Characteristics of Sleep Slow Waves in Children and Adolescents

SLEEP SLOW WAVES IN CHILDREN AND ADOLESCENTS

Characteristics of Sleep Slow Waves in Children and Adolescents Salomé Kurth, MS1,2; Oskar G. Jenni, MD1,2; Brady A. Riedner, BS3; Giulio Tononi, MD, PhD3; Mary A. Carskadon, PhD4; Reto Huber, PhD1,2 Child Development Center, University Children’s Hospital Zurich, Switzerland; 2Pediatric Sleep Disorders Center, University Children’s Hospital Zurich, Switzerland; 3Psychiatry Department,University of Wisconsin, Madison, WI; 4E. P. Bradley Hospital Chronobiology and Sleep Research Laboratory, Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI 1

Study Objectives: Slow waves, a major electrophysiological characteristic of non-rapid eye movement sleep, undergo prominent changes across puberty. This study provides a detailed description of sleep slow waves of prepubertal children and mature adolescents to better understand the mechanisms underlying the decrease of activity in the slow-wave frequency range across puberty. Design: All-night sleep electroencephalographic recordings were performed for baseline and after sleep deprivation. Setting: N/A. Participants: Eight prepubertal children (Tanner 1/2, 11.9 ± 0.8 years, 3 boys) and 6 mature adolescents (Tanner 4/5, 14.3 ± 1.4 years, 3 boys). Interventions: Thirty-six hours of sleep deprivation. Measurements and Results: Both during baseline and after sleep deprivation, a steeper slope of slow waves was observed in prepubertal children (351.0 ± 49.5 µV/s), compared with mature adolescents (215.0 ± 27.2 µV/s, P < 0.05; mean of first 5 NREM sleep episodes from baseline), even accounting for overall amplitude differences. Conclusions: Based on a recent thalamocortical computer model, these findings may indicate a greater synaptic strength of neurons involved in the generation of sleep slow waves in prepubertal children, compared with mature adolescents. Such increased synaptic strength may be due to greater density or greater efficacy of cortical synapses or both. Keywords: Sleep, adolescent development, synaptic strength, slope of slow waves, slow-wave activity Citation: Kurth S; Jenni OG; Riedner BA; Tononi G; Carskadon MA; Huber R. Characteristics of sleep slow waves in children and adolescents. SLEEP 2010;33(4):475-480.

THE MOST PROMINENT ELECTROENCEPHALOGRAPHIC (EEG) CHARACTERISTIC OF DEEP NON-RAPID EYE MOVEMENT (NREM) SLEEP IS SLOW WAVES. THE neuronal correlate of slow waves is the slow oscillation first described in detail by Steriade and coworkers, who showed that membrane potentials of cortical neurons alternate about every second between a depolarized upstate and a hyperpolarized downstate during slow oscillations.1 When these oscillations are near synchronous and involve the majority of the cortical neurons in a given region, they become visible in the surface EEG as slow waves of large amplitude. The alternation of upstates and downstates in cortical neurons is thought to be involved in memory consolidation,2,3 synaptic homeostasis,4 and the recuperative function of sleep.5 Slow waves in the sleep EEG undergo dramatic changes during brain maturation. As early as the 1980s, Feinberg and colleagues reported that the amplitude of slow waves increases until shortly before puberty and then shows a prominent decrease across adolescence.6 Sleep slow-wave activity (SWA, EEG spectral power between 0.5 and 4.5 Hz), a quantitative measure of the activity in the slow-wave frequency range reflecting the homeostatic regulation of sleep,5 follows a similar time course. SWA sharply declines across puberty,7-9 followed by a smaller decrease during the twenties.6,10

The brain undergoes striking morphologic and functional changes during early human development.11-13 For example, Huttenlocher and Dabholkar11 reported that maximum cortical synapse density is achieved shortly before puberty. Furthermore, the course of adolescence is accompanied by a reduction in density of synapses in the grey matter, a process termed pruning.14-17 Longitudinal neuroimaging findings of cortical maturation corroborate these findings and reveal concomitant changes in grey matter. For instance, grey-matter volume has been shown to be greatest in frontal regions of the cortex around early puberty and to decrease thereafter.18,19 With the exception of the temporal lobe, the majority of the other cortical areas show a similar time course.18 In view of the similar time course of cortical synapse density and the amplitude of sleep slow waves, some authors have proposed a link between the two.6,8,9 In fact, recent computer simulations of the thalamocortical system have provided evidence for an association of the number and strength of synapses involved in the generation of sleep slow waves and their amplitude.20 In this simulation, the morphology of sleep slow waves changed as a function of the level of synchronization of cortical neurons. The synchronization level, on the other hand, depended on synaptic strength and synaptic density. Thus, the stronger and denser synapses are on neurons of a certain population, the faster those neurons can synchronize their activity; the faster they show synchronized activity, the steeper is the resulting potential change within that population. It is those potential changes that are measured by EEG recordings.21 Therefore, this computer model implied that the slope of slow waves is a good measure of the synchronization level of the neurons in the cortex, i.e., the slope of slow waves may represent a direct electrophysiological measure of changes in synaptic strength or density.20

Submitted for publication May, 2009 Submitted in final revised form July, 2009 Accepted for publication July, 2009 Address correspondence to: Reto Huber, PhD, Pediatric Sleep Disorders Center, University Children’s Hospital Zurich, Steinwiesstrasse 75, CH – 8032 Zürich, Switzerland; Tel: 0041 44 266 8160; Fax: 0041 44 266 7866; Email: [email protected] SLEEP, Vol. 33, No. 4, 2010

475

Sleep Slow-Waves in Children and Adolescents—Kurth et al

provided every 2 hours. The research team provided 1-to-1 observation of and interaction with the children during the extended waking procedure, during which wakefulness was enabled through planned games and activities. Although participants became tired and sleepy during the 36 hours of wakefulness, none experienced any adverse response. EEG Recordings During each night, EEG (referential derivations, C3/A2, C4/ A1, O2/A1, O1/A2), electrooculogram (right and left outer canthus), electromyogram (mentalis, submentalis), and electrocardiogram were registered using the Grass System (Astromed, Grass, West Warwick, RI). The signals were sampled at 100 Hz and analogue filtered (high-pass, -6 dB at 0.3 Hz; low-pass -6 dB at 35 Hz). The current analysis was based on EEG data of the C3/A2 derivation. Since the O2/A1 derivation revealed the same results as C3/A2, only data for the latter are presented. Artifacts were visually excluded, and sleep stages were scored in 30-second epochs according to the standard criteria.24 NREM sleep episodes were also defined according to standard criteria,24,25 which were adapted according to Jenni and Carskadon7 because of the frequent occurrence of a “skipped” rapid eye movement (REM) sleep episode after the first NREM sleep episode (see Table 1). The first NREM sleep episode was subdivided at the lowest SWA if it exceeded 240 epochs and if slow-wave sleep (SWS, stage 3 and 4) was interrupted for at least 12 continuous minutes of stage 1 or 2 sleep, wakefulness, or movement time. Only sleep episodes containing a minimum of 50 epochs of NREM sleep were included in the episode comparisons (for more details about methods, see7).

Figure 1—Schematic representation of the slope of slow waves. The slope was defined as the amplitude of the most negative peak (a) divided by the time until the next zero crossing (t).

On the basis of this observation, we hypothesized that the higher slow-wave amplitude in prepubertal children, compared with mature adolescents, results from higher synaptic strength or density, which should also be seen as a steeper slope of sleep slow waves. Therefore, we compare the characteristics of sleep slow waves in prepubertal children and mature adolescents. With this study, we propose the analysis of slow-wave characteristics as a novel approach to assessing developmental differences in overall cortical structure with possible functional associations. METHODS

Data Analysis We used the same signal-processing procedures and slowwave detection criteria as described by Riedner and coworkers.26 In short, after applying a low-pass filter at 30 Hz, the signal was rereferenced to the mastoid and band-pass filtered (0.5-4.0 Hz, stopband 0.1 and 10 Hz). Sleep slow waves were identified as negative deflections of the EEG signal (C3/A2) between 2 zero-crossings. Only waves of any amplitude but with consecutive zero crossings separated by 0.25 and 1.0 seconds (within an artifact-free NREM sleep episode) were considered as slow waves. Analysis of wave characteristics included NREM stages 2, 3, and 4. All analyses are based on the first 5 NREM episodes, with the exception of sleep variables (all NREM sleep episodes included, Table 1). Artifact rejection did not differ between groups (in the first 5 NREM episodes, 3.7% of artifacts were excluded in prepubertal children and 5.2% in mature adolescents in baseline and 3.0% in prepubertal children and 4.2% in mature adolescents in recovery sleep). The characteristics of slow waves were delineated by the following parameters: The amplitude was defined as the most negative peak of the signal (defined as “a” in Figure 1). The incidence indicated the total number of slow waves of the first 5 NREM episodes. The slowwave slope was defined as the amplitude over time calculated by the straight line connecting the most negative peak of the signal with the following zero crossing (Figure 1).26 Both the ascending and descending slopes were calculated, but, because they led to similar results, only the ascending slope is reported in this manuscript.

Participants and Experimental Design We analyzed previously presented data from polysomnographic recordings from children and adolescents studied at the Bradley Hospital Sleep Lab between 1999 and 2002.7,22 The study protocols were approved by the Lifespan/Brown University Institutional Review Board for the Protection of Human Subjects, and the studies were performed according to the Declaration of Helsinki. A description of the study procedures was given to the participants and their parents. A parent signed informed consent, and the participants signed to give their assent. Participants received monetary compensation. Data from 8 prepubertal children (pubertal stages Tanner 1 or 2, age 11.9 ± 0.8 years, range 10.3-12.9 years, 3 boys) and 6 mature adolescents (pubertal stages Tanner 4 or 5, age 14.3 ± 1.4 years, range 11.8-15.9 years, 3 boys) were selected for the analysis. Pubertal status was determined using the standardized assessment scales of Tanner.23 The participants were asked to keep regular bedtimes (2200 to 0800) at home for 10 days before sleeping in the lab. During that period, no daytime naps were allowed. The children and adolescents were subjected to an extensive screening procedure about general health and sleep quality, which has been described earlier.22 All subjects underwent polysomnographic recordings, including a baseline night (10 hours in bed) followed by a 36-hour sleep-deprivation period and a recovery night (11 hours 40 minutes in bed). During sleep deprivation, the participants were sitting in bed in a semirecumbent posture in a dimly lit room; small meals were SLEEP, Vol. 33, No. 4, 2010

476


Table 1—Sleep variables Baseline Parameter Time in bed, min Total sleep time, min Sleep efficiency, % Sleep latency, min REM sleep latency, min WASO, min Sleep stage, % 1 2 SWS REM sleep NREM episode, min Arousals, no.

Within-group comparisons (P value)

Recovery

Tanner 1/2 610.9 ± 0.9 546.7 ± 11.6 89.5 ± 1.9 29.1 ± 2.2 107.7 ± 6.7 25.6 ± 10.7

Tanner 4/5 610.3 ± 1.0 516.0 ± 19.8 84.6 ± 3.3 25.8 ± 5.6 115.5 ± 7.0 50.8 ± 15.5

P value 0.694 0.183 0.194 0.555 0.443 0.192

Tanner 1/2 695.8 ± 11.5 667.3 ± 12.6 95.9 ± 0.3 13.0 ± 1.3 90.7 ± 5.0 2.4 ± 1.0

Tanner 4/5 709.1 ± 1.3 660.8 ± 9.2 93.2 ± 1.4 12.4 ± 1.2 104.4 ± 16.2 11.8 ± 6.5

P value 0.343 0.708 0.054 0.757 0.376 0.126

Tanner 1/2 0.017 0.001 0.003 0.074

Tanner 4/5 0.064 0.030 0.394 0.092

7.4 ± 1.3 30.1 ± 2.7 28.1 ± 2.8 23.9 ± 2.3 89.9 ± 5.1 2.9 ± 2.2

8.4 ± 1.4 36.2 ± 3.1 19.3 ± 1.7 20.6 ± 1.2 90.8 ± 2.9 6.2 ± 1.5

0.597 0.166 0.030 0.281 0.899 0.270

3.2 ± 0.6 32.5 ± 3.1 37.0 ± 3.6 23.1 ± 1.2 91.6 ± 3.0 0.3 ± 0.3

2.7 ± 0.4 36.8 ± 1.9 30.5 ± 3.1 23.3 ± 1.1 90.6 ± 4.0 1.8 ± 1.5

0.522 0.307 0.209 0.910 0.849 0.243

0.015 0.105 0.004 0.576 0.773 0.279

0.009 0.828 0.003 0.303 0.965 0.026

Data are presented as mean ± SEM for prepubertal children (n = 8; Tanner 1/2) and mature adolescents (n = 6; Tanner 4/5). For the analysis, all non-rapid eye movement (NREM) sleep episodes were included, except for NREM sleep-episode duration, in which only the first 5 NREM sleep episodes were considered. Group differences were compared using posthoc unpaired t test; within-group effects were compared using paired t test (both 2-tailed). P < 0.05 was considered statistically significant. Sleep efficiency is expressed as a percentage of time in bed; sleep latency, latency to the first occurrence of stage 2 sleep; rapid eye movement (REM) sleep latency to the first occurrence of REM sleep or to “skipped” REM sleep; sleep stages are expressed as a percentage of total sleep time. WASO refers to wake time after sleep onset; SWS, slow-wave sleep, i.e., stage 3 and 4. Arousals were defined as epochs of wakefulness after sleep onset. At least 1 epoch of stage 2, 3, 4 or REM sleep had to occur between 2 arousals. Different sleep allowance between baseline and recovery (see Methods) did not allow within-group comparison for time in bed and total sleep time. It must be pointed out that REM sleep latency is strongly affected by the variable occurrence of “skipped” first REM sleep episodes (Tanner 1/2: baseline n = 1, recovery n = 3; Tanner 4/5: baseline n = 3, recovery n = 2).

Analyses were performed with the software package MATLAB (The MathWorks, Inc., Natick, MA). Analyses of variance (ANOVA, 2- or 3-way) included the factors Group (Tanner 1/2, Tanner 4/5), Night (baseline or recovery condition), or Time (5 NREM sleep episodes), or a combination thereof. Paired or unpaired 2-tailed t tests were used to compare within- or between-group effects. The significance level was set at P < 0.05. Throughout the manuscript, data variability is given as standard errors.

showed a substantial decrease in the slope of slow waves across the sleep episode for the baseline and the recovery nights (Figure 3, statistical details are given in the legend). During baseline, however, this slope decrease across the night was greater in prepubertal children than in mature adolescents (absolute decrease from first to fifth NREM sleep episode: 329.5 ± 40.4 µV in prepubertal children; 144.1 ± 30.8µV in mature adolescents, P < 0.01 for both within-group comparisons; between-group difference P < 0.005). The amplitude of slow waves showed a similar time course as the slope (2-way ANOVA, factor Time P < 0.001, F = 51.4, df = 4; Group P < 0.001, F = 81.9, df = 1; interaction Time × Group P < 0.01, F = 3.7, df = 4; data not shown). Furthermore, the incidence of slow waves decreased in the course of the night in both prepubertal children and adolescents, although the groups did not differ significantly (2way ANOVA, factor Time P < 0.001, F = 27.1, df = 4; Group P = 0.06, F = 3.6, df = 1; interaction NS; data not shown).

RESULTS Prepubertal Children Exhibited a Steeper Slope than Did Mature Adolescents In a first step, we examined the macrostructure of sleep of the 2 groups. We found no difference in sleep variables except that the percentage of SWS was lower in mature adolescents (Table 1). We then applied the slow-wave detection algorithm to the EEG data of the first 5 NREM sleep episodes for the characterization of sleep slow waves. During baseline as well as recovery sleep, the slope of slow waves was lower in mature adolescents, as compared with prepubertal children (Figure 2, upper panel). Also, the amplitude of slow waves was smaller in mature adolescents than in prepubertal children (Figure 2, middle panel), whereas the average incidence of slow waves was similar in both groups (Figure 2, lower panel). We next assessed the time course of the slow-wave slope across the first 5 NREM sleep cycles of the night. Both groups SLEEP, Vol. 33, No. 4, 2010

Even When Accounting for Amplitude, the Slope of Sleep Slow Waves Was Greater in Prepubertal Children As expected, a close association was found between amplitude and slope: the higher the amplitude, the steeper the slope for both groups and all nights of sleep (Figure 4). Because overall amplitude was greater in the prepubertal group, we tested whether this relationship fully explains the differences in the slope of slow waves between prepubertal children and mature adolescents by performing a regression analysis between amplitude and slope of slow waves (a detailed description of the calculation is given in the legend of Figure 4). Pointwise com477


Figure 3—Time course of slow-wave slopes in prepubertal children (closed circles, Tanner 1/2, n = 8) and mature adolescents (open circles, Tanner 4/5, n = 6). Values represent the average slope of slow waves (± SEM) for consecutive non-rapid eye movement (NREM) sleep episodes. The slope decreased in the course of baseline and recovery sleep for both groups. During both baseline and recovery sleep, prepubertal children exhibited steeper slopes, compared with mature adolescents (3way analysis of variance, P < 0.001 [ F = 69.8, df = 1] for factor Group [prepubertal versus mature]; P < 0.001 [F = 46.7, df = 4] for factor Time [NREM sleep episode]; P < 0.05 [F = 6.7, df = 1] for factor Night [baseline versus recovery]; and P < 0.005 [F = 4.9, df = 4] for the interaction Group and Time). Group differences were assessed by posthoc t test, *P < 0.05, **P < 0.005.

mental groups. Sleep following sleep deprivation was characterized by enhanced sleep efficiency, and SWS, as well as by a reduction in sleep latency and stage 1 sleep for both groups (Table 1). REM sleep latency was reduced in prepubertal children after sleep deprivation. The number of arousals was significantly reduced in mature adolescents after sleep deprivation, whereas prepubertal children already exhibited a low level of arousals in the baseline, which was not further diminished in recovery sleep. The slope of slow waves also increased in both groups after sleep deprivation (Figure 2, right panels). In the first NREM sleep episode, the amount of slope increase was similar in mature adolescents and prepubertal children (mature adolescents, 96.4 ± 10.6 µV; prepubertal children, 90.2 ± 31.5 µV; P > 0.8; Figure 3). The amplitude of slow waves in the first NREM sleep episode was also increased after sleep deprivation in both groups (Figure 2) but did not differ significantly (adolescents, 13.8 ± 1.0 µV; prepubertal children, 5.9 ± 4.3 µV; P > 0.1). Also, the increased incidence of slow waves in the first NREM sleep episode did not differ significantly between the 2 groups (adolescents, 571.0 ± 665.9; prepubertal children, 49.1 ± 307.5; P > 0.5). Over the entire recording period, the average incidence of slow waves was enhanced after sleep deprivation in both groups (Figure 2, right panel).

Figure 2—Characteristics of slow waves in non-rapid eye movement (NREM) sleep (slope, amplitude, and incidence) in prepubertal children (black bars, Tanner 1/2, n = 8) and mature adolescents (white bars, Tanner 4/5, n = 6) are illustrated for baseline and recovery sleep for the first 5 NREM sleep episodes. A 2-way analysis of variance revealed a significant Group effect in slope (prepubertal children versus mature adolescents, P < 0.001, F = 23.2, df = 1), amplitude, (P < 0.001, F = 27.1, df = 1), and incidence (P < 0.05, F = 4.8, df = 1). Moreover, we found a significant Night effect (baseline versus recovery sleep) for the incidence of slow waves (P < 0.001, F = 25.2, df = 1) but no interaction between the factors Group and Night. Posthoc t tests were used to test group differences (*P < 0.01). Lines above the bars refer to significant withingroup differences after sleep deprivation (P < 0.05).

parisons of the slope of slow waves at chosen amplitudes differed between the 2 groups significantly between 40 and 130 µV (in steps of 10 µV). This analysis showed an increased slope of slow waves in prepubertal children, compared with mature adolescents, even accounting for the group difference in slowwave amplitude (Figure 4).

DISCUSSION The comparison of characteristics of sleep EEG slow waves in prepubertal children and mature adolescents revealed that (1) the slope of slow waves was steeper in prepubertal children than in mature adolescents, even when accounting for amplitude differences; (2) the slope and amplitude of slow waves decreased across the night of sleep in both groups; and (3) sleep deprivation resulted in a similar initial increase in the slope of slow waves for mature adolescents and prepubertal children.

Slow-wave Slope Responded Similarly to Sleep Deprivation in Mature Adolescents and in Prepubertal Children Sleep deprivation was performed to examine the differences in the homeostatic regulation of sleep between the 2 developSLEEP, Vol. 33, No. 4, 2010

478


The finding of EEG slow-wave amplitude group differences is in agreement with the well-known decrease of sleep EEG slowwaves amplitude across puberty6 and the lower SWA in mature adolescents, as compared with prepubertal children.7,22,27 A question remains, however, whether the age differences in other slow-wave characteristics are a simple reflection of differences in SWA, the classical electrophysiological marker of sleep propensity,5 or whether other neurophysiologic processes may be involved. EEG power is a function of the incidence and amplitude of brain waves. Given that we found no age-group difference in the incidence, the age-related difference in SWA (as shown in 2005 by Jenni and colleagues22) is likely primarily due to the amplitude difference of sleep slow waves. On the other hand, the amplitude difference may not account for all contributions of other facets of cortical function. For example, attributing all differences to EEG slow-wave amplitude likely underestimates the levels of synchronization of cortical assemblies. This view is exemplified by multipeak waves that occur in abundance toward the end of sleep in adult humans.26 The amplitude of these waves may not accurately represent the actual level of cortical synchronization. Thus, if there were only a single peak with identical power, amplitude and slope may not grow in the same manner. Indeed, our findings point to a level of independence of EEG sleep slow-wave amplitude and slope, wherein slope was significantly greater in the prepubertal group, even accounting for amplitude. Another rationale for the introduction of slow-wave slope as a sleep-analysis tool may be best demonstrated within the framework of the recent synaptic homeostasis hypothesis of sleep function.4 According to this hypothesis, sleep regulates cortical plasticity and also may actively support cortical processes in the developing brain through global synaptic depression, which some suggest is favored by sleep. Recent findings support the synaptic homeostasis hypothesis and suggest that the slope of slow waves in the sleep EEG is a good measure for synaptic strength.20,26,28 Our finding of a decrease of amplitude and slope of slow waves throughout sleep tends to support this concept. As indicated by the significant interaction between Group and Time, the decrease in slope and amplitude may also manifest age-related differences. Thus, when comparing the 2 developmental groups, we found a steeper slope and higher amplitude of slow waves in prepubertal children compared with mature adolescents. This age-related difference in the steepness of the slope of slow waves supports the proposed relationship between the age-programmed synaptic pruning and the decline in slow-wave amplitude.6,8 Thus, the elimination of synapses across puberty decreases cortical connectivity, which then results in a reduction of the synchronized oscillations of neuronal populations. As a consequence of the lower simultaneous firing, slow waves in the cortical EEG may show smaller amplitudes, as before the pruning process. The reduced slope of slow waves observed in mature adolescents may therefore reflect a reduced rate of neuronal recruitment due to lower synaptic strength or density.20 A number of maturational brain changes support the generally lower synaptic strength or density in mature adolescents. Most directly, synaptic density is reduced after puberty.14 Fewer synapses may need less space and energy, which may be reflected in reduced cortical thickness29 and lower cortical metabolic rate.30 It is noteworthy that age-related changes in SLEEP, Vol. 33, No. 4, 2010

Figure 4—Regression of slope and amplitude of slow waves in prepubertal children (black symbols, R2 = 0.97, P < 0.001, n = 8) and mature adolescents (white symbols, R2 = 0.96, P < 0.001, n = 6). For each participant and each night, we divided the total number of slow waves of the first non-rapid eye movement (NREM) sleep episode into 5 equal percentiles arranged according to their amplitude. We then calculated the average slope for all slow waves for each amplitude percentile. In the graph, group means of the slope for each amplitude percentile for baseline (BL, circles) and recovery (REC, triangles) are presented. In addition, the regression between these amplitude and slope values is included as dashed lines. For a statistical comparison, the same regression analysis was repeated for each subject, and the regression coefficients slope and intercept were compared with unpaired t tests. The regression coefficient intercept tended to be larger in prepubertal children than in mature adolescents (prepubertal children, 150.4 ± 15.7 µV; mature adolescents, 104.6 ± 14.6 µV; P = 0.06, unpaired t test) and the slope did not differ between the groups (prepubertal children, 4.2 ± 0.3 µV/s; mature adolescents, 3.7 ± 0.2 µV/s; P > 0.1). The regression coefficients were also used to compare the slope of slow waves at chosen amplitudes between the 2 groups. At all amplitudes ranging from 40 to 130 µV (encompassing data range of both groups), the slopes of slow waves were steeper in prepubertal children compared with mature adolescents (P < 0.05, unpaired t test, as indicated by an asterisk). For example, at an average amplitude of 100 µV, prepubertal children showed a slope of 571.6 µV/s, and mature adolescents of 473.5 µV/s.

synaptic strength or density not only should result in changes of the sleep EEG in the SWA frequency range, but may also explain the age-related amplitude changes of oscillations in other frequency ranges and other vigilance states observed.10,31,32 Here, we also report effects of sleep deprivation on slowwave slope in subjects before and after puberty. Sleep deprivation resulted in similar increases in the slope, amplitude, and incidence of slow waves in mature adolescents and prepubertal children. This is in contrast with the study by Jenni et al.,22 showing a more pronounced homeostatic response to sleep deprivation, i.e., a larger increase in SWA, in mature adolescents compared with prepubertal children. Those data were, however, presented in relative values. If our data are presented in the same way, we observe a similar age-related difference in the response to sleep deprivation for the slope of slow waves (mature adolescents, 130.6% ± 2.4%; prepubertal children, 116.5% ± 5.2%; P < 0.05). The lack of age-related difference in the increase of the slope of slow waves after sleep deprivation, if presented in absolute values, may indicate a similar build-up of synaptic 479


strength during wakefulness for prepubertal children and mature adolescents. We conclude that the slope of slow waves may be used as an indicator of synaptic strength or density, independent of slowwave incidence and amplitude. Thus, possible implications of the present study may be that, in clinical populations in which the level of sleep pressure and presumably synaptic strength is not properly reflected by the level of sleep SWA, the slope of slow waves may provide a useful alternative measure because of its independence of amplitude and incidence. This may be of particular importance because SWA is composed of amplitude and incidence of slow waves, and its expression depends on the continuity of NREM sleep.33 Therefore, sleep fragmentation, as for example found in patients suffering from restless legs syndrome34 or with narcolepsy-cataplexy,33,35 may prevent the full expression of SWA. Under these circumstances, the analysis of the slow-wave characteristics may provide additional information. Thus, in the future, the slope of slow waves may serve as a clinical tool, allowing the recognition of abnormalities of brain functioning and maturation with a minimally invasive method. Finally, the application of this novel analysis tool to EEG recordings with improved spatial resolution, i.e., high-density EEG recordings, may uncover local aspects of cortical maturation, as for example found in magnetic resonance images.13,18,19

9. Campbell IG, Feinberg I. Longitudinal trajectories of non-rapid eye movement delta and theta EEG as indicators of adolescent brain maturation. Proc Natl Acad Sci USA 2009;106:5177-80. 10. Gaudreau H, Carrier J, Montplaisir J. Age-related modifications of NREM sleep EEG: from childhood to middle age. J Sleep Res 2001;10:165-72. 11. Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol 1997;387:167-78. 12. Shaw P. Intelligence and the developing human brain. Bioessays 2007;29:962-73. 13. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW. Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci 2004;24:8223-31. 14. Huttenlocher PR. Synaptic density in human frontal cortex—developmental changes and effects of aging. Brain Res 1979;163:195-205. 15. Huttenlocher PR, de Courten C, Garey LJ, Van der Loos H. Synaptogenesis in human visual cortex--evidence for synapse elimination during normal development. Neurosci Lett 1982;33:247-52. 16. Rakic P, Bourgeois JP, Eckenhoff MF, Zecevic N, Goldmanrakic PS. Concurrent overproduction of synapses in diverse regions of the primate cerebral-cortex. Science 1986;232:232-5. 17. Zuo Y, Lin A, Chang P, Gan WB. Development of long-term dendritic spine stability in diverse regions of cerebral cortex. Neuron 2005;46:181-9. 18. Giedd JN. Structural magnetic resonance imaging of the adolescent brain. Ann NY Acad Sci 2004;1021:77-85. 19. Giedd JN, Blumenthal J, Jeffries NO, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 1999;2:861-3. 20. Esser SK, Hill SL, Tononi G. Sleep homeostasis and cortical synchronization: I. Modeling the effects of synaptic strength on sleep slow waves. Sleep 2007;30:1617-30. 21. Lopes da Silva F. Functional localization of brain sources using EEG and/ or MEG data: volume conductor and source models. Magn Reson Imaging 2004;22:1533-8. 22. Jenni OG, Achermann P, Carskadon MA. Homeostatic sleep regulation in adolescents. Sleep 2005;28:1446-54. 23. Tanner JM. Growth at Adolescence. Oxford: Blackwell 1962. 24. Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Washington, DC: US Public Health Service, US Government Printing Office; 1968. 25. Feinberg I, Floyd TC. Systematic trends across the night in human sleep cycles. Psychophysiology 1979;16:283-91. 26. Riedner BA, Vyazovskiy VV, Huber R, et al. Sleep homeostasis and cortical synchronization: III. A high-density EEG study of sleep slow waves in humans. Sleep 2007;30:1643-57. 27. Campbell IG, Higgins LM, Trinidad JM, Richardson P, Feinberg I. The increase in longitudinally measured sleepiness across adolescence is related to the maturational decline in low-frequency EEG power. Sleep 2007;30:1677-87. 28. Vyazovskiy VV, Riedner BA, Cirelli C, Tononi G. Sleep homeostasis and cortical synchronization: II. A local field potential study of sleep slow waves in the rat. Sleep 2007;30:1631-42. 29. Jernigan TL, Tallal P. Late childhood changes in brain morphology observable with MRI. Dev Med Child Neurol 1990;32:379-85. 30. Chugani HT, Phelps ME, Mazziotta JC. Positron emission tomography study of human brain functional development. Ann Neurol 1987;22: 487-97. 31. Feinberg I, Thode HC, Jr., Chugani HT, March JD. Gamma distribution model describes maturational curves for delta wave amplitude, cortical metabolic rate and synaptic density. J Theor Biol 1990;142:149-61. 32. Gasser T, Verleger R, Bacher P, Sroka L. Development of the EEG of school-age children and adolescents. I. Analysis of band power. Electroencephalogr Clin Neurophysiol 1988;69:91-9. 33. Khatami R, Landolt HP, Achermann P, et al. Challenging sleep homeostasis in narcolepsy-cataplexy: implications for non-REM and REM sleep regulation. Sleep 2008;31:859-67. 34. Sforza E, Jouny C, Ibanez V. Time course of arousal response during periodic leg movements in patients with periodic leg movements and restless legs syndrome. Clin Neurophysiol 2003;114:1116-24. 35. Khatami R, Landolt HP, Achermann P, et al. Insufficient non-REM sleep intensity in narcolepsy-cataplexy. Sleep 2007;30:980-9.

ACKNOWLEDGMENTS Financial support was provided by the Swiss National Science Foundation (PP00A3-114923 to RH) and the National Institutes of Health (MH52415 to MAC). DISCLOSURE STATEMENT This was not an industry supported study. Dr. Huber and Ms. Kurth were involved in a study in which some components of EEG equipment were used through a loan by Geodesics Inc. Mr. Reidner is involved in studies supported by Respironics. Dr. Tononi is involved in studies supported by Respironics and has participated in speaking engagements for Sanofi and Respironics. The other authors have indicated no financial conflicts of interest. REFERENCES

1. Steriade M, McCormick DA, Sejnowski TJ. Thalamocortical oscillations in the sleeping and aroused brain. Science 1993;262:679-85. 2. Sejnowski TJ, Destexhe A. Why do we sleep? Brain Res 2000;886: 208-23. 3. Steriade M, Timofeev I. Neuronal plasticity in thalamocortical networks during sleep and waking oscillations. Neuron 2003;37:563-76. 4. Tononi G, Cirelli C. Sleep function and synaptic homeostasis. Sleep Med Rev 2006;10:49-62. 5. Borbély AA, Achermann P. Sleep homeostasis and models of sleep regulation. In: Kryger MH, Roth T, Dement WC, eds. Principles and Practice of Sleep Medicine. Philadelphia, PA: Elsevier Saunders, 2005:405-17. 6. Feinberg I. Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence? J Psychiatr Res 1982;17:319-34. 7. Jenni OG, Carskadon MA. Spectral analysis of the sleep electroencephalogram during adolescence. Sleep 2004;27:774-83. 8. Feinberg I, Higgins LM, Khaw WY, Campbell IG. The adolescent decline of NREM delta, an indicator of brain maturation, is linked to age and sex but not to pubertal stage. Am J Physiol Regul Integr Comp Physiol 2006;291:R1724-9.

SLEEP, Vol. 33, No. 4, 2010

480
