The Distribution of EEG Frequencies in REM and NREM Sleep Stages in Healthy Young Adults

Transcription

1 Sleep, 18(5): American Sleep Disorders Association and Sleep Research Society The Distribution of G Frequencies in RM and NRM Sleep Stages in Healthy Young Adults Roseanne Armitage The University of Texas Southwestern Medical Center at Dallas, Dallas, Texas U.S.A. Summary: Period-analyzed electroencephalographic (G) activity was evaluated in men and 11 women to explore the distribution ofg frequencies during sleep and potential gender differences. Significant stage-of-sleep main effects were noted for both incidence and amplitude measures. Power measures seemed to best differentiate between non-rapid eye movement (NRM) stages, although incidence measures showed roughly the same distributions across sleep stages. Beta incidence and amplitude was highest in stage sleep followed in descending order by rapid eye movement (RM), stage 2, and slow-wave sleep (SWS). Delta incidence and amplitude were highest in SWS, with slightly lower values in stage 2. nterestingly, RM was characterized by higher incidence and amplitude delta than those found in stage 1 sleep. G variables did not show striking sex differences in any sleep stage, although a global measure of delta power in NRM sleep was higher among women. Hemispheric asymmetries were small throughout RM and NRM stages. These findings suggest that period analysis provides a detailed description of G frequency characteristics during sleep but does not reveal dramatic gender differences. Key Words: Computer quantification-gender-period analysis-sleep G. nterest in the application of computer analysis to sleep electroencephalograms (G) has grown steadily over the past 3 decades. Some studies have focused attention on the development of computer-scoring algorithms for sleep staging (e.g. 1-4). Most ofthe work, however, has focused primarily on delta bands (5-11), on a restricted set of measures (5,12) or on periodamplitude analysis (PAA) variation within sleep stages (12,13). A notable exception is the early work of J ohnson et al. (14). Although there has been considerable controversy over the choice of P AA or power spectral analysis (PSA) to quantify G activity (see Discussion), the overlap in variance between the two techniques may be greater than 80%, particularly for slow frequencies (15,16). Regardless of the choice of algorithm, only one study to our knowledge has evaluated gender differences in computer-quantified G during sleep among normal young adults (17). Although Dijk et al. found higher power in lower frequencies ( Hz) in females in non-rapid eye movement (NRM) sleep, the distribution of G frequencies in NRM sleep did not show sex differences (17). From these data, Dijk et al. (17) concluded that gender effects were likely due to Accepted for publication February Address. correspondence and reprint requests to Dr. R. Armitage, Director, Sleep Study Unit, University of Texas Southwestern Medical Center, Department of Psychiatry, 5323 Harry Hines Boulevard, Dallas, TX , U.S.A. 334 skull size and thickness differences between men and women, resulting in differential potentiation of bioelectrical signals. Dijk et al. (17) recorded G activity from C3 and C4 sites but did not report an evaluation of potential interhemispheric differences between males and females. The purpose of the present study was to compare sex differences in the distribution of period-analyzed G data in all sleep stages, evaluating potential interhemispheric differences. Subjects MTHODS leven males (mean age 25.6 ± 4.2 years) and 11 females (mean age 24.8 ± 3.9 years) participated in the study. All subjects were screened for medical and psychiatric wellness through a non patient version of the structured clinical interview for depression (SCD) (18) and detailed medical history. A family history of first-degree relatives with psychiatric disorders excluded subjects from study. Regular rise- and bed-times were maintained for 5 days prior to study, as assessed by home diary. Alcohol and caffeine restrictions were in place during the 5-day interval. None of the subjects were taking medication at the time of study, with the exception of oral or implant contraceptive use among all females.

2 Procedures SX DFFRNCS N SLP G 335 ach subject spent two consecutive nights in the University of Texas Southwestern Medical Center Sleep Study Unit. Night 1 served as both an adaptation night and as a screening for sleep disorders. A full electrode montage, including leg leads, chest and abdomen respiration bands and nasal oral thermistors was used during the screening night. G activity was recorded from left (C3) and right (C4) central sites, referenced to ear lobes connected to a 10 kohm common resistor. This reference system was chosen to control for nonhomogeneous current flow from reference electrodes that may produce artifactual hemispheric asymmetries (19). lectrooculographic (OG) activity was recorded from the upper and lower canthi of the left and right eyes. lectromyographic (MG) activity was recorded from a bipolar chin--cheek montage. G was amplified on Grass P511 AC amplifiers at a sensitivity of 5 (50 /lv, 0.5- second duration calibration). The half-amp low- and high-bandpass filters were set at 0.3 and 30 Hz. A 60- Hz notch filter attenuated electrical noise. Signals were digitized on-line at 250 Hz through a 16-bit Microstar analogue-to-digital (AD) converter and displayed on a computer monitor in analogue form. No additional bandpass filtering was performed. Raw digitized data were stored on a write-once-read-many (WORM) optical disk for off-line period analysis. The period-analysis algorithm used here has been described in detail elsewhere (2,12). The algorithm included half-wave zero-cross, full-wave first-derivative and power analyses. Zero-cross analysis is preferentially sensitive to slow-frequency variation, whereas first-derivative analysis preferentially quantifies fastfrequency bands. The algorithm evaluates elapsed time in five conventional frequency categories: delta (0.5 to <4 Hz), theta (4 to <8 Hz), alpha (8 to < 12 Hz), sigma (12 to < 16 Hz) and beta (16-32 Hz). A zero-cross event is a polarity change in the signal voltage. Half-wave zero-crosses evaluate both negative to positive and positive to negative signal changes. The algorithm for the half-wave zero-cross analyses computes the time interval between successive zero-voltage crossings, thereby determining the frequency of each wave. A time-in-frequency category accumulator is incremented and, at the end of each 30-second epoch, the percentage of total time in each frequency is computed for the zero-cross events. A power measure for each frequency is computed from the absolute value of the area of the half-waves squared in that frequency category. This analysis produces measures that are roughly equivalent to FFT power (15, 16). A total power value for all frequencies is also calculated. Our complete algorithm also includes full-wave zerocross analyses, but because the overlap between the two measures is very high (r > 0.80), only half-wave zero-cross data are reported here. The fourth analysis, which is preferentially sensitive to fast-frequency G bands, is a full-wave, first-derivative analysis. A first-derivative event is a negative signal inflection that does not necessarily cross zero volts. These events are instances of zero slope (the midpoint of the inflection is lower than the two endpoints) in the wave. A time-in-frequency counter is incremented, as in the zero-cross analyses, and percentage oftime for each first-derivative frequency category is determined at the end of every 30-second sampling epoch. ach percent time-in-frequency adds up to 100 for full-wave zero-crosses, half-wave zero-crosses, and first-derivative events. A given 30-second epoch, for example, might be comprised of80% beta and 20% alpha first-derivative, with 0% in other frequency bands. The zero-cross measures could be 40% delta, 20% theta, 30% alpha and 10% sigma, with 0% beta zero-cross. n an average 8-hour night, 21 G measures are generated (5 of each first-derivative, halfwave zero-cross, full-wave zero-cross, power, and 1 total power) in every 1 of the 960 epochs. Visual stage-scoring was conducted according to standard criteria (20) by personnel trained at better than 90% agreement on a epoch-by-epoch basis. The stage scores were then used to categorize the periodanalyzed data. Data were inspected visually to remove all epochs with MG or OG artifact and periodic awakening. Means and standard deviations were computed for each of the 20 P AA measures described above (3 measures,s frequency bands) in rapid eye movement (RM), stage 1, stage 2, stage 3 and stage 4 sleep. Too few epochs of wakefulness were obtained to be included in analysis. Data were then coded for sex (between-group variable), and split-plot analysis of variance (ANOV A) was computed separately for the three dependent measures: half-wave zero-cross, firstderivative and power. n each ANOV A, frequency band (beta-delta) and stage of sleep (RM, stages 1-4) were treated as five-level repeated measures. Hemisphere (C3 or C4) was treated as a two-level repeated measure. Univariate analyses were only computed if overall analysis showed a significant sleep stage main effect or interaction. All statistical analyses were conducted using SASTM (Cary, NC) routines. Half-wave zero-cross RSULTS The overall ANOV A indicated a significant frequency band by sleep stage interaction (F = , df = 16, 320, p < ). Figure 1 illustrates the dis- Sleep. Vol. 18. No

3 336 R.ARMTAG HALF-WAV ZRO-CROSS BY STAG % 80~ ~ T M 60 SLP STAG D RM N F R Q U N C Y o BTA SGMA ALPHA THTA DLTA FRQUNCY BAND ~ 1 1«1 2 _ 3 4 N=22 FG.. Average half-wave zero-cross PAA measures for all frequency bands by sleep stage. Values are collapsed across males and females. tribution ofg frequencies across sleep stages. Note that delta half-wave zero-cross (DHZ) was maximal in stages 3 and 4, as expected, accounting for 60 and 70% of all time-in-frequency. Frequency distributions in RM sleep, however, were somewhat unexpected. More DHZ activity was found in RM sleep than in stage 1. Beta half-wave zerocross (BHZ) was lowest in stages 3 and 4 and highest in stage 1 sleep. To further investigate this effect, simple main effects and interactions were compared independently for each sleep stage. Stage 2 sleep was characterized by low amounts of beta, and alpha, and surprisingly, low sigma incidence. DHZ and theta halfwave zero-cross (THZ) accounted for approximately 45 and 26% of time-in-frequency, respectively. Frequency band main effects were obtained for all sleep stages. The smallest F ratio was obtained for stage 1 sleep (F = 147.2, df = 4, 80, p < 0.000). Stage 2 sleep also showed a significant frequency band by hemisphere interaction (F = 4.40, df = 4, 80, p < 0.003) that was not significant when a Bonferroni correction factor was applied. Significant frequency band by hemisphere interactions were also obtained for stages 3 and 4 sleep (F= 5.52,4.10, respectively, df= 4,80, p < 0.005), although the effect size was very small. Stage 3 sleep also showed a significant frequency band by sex by hemisphere interaction (F = 7.50, df = 4, 80, p < 0.000). However, none of the univariate analyses revealed significant main effects or interaction for sex. The sex interaction was only inherent in the repeated measure. Thus, the effect is attributable to slight differences in the distribution of G activity with sleep stages. This interaction is shown in Table 1. From examining the means in Table 1, it can be seen that males showed less DHZ than females in stages 1, 2 and RM sleep. Males also showed slightly more Sleep, Vol. 18, No.5, 1995

4 SXDFFRNCSNSLPG 337 TABL 1. Means and standard deviations (SD) for period-analyzed data with significant frequency by sex by hemisphere interactions Sleep stage RM Variables Total epochs, male 4,236 6,188 4,290 2,980 7,168 Total epochs, female 2,673 6,114 4,836 3,660 7,316 Beta half-wave zero-cross Male MeanU SDL MeanR SDR Female MeanL SDL MeanR SDR Delta half-wave zero-cross Male MeanL SDL MeanR SDR Female MeanL SDL MeanR SDR Sleep stages 1-4 and RM are represented in columns. Total average epochs used in analysis are listed below stages. a Mean L represents the average PAA value from C3 recordings; SD from C3 and C4 recordings are identified as SD Land SD R, respectively; Mean R denotes C4 average values. BHZ than females, overall. Females, on the other hand, showed more BHZ in the right than in the left hemisphere in all stages except 4. Males showed more left than right hemisphere BHZ, except in RM sleep. These differences likely account for the statistically significant, but very small, three-way interaction. First-derivative Overall ANOV A revealed a significant frequency band by sleep stage interaction (F = 70.58, df = 16,320, p < ). The distribution of G frequencies in each sleep stage is illustrated in Fig. 2. More beta firstderivative (BFD) was observed in stage 1 sleep than in any other stage. n contrast to the half- and fullwave zero-cross analyses, more theta first-derivative (TFD) was found in slow-wave sleep (SWS) than in RM. As expected, little delta first-derivative (DFD) was obtained in any sleep stage, owing to the bias of first-derivative analyses to detect faster-frequency activity. Alpha and sigma first-derivative (AFD) did not discriminate between sleep stages. Simple main effects were compared by ANOV A, separately for each sleep stage. All stages showed significant frequency band main effects, with the smallest F ratio again found for stage 1 sleep (F = 79.7, df = 4,80, p < 0.000). No interactions with sex or hemisphere were found. Power Overall ANOV A revealed a significant frequency band by sleep stage interaction (F = , df = 16,320, p < ). This interaction is shown in Fig. 3. As expected, delta power was highest in SWS and lowest in stage 1 sleep. Stage 1 sleep was also characterized by lowest theta power (TP), although the distribution of TP was roughly equivalent across sleep stages. RM sleep showed more power in the theta band than in any other frequency. nterestingly, stage 2 sleep also showed more power in theta than in any other frequency, and more TP than RM sleep. Relatively low power was observed for both sigma and beta frequencies in all stages. Beta power was lowest in SWS and highest in RM and stage 1 sleep. Slightly more sigma was found in stages 1 and 2 sleep, in contrast with other stages. Delta, theta and alpha power Sleep. Vol. 18. No

5 338 R.ARMTAG FRST DRVATV BY STAG % 80~ ~ T M N F R Q U N C Y BTA SGMA ALPHA THTA DLTA FRQUNCY BAND SLP STAG D ~ 1 ~.'...'..'. L:1id 2 _ 3 RM 4 N=22 FG. 2. Average full-wave first-derivative period-amplitude analysis measures for all frequency bands by sleep stage. Values are collapsed across males and females. were higher among women in all sleep stages, although these effects did not reach statistical significance. A comparison of simple main effects, with ANOV A, of each sleep stage revealed only a main effect for frequency band, with the smallest F ratio for RM sleep (F= 99.7, df= 4, 80, p < 0.000). No sex or hemisphere interactions were obtained. Several authors have noted that categorizing quantitative G by sleep stage may not adequately capture temporal variation in G activity (2,11,12,17,21). Thus, a global NRM and RM delta measure was computed to evaluate gender differences in delta activity more fully. Delta power was averaged for all artifact-free stage 2, 3 and 4 sleep times and was significantly higher in women (F = 6.24, df = 1,20, p < 0.02), as previously reported (17). Global delta power in all RM time failed to differentiate men from women (F = 1.50, df = 1,20, p > 0.05). Gender differences DSCUSSON To summarize, little evidence of gender differences was found in the distribution of period-analyzed G during sleep. Frequency band by hemisphere by sex interactions were obtained for full-wave and half-wave zero-cross, indicating significant, but small, gender differences. ffect sizes were extremely small, and it appeared that a slight hemispheric shift in beta activity accounted for the significant effect. Such a finding requires replication. As reported previously (17), females tended to show Sleep, Vol. 18, No.5, 1995

6 SXDFFRNCSNSLPG 339 POWR BY STAG 1400 A M P L T U D ()JV2) SLP STAG CJ RM ~ 1 CJ 2 _ BTA SGMA ALPHA THTA DLTA FRQUNCY BAND N=22 FG. 3. Average power (amplitude measured in /LV2), derived from the half-wave zero-cross measures. Values are collapsed across males and females. higher amplitude delta activity in all sleep stages, but these effects failed to reach significance. The global delta power measure in NRM sleep was significantly higher among women, whereas delta power in RM sleep did not differentiate men and women. These data are not inconsistent with the conclusion of Dijk et al. (17) that sex differences in G may be due to differences in skull size and thickness. The influence of contraceptives cannot be ruled out as a contributing factor in either study. All of the females in the present study were either taking oral contraceptives or had a contraceptive implant. Perhaps sex differences may be more evident in comparing males to females with normal hormonal variation across the menstrual cycle. Nevertheless, these data suggest that gender plays little role in moderating the distribution of G frequencies within sleep, with perhaps the exception of stage-independent delta amplitude. Distributiou of G frequeucies across sleep stages Frequency-band main effects were obtained for all PAA measures within each sleep stage, indicating significantly different frequency characteristics across stages. Perhaps surprisingly, RM sleep was characterized by higher incidence of delta and theta than any other half-wave zero-cross frequency. Stage 1 sleep was not strikingly different from RM sleep, with the exception of greater delta incidence in RM. Stage 2 sleep was characterized by more delta but less beta than either RM or stage 1 sleep. Delta incidence was, as expected, highest in SWS. Beta incidence was lowest in SWS. These findings are in general agreement with those of Hoffmann et al. (22), with some notable differences, namely reduced sigma and alpha activity in all sleep stages. Sleep, Vol. 18, No.5, 1995

7 340 R.ARMTAG Several findings in this study appear surprising. Sigma activity, for example, is roughly equivalent in stage 1 and 2 sleep, although one might expect elevated sigma in stage 2 sleep because of the presence of spindle activity. Spindles, however, make up a small portion of the total sigma bandwidth and are of a relatively short duration. As such, they are not likely to produce a large increase in time-in-frequency estimates. Moreover, the sigma values obtained in this study have the same distribution across sleep and are equivalent to those values reported by Hoffmann et al. (22). Additionally, theta zero-cross measures are higher in RM than in SWS, as expected from classic sleep stage descriptions (20). TFD, however, is higher in stage 3 and 4 than in RM sleep. t is possible that the first-derivative measure detected theta activity was superimposed on a background of delta in SWS, events that may not be evident visually. Alternatively, because more fast-frequency activity is prominent in RM sleep, and because first-derivative analyses are biased to the fastest frequency that can be detected, the algorithm may miss theta events in RM. t is also possible that the theta events in RM sleep represent background G activity and are more likely to be detected by the zero-cross measures. Regardless, the theta values and distributions within sleep stages reported here are very similar to those described previously (22). The amplitude and the incidence ofg frequencies within sleep stages showed small differences. THZ was lowest in SWS, whereas theta power was highest in stage 2 and 3 sleep. This difference suggests that whereas the incidence of theta is lower in SWS, the amplitude of theta activity is relatively high. The delta power measures also seem to provide a good discriminator of sleep stage, maximizing differences between NRM stages. The pattern of decreased delta power from stage 4 to stage 1 sleep was inherent in both measures. As noted previously, delta amplitude and incidence were higher in RM than in stage 1 sleep. Overall, power measures seemed to provide better discriminations between NRM sleep stages than did the incidence measures. First-derivative measures are, in general, more sensitive to fast-frequency activity than are zero-cross measures (2,12). n this study, more beta activity was detected by the first-derivative measure, although the frequency distribution across sleep stages was equivalent in both analyses. First-derivative analysis also detected more alpha activity in all sleep stages than did the zero-cross analyses. Because slow-frequency events usually cross zero volts, it was not surprising that the first-derivative analysis produced little delta activity. n a recent study, Uchida et al. (23) showed that beta and delta activity are out-of-phase, as evidenced by negative correlations between these two frequency bands. They cited the report of Hoffmann et al. (22) of lowest BHZ and BFD in SWS in support of their findings. n the present study, low BHZ was obtained in SWS in addition to stage 1 sleep. The reader should be cautioned against using such findings as support for a phase relationship between G frequency bands, however, because mean measures are not sensitive to temporal changes. Cross-spectral analysis of periodicity and phase relationships between beta and delta would be a more appropriate assessment. Nevertheless, the findings of this study are in agreement with the earlier report by Hoffmann et al. (22); this is not particularly surprising because the P AA algorithms used were virtually identical. The reliability and validity of PAA algorithms in quantifying sleep G has recently been questioned (14,24). Geering et al. (25) and colleagues have suggested that P AA can produce spurious frequency classification, especially in faster frequency bands. Because the frequency of a given wave is defined differently for PSA and PAA, it is not surprising that some events would be differentially categorized by the two techniques, particularly if single Hz band-widths are used (26,27; Ktonas, personal communication). Feinberg et al. have demonstrated substantial overlap between PSA and P AA, especially among slower frequency bands. Further, Pigeau et al. (15) have shown that the shared variance between the two techniques is greater than 80%. Moreover, if PAA misquantified or overestimated that amount offast-frequency activity, the lowest correlation between PSA and PAA would be expected in the beta band. Pigeau et al. (15) reported zero-order correlations >0.55 between PSA and all PAA measures of beta in sleeping subjects. With the exception of theta, the amplitude measures showed strong positive correlations between the two techniques, in contrast to the suggestion of Geering et al. (24,25). The high correspondence of PAA and PSA data was recently confirmed in a study of all-night G in 40 healthy young adults (28). This study (28) followed data acquisition procedures identical to those outlined in the present paper. Moreover, Armitage et al. (28) did not utilize secondary bandpass filtering, but they demonstrated high comparability (88% shared variance) between PSA and PAA. These data, coupled with those reported here, suggest that PAA adequately describes within- and between-stage variation in G activity during sleep without secondary bandpass filtering. Within sleep stages, however, PAA data provide little evidence of gender differences in G frequency distributions. Overall, the variability of all of the P AA measures was relatively low, indicating good stability within sleep stages in normal individuals, suggesting that PAA pro- Sleep. Vol. 18. No.5, 1995

8 SX DFFRNCS N SLP G 341 vides a relatively stable description ofg characteristics across sleep stages. As many authors have pointed out, visual sleep-stage scoring algorithms result in a tremendous loss of information and imply a discontinuity among sleep stages that may not be accurate (2,12,16,29). One advantage of computer analysis is the potential to quantify variability and stability of G activity within sleep stages (22). t should be noted, however, that the distribution of stage-defined G frequencies may not adequately capture temporal variation in G activity, as noted by many researchers (2,11,12,17,21). Moreover, it is somewhat circular to define stage on the basis of visually discernible G characteristics and to then quantify the G frequencies that describe each sleep stage. Within-stage G characteristics provide a bridge to visual stage-scoring strategies, although stage-independent G frequency analyses provide a more complete description of sleep microarchitecture. Sex differences may also be more evident in global power measures ofnrm sleep. For example, in the present study, females did show higher delta amplitude than males when amplitude was collapsed across NRM stages, as previously noted (17). Nevertheless, the gender effects in normal individuals were not striking, regardless of the method of analysis. Acknowledgements: This work was supported in part by NMH and am indebted to the Mental Health Clinical Research Center, under the direction of A. John Rush, M.D., for providing psychiatric evaluations of normal controls individuals, to Paula Pechacek and Gerardo Zambrano for data tabulation and to Doris Burton for secretarial support. Administrative support was provided by Kenneth Z. Altshuler, M.D., (Chair) Department of Psychiatry. RFRNCS. Gaillard J, Tissot R. Principles of automatic analysis of sleep records with a hybrid system. Comput Biomed Res 1973;6: Hoffmann R, Moffitt A, Wells R, Sussman P, Pigeau R, Shearer J. Quantitative description of sleep stage electrophysiology using digital period analytic techniques. Sleep 1984;7: Smith JR, Karacan, Yang M. Automated analysis of the human sleep G. Waking and Sleeping 1978;2: Kuwahara H, Higashi H, Mizuki Y, Matsunari S, Tanaka M, nanaga K. Automatic real-time analysis of human sleep stages by an interval histogram method. lectroencephalogr Clin Neurophysio1988;70: Armitage R, RoflWarg HP. Distribution of period-analyzed delta activity during sleep. Sleep 1992;15: Feinberg. Across-night changes in the G characteristics of human sleep. Sleep Res 1975;4: Feinberg, March JD, Fein G, floyd TC, Walker JM, Price L. Period and amplitude analysis of c/sec activity in sleep of young adults. lectroencephalogr Clin Neurophysio1978;44: Feinberg, floyd TC. Systematic trends across the night in human sleep cycles. Psychophysiology 1979; 16: Feinberg, Fein G, floyd TC. Period and amplitude analysis ofnrm G in sleep: repeatability of results in young adults. lectroencephalogr Clin Neurophysiol 1980;48: Feinberg, Baker T, Leder R, March JD. Response of delta (0-3 Hz) G and eye movement density to a night with 100 minutes of sleep. Sleep 1988;11: Borbely A, Baumann F, Brandeis D, Strauch, Lehmann D. Sleep deprivation: effects on sleep stages and G power density in man. lectroencephalogr Clin Neurophysiol1981 ;51 : Armitage R, RoflWarg HP, Rush AJ, Calhoun S, Purdy DG: Digital period analysis of sleep G in depression. Bioi Psychiatry 1992;32: Feinberg, floyd TC, March JD. ffects of sleep loss on delta (0.3-3 Hz) G and eye movement density: new observations and hypotheses. lectroencephalogr Clin Neurophysiol1981 ;67: Johnson L, Lubin P, Naitoh P, Nute C, Austin M. Spectral analysis ofthe G of dominant and non-dominant alpha subjects during waking and sleeping. Clin Neurophysiol 1969;26: Pigeau R, Hoffmann R, Moffitt A. A multi-variate comparison of two G analysis techniques: period analysis and fast Fourier transforms. lectroencephalogr Clin Neurophysiol 1981;52: Feinberg. ffects of maturation and aging on slow-wave sleep in man. n: Wauquier A, Dugovic C, Radulovacki A, eds. Slow wave sleep: physiological pathophysiological and functional aspects. New York: Raven Press, 1989: Dijk D-J, Beersma DGM, Bloem GM. Sex differences in the sleep G of young adults: visual scoring and spectral analysis. Sleep 1989;12: Spitzer RL, Williams JBS, Gibbons M. The structured clinical interview for DSM--R (SCD). New York: New York State Psychiatric Research nstitute, Lehmann D, Michel CM. ntracerebral dipole source localization for FFT power maps. lectroencephalogr Clin Neurophysiol 1990;76: Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Washington, DC: U.S. Government Printing Office, Feinberg. Period/amplitude measures of delta show robust declines across nonrapid eye movement sleep episodes: a comment on Armitage and RoflWarg. Sleep 1993;16: Hoffmann RF, Moffitt AR, Shearer JC, Sussman PS, Wells RB. Conceptual and methodological considerations towards the development of computer-controlled research on the electrophysiology of sleep. Waking and Sleeping 1979;3: Uchida S, Malony T, Feinberg. Beta (20-28 Hz) and delta (0.3-3 Hz) Gs oscillating reciprocally across NRM and RM sleep. Sleep 1992;15: Geering B, ggimann F, Borbely A. Period-amplitude analysis of the sleep G: methodological considerations. J Sleep Res 1992; 1, Suppl, Geering B, Achermann P, ggimann F, Borbely A. Period-amplitude analysis and power spectral analysis: a comparison based on all-night sleep G recordings. J Sleep Res 1993;2: Ktonas P, Gosalia A. Spectral analysis vs period-amplitude analysis of narrowband G activity: a comparison based on the sleep delta-frequency band. Sleep 1981;4: Ktonas P. Period-amplitude G analysis (editorial comment). Sleep 1987;10: Armitage R, Hoffmann R, Fitch T, Morel C, Bonato R. A comparison of period amplitude and power spectral analysis of sleep G in normal and depressed outpatients. Psychiatry Res, 1995 (in press). 29. Armitage R, Hoffmann R, Loewy D, Moffitt A. Variations in period-analysed G asymmetry in RM and NRM sleep. Psychophysiology 1989;26: Sleep, Vol. 18, No.5, 1995