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Temporal EEG dynamics of non-REM sleep episodes in humans
Brain Research 861 Ž2000. 233–240 www.elsevier.comrlocaterbres
Research report
Temporal EEG dynamics of non-REM sleep episodes in humans Hirokuni Tagaya ) , Lorenz Trachsel, Harald Murck, Irina Antonijevic, Axel Steiger, Florian Holsboer, Elisabeth Friess Max Planck Institute of Psychiatry, Clinical Institute, Kraepelinstrasse 10, D-80804 Munich, Germany Accepted 28 December 1999
Abstract The process of the human non-rapid eye movement Žnon-REM. sleep period has not been clarified. Time-based analysis on sleep EEG may provide an explanation. We focused on chronological aspects of initiation and termination of non-REM episodes, using spectral analysis of sleep EEG. The subjects were healthy male volunteers Ž n s 32, mean age " S.D.: 25.5 " 3.5 years.. The rise latencies from non-REM sleep onset to the maximal power value and the decay latencies from the maximal power value to non-REM sleep offset were determined in the initial and final 21-min windows of individual non-REM episodes in each EEG band ranges. Low Ž12.1–13.7 Hz. and high Ž14.1–16.0 Hz. sigma ranges were analyzed separately. The rise and decay latencies were shorter in higher frequency ranges Ž) 14 Hz. and longer in lower frequency ranges Ž- 14 Hz.. There were significant differences in the rise and decay latencies between low and high sigma ranges, indicating that the whole frequency ranges were clearly separated at the middle of the sigma range Ž14 Hz.. The rise and decay latencies were significantly different in lower frequency ranges. The clock time of the night significantly affected only the rise latencies of the delta Ž0.78–3.9 Hz., alpha Ž8.2–11.7 Hz. and low sigma Ž12.1–13.7 Hz. ranges. In conclusion, initiation and termination of non-REM sleep was represented by higher frequency ranges, whereas further evolution and devolution of non-REM sleep was represented by lower frequency ranges, and only the evolution process was affected by the clock time of the night. q 2000 Published by Elsevier Science B.V. All rights reserved. Keywords: Polysomnography; Spectral analysis; Sleep spindle; Sleep slow wave; Initiation and termination of non-REM sleep episode
1. Introduction Previous studies on human sleep EEG have revealed that 2 major components of the sleep EEG Ži.e., sleep slow waves and sleep spindles. possibly reflect the underlying brain activity that regulates human non-rapid eye movement Žnon-REM. sleep. In the homeostatic or circadian processes through the night, delta activity decreases as a function of sleep period time, and the initial build-up reflects the duration of the preceding period of wakefulness w5x, whereas sigma activity increases throughout sleep and was shown to be modulated by circadian factors w8x. In their respective non-REM sleep episodes, delta and sigma EEG activities are thought to show a reciprocal relationship. During the initiation process of non-REM
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Corresponding author. National Center of Neurology and Psychiatry, National Institute of Mental Health, Kounodai 1-7-1, Ichikawa, Chiba, J-2720827, Japan. Fax: q81-47-375-4771; e-mail: [email protected]
sleep, the sleep EEG activity shifts from a sigma dominant state to a delta dominant state, and an inverse direction of shift from the delta dominant state to the sigma dominant state was observed during the termination process of nonREM sleep w7,23x. The putative mechanism underlying this characteristic pattern of non-REM sleep EEG activity was supported by findings based on animal experiments showing that thalamocortical neurons oscillate cortical neurons in two different modes, depending on the degree of membrane hyperpolarization w20,21x. Thus, the shift from the sigma to the delta EEG frequencies may be driven by a decrease in the membrane potential of the thalamic relay neurons, and vice versa. Numerous sleep-EEG studies have identified two different types of sleep spindles w13–15,19,24x. The fast spindles, ranging from 12.5 to 15 Hz, appear predominantly at centralotemporal scalp electrodes at the initiation and termination of non-REM sleep, while the slow spindles, ranging from 11 to 13.5 Hz, appear during deeper non-REM sleep and show a frontocentral distribution. Moreover, the standard criteria exclude the fast spindles from the definition of sleep spindles w18x. Recent studies suggested that
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the fast and slow spindles are differently affected by a variety of factors: the circadian phase w2,3,8x, menstrual cycle and pregnancy w6,10x, circulating neuroactive steroids w12x and various substances such as melatonin w9x and benzodiazepines w3x. Therefore, to analyze the initiation and termination process of non-REM sleep episodes, low sigma and high sigma should be separated. The typical transitional pattern from sigma to delta and from delta to sigma is only seen in the late evening, and there are some conflicting findings as to whether the termination process of non-REM sleep is simply the reverse of non-REM initiation w7,23x. This disagreement may be due to two methodological issues: Ži. Most previous studies have analyzed the whole non-REM period. Thus the lengths of non-REM sleep periods had to be standardized, or regression andror correlation analyses were performed regardless of time. Žii. Slow and fast spindle ranges were not distinguished in most studies. As for the first issue, we defined the time point of maximal EEG activity for each frequency range in the initial and final 21-min windows of non-REM sleep periods. Then we determined the rise latency from non-REM sleep onset to maximal EEG values and the decay latency from maximal EEG values to non-REM offset. Thus, we were able to analyze the time sequence of the maximal EEG activity of all frequency ranges during the initiation and the termination process of non-REM sleep. In this study, we tried to overcome the shortcomings described above to investigate the initiation and termination process of non-REM sleep.
2. Materials and method 2.1. Subjects The subjects were 32 healthy male volunteers Žmean age " S.D.: 25.5 " 3.5 years, range: 19–33 years.. They had no sleep disturbance and no family or personal history of psychiatric or neurological disorders, and they had received no medical treatment for the previous 3 months. Shift workers and subjects who had recently made transmeridian flights were excluded. After written informed consent had been obtained, the subjects spent two consecutive nights in our sleep laboratories; the first night for adaptation to the experimental setting, and the second night for sleep recordings. 2.2. EEG recording Polysomnographic sleep recordings consisted of two EEGs ŽC3-A2rC4-A1., vertical and horizontal electrooculograms ŽEOGs., an electromyogram ŽEMG. and an elec-
trocardiogram ŽECG.. Polysomnographic recordings were carried out from 2300 Žlight out. to 0700 h Žlight on.. Sleep stages were scored visually by experienced raters for consecutive 30-s epochs according to the criteria of Rechtschaffen and Kales w18x. 2.3. EEG spectral analysis The EOG, EEG, EMG, and ECG signals were filtered Žhigh-pass filter at 0.5 Hz, notch filter at 50 Hz. and transmitted to the polygraph ŽSchwartzer, ED 24.. The digitized data Ž8-bit analog-to-digital converter, sampling rate 100 Hz. were stored on the disk and calibrated with a 50-mV 10-Hz sine wave. The C3-A2 EEG derivation was subjected to spectral analysis using a fast Fourier transformation. The sleep-stage-specific EEG power spectra were computed using twelve 2.56-s epochs which had overlapping to match the 30-s epoch of sleep stages w22x. The frequency resolution was set to 0.39 Hz between 0.78 and 19.1 Hz inclusively. Artifacts, for example, those caused by brief body movements, were excluded manually by referring to the EEG recordings. The EEG activities of the 48 frequency bins were handled as described in result section. For latency analyses of band ranges, variables were averaged for delta Ž0.78–3.9 Hz., theta Ž4.3–7.8 Hz., alpha Ž8.2–11.7 Hz., low sigma Ž12.1–13.7 Hz., high sigma Ž14.1–16.0 Hz. and beta Ž16.4–19.1 Hz. ranges. 2.4. Statistical analysis The data were examined by analysis of variance ŽANOVA. with repeated measures designs. Two different analyses were done for six EEG bands Ždelta, theta, alpha, low sigma, high sigma and beta. and for 11 EEG frequency bins within sigma range Ž12.1–16.1 Hz. for testing the appropriateness of our sigma separation. In the analyses of the initial and the final parts of the non-REM episodes, band and clock time Žthirds of night sleep. were
Table 1 Sleep variables Values are indicated as means and S.D. Ž ns 32.. Sleep onset latency was defined as the time from lights off to the first epoch of stage 2 sleep lasting longer than 3 min, and REM Iatency was defined as the time between sleep onset and the first epoch of REM sleep. Variable
Mean"S.D.
Time in bed Žmin. Sleep period time Žmin. Sleep onset latency Žmin. REM latency Žmin. Stage 1 sleep Žmin. Stage 2 sleep Žmin. Slow wave sleep Žmin. REM sleep Žmin. Sleep efficiency Ž%.
477.5"9.3 454.9"14.2 20.9"14.3 79.6"31.1 33.3"15.7 256.0"28.7 45.6"25.6 90.5"20.6 90.9"3.8
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the within-subject factors, with 6 and 3 levels for EEG band analyses, and frequency bins and clock time were the within-subject factors, with 11 and 3 levels for frequency bins within sigma range, respectively. In the analyses between the initial and the final parts of the non-REM episodes, initial vs. final, band and clock time were the within-subject factors, with 2, 6 and 3 levels for EEG band analysis, and initial vs. final, frequency bins and clock time were the within-subjects factors, with 2, 11 and 3 levels for frequency bins analysis within sigma range, respectively. When a significant main effect or interaction was found, tests with contrasts were performed to identify the factor levels with significant differences in the ob-
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served quantities. a s 0.05 was accepted as the nominal level of significance, and corrected Žreduced according to the Bonferroni procedure. for all post-hoc tests Žtests with contrasts. to keep the type I error rate less than 0.05.
3. Results 3.1. Sleep cycles The sleep variables obtained by visual scoring of the sleep EEG are summarized in Table 1. In accordance with the modified criteria of Feinberg and Floyd w11x, a sleep
Fig. 1. Extraction of the initial and final parts of non-REM sleep periods. The extracting procedure of a representative frequency bin Ž0.78 Hz. in one subject is shown. Ža. Hypnogram and activity of 0.78-Hz bin: Initial and final 21-min parts of non-REM periods were determined by correspondent 30-s sleep stages. Žb. The 3-min averaged profiles of initial and final 21-min activities of 0.78-Hz bin: The average activity of consecutive 3-min epochs in the initial and final 21-min of non-REM sleep periods were calculated. The initial and final parts of the non-REM sleep episodes were assigned to one of three clock time zones of night sleep Ž2300–0140, 0140–0420 and 0420–0700 h. according to their median time point. Žc. The 0.78-Hz bin activity of initial and final parts of non-REM sleep periods in each time zones time-locked by the non-REM onset and offset.
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cycle was defined as a non-REM sleep episode of at least 35-min duration followed by a REM sleep episode of at least 5-min duration. The non-REM sleep episodes were defined as the interval between the first occurrence of stage 2 sleep and the last occurrence of stage 2 prior to the first occurrence of REM sleep within a cycle. The first and last 21-min periods of each non-REM episode were extracted as the initial and final parts of the episode, and the average EEG values of consecutive 3-min epochs were calculated for individual frequency bins ŽFig. 1.. The initial and final parts of the non-REM sleep episodes were assigned to one of three clock time zones of night sleep Ž2300–0140, C0140–0420 and 0420–0700 h. according to their median time points. Thus the clock time zone was not always the same for the initial and final parts of a given non-REM episode. We observed 147 non-REM sleep episodes that met the criteria from 32 night recordings. The mean length Ž"S.D.. of the sleep cycles was 102.0 " 36.1 min. There were 11 sleep cycles with a non-REM sleep episode of less than 42 min. However, the mean overlapping time between the initial and final parts of these sleep cycles was 0.76 " 3.2 min. Since several nights did not contain at least one sleep cycle during each of the three clock time zones, ANOVA of the initial parts of the non-REM sleep episodes was restricted to 31 nights, and ANOVA of the final part to 28 nights. For the same reason, statistical analyses involving both the initial and final parts of the non-REM sleep episodes were limited to 27 nights. 3.2. Initial and final parts of non-REM episodes 3.2.1. General Fig. 2 shows the time course of the EEG activities in representative frequency bins for the six band ranges. All the frequency bins except for the beta range showed smallest values at non-REM onset and offset. 3.2.2. Initial part In the initial part of non-REM sleep, the EEG activities increased or decreased continuously in the delta, theta and beta ranges. In the alpha and low sigma ranges, however, as represented by the 10.2- and 12.1-Hz bins, the values seemed to reach a plateau between 10 and 20 min after non-REM onset. In the high sigma range, represented by the 15.2-Hz bin, there was a distinct peak shortly after the onset of non-REM sleep. 3.2.3. Final part In the final part of the non-REM sleep episodes, the activities from the delta to low sigma showed a uniform decrease towards the offset of non-REM sleep, with the steepest decline in the 12.1-Hz bin. On the other hand, the high sigma activity, together with the beta activity, increased within the final part of non-REM sleep. However again, the high sigma range declined rapidly towards the offset of non-REM sleep.
Fig. 2. Profiles of representative six frequency bins for six band ranges in the initial and final 21-min of non-REM episodes. Profiles of representative six frequency bins for delta Ž0.78 Hz., theta Ž5.1 Hz., alpha Ž10.2 Hz., low sigma Ž12.1 Hz., high sigma Ž15.2 Hz. and beta Ž17.6 Hz. ranges in the initial and final 21-min of non-REM episodes are shown. Data represent mean values of consecutive 3-min epochs after non-REM onset and before non-REM offset, expressed as a percentage of an average power density of total sleep periods of each night in the corresponding frequency bins. Bars represent standard errors of mean. Profiles for three clock time zones are shown superimposed. The values on the abscissa represent median time points in 3-min epochs.
3.3. Temporal dynamics of the EEG frequencies 3.3.1. General To further investigate the temporal dynamics of the EEG activities, we calculated the rise latencies from the onset of non-REM sleep to the maximum value of each frequency bins and decay latencies from the maximum value to the offset of non-REM sleep within the three clock time zones of night sleep. When there were two cycles or more within one clock time zone for a given
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Fig. 3. Rise latencies in the initial part of the non-REM episodes and decay latencies in the final part. Rise latencies from the onset of non-REM sleep to the maximum EEG values in the initial part of the non-REM episodes Žleft. and decay latencies from the maximum EEG values to the offset of non-REM sleep in the final part Žright. are shown. Bars represent standard errors of mean. Asterisks represent significant clock time effects within each band range in the analyses of initial or final part of non-REM sleep period. In the initial part of the non-REM episodes, the rise latencies were shorter in the higher frequency ranges Žhigh sigma and beta; ) 14 Hz. and longer in the lower frequency ranges Ždelta, theta, alpha and low sigma; - 14 Hz. ŽWilks multivariate test of significance: effect of band; F5,26 s 46.35, p - 0.001, band = clock time interaction; F10,21 s 9.33, p - 0.001, tests with contrasts: effect of the band between the lower and the higher frequency ranges; F1,30 s 407.86, p - 0.001, effect of clock time on delta, alpha and low sigma indicated by asterisks; F2,30 s 5.64, p - 0.008.. In the final part of the non-REM episodes, the decay latencies were shorter in the higher frequency ranges Ž) 14 Hz. and longer in the lower frequency ranges Ž- 14 Hz. ŽWilks multivariate test of significance: effect of the band; F5,25 s 10.070, p - 0.001. In the lower frequency ranges Ž- 14 Hz., the rise latencies in the initial part of the non-REM episodes were longer compared with the decay latencies in the final part of the non-REM episodes ŽWilks multivariate test of significance: initial vs. final = band interaction; F5,130 s 11.173, p - 0.001; band = clock time interaction; F10,260 s 6.329, p - 0.001, tests with contrasts: effect of the band between the lower frequency ranges Ž- 14 Hz. and the higher frequency ranges Ž) 14 Hz.; F1,26 s 278.613, p - 0.001, initial vs. final effect in the lower frequency ranges; F1,26 s 12.966, p - 0.002..
subject, we calculated the mean latency. Finally, average rise and decay latencies for each band ranges within three clock time zone were calculated ŽFig. 3.. 3.3.2. Initial part The initial part of the non-REM episodes was characterized by relatively shorter rise latencies in the higher frequency ranges Žhigh sigma and beta ranges; ) 14 Hz., and longer latencies in the lower frequency ranges Ždelta, theta, alpha and low sigma ranges; - 14 Hz.. Statistical analysis revealed significant differences in the rise latencies between the lower and the higher frequency ranges, and the temporal dynamics of the delta, alpha and low sigma ranges were significantly affected by the clock time of the night ŽWilks multivariate test of significance: effect of band; F5,26 s 46.35, p - 0.001, band = clock time interaction; F10,21 s 9.33, p - 0.001, tests with contrasts: effect of the band factor between the lower and the higher frequency ranges; F1,30 s 407.86, p - 0.001, effect of clock time on delta, alpha and low sigma; F2,30 s 5.64, p - 0.008..
By the analysis of 11 frequency bins in the sigma range Ž12.1–16.1 Hz., bins in low sigma and bins in high sigma range were statistically separated ŽWilks multivariate test of significance: effect of frequency bin; F10,300 s 39.304, p - 0.001, tests with contrasts: effect of the frequency bin between the bins in low sigma range Ž12.1–13.7 Hz. and bins in high sigma range Ž14.1–16.0 Hz.; F1,30 s 341.83, p - 0.001.. 3.3.3. Final part In the final part of the non-REM episodes, the decay latencies were shorter in the higher frequency ranges Ž) 14 Hz. and longer in the lower frequency ranges Ž- 14 Hz.. Statistical analysis revealed significant differences between the lower and higher frequency ranges ŽWilks multivariate test of significance: effect of band; F5,25 s 10.070, p - 0.001, tests with contrasts; effect of the band between the lower frequency ranges and the higher frequency ranges; F1,27 s 57.879, p - 0.001.. Bins in low and bins in high sigma range were again separated statistically ŽWilks multivariate test of signifi-
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cance: effect of frequency bin; F10,270 s 12.498, p - 0.001, tests with contrasts; effect of the frequency bin between the bins in low sigma range and the bins in high sigma range; F1,27 s 114.367, p - 0.001.. 3.4. Initial Õs. final part ANOVA with the mean values of six band ranges revealed a significant difference between lower frequency ranges Ž- 14 Hz. and higher frequency ranges Ž) 14 Hz., and a significant difference between the initial and the final part in lower frequency ranges ŽWilks multivariate test of significance: effect of initial vs. final; F1,20 s 94.906, p - 0.001, effect of band; F5,130 s 11.173, p 0.001, initial vs. final = band interaction; F5,130 s 11.173, p - 0.001, band = clock time interaction; F10,260 s 6.329, p - 0.001, tests with contrasts: effect of the band between the lower frequency ranges and the higher frequency ranges; F1,26 s 278.613, p - 0.001, initial vs. final effect in the lower frequency ranges; F1,26 s 12.966, p - 0.002.. Bins in low sigma range and bins in high sigma range were also statistically separated ŽWilks multivariate test of significance: effect of initial vs. final; F1,26 s 13.210, p 0.002, effect of frequency bin; F10,260 s 86.195, p - 0.001, effect of clock time; F2,52 s 7.667, p - 0.002, tests with contrasts: effect of frequency bin between low sigma range Ž12.1–13.7 Hz. and high sigma range Ž14.1–16.0 Hz.; F1,26 s 319.075, p - 0.001.. 4. Discussion In the present study, we investigated the temporal dynamics of EEG activity during non-REM sleep periods in healthy men, taking in account the time information within non-REM episodes and the heterogeneity of the sigma frequency range. Previous reports analyzed regression or correlation of EEG dynamics by plotting the power densities of delta and sigma ranges for individual epochs w7,23x. Their results did not contain time information. Moreover, a short sigma state had to be ignored, when such a state was not long enough to collect a certain amount of epochs to satisfy statistical tests. Some studies standardized the length of
individual non-REM episodes to analyze the effect of the clock time of the night w2x, and other calculated additional variables such as rise rates of EEG activity w16,17x. These standardizations allow for discussion of the time percent of certain states of sleep or the process shifts within non-REM episodes, but it is difficult to discuss the definitive temporal dynamics of initiation and termination of non-REM sleep, as we clarified here. We described the temporal dynamics in relation to the latencies to the maximum EEG activities reached within the initial and final 21-min of the non-REM episodes. We set the window width to 21 min, which is long enough to cover the process of shifting from the sigma dominant state to the delta dominant state and vice versa, regardless of the length of the individual non-REM episode. Thus, we kept the time information within these windows, which is essential for analyzing and discussing the initiation and termination of non-REM sleep. By calculating the rise latencies and decay latencies, we performed ANOVA analyses without considering the differences of power densities, which are largely affected by inter-individual differences and clock time of the night. We observed that the initiation of non-REM sleep is characterized by a temporal phase shift from a high sigma to a low sigma-dominant state, and finally to a delta-dominant state, and the termination of non-REM sleep showed in part an inverse pattern of EEG activities. Statistical analyses revealed the independence of low sigma and high sigma both in the initial and final part of non-REM episodes. Only the initiation process of non-REM sleep in delta, alpha and low sigma was significantly affected by clock time of the night ŽTable 2.. The durations of the build-up and of the plateau state in the low sigma were shorter in the earlier non-REM sleep episodes. Clock time effect of delta and theta in our result was in contrast to a previous study which reported longer rise times of delta and theta ranges as well as a lengthening of the rise time from cycles 1 to 3 w1x. This inconsistency may be due to a different aim and methodology, as our 21-min window was determined to investigate the initiation and termination process, whereas they rather aimed to investigate evolution processes of non-REM sleep.
Table 2 Summary of temporal dynamics of latencies in each band range Part of non-REM sleep
Delta
Theta
Alpha
Low sigma
High sigma
Beta
Initial part of non-REM Build-up Significant effect of clock time Towards morning
slow yes shorter
slow no
slow yes longer
slow yes longer
rapid no
rapid no
Final part of non-REM Decay Significant effect of clock time
slow no
slow no
slow no
slow no
rapid no
rapid no
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Aeschbach and Borbely ´ w2x performed analyses of rise and decay time between non-REM onsetroffset and maximal activity. However, they analyzed only three separate sigma ranges and only from the first to the fourth non-REM episodes. In the present study, for the first time, we analyzed the non-REM EEG dynamics across the whole frequency range. Two types of frequency ranges, a lower frequency range Ždelta, theta, alpha and low sigma ranges; - 14 Hz. with longer latencies and a higher frequency range Žhigh sigma and beta; ) 14 Hz. with shorter latencies, were differentiated statistically to their maximal power values. Our analyses also revealed the independence of low sigma range from high sigma range for the first time. Our findings provide evidence for heterogeneity of sleep spindles observed in studies using pattern-recognizing analysis and those with pharmaco-endocrinologic intervention. Topographically, fast spindles, ranging from 12.5 to 16 Hz, occur predominantly in the central derivations, whereas slow spindles, ranging from 11 to 12 Hz, are located mainly in the frontal brain regions w13,19,24,25x. During the progress of non-REM sleep, the amount of fast spindles decreases in favor of slow spindles w15x. Moreover, there is evidence that the two types of sleep spindles are affected differentially by homeostatic or circadian mechanisms w2,4,8x. This was shown in the case of melatonin w9x, the menstrual cycle w10x, pregnancy w6x, neurosteroids w12x and substances acting on the GABA A -benzodiazepine receptor complex w3x. Our results suggest that sleep pressure affects the temporal dynamics of slow spindles, but not those of fast spindles. At the beginning of night sleep, when sleep pressure is supposed to be high, there was rapid progress of the peak power values from the high sigma to the low sigma towards the delta range. In the course of the night, as sleep pressure decays, the profile of the low sigma changed to a more gradual increase in power values. However, the pattern of the fast spindle range, which has clear peaks at the beginning and end of non-REM episode was preserved. In contrast, the EEG profiles in the final parts of the non-REM episodes remained fairly stable. This observation is of interest, given that the termination process of non-REM sleep always follows the same schedule, although the process might be triggered by different factors, such as non-REM pressure and REM pressure. Recent electrophysiological findings suggest that the process of EEG synchronization during non-REM sleep might be critically related to an interplay between the reticular thalamic and thalamocortical relay neurons. The generation of the sleep spindles and of slow waves seems to depend on the progressive membrane hyperpolarization of these thalamic neurons w20,21x. Although there are no neurophysiological findings confirming the differential generation of slow and fast spindles at present, our data together with the previous reports strongly suggest the heterogeneity of slow and fast spindles. It might be specu-
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lated that oscillation of fast spindles is different from that of slow spindles, or that it is affected by other factors, such as the excitability of the thalamocortical and cortical neurons. Previous studies using sleep manipulation or pharmacological treatment Že.g., hypnotics. have addressed build up of delta andror sigma activities after the interventions w1,3,7x. Functional significance of these changes observed in the different frequency bands has not been fully clarified and needs further investigations. In summary, the present results clarified the temporal dynamics of initiation and termination of non-REM sleep within non-REM periods. First, the temporal dynamics of lower frequency ranges Ž- 14 Hz. and higher frequency ranges Ž) 14 Hz. were significantly different, suggesting the heterogeneity of slow and fast spindles. Second, in the initiation of non-REM sleep, temporal dynamics of delta, alpha and low sigma ranges were significantly affected by the clock time of the night, whereas the temporal EEG dynamics of the termination of the non-REM sleep remained fairly stable. Third, temporal dynamics of nonREM termination in only higher frequency ranges Ž) 14 Hz. showed an inverted process of that of non-REM initiation, but not in lower frequency ranges Ž- 14 Hz..
Acknowledgements The authors thank Werner Laimgruber and Norbert Tauchmann for their technical assistance, and Susanne Heim for her support. We also thank Dr. Makoto Uchiyama for comments on this manuscript.
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ogy, Techniques and Scoring Systems of Sleep Stages of Human Subjects, US Department of Health, Education and Welfare, Bethesda, MD, 1968. W. Scheuler, S. Kubicki, G. Scholz, J. Marquerdt, Two different activities in the sleep spindle frequency band-discrimination based on the topographical distribution of spectral power and coherence, in: J. Horne ŽEd.., Sleep ‘90, Pontenagel Press, Bochum, 1990, pp. 13–16. M. Steriade, D.A. McCormick, T.J. Sejnowski, Thalamocortical oscillations in the sleeping and aroused brain, Science 262 Ž1993. 679–685. M. Steriade, D. Contreras, F. Amzica, Synchronized sleep oscillations and their paroxysmal developments, Trends Neurosci. 17 Ž1994. 199–208. L. Trachsel, W. Schreiber, F. Holsboer, T. Pollmacher, Endotoxin ¨ enhances EEG alpha and beta power in human sleep, Sleep 17 Ž1994. 132–139. S. Uchida, T. Maloney, J.D. March, R. Azari, I. Feinberg, Sigma Ž12–15 Hz. and delta Ž0.3–3 Hz. EEG oscillate reciprocally within NREM sleep, Brain Res. Bull. 27 Ž1991. 93–96. E. Werth, P. Achermann, D.-J. Dijk, A.A. Borbely, ´ Spindle frequency activity in the sleep EEG: individual differences and topographic distribution, Electroencephalogr. Clin. Neurophysiol. 103 Ž1997. 535–542. J. Zeitlhofer, G. Gruber, P. Anderer, S. Asenbaum, P. Schimicek, B. Saletu, Topographic distribution of sleep spindles in young healthy subjects, J. Sleep Res. 6 Ž1997. 149–155.
Research report
Temporal EEG dynamics of non-REM sleep episodes in humans Hirokuni Tagaya ) , Lorenz Trachsel, Harald Murck, Irina Antonijevic, Axel Steiger, Florian Holsboer, Elisabeth Friess Max Planck Institute of Psychiatry, Clinical Institute, Kraepelinstrasse 10, D-80804 Munich, Germany Accepted 28 December 1999
Abstract The process of the human non-rapid eye movement Žnon-REM. sleep period has not been clarified. Time-based analysis on sleep EEG may provide an explanation. We focused on chronological aspects of initiation and termination of non-REM episodes, using spectral analysis of sleep EEG. The subjects were healthy male volunteers Ž n s 32, mean age " S.D.: 25.5 " 3.5 years.. The rise latencies from non-REM sleep onset to the maximal power value and the decay latencies from the maximal power value to non-REM sleep offset were determined in the initial and final 21-min windows of individual non-REM episodes in each EEG band ranges. Low Ž12.1–13.7 Hz. and high Ž14.1–16.0 Hz. sigma ranges were analyzed separately. The rise and decay latencies were shorter in higher frequency ranges Ž) 14 Hz. and longer in lower frequency ranges Ž- 14 Hz.. There were significant differences in the rise and decay latencies between low and high sigma ranges, indicating that the whole frequency ranges were clearly separated at the middle of the sigma range Ž14 Hz.. The rise and decay latencies were significantly different in lower frequency ranges. The clock time of the night significantly affected only the rise latencies of the delta Ž0.78–3.9 Hz., alpha Ž8.2–11.7 Hz. and low sigma Ž12.1–13.7 Hz. ranges. In conclusion, initiation and termination of non-REM sleep was represented by higher frequency ranges, whereas further evolution and devolution of non-REM sleep was represented by lower frequency ranges, and only the evolution process was affected by the clock time of the night. q 2000 Published by Elsevier Science B.V. All rights reserved. Keywords: Polysomnography; Spectral analysis; Sleep spindle; Sleep slow wave; Initiation and termination of non-REM sleep episode
1. Introduction Previous studies on human sleep EEG have revealed that 2 major components of the sleep EEG Ži.e., sleep slow waves and sleep spindles. possibly reflect the underlying brain activity that regulates human non-rapid eye movement Žnon-REM. sleep. In the homeostatic or circadian processes through the night, delta activity decreases as a function of sleep period time, and the initial build-up reflects the duration of the preceding period of wakefulness w5x, whereas sigma activity increases throughout sleep and was shown to be modulated by circadian factors w8x. In their respective non-REM sleep episodes, delta and sigma EEG activities are thought to show a reciprocal relationship. During the initiation process of non-REM
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Corresponding author. National Center of Neurology and Psychiatry, National Institute of Mental Health, Kounodai 1-7-1, Ichikawa, Chiba, J-2720827, Japan. Fax: q81-47-375-4771; e-mail: [email protected]
sleep, the sleep EEG activity shifts from a sigma dominant state to a delta dominant state, and an inverse direction of shift from the delta dominant state to the sigma dominant state was observed during the termination process of nonREM sleep w7,23x. The putative mechanism underlying this characteristic pattern of non-REM sleep EEG activity was supported by findings based on animal experiments showing that thalamocortical neurons oscillate cortical neurons in two different modes, depending on the degree of membrane hyperpolarization w20,21x. Thus, the shift from the sigma to the delta EEG frequencies may be driven by a decrease in the membrane potential of the thalamic relay neurons, and vice versa. Numerous sleep-EEG studies have identified two different types of sleep spindles w13–15,19,24x. The fast spindles, ranging from 12.5 to 15 Hz, appear predominantly at centralotemporal scalp electrodes at the initiation and termination of non-REM sleep, while the slow spindles, ranging from 11 to 13.5 Hz, appear during deeper non-REM sleep and show a frontocentral distribution. Moreover, the standard criteria exclude the fast spindles from the definition of sleep spindles w18x. Recent studies suggested that
0006-8993r00r$ - see front matter q 2000 Published by Elsevier Science B.V. All rights reserved. PII: S 0 0 0 6 - 8 9 9 3 Ž 0 0 . 0 1 9 8 2 - X
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the fast and slow spindles are differently affected by a variety of factors: the circadian phase w2,3,8x, menstrual cycle and pregnancy w6,10x, circulating neuroactive steroids w12x and various substances such as melatonin w9x and benzodiazepines w3x. Therefore, to analyze the initiation and termination process of non-REM sleep episodes, low sigma and high sigma should be separated. The typical transitional pattern from sigma to delta and from delta to sigma is only seen in the late evening, and there are some conflicting findings as to whether the termination process of non-REM sleep is simply the reverse of non-REM initiation w7,23x. This disagreement may be due to two methodological issues: Ži. Most previous studies have analyzed the whole non-REM period. Thus the lengths of non-REM sleep periods had to be standardized, or regression andror correlation analyses were performed regardless of time. Žii. Slow and fast spindle ranges were not distinguished in most studies. As for the first issue, we defined the time point of maximal EEG activity for each frequency range in the initial and final 21-min windows of non-REM sleep periods. Then we determined the rise latency from non-REM sleep onset to maximal EEG values and the decay latency from maximal EEG values to non-REM offset. Thus, we were able to analyze the time sequence of the maximal EEG activity of all frequency ranges during the initiation and the termination process of non-REM sleep. In this study, we tried to overcome the shortcomings described above to investigate the initiation and termination process of non-REM sleep.
2. Materials and method 2.1. Subjects The subjects were 32 healthy male volunteers Žmean age " S.D.: 25.5 " 3.5 years, range: 19–33 years.. They had no sleep disturbance and no family or personal history of psychiatric or neurological disorders, and they had received no medical treatment for the previous 3 months. Shift workers and subjects who had recently made transmeridian flights were excluded. After written informed consent had been obtained, the subjects spent two consecutive nights in our sleep laboratories; the first night for adaptation to the experimental setting, and the second night for sleep recordings. 2.2. EEG recording Polysomnographic sleep recordings consisted of two EEGs ŽC3-A2rC4-A1., vertical and horizontal electrooculograms ŽEOGs., an electromyogram ŽEMG. and an elec-
trocardiogram ŽECG.. Polysomnographic recordings were carried out from 2300 Žlight out. to 0700 h Žlight on.. Sleep stages were scored visually by experienced raters for consecutive 30-s epochs according to the criteria of Rechtschaffen and Kales w18x. 2.3. EEG spectral analysis The EOG, EEG, EMG, and ECG signals were filtered Žhigh-pass filter at 0.5 Hz, notch filter at 50 Hz. and transmitted to the polygraph ŽSchwartzer, ED 24.. The digitized data Ž8-bit analog-to-digital converter, sampling rate 100 Hz. were stored on the disk and calibrated with a 50-mV 10-Hz sine wave. The C3-A2 EEG derivation was subjected to spectral analysis using a fast Fourier transformation. The sleep-stage-specific EEG power spectra were computed using twelve 2.56-s epochs which had overlapping to match the 30-s epoch of sleep stages w22x. The frequency resolution was set to 0.39 Hz between 0.78 and 19.1 Hz inclusively. Artifacts, for example, those caused by brief body movements, were excluded manually by referring to the EEG recordings. The EEG activities of the 48 frequency bins were handled as described in result section. For latency analyses of band ranges, variables were averaged for delta Ž0.78–3.9 Hz., theta Ž4.3–7.8 Hz., alpha Ž8.2–11.7 Hz., low sigma Ž12.1–13.7 Hz., high sigma Ž14.1–16.0 Hz. and beta Ž16.4–19.1 Hz. ranges. 2.4. Statistical analysis The data were examined by analysis of variance ŽANOVA. with repeated measures designs. Two different analyses were done for six EEG bands Ždelta, theta, alpha, low sigma, high sigma and beta. and for 11 EEG frequency bins within sigma range Ž12.1–16.1 Hz. for testing the appropriateness of our sigma separation. In the analyses of the initial and the final parts of the non-REM episodes, band and clock time Žthirds of night sleep. were
Table 1 Sleep variables Values are indicated as means and S.D. Ž ns 32.. Sleep onset latency was defined as the time from lights off to the first epoch of stage 2 sleep lasting longer than 3 min, and REM Iatency was defined as the time between sleep onset and the first epoch of REM sleep. Variable
Mean"S.D.
Time in bed Žmin. Sleep period time Žmin. Sleep onset latency Žmin. REM latency Žmin. Stage 1 sleep Žmin. Stage 2 sleep Žmin. Slow wave sleep Žmin. REM sleep Žmin. Sleep efficiency Ž%.
477.5"9.3 454.9"14.2 20.9"14.3 79.6"31.1 33.3"15.7 256.0"28.7 45.6"25.6 90.5"20.6 90.9"3.8
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the within-subject factors, with 6 and 3 levels for EEG band analyses, and frequency bins and clock time were the within-subject factors, with 11 and 3 levels for frequency bins within sigma range, respectively. In the analyses between the initial and the final parts of the non-REM episodes, initial vs. final, band and clock time were the within-subject factors, with 2, 6 and 3 levels for EEG band analysis, and initial vs. final, frequency bins and clock time were the within-subjects factors, with 2, 11 and 3 levels for frequency bins analysis within sigma range, respectively. When a significant main effect or interaction was found, tests with contrasts were performed to identify the factor levels with significant differences in the ob-
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served quantities. a s 0.05 was accepted as the nominal level of significance, and corrected Žreduced according to the Bonferroni procedure. for all post-hoc tests Žtests with contrasts. to keep the type I error rate less than 0.05.
3. Results 3.1. Sleep cycles The sleep variables obtained by visual scoring of the sleep EEG are summarized in Table 1. In accordance with the modified criteria of Feinberg and Floyd w11x, a sleep
Fig. 1. Extraction of the initial and final parts of non-REM sleep periods. The extracting procedure of a representative frequency bin Ž0.78 Hz. in one subject is shown. Ža. Hypnogram and activity of 0.78-Hz bin: Initial and final 21-min parts of non-REM periods were determined by correspondent 30-s sleep stages. Žb. The 3-min averaged profiles of initial and final 21-min activities of 0.78-Hz bin: The average activity of consecutive 3-min epochs in the initial and final 21-min of non-REM sleep periods were calculated. The initial and final parts of the non-REM sleep episodes were assigned to one of three clock time zones of night sleep Ž2300–0140, 0140–0420 and 0420–0700 h. according to their median time point. Žc. The 0.78-Hz bin activity of initial and final parts of non-REM sleep periods in each time zones time-locked by the non-REM onset and offset.
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cycle was defined as a non-REM sleep episode of at least 35-min duration followed by a REM sleep episode of at least 5-min duration. The non-REM sleep episodes were defined as the interval between the first occurrence of stage 2 sleep and the last occurrence of stage 2 prior to the first occurrence of REM sleep within a cycle. The first and last 21-min periods of each non-REM episode were extracted as the initial and final parts of the episode, and the average EEG values of consecutive 3-min epochs were calculated for individual frequency bins ŽFig. 1.. The initial and final parts of the non-REM sleep episodes were assigned to one of three clock time zones of night sleep Ž2300–0140, C0140–0420 and 0420–0700 h. according to their median time points. Thus the clock time zone was not always the same for the initial and final parts of a given non-REM episode. We observed 147 non-REM sleep episodes that met the criteria from 32 night recordings. The mean length Ž"S.D.. of the sleep cycles was 102.0 " 36.1 min. There were 11 sleep cycles with a non-REM sleep episode of less than 42 min. However, the mean overlapping time between the initial and final parts of these sleep cycles was 0.76 " 3.2 min. Since several nights did not contain at least one sleep cycle during each of the three clock time zones, ANOVA of the initial parts of the non-REM sleep episodes was restricted to 31 nights, and ANOVA of the final part to 28 nights. For the same reason, statistical analyses involving both the initial and final parts of the non-REM sleep episodes were limited to 27 nights. 3.2. Initial and final parts of non-REM episodes 3.2.1. General Fig. 2 shows the time course of the EEG activities in representative frequency bins for the six band ranges. All the frequency bins except for the beta range showed smallest values at non-REM onset and offset. 3.2.2. Initial part In the initial part of non-REM sleep, the EEG activities increased or decreased continuously in the delta, theta and beta ranges. In the alpha and low sigma ranges, however, as represented by the 10.2- and 12.1-Hz bins, the values seemed to reach a plateau between 10 and 20 min after non-REM onset. In the high sigma range, represented by the 15.2-Hz bin, there was a distinct peak shortly after the onset of non-REM sleep. 3.2.3. Final part In the final part of the non-REM sleep episodes, the activities from the delta to low sigma showed a uniform decrease towards the offset of non-REM sleep, with the steepest decline in the 12.1-Hz bin. On the other hand, the high sigma activity, together with the beta activity, increased within the final part of non-REM sleep. However again, the high sigma range declined rapidly towards the offset of non-REM sleep.
Fig. 2. Profiles of representative six frequency bins for six band ranges in the initial and final 21-min of non-REM episodes. Profiles of representative six frequency bins for delta Ž0.78 Hz., theta Ž5.1 Hz., alpha Ž10.2 Hz., low sigma Ž12.1 Hz., high sigma Ž15.2 Hz. and beta Ž17.6 Hz. ranges in the initial and final 21-min of non-REM episodes are shown. Data represent mean values of consecutive 3-min epochs after non-REM onset and before non-REM offset, expressed as a percentage of an average power density of total sleep periods of each night in the corresponding frequency bins. Bars represent standard errors of mean. Profiles for three clock time zones are shown superimposed. The values on the abscissa represent median time points in 3-min epochs.
3.3. Temporal dynamics of the EEG frequencies 3.3.1. General To further investigate the temporal dynamics of the EEG activities, we calculated the rise latencies from the onset of non-REM sleep to the maximum value of each frequency bins and decay latencies from the maximum value to the offset of non-REM sleep within the three clock time zones of night sleep. When there were two cycles or more within one clock time zone for a given
H. Tagaya et al.r Brain Research 861 (2000) 233–240
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Fig. 3. Rise latencies in the initial part of the non-REM episodes and decay latencies in the final part. Rise latencies from the onset of non-REM sleep to the maximum EEG values in the initial part of the non-REM episodes Žleft. and decay latencies from the maximum EEG values to the offset of non-REM sleep in the final part Žright. are shown. Bars represent standard errors of mean. Asterisks represent significant clock time effects within each band range in the analyses of initial or final part of non-REM sleep period. In the initial part of the non-REM episodes, the rise latencies were shorter in the higher frequency ranges Žhigh sigma and beta; ) 14 Hz. and longer in the lower frequency ranges Ždelta, theta, alpha and low sigma; - 14 Hz. ŽWilks multivariate test of significance: effect of band; F5,26 s 46.35, p - 0.001, band = clock time interaction; F10,21 s 9.33, p - 0.001, tests with contrasts: effect of the band between the lower and the higher frequency ranges; F1,30 s 407.86, p - 0.001, effect of clock time on delta, alpha and low sigma indicated by asterisks; F2,30 s 5.64, p - 0.008.. In the final part of the non-REM episodes, the decay latencies were shorter in the higher frequency ranges Ž) 14 Hz. and longer in the lower frequency ranges Ž- 14 Hz. ŽWilks multivariate test of significance: effect of the band; F5,25 s 10.070, p - 0.001. In the lower frequency ranges Ž- 14 Hz., the rise latencies in the initial part of the non-REM episodes were longer compared with the decay latencies in the final part of the non-REM episodes ŽWilks multivariate test of significance: initial vs. final = band interaction; F5,130 s 11.173, p - 0.001; band = clock time interaction; F10,260 s 6.329, p - 0.001, tests with contrasts: effect of the band between the lower frequency ranges Ž- 14 Hz. and the higher frequency ranges Ž) 14 Hz.; F1,26 s 278.613, p - 0.001, initial vs. final effect in the lower frequency ranges; F1,26 s 12.966, p - 0.002..
subject, we calculated the mean latency. Finally, average rise and decay latencies for each band ranges within three clock time zone were calculated ŽFig. 3.. 3.3.2. Initial part The initial part of the non-REM episodes was characterized by relatively shorter rise latencies in the higher frequency ranges Žhigh sigma and beta ranges; ) 14 Hz., and longer latencies in the lower frequency ranges Ždelta, theta, alpha and low sigma ranges; - 14 Hz.. Statistical analysis revealed significant differences in the rise latencies between the lower and the higher frequency ranges, and the temporal dynamics of the delta, alpha and low sigma ranges were significantly affected by the clock time of the night ŽWilks multivariate test of significance: effect of band; F5,26 s 46.35, p - 0.001, band = clock time interaction; F10,21 s 9.33, p - 0.001, tests with contrasts: effect of the band factor between the lower and the higher frequency ranges; F1,30 s 407.86, p - 0.001, effect of clock time on delta, alpha and low sigma; F2,30 s 5.64, p - 0.008..
By the analysis of 11 frequency bins in the sigma range Ž12.1–16.1 Hz., bins in low sigma and bins in high sigma range were statistically separated ŽWilks multivariate test of significance: effect of frequency bin; F10,300 s 39.304, p - 0.001, tests with contrasts: effect of the frequency bin between the bins in low sigma range Ž12.1–13.7 Hz. and bins in high sigma range Ž14.1–16.0 Hz.; F1,30 s 341.83, p - 0.001.. 3.3.3. Final part In the final part of the non-REM episodes, the decay latencies were shorter in the higher frequency ranges Ž) 14 Hz. and longer in the lower frequency ranges Ž- 14 Hz.. Statistical analysis revealed significant differences between the lower and higher frequency ranges ŽWilks multivariate test of significance: effect of band; F5,25 s 10.070, p - 0.001, tests with contrasts; effect of the band between the lower frequency ranges and the higher frequency ranges; F1,27 s 57.879, p - 0.001.. Bins in low and bins in high sigma range were again separated statistically ŽWilks multivariate test of signifi-
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cance: effect of frequency bin; F10,270 s 12.498, p - 0.001, tests with contrasts; effect of the frequency bin between the bins in low sigma range and the bins in high sigma range; F1,27 s 114.367, p - 0.001.. 3.4. Initial Õs. final part ANOVA with the mean values of six band ranges revealed a significant difference between lower frequency ranges Ž- 14 Hz. and higher frequency ranges Ž) 14 Hz., and a significant difference between the initial and the final part in lower frequency ranges ŽWilks multivariate test of significance: effect of initial vs. final; F1,20 s 94.906, p - 0.001, effect of band; F5,130 s 11.173, p 0.001, initial vs. final = band interaction; F5,130 s 11.173, p - 0.001, band = clock time interaction; F10,260 s 6.329, p - 0.001, tests with contrasts: effect of the band between the lower frequency ranges and the higher frequency ranges; F1,26 s 278.613, p - 0.001, initial vs. final effect in the lower frequency ranges; F1,26 s 12.966, p - 0.002.. Bins in low sigma range and bins in high sigma range were also statistically separated ŽWilks multivariate test of significance: effect of initial vs. final; F1,26 s 13.210, p 0.002, effect of frequency bin; F10,260 s 86.195, p - 0.001, effect of clock time; F2,52 s 7.667, p - 0.002, tests with contrasts: effect of frequency bin between low sigma range Ž12.1–13.7 Hz. and high sigma range Ž14.1–16.0 Hz.; F1,26 s 319.075, p - 0.001.. 4. Discussion In the present study, we investigated the temporal dynamics of EEG activity during non-REM sleep periods in healthy men, taking in account the time information within non-REM episodes and the heterogeneity of the sigma frequency range. Previous reports analyzed regression or correlation of EEG dynamics by plotting the power densities of delta and sigma ranges for individual epochs w7,23x. Their results did not contain time information. Moreover, a short sigma state had to be ignored, when such a state was not long enough to collect a certain amount of epochs to satisfy statistical tests. Some studies standardized the length of
individual non-REM episodes to analyze the effect of the clock time of the night w2x, and other calculated additional variables such as rise rates of EEG activity w16,17x. These standardizations allow for discussion of the time percent of certain states of sleep or the process shifts within non-REM episodes, but it is difficult to discuss the definitive temporal dynamics of initiation and termination of non-REM sleep, as we clarified here. We described the temporal dynamics in relation to the latencies to the maximum EEG activities reached within the initial and final 21-min of the non-REM episodes. We set the window width to 21 min, which is long enough to cover the process of shifting from the sigma dominant state to the delta dominant state and vice versa, regardless of the length of the individual non-REM episode. Thus, we kept the time information within these windows, which is essential for analyzing and discussing the initiation and termination of non-REM sleep. By calculating the rise latencies and decay latencies, we performed ANOVA analyses without considering the differences of power densities, which are largely affected by inter-individual differences and clock time of the night. We observed that the initiation of non-REM sleep is characterized by a temporal phase shift from a high sigma to a low sigma-dominant state, and finally to a delta-dominant state, and the termination of non-REM sleep showed in part an inverse pattern of EEG activities. Statistical analyses revealed the independence of low sigma and high sigma both in the initial and final part of non-REM episodes. Only the initiation process of non-REM sleep in delta, alpha and low sigma was significantly affected by clock time of the night ŽTable 2.. The durations of the build-up and of the plateau state in the low sigma were shorter in the earlier non-REM sleep episodes. Clock time effect of delta and theta in our result was in contrast to a previous study which reported longer rise times of delta and theta ranges as well as a lengthening of the rise time from cycles 1 to 3 w1x. This inconsistency may be due to a different aim and methodology, as our 21-min window was determined to investigate the initiation and termination process, whereas they rather aimed to investigate evolution processes of non-REM sleep.
Table 2 Summary of temporal dynamics of latencies in each band range Part of non-REM sleep
Delta
Theta
Alpha
Low sigma
High sigma
Beta
Initial part of non-REM Build-up Significant effect of clock time Towards morning
slow yes shorter
slow no
slow yes longer
slow yes longer
rapid no
rapid no
Final part of non-REM Decay Significant effect of clock time
slow no
slow no
slow no
slow no
rapid no
rapid no
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Aeschbach and Borbely ´ w2x performed analyses of rise and decay time between non-REM onsetroffset and maximal activity. However, they analyzed only three separate sigma ranges and only from the first to the fourth non-REM episodes. In the present study, for the first time, we analyzed the non-REM EEG dynamics across the whole frequency range. Two types of frequency ranges, a lower frequency range Ždelta, theta, alpha and low sigma ranges; - 14 Hz. with longer latencies and a higher frequency range Žhigh sigma and beta; ) 14 Hz. with shorter latencies, were differentiated statistically to their maximal power values. Our analyses also revealed the independence of low sigma range from high sigma range for the first time. Our findings provide evidence for heterogeneity of sleep spindles observed in studies using pattern-recognizing analysis and those with pharmaco-endocrinologic intervention. Topographically, fast spindles, ranging from 12.5 to 16 Hz, occur predominantly in the central derivations, whereas slow spindles, ranging from 11 to 12 Hz, are located mainly in the frontal brain regions w13,19,24,25x. During the progress of non-REM sleep, the amount of fast spindles decreases in favor of slow spindles w15x. Moreover, there is evidence that the two types of sleep spindles are affected differentially by homeostatic or circadian mechanisms w2,4,8x. This was shown in the case of melatonin w9x, the menstrual cycle w10x, pregnancy w6x, neurosteroids w12x and substances acting on the GABA A -benzodiazepine receptor complex w3x. Our results suggest that sleep pressure affects the temporal dynamics of slow spindles, but not those of fast spindles. At the beginning of night sleep, when sleep pressure is supposed to be high, there was rapid progress of the peak power values from the high sigma to the low sigma towards the delta range. In the course of the night, as sleep pressure decays, the profile of the low sigma changed to a more gradual increase in power values. However, the pattern of the fast spindle range, which has clear peaks at the beginning and end of non-REM episode was preserved. In contrast, the EEG profiles in the final parts of the non-REM episodes remained fairly stable. This observation is of interest, given that the termination process of non-REM sleep always follows the same schedule, although the process might be triggered by different factors, such as non-REM pressure and REM pressure. Recent electrophysiological findings suggest that the process of EEG synchronization during non-REM sleep might be critically related to an interplay between the reticular thalamic and thalamocortical relay neurons. The generation of the sleep spindles and of slow waves seems to depend on the progressive membrane hyperpolarization of these thalamic neurons w20,21x. Although there are no neurophysiological findings confirming the differential generation of slow and fast spindles at present, our data together with the previous reports strongly suggest the heterogeneity of slow and fast spindles. It might be specu-
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lated that oscillation of fast spindles is different from that of slow spindles, or that it is affected by other factors, such as the excitability of the thalamocortical and cortical neurons. Previous studies using sleep manipulation or pharmacological treatment Že.g., hypnotics. have addressed build up of delta andror sigma activities after the interventions w1,3,7x. Functional significance of these changes observed in the different frequency bands has not been fully clarified and needs further investigations. In summary, the present results clarified the temporal dynamics of initiation and termination of non-REM sleep within non-REM periods. First, the temporal dynamics of lower frequency ranges Ž- 14 Hz. and higher frequency ranges Ž) 14 Hz. were significantly different, suggesting the heterogeneity of slow and fast spindles. Second, in the initiation of non-REM sleep, temporal dynamics of delta, alpha and low sigma ranges were significantly affected by the clock time of the night, whereas the temporal EEG dynamics of the termination of the non-REM sleep remained fairly stable. Third, temporal dynamics of nonREM termination in only higher frequency ranges Ž) 14 Hz. showed an inverted process of that of non-REM initiation, but not in lower frequency ranges Ž- 14 Hz..
Acknowledgements The authors thank Werner Laimgruber and Norbert Tauchmann for their technical assistance, and Susanne Heim for her support. We also thank Dr. Makoto Uchiyama for comments on this manuscript.
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