM described by a number of authors (Lancel et al., 1992
; Aeschbach and Borbély, 1993
, Dijk et al., 1993
; Uchida et al., 1994
; Merica and Blois, 1997
) is similar to what we observe here in the 1518 Hz range and very different from that of the upper (1825Hz) beta range. The shape argument can be seen in the context of the continuum of shapes from delta through all bands up to high beta, broken only by the discontinuity at 18 Hz. Figure 1 shows this continuum and the NTP model simulation of it. The superposed loci of time-course maxima, despite the abrupt shape discontinuity at 18 Hz, are continuous all the way from the single-peak right-skewed delta to the fully separated double peak of U-beta. The second sigma peak starts to separate out at
13 Hz, so we could expect that if the sigma band did extend above 15 Hz it would have the more widely spaced and sharper peaks we actually see in the 1518 Hz range. A final decision on whether this range represents spindles or beta oscillations or both must await measurement of the relative proportion of power manifested as high frequency spindles to that manifested as beta oscillations not having spindle characteristics.
In conclusion, a hitherto unnoticed sharp discontinuity in sleep EEG time-course shape at 18.1 ± 1.5 Hz cannot be ignored if precise measurement is to be made of the progression of sleep states corresponding to different frequency bands on the sleep EEG. The importance of such precision is clear in the study of sleep-state transitions and when attempting to relate EEG time-courses to modulatory activity at subcortical level. Of perhaps greater importance, the discontinuity appears to be related to the fundamental physiology of sleep structure as described in the NTP model. The abrupt change of shape at 18 Hz divides the higher neuronal oscillation frequencies 1530 Hz into two categories: those that are specific to NREM sleep and those that are specific to the brain activated states wake and REM.
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Supplementary Material |
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Acknowledgments |
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References |
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