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: [email protected] (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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