Paradoxes of the first-night effect: a quantitative

analysis of antero-posterior EEG topography

Giuseppe Curcioa,*, Michele Ferraraa,b, Assunta Piergiannia,Fabiana Fratelloa, Luigi De Gennaroa

aLaboratorio di Psicofisiologia del Sonno, Department of Psychology, University of Rome “La Sapienza”, Via dei Marsi 78, 00185 Rome, ItalybDepartment of Internal Medicine and Public Health, University of L’Aquila, L’Aquila, Italy

Accepted 15 December 2003

Abstract

Objective: The first-night effect (FNE) is a common issue in sleep research. Being considered fragmented and poorly efficient, the

adaptation night is discarded for data analysis. The present study aims to provide a quantitative and topographical EEG analysis of this

phenomenon.

Methods: Eight healthy subjects slept for two consecutive nights (adaptation (AD) and baseline (BSL)), and their polysomnography was

visually scored and then submitted to spectral power analysis.

Results: The results showed a decreased quality and quantity of first-night sleep as indicated by more stage 1 and intrasleep wake,

paralleled by a reduced sleep efficiency and a longer sleep onset latency. On the other hand, EEG quantitative data showed a more complex

and apparently paradoxical picture. An increase in delta power was observed, particularly over the central areas during the first night,

paralleled by an increased power in beta bin frequencies solely at posterior scalp locations.

Conclusions: These results have been interpreted as caused by, respectively, a reduced total sleep time during the adaptation night and a

cortical hyperactivity, typical of psychophysiological insomnia. The present results confirm the need to exclude the laboratory sleep

adaptation night from data analysis since it is not a reliable index of sleep on subsequent nights as regards both visual scoring and quantitative

EEG analysis. Finally, regional differences between REM and NREM sleep have been confirmed.

Significance: This is the first attempt to evaluate the FNE with a quantitative approach to the antero-posterior EEG topography, providing

both a Hz-by-Hz and a classical EEG band-based analysis.

q 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Keywords: First night effect; Sleep adaptation; Cortical topography; Antero-posterior changes

1. Introduction

The first night of sleep recording in the laboratory is not

normally considered a reliable index of sleep on subsequent

nights and is thus disregarded for data analysis (Browman

and Cartwright, 1980). The term first-night effect (FNE) is

used by sleep researchers to refer to a series of phenomena

including reduced total sleep time and rapid eye movement

(REM) sleep, increased duration of stage 1 and intra-sleep

wake, lowered sleep efficiency and increased latency to both

slow-wave (SWS) and REM sleep (Rechtschaffen and

Verdone, 1964; Agnew et al., 1966; Mendels and Hawkins,

1967; Schmidt and Kaelbing, 1971). A multifactorial

explanation was proposed for this maladaptation effect:

the change in sleeping environment might be worsened by

the discomfort induced by electrodes and cables and,

potentially, by the psychological status of being the object

of study (Agnew et al., 1966; Mendels and Hawkins, 1967;

Le Bon et al., 2001). This led some authors to suggest an

enhancement of comfort in the sleep setting by means of a

hotel-type environment arrangement and a friendly and

open laboratory staff (e.g. Coble et al., 1974). Moreover,

personality traits (such as trait anxiety) were also suggested

as a potential cause of FNE (e.g. Saletu et al., 1996;

Kajimura et al., 1998).

Most of the studies have been carried out by polysomno-

graphy (PSG) of healthy subjects, pointing to the issue of

Clinical Neurophysiology 115 (2004) 1178–1188

www.elsevier.com/locate/clinph

1388-2457/$30.00 q 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/j.clinph.2003.12.018

* Corresponding author. Tel.: þ39-06-4991-7508; fax: þ39-06-445-

1667.

E-mail address: giuseppe.curcio@uniroma1.it (G. Curcio).

a lightened and more disturbed sleep. Nevertheless,

attempts were made to find similar effects in patients

affected by psychiatric or sleep disorders, mainly for their

relevance to clinical and therapeutic settings (Kupfer et al.,

1989). In these cases, a modulation of responses to

laboratory first-night was observed, ranging from the

absence of the FNE in depressed patients (Kupfer et al.,

1974; Rotenberg et al., 1997) to an attenuation of the

phenomenon with post-traumatic stress disorder patients

(Woodward et al., 1996; Ross et al., 1999), and up to a

reversed FNE in insomniacs (Hauri and Olmstead, 1989;

Riedel et al., 2001). Furthermore, the issue of a possible

repeated FNE was addressed. Some authors, in fact,

reported data about the possible FNE following multiple

laboratory sessions (e.g. Stepanski et al., 1981), with the

conclusion that the FNE is almost totally confined to the first

recording session, configuring the ‘very first night effect’

(Lorenzo and Barbanoj, 2002).

Only a few studies have provided data on quantitative

changes of EEG during the adaptation to a sleep

laboratory. The first study (Rosadini et al., 1983) reported

a variation on delta and sigma bands that the authors

properly discussed as a consequence of the high inter- and

intra-individual variability. Kupfer et al. (1989) studied the

adaptation to sleep recording in young normal and

depressed subjects: the main differences between the two

groups concerned the features of stage 2, delta sleep and

several REM variables. Moreover, with healthy subjects,

they observed a higher delta sleep presence on the first

night with respect to the second one: unfortunately, no

comparison between nights was carried out. Touissant et al.

(1997) found that the FNE mainly affected REM-related

variables with an increased delta, theta and beta1 power

densities during the second night, while non-rapid-eye

movement (NREM) sleep was affected only as a function

of sleep cycles and probably as a consequence of a changed

REM pressure. A later study by the same group (Touissant

et al., 2000) focused on the FNE of depressed inpatients. In

this case, spectral power was unaffected during REM sleep,

whereas a reduced delta and theta power was observed on

the NREM sleep of the first night. In a set of studies by

Gingras et al. (2000, 2001, 2002), the spectral power of

REM sleep, interhemispheric asymmetries of EEG power

and the spectral content of post adaptation waking EEG

were evaluated. These short reports showed a mild

prevalence of delta power during the REM sleep of the

first night compared to the second night (Gingras et al.,

2000, 2001) and the presence of a significant lateralization

in some bins of the theta range, again during REM sleep

(Gingras et al., 2000). The authors reported no data about

the spectral content of NREM sleep. Finally, in order to

test the dynamics of habituation to polysomnography, Le

Bon et al. (2001) observed a clear discordance between

visual scoring and EEG spectral power of sleep. Although

REM sleep appeared more affected during the first night of

recording, the delta spectral power of NREM sleep showed

a kind of rebound during the second night with respect to

the first one. Moreover, it is well known that some aspects

of the regulatory processes of human sleep are local in

nature (e.g. Ferrara et al., 2002a,b; Finelli et al., 2001),

showing use-dependent characteristics (Kruger and Obal,

2003). Nevertheless, none of these studies provided a

topographical EEG data collection, recorded from different

scalp derivations.

In the light of these puzzling data, the aim of the present

study was to evaluate the FNE on healthy subjects by

providing a quantitative analysis of antero-posterior EEG

topography. Thus, the spectral power of REM and NREM

sleep was evaluated separately, and both a Hz-by-Hz

analysis and a broader analysis, concerning the classical

EEG bands, were carried out.

2. Materials and methods

2.1. Subjects

Eight normal right-handed male subjects (age range:

20–28 years; mean age: 23 years) were selected as paid

volunteers from a university student population. All subjects

signed an informed consent before participating in the

study. They were right-handed and reported drinking less

than 3 caffeinated beverages per day. They usually slept

7–8 h per night, went to bed between 23:00 and 00:00 h, and

did not take naps during the day. Other requirements for

inclusion were: no excessive daytime sleepiness, no other

sleep, medical or psychiatric disorder. These characteristics

were assessed by a 1-week sleep log and by a clinical

interview.

2.2. Procedure

This work was performed in the sleep laboratory of the

University of Rome ‘La Sapienza.’ The study protocol was

approved by the local Institutional Review Board and was

conducted in accordance with the Declaration of Helsinki.

Participants slept for two consecutive undisturbed nights in

a soundproof, temperature-controlled room. According to

sleep research terminology, the first night will be called

‘adaptation’ (AD) and the second one ‘baseline’ (BSL).

Each night, subjects arrived in the laboratory at about

21:00 h for electrode hook-up. Polygraphic sleep recordings

always started at 23:30 h (^30 min) and ended after 7.5 h of

accumulated sleep. Upon final awakening, after electrode

removal, the subjects were free to leave the laboratory to

attend to their normal life schedule. During the daytime,

they attended their university courses and/or studied in their

own homes or in the Faculty’s library. Participants were

required to avoid napping and strenuous physical exercise

throughout the experiment; their compliance was confirmed

by wrist actigraphic recordings (AMI Basic Mini Motion-

logger).

G. Curcio et al. / Clinical Neurophysiology 115 (2004) 1178–1188 1179

2.3. Polygraphic recordings

An Esaote Biomedica VEGA 24 polygraph set at a paper

speed of 10 mm/s was used for polygraphic recordings. EEG

signals were high-pass filtered with a time constant of 0.3 s

and low-pass filtered at 30 Hz (30 dB/octave); 4 unipolar

EEG channels (Fz-A1, Cz-A1, Pz-A1, Oz-A1) were applied

using the International 10–20 system.

The submental electromyogram (EMG) was recorded

with a time constant of 0.03 s. Bipolar horizontal and

vertical eye movements were recorded with a time constant

of 1 s. The bipolar horizontal electrooculogram (EOG) was

recorded from electrodes placed about 1 cm from the

medial and lateral canthi of the dominant eye, and bipolar

vertical EOG from electrodes located about 3 cm above

and below the right eye pupil. Electrode impedance was

kept below 5 kV. Central EEG (Cz-A2), EMG, and

horizontal and vertical EOG were used to visually score

sleep stages in 20 s epochs, according to the standard

criteria (Rechtschaffen and Kales, 1968). With regard to

delta sleep scoring, the amplitude criterion (.75 mV)

expressed by Rechtschaffen and Kales (1968) was strictly

followed.

2.4. Quantitative analysis of signals

The polygraphic signals (4 EEG channels, 2 EOG and

EMG) were analog-to-digital converted on-line with a

sampling rate of 128 Hz and stored on the disk of a personal

computer. Artifacts were excluded off-line on a 4-s basis by

visual inspection. As regards REM sleep, only tonic periods

were included in the analyses in order to avoid artifactual

influences of rapid eye movements on EEG power. Power

spectra of 4 derivations along the antero-posterior axis

(Fz-A1, Cz-A1, Pz-A1, Oz-A1) were computed by a fast

Fourier transform routine for consecutive 4-s epochs,

resulting in a frequency resolution of 0.25 Hz. Values

above 25 Hz were not used in the analysis. By collapsing 4

adjacent 0.25 Hz bins (1–24 Hz), the data were reduced to a

1 Hz bin width. A further data reduction of power spectra

was achieved by averaging 15 consecutive 4-s epochs to

yield a 60 s spectrum. As a result, this spectrum comprised 3

consecutive 20 s visually scored epochs. Power spectra were

calculated separately for NREM (stage 2 þ 3 þ 4) and

REM sleep.

Bins are referred to and plotted in this study by the

lowest frequency included (e.g. the 2 Hz bin refers to the

averaged values of the bins centered at 2.00, 2.25, 2.50 and

2.75 Hz).

2.5. Data analysis

As regards visually scored sleep parameters, one-way

repeated measure analyses of variance (ANOVAs) with

Night as a 2-level factor (AD vs. BSL) were carried out on

the duration of each sleep stage, sleep onset latency, REM

latency, total bed time (TBT), total sleep time (TST),

and sleep efficiency index (SE). Statistical significance was

set at a probability level of # 0:05.

As regards sleep EEG power, absolute power values

were log-transformed before the statistical tests in order to

approximate a normal distribution. Twenty-four two-way

repeated measure ANOVAs, Night (AD, BSL) £ Derivation

(Fz, Cz, Pz, Oz), were carried out on EEG power for each

frequency bin (from 1 to 24 Hz). The same set of ANOVAs

was repeated for NREM and REM sleep. When the main

effect for the Derivation factor or the interaction was

significant, the means were compared by post hoc tests

(t tests). To correct for multiple comparisons, the Bonferroni

correction was applied. To define the alpha level, we took

into account the existence of correlations between all

variables considered in the analysis, and the mean

correlation between them (the more the variables are

correlated, the less the Bonferroni correction is conserva-

tive; Sankoh et al., 1997). Taking into account the mean

correlation between the variables considered in this

ANOVA (r ¼ 0:82 for NREM sleep and r ¼ 0:69 for

REM sleep), the alpha levels were then adjusted respec-

tively to # 0:03 and # 0:02.

To assess time course of EEG changes across successive

NREM episodes, power values for each frequency bin were

grouped in the following bands: delta (1.00–4.00 Hz), theta

(5.00 – 7.00 Hz), alpha (8.00 – 11.00 Hz), sigma

(12.00–14.00 Hz), and beta (15.00–24.00 Hz). Power

values for each frequency band also were log transformed,

and then submitted to two-way repeated measure ANOVAs

Night (AD vs. BSL) £ NREM cycle(1st, 2nd, 3rd, 4th). This

ANOVA design was carried out separately for each cortical

lead.

When the main effect for the NREM cycle factor was

significant, the existence of a linear trend was evaluated.

When an interaction was significant, the means were

compared by post hoc tests (paired t tests). To correct for

multiple comparisons, the Bonferroni correction was

applied. Taking into account the mean correlation between

the variables considered in this ANOVA (r ¼ 0:65), the

alpha level was then adjusted to # 0:01.

3. Results

3.1. Polysomnography

Table 1 shows the means, standard deviations and

ANOVA results of the sleep variables during adaptation

and recovery nights. The ANOVAs indicate that the

adaptation night was characterized by an increase of stage

1, sleep latency, intra-sleep wake and total bed time,

paralleled by a decrease of the sleep efficiency index.

Although not significant, there was also a lengthening of

REM latency during the adaptation night.

G. Curcio et al. / Clinical Neurophysiology 115 (2004) 1178–11881180

3.2. Sleep EEG power during non-REM sleep

3.2.1. Regional differences

The main effects for the Derivation factor point to

significant topographic differences regarding most of the

1.00–24.00 Hz frequency range. More specifically, these

differences were significant in the 1.00 –14.00 and

16.00–24.00 Hz ranges. A common trend of post hoc

differences were found within the 1.00 – 11.00,

12.00–14.00, 16.00–24.00 Hz: the 1.00–11.00 Hz and

16.00–24.00 Hz ranges showed a clear antero-posterior

gradient, while the 12.00–14.00 Hz range showed a

centro-parietal prevalence. Fig. 1 depicts these regional

differences by grouping single-Hz frequencies as a

function of the same trend of regional differences in post

hoc tests.

3.2.2. Between-night differences

Significant main effects for the Night factor were found

for slow frequencies (1.00–5.00 Hz), indicating a higher

power during the adaptation night compared to the baseline

one. Although not significant (0:03 , P , 0:05), a similar

prevalence was also observed within the 16.00–17.00 Hz

frequency range. Fig. 2A reports values of EEG power at

each frequency bin in the two nights, and the results of the

statistical comparisons.

3.2.3. Regional differences in the two nights

The existence of antero-posterior differences in the

between-night differences is expressed by the signifi-

cance of the Night £ Derivation interactions. The

interaction was significant for ANOVAs on fast

frequencies (21.00–24.00 Hz). As detailed by Fig. 3,

these effects pointed to higher EEG power in these

frequency ranges over posterior sites (Pz and Oz) during

the adaptation night.

3.3. Sleep EEG power during REM sleep

3.3.1. Regional differences

The main effects for the Derivation factor point to

significant topographic differences regarding the 1.00–6.00,

Table 1

Means and standard errors (within parentheses), and ANOVA results of the sleep variables during adaptation and baseline nights

Adaptation Baseline F1,7 P

Stage 1 (%) 13.31 (5.64) 7.08 (3.29) 17.74 0.004*

Stage 2 (%) 56.81 (7.81) 58.93 (4.48) 1.25 0.30

SWS (%) 9.81 (7.18) 11.83 (6.06) 2.37 0.17

REM (%) 20.03 (4.10) 22.16 (2.86) 1.31 0.29

NREM (min) (stage 2 þ SWS) 303.71 (41.6) 330.87 (25.65) 3.98 0.09

ISW (min) 70.76 (15.18) 15.25 (2.23) 13.95 0.007*

SE (%) 83.31 (2.71) 94.59 (0.88) 17.31 0.004*

TBT (min) 547.32 (9.89) 495.25 (13.06) 21.60 0.002*

TST (min) 454.66 (10.86) 468.25 (11.92) 1.31 0.29

S1 latency (min) 21.91 (5.00) 11.75 (3.19) 12.82 0.009*

REM latency (min) 146.00 (26.89) 86.56 (9.59) 4.10 0.08

SWS, slow-wave sleep (stages 3 þ 4); NREM, non rapid eye movement sleep; ISW, intra-sleep wake; SE, sleep efficiency index (total bed time/total sleep

time £ 100); TBT, total bed time; TST, total sleep time. *Significant differences.

Fig. 1. Mean EEG power (and standard errors) at 1–11, 12–14 and 16–24

Hz grouped frequencies, recorded during NREM sleep from antero-

posterior scalp locations. Single-Hz frequencies were grouped as a function

of the same trend at the post hoc tests, comparing scalp locations. Raw data

were log transformed.

G. Curcio et al. / Clinical Neurophysiology 115 (2004) 1178–1188 1181

8.00–14.00 and 20.00–24.00 Hz ranges. Single frequency

bins within these ranges showed a common trend of post hoc

differences. The 1.00–6.00 Hz range had an antero-

posterior gradient without significant differences between

frontal and central sites. Both the other two frequency

ranges were characterized by a posterior prevalence: the

middle frequencies with a clear postero-anterior gradient,

while the faster ones did not show any significant difference

between central and parietal sites (Fig. 4).

3.3.2. Between-night differences

A significant main effect for the Night factor was found

only at 5.00 Hz with a higher power during the adaptation

night compared to the baseline one (Fig. 2B).

Fig. 3. Mean relative EEG power (and standard errors) for single-Hz bins during NREM sleep, expressed as ratios between values of the adaptation night and

those of the baseline night. The horizontal line represents the baseline level of EEG power (value ¼ 1). Open triangles indicate the significantly different bins

between the two considered nights.

Fig. 2. Mean EEG power for NREM (A) and REM sleep (B) during adaptation (W) and baseline (X) nights, expressed as log transformed values. The ANOVA

results (F values) are reported for each bin, in the bottom of each panel: the continuous line indicates the level of statistical significance after the Bonferroni

correction (P # 0:01).

G. Curcio et al. / Clinical Neurophysiology 115 (2004) 1178–11881182

3.3.3. Regional differences in the two nights

The Night £ Derivation interaction was significant for

ANOVAs on the 15.00 frequency bin with higher EEG

power during the baseline night compared to the adaptation

one over the centro-parietal derivations (Fig. 5). Although

not significant (0:02 , P , 0:05), it should be mentioned

that this prevalence during the baseline night concerned

a wider range of middle frequencies (14.00–17.00 Hz) on

the central site.

3.4. Time course of EEG power across successive

NREM episodes

3.4.1. Delta band

All Night £ NREM Sleep Cycle ANOVAs on delta

power values yielded no significant effect for the Night

factor and no interaction on any derivation. On the other

hand, a significant effect for the NREM Sleep Cycle factor

was found on all cortical sites (Fz: F3;21 ¼ 39:525,

P ¼ 0:0000001; Cz: F3;21 ¼ 21:126, P ¼ 0:0000015; Pz:

F3;21 ¼ 24:464, P ¼ 0:0000005; Oz: F3;21 ¼ 23:418,

P ¼ 0:0000007), with a linear decrease of delta activity

across successive sleep episodes (Fz: F1;7 ¼ 199:103,

P ¼ 0:000002; Cz: F1;7 ¼ 47:523, P ¼ 0:00023). Fig. 6

shows these linear decreases of delta power across sleep

episodes.

3.4.2. Theta band

The results on this EEG band exactly overlap those of

delta power: no significant effect for the Night factor, no

interaction, and a significant effect for the NREM

Sleep Cycle factor was found on all cortical sites

(Fz: F3;21 ¼ 31:583, P ¼ 0:0000001; Cz: F3;21 ¼ 25:061,

P ¼ 0:0000004; Pz: F3;21 ¼ 19:951, P ¼ 0:000002; Oz:

F3;21 ¼ 17:721, P ¼ 0:000006), with a linear decrease

across successive sleep episodes (Fz: F1;7 ¼ 62:598,

P ¼ 0:00009; Cz: F1;7 ¼ 34:661, P ¼ 0:0006) (Fig. 6).

3.4.3. Alpha band

ANOVA results showed no significant effect for the

Night factor and no interaction on any derivation. Again, a

significant effect for the NREM Sleep Cycle factor was

Fig. 4. Mean EEG power (and standard errors) at 1–6, 8–14 and 20–24 Hz

grouped frequencies, recorded during REM sleep from antero-posterior

scalp locations. Single-Hz frequencies were grouped as a function of the

same trend at the post hoc tests, comparing scalp locations. Raw data were

log transformed.

Fig. 5. Mean relative EEG power (and standard errors) for single-Hz bins during REM sleep, expressed as ratios between values of adaptation night and those of

the baseline night. The horizontal line represents the baseline level night of EEG power (value ¼ 1). Open triangles indicate the significantly different bins

between the two considered nights.

G. Curcio et al. / Clinical Neurophysiology 115 (2004) 1178–1188 1183

found on all cortical sites (Fz: F3;21 ¼ 28:573,

P ¼ 0:0000001; Cz: F3;21 ¼ 15:729, P ¼ 0:000014; Pz:

F3;21 ¼ 6:934, P ¼ 0:002; Oz: F3;21 ¼ 8:007, P ¼ 0:0009),

with a linear decrease of alpha activity across successive

sleep episodes (Fz: F1;7 ¼ 58:834, P ¼ 0:00012; Cz:

F1;7 ¼ 37:651, P ¼ 0:0005).

3.4.4. Sigma band

No main effect or interaction was significant for this

frequency band.

3.4.5. Beta band

ANOVAs on beta power at Fz showed only the

significant effect for the NREM Sleep Cycle factor

(F3;21 ¼ 8:484, P ¼ 0:0007). At Cz, the same main effect

was found (F3;21 ¼ 7:702, P ¼ 0:001), and a significant

Night £ NREM Sleep Cycle interaction (F3;21 ¼ 4:24,

P ¼ 0:01) indicated a prevalence of beta power during the

4th NREM sleep episode of the adaptation night (P ¼ 0:01).

At Pz, the NREM Sleep Cycle effect was found

(F3;21 ¼ 5:642, P ¼ 0:005), and a close-to-significance

main effect for Night (F1;7 ¼ 8:56, P ¼ 0:02) with higher

power in the adaptation night (M ¼ 20:555) compared to

the baseline one (M ¼ 20:629). At Oz, there was only a

close-to-significance main effect for Night (F1;7 ¼ 6:81,

P ¼ 0:03) with higher power in the adaptation night

(M ¼ 20:563) compared to the baseline one

(M ¼ 20:640). Fig. 7 shows these differences regarding

Fz, Cz, Pz and Oz sites.

4. Discussion

The results of visual scoring of the first night confirm that

the process of adaptation to sleep recording affects

polysomnographic measures according to a model of a

more fragmented and less efficient sleep. REM pressure, as

expressed by REM latency, also seems to be decreased.

Fig. 6. Mean absolute EEG power for delta, theta, alpha, sigma and beta bands during successive NREM sleep cycles, plotted as a function of different scalp

locations (Fz, Cz, Pz, Oz).

Fig. 7. Mean relative EEG power for delta, theta, alpha, sigma and beta bands during successive NREM sleep cycles, plotted as a function of different scalp

locations (Fz, Cz, Pz, Oz). The horizontal line represents the baseline night level of EEG power (value ¼ 1).

G. Curcio et al. / Clinical Neurophysiology 115 (2004) 1178–11881184

On the other hand, the quantitative analysis of antero-

posterior EEG changes during NREM and REM sleep

provides an apparently paradoxical picture. NREM sleep of

the first night is characterized by a significant increase in

power of slow frequencies (1–5 Hz) without regional

differences, while an increase was also found for fast

frequencies (20–24 Hz) but solely for posterior sites. This

posteriorization of faster frequencies during the NREM

sleep of the adaptation night could also extend to REM

sleep. However, during REM sleep, the only significant

difference was found at 5 Hz, with higher power during the

adaptation night, regardless of derivations, while in the

middle frequency range, EEG power was higher during

the baseline night.

The antero-posterior differences during NREM and REM

sleep confirm the recent topographical findings. Clear

antero-posterior gradients were found during NREM for

the 1.00–11.00 Hz and the 16.00–24.00 Hz ranges, while

the 12.00 –14.00 Hz range showed a centro-parietal

prevalence. In REM sleep, 1.00–6.00 Hz frequencies

peaked at more anterior areas, and 8.00–14.00 and

20.00–24.00 showed a postero-anterior gradient. This

pattern of regional differences is coherent with Finelli

et al. (2001) and Ferrara et al. (2002a,b), and confirms the

growing issue of local processes during sleep. In fact, these

recent studies found specific EEG dynamics at different

scalp derivations, suggesting that sleep processes might

occur in a topographically graded manner (Werth et al.,

1996), especially involving those neuronal populations that

have been mostly activated during waking (e.g. Kruger and

Obal, 2003).

Moreover, these data confirm the presence of an anterior

predominance of alpha rhythm during NREM sleep

(De Gennaro et al., 2001), and a posterior shift during

REM sleep (De Gennaro et al., 2002), strongly suggesting the

existence of a frontal alpha linked to the synchronization

process (Ferrara et al., 2002a,b). Taken together, these results

clearly indicate the need to partially revise the functional

meaning of traditional EEG bands during sleep, pointing to a

simplification of the different cortical rhythms (e.g. Steriade,

2000). In fact, from a neurophysiological point of view the

1–14 Hz rhythms can be reduced to a few basic cellular

operations, generated in the thalamus and cortex, and

grouped within complex wave sequences by a cortical

generated slow (,1 Hz) oscillation (Steriade, 2000).

The unexpected finding is represented by the increase of

slow wave activity (SWA) and part of theta activity during a

night characterized by a more fragmented and less efficient

sleep. The picture partially resembles that of a recovery

night after sleep deprivation or, at least, characterized by

some rebound processes. The increase of fast frequencies,

encompassing part of the beta range, does not necessarily

contradict this view since it has been observed also in

recovery nights after SWS deprivation (Ferrara et al.,

2002b) and after total sleep deprivation (Finelli et al., 2001).

But our subjects were not sleep deprived, since they were

selected because they usually slept 7–8 h per night, and

their home sleep was assessed by a 1-week sleep log.

Furthermore, their visually scored SWS did not increase; on

the contrary, mean percentages were in the opposite

direction.

A closer view of the previous quantitative analyses of the

FNE is not fully contradictory with our current data. As

mentioned in the Introduction section, only a few studies

have provided data on quantitative changes of EEG during

adaptation to a sleep laboratory. More specifically, Rosadini

et al. (1983) found a high inter- and intra-individual

variability without any significant difference between the

first and the following night; a slight (not significant)

prevalence of delta activity was actually present during the

first night.

In a study on depressed patients (Kupfer et al., 1989), the

average delta activity of normal controls, measured by

period amplitude analysis, was higher in the adaptation than

in the baseline night, although no statistical comparison

between these nights was carried out.

Toussaint et al. (1997) did not find any difference

between the first and the second night (means and statistics

of the whole nights were not provided) for both NREM and

REM sleep. Analyses of time course of EEG power showed

only an increased delta, theta and beta1 power during REM

sleep of the second night, while the first and the third night

were not different. Again, values provided of NREM delta

power across 4 consecutive sleep cycles showed a non-

significant prevalence in the first 3 cycles of the adaptation

as compared to the second night.

Gingras et al. (2000) analyzed the EEG power of REM

sleep without any significant difference between baseline

and adaptation. Again the means were in the direction of a

slight prevalence of delta power during the adaptation night.

Unfortunately, the same group studied EEG power during

REM sleep (Gingras et al., 2000), interhemispheric

asymmetries of EEG power (Gingras et al., 2002), and the

effect of the adaptation on daytime EEG (Gingras et al.,

2001), without analyzing NREM sleep.

Le Bon et al. (2001) published the only results clearly

contrasting with the current ones. They found a significant

prevalence of delta power during the second as compared to

the first night (no difference for the other frequency bands).

However, delta power of the first night was not different

from the third and 4th nights. The authors interpreted these

results as a SWA rebound during the second night.

It should, however, be mentioned that all these studies,

with the exception of those by Gingras et al. (2000, 2001,

2002), used bipolar recordings. Since it is known that inter-

electrode distance influences EEG spectra and coherence

(Achermann and Borbely, 1998), some discrepancy may be

due to differences in the referential recording procedure.

There are two apparent paradoxes in our results: a kind of

recuperative process during the first night in a sleep

laboratory, and the incongruence between visually scored

SWS and changes in SWA. One could speculate that

G. Curcio et al. / Clinical Neurophysiology 115 (2004) 1178–1188 1185

the kind of sleep fragmentation constituting the FNE does

not affect the strength of Process S, and that the due amount

of SWA is expressed even in a relatively fragmented sleep

night. If this is true, the mean EEG activity of the baseline

night would contain ‘lighter’ NREM sleep. In fact, it should

be remembered that quantitative analysis of NREM sleep

EEG is conventionally carried out without taking stage 1

sleep into consideration, and that the baseline night lasts

about 27 min of NREM sleep more than the adaptation one

(Table 1). In line with this interpretation, the analysis of the

temporal dynamics of EEG power across successive NREM

sleep episodes shows the lack of any between-night

difference, when NREM sleep duration is equated. Further-

more, as shown by Fig. 7, the adaptation night is

characterized (at least for some subjects) by a trend toward

an increase of delta activity in the 4th NREM cycle.

Therefore, the whole-night differences in delta power may

actually be explained by a late part of night in which

baseline sleep is characterized by a progressive decay of

slow EEG activity, while the fragmentation and the amount

of wake after sleep onset seems to induce a re-emergence of

slow wave activity during the adaptation night.

Another (not alternative) possible explanation could

differently involve the arousal mechanisms. It is now

acknowledged that phasic EEG events of NREM sleep can

be interpreted by means of the cyclic alternating pattern

(CAP), a scoring system based on the arousal level of EEG

patterns (Terzano et al., 1985). Significant CAP changes

have been found in several kinds of sleep disorders and

neurological diseases, after drug consumption or as a

consequence of circadian phase delay (for a review see

Terzano and Parrino, 2000). In light of this, it could be

hypothesized that during the adaptation night, subjects

might show slow EEG activities as a reflection of the delta

cyclic oscillations during NREM sleep related to the CAP.

In fact, since the CAP is characterized by phases of higher

(A) and lower (B) arousal, it is possible to hypothesize that

the FNE involves an increase in CAP phase A1, defined by

bursts of synchronized EEG patterns (with intermittent

alpha rhythm in stage 1 and sequences of K-complexes or

delta bursts in other NREM stages).

Coherently with the involvement of the arousal mech-

anisms in the observed effects, the present data also showed

an increased beta spectral power content, a rhythm widely

known as an index of physiological arousability and

cognitive functioning in humans (Perlis et al., 2001a).

This increase in high-frequency activity should be an effect

of the higher amount of wake and stage 1 after sleep onset

(see Table 1), and leads us to hypothesize that subjects were

more vigilant and aroused during the adaptation night. The

same pool of symptoms can be found on the so-called

psychophysiological insomnia (Merica et al., 1998) and

could lead us to put forward the FNE as a possible model for

acute insomnia.

It is well known that psychophysiological insomnia is

characterized by somatic, cognitive and Central Nervous

System arousal (Perlis et al., 1997, 2001b). As indicated by

several authors, insomniacs suffer from hypervigilance and

ruminative thoughts (Freedman, 1986; Perlis et al., 2001a),

process and structural hyperactivity (Perlis et al., 2001b;

Bonnet and Arand, 1995, 1996) and from an increase in both

beta (Merica et al., 1998; Nofzinger et al., 1999) and higher

frequency activity power (Perlis et al., 2001a). The feature

of excessive ruminative thoughts is specific of a disturbed

and fragmented sleep night (Hall et al., 1996), whereas the

hyperactivity of sensory and information processing relates

to sensory and prefrontal cortices functioning and thus to

maintenance problems (Perlis et al., 1997). This cognitive

and cortical hyperactivity can be observed at both the

metabolic (Bonnet and Arand, 1995) and EEG level, where

an increase in beta and higher frequencies has been

observed (Freedman, 1986; Merica et al., 1998; Perlis

et al., 2001a). Thus, we could hypothesize that subjects

studied during their first night of laboratory sleep behave

similarly to patients suffering from psychophysiological

insomnia. Their hyperactivity is evident from an increased

sleep fragmentation (more wake after sleep onset), a

lightened sleep and an enhanced beta power particularly

evident on posterior areas. These areas are, in turn, the same

as the ones indicated by some authors as being more

engaged in the increased sensory processing of insomniacs

(Perlis et al., 1997).

With respect to topographical differences, quantitative

analysis of the NREM sleep EEG showed a clear antero-

posterior gradient of most frequencies, whereas the middle

frequencies (12–14 Hz) were more prominent at centro-

parietal sites. A different topographical picture was seen for

REM sleep, where an anteriorization of low frequencies was

paralleled by a posteriorization of middle and high

frequencies. When a topographical analysis was made by

comparing the spectral power of NREM sleep EEG during

the two nights, an increased power of higher frequencies

(21.00–24.00 Hz) was observed on posterior sites during

the adaptation night as compared to the baseline one.

Although not statistically significant, a similar picture was

found during REM sleep, paralleled by a significant

decrease of the 15.00 frequency bin power during the first

with respect to the second night. Taken together, these data

confirm the fact that sleep is local in nature (e.g. De Gennaro

et al., 2001) and suggest that the fragmented and poorly

efficient sleep of the laboratory adaptation night differently

affects different cerebral regions. In fact, as partially

discussed above, the increase of beta frequency was

particularly evident over the posterior areas during both

NREM (Fig. 3) and REM sleep (Fig. 5), that is, those areas

more affected by sleep alterations compared to the more

‘resistant’ frontal ones (Ferrara et al., 2002b; Finelli et al.,

2001). Thus, it could be said that a particular model of sleep

alteration and fragmentation, as the FNE is, may disrupt the

normal activity mainly in posterior sensory areas (e.g. Perlis

et al., 2001b), during both NREM and REM sleep, and thus

G. Curcio et al. / Clinical Neurophysiology 115 (2004) 1178–11881186

lends further support to the hypothesis of the FNE as a

model of acute, psychophysiological insomnia.

In conclusion, the adaptation to laboratory sleep

affects polysomnographic parameters, indicating a more

fragmented and less efficient sleep. Nevertheless, visual

scoring does not match quantitative analysis of EEG,

where a higher spectral power of slow activity (delta and

part of theta range) and beta rhythm are present in the

adaptation night. Specifically, if the former effect may be

explained as a consequence of a shorter adaptation night,

the latter indicates an increased cognitive and cortical

arousal typical of a disturbed night (as in psycho-

physiological insomnia) that can be seen as responsible

for the FNE. Moreover, these effects on the EEG spectra

showed a clear topographical picture, with an antero-

posterior gradient for slow (1.00–11.00 Hz) and fast

(16.00–24.00 Hz) frequencies during NREM sleep and a

postero-anterior gradient for middle (8.00–14.00 Hz) and

fast (20.00–24.00 Hz) bins during REM sleep, again

pointing to the need to reassess the functional role of

EEG rhythms during sleep. In the light of the present

data, the need to exclude the laboratory sleep adaptation

night from data analysis is confirmed, since it is not a

reliable index of sleep on subsequent nights.

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