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. Author manuscript; available in PMC: 2015 Jun 1.
Published in final edited form as: Vision Res. 2013 Nov 7;99:154–161. doi: 10.1016/j.visres.2013.10.023

The first-night effect suppresses the strength of slow-wave activity originating in the visual areas during sleep

Masako Tamaki 1, Ji Won Bang 1, Takeo Watanabe 1, Yuka Sasaki 1
PMCID: PMC4013254  NIHMSID: NIHMS544566  PMID: 24211789

Abstract

Our visual system is plastic and adaptive in response to the stimuli and environments we experience. Although visual adaptation and plasticity have been extensively studied while participants are awake, little is known about what happens while they are asleep. It has been documented that sleep structure as measured by sleep stages using polysomnography is altered specifically in the first sleep session due to exposure to a new sleep environment, known as the first-night effect (FNE). However, the impact of the FNE on spontaneous oscillations in the visual system is poorly understood. How does the FNE affect the visual system during sleep? To address this question, the present study examined whether the FNE modifies the strength of slow-wave activity (SWA, 1–4 Hz)—the dominant spontaneous brain oscillation in slow-wave sleep—in the visual areas. We measured the strength of SWA originating in the visual areas during the first and the second sleep sessions. Magnetoencephalography, polysomnography, and magnetic resonance imaging were used to localize the source of SWA to the visual areas. The visual areas were objectively defined using retinotopic mapping and an automated anatomical parcellation technique. The results showed that the strength of SWA was reduced in the first sleep session in comparison to the second sleep session, especially during slow-wave sleep, in the ventral part of the visual areas. These results suggest that environmental novelty may affect the visual system through suppression of SWA. The impact of the FNE may not be negligible in vision research.

Keywords: sleep, first-night effect, slow-wave activity, adaptation, plasticity

1. Introduction

A growing body of evidence suggests that our visual system continues to be plastic during sleep. In the early postnatal brain, ocular dominance plasticity takes place not only during wakefulness after visual experiences, but also during subsequent sleep in young cats and mice (Aton et al., 2009; Aton et al., 2013; Frank, Issa, and Stryker, 2001; Hensch, 2005). Performance in visual tasks significantly improves after sleep in adult humans (Gais et al., 2002; Karni et al., 1994; Stickgold, James, and Hobson, 2000; Yotsumoto et al., 2009). Moreover, sleep deprivation has been shown to nullify sleep facilitation effect in visual tasks (Stickgold, James, and Hobson, 2000). These studies suggest that sleep plays a critical role in visual plasticity. However, the detailed neural mechanism of this sleep-dependent visual plasticity is not completely understood.

Importantly, to investigate the neural mechanism of sleep-dependent visual plasticity, sleep quality would be a crucial factor that needs to be controlled, because it may affect brain activities and plasticity during sleep (Dresler et al., 2010; Manoach and Stickgold, 2009). The first-night effect (FNE) is a sleep disturbance that is observed particularly in the first session of sleep experiments (Agnew, Webb, and Williams, 1966; Rechtschaffen and Verdone, 1964; Tamaki et al., 2005a), which could be a confounding factor in the field of sleep research. In the first sleep session, latencies to sleep onset and to rapid-eye movement (REM) sleep are longer, the proportion of wakefulness during bed time is increased, and total sleep time is decreased, compared to the second sleep session (Agnew, Webb, and Williams, 1966; Curcio et al., 2004; Tamaki, et al., 2005a; Tamaki et al., 2005b; Tamaki, Nittono, and Hori, 2005). Sleep quality improves significantly in the second sleep session in younger and healthy participants (Agnew, Webb, and Williams, 1966; Lorenzo and Barbanoj, 2002). To control for the FNE, it has been recommended to incorporate adaptation sleep sessions before experimental sleep sessions, which would let participants adapt to a new sleep environment (Tamaki, Nittono, and Hori, 2005).

Although the FNE impact on sleep structures has been well documented, the FNE impact on spontaneous oscillations and plasticity during sleep is yet to be investigated. In particular, it is imperative to examine how the FNE modifies slow-wave activity (SWA, 1-–4 Hz), one of the spontaneous oscillatory activities during non-rapid-eye movement (NREM) sleep, in the visual area, because SWA is suggested to be involved in various types of learning including visual perceptual learning (Aeschbach, Cutler, and Ronda, 2008; Born and Wilhelm, 2012; Tamaki et al., 2013; Tononi and Cirelli, 2003). If SWA in the visual area is affected by the FNE, this will pose a critical confound for studies investigating the mechanisms of visual plasticity during sleep. In this case, using the data of the first sleep session should be discouraged. On the other hand, if the impact of the FNE on SWA originating in the visual area is small, we may not need to consider the FNE in future studies of sleep-dependent visual plasticity, which would save time and money for vision sleep research. Thus, it is necessary to ask whether the FNE modulates SWA in the visual cortex.

To our knowledge, however, there is no clear consensus on whether the FNE modifies SWA in the visual area, as argued below. First, we have found 58 papers on the FNE, among which there were only 14 papers that employed a young (age range: 16–39 yrs) and healthy population to investigate the FNE using polysomnography (PSG) measurement of at least two nights (Agnew, Webb, and Williams, 1966; Browman and Cartwright, 1980; Coble et al., 1974; Curcio, et al., 2004; Kajimura et al., 1998; Lorenzo and Barbanoj, 2002; Rechtschaffen and Verdon, 1964; Rosadini et al., 1983; Sharpley, Solomon, and Cowen, 1988; Suetsugi et al., 2007; Tamaki, et al., 2005a; Tamaki, et al., 2005b; Tamaki, Nittono, and Hori, 2005; Toussaint et al., 1997). Among those, there were only a handful of studies that examined the impact of the FNE on the strength of SWA, using a power spectral analysis (Curcio, et al., 2004; Tamaki, et al., 2005b; Toussaint, et al., 1997). The impacts reported were contradictory: increase in central brain regions (Curcio, et al., 2004), decrease in frontal brain regions (Tamaki, et al., 2005b), and no changes (Toussaint, et al., 1997) in the first compared to the second sleep session. Unfortunately, there is no study that has investigated SWA in the occipital area.

Second, which brain regions, including in the visual areas, are susceptible to the FNE is also unclear, as modification of SWA strength could take place not only evenly in the brain but also in a particular region. Recent studies have suggested that SWA can be modulated and generated locally (Huber et al., 2004; Tononi and Cirelli, 2003; Vyazovskiy et al., 2011), not uniformly across brain regions, and may propagate across other brain regions from the frontal regions (Massimini et al., 2004; Nir et al., 2011). Moreover, the susceptibility of SWA to the FNE may not be consistent across the brain (Cajochen et al., 2006). The study by Cajochen et al. (2006) reported that the age-related reduction of SWA was prominent only in the frontal region, but not in the occipital region. This line of studies suggests that SWA in the visual area could be either suppressed by or resilient to the FNE locally, independent of other brain regions such as the frontal (where SWA decreased, as reported by Tamaki et al, 2005b) or central (where SWA increased, as reported by Curcio et al., 2004) brain regions. On the other hand, global decreases of SWA, including occipital areas, is also possible, due to sleep deterioration (Aeschbach, Cutler, and Ronda, 2008; Carrier et al., 2011; Walsh, 2009; Westerberg et al., 2012). Thus, it is also possible that SWA could be decreased globally including the visual area by the FNE.

In other words, whether the FNE impacts SWA in the visual area may depend on how globally the FNE impacts the brain. That is, if the FNE impacts the brain globally, it is possible that the strength of SWA is decreased in the visual areas, as well as in the frontal areas, due to deterioration in sleep quality. On the other hand, if the FNE impacts locally in a limited region (i.e., frontal areas), the strength of SWA may not be decreased in the visual area in the first compared to the second session. Moreover, the impact of the FNE on SWA may differ among the visual areas, because functional distinctions including attention and perception among the visual areas, such as the ventral and dorsal early visual areas, and the higher visual areas, have been suggested (Goodale, 1993; He, Cavanagh, and Intriligator, 1996; Mishkin and Ungerleider, 1982; Previc and Mullen, 1990; Rubin, Nakayama, and Shapley, 1996). Furthermore, the strength of SWA could even be increased in the visual area in the first sleep session: in the first sleep session, subjects are exposed to a new sleep environment with new objects and equipment, which could increase visual usage during wakefulness, thereby potentiating visual neurons to facilitate visual plasticity (Torasdotter et al., 1996; Torasdotter et al., 1998). According to the synaptic homeostasis hypothesis (Tononi and Cirelli, 2003), SWA should be increased in the brain regions that have been extensively used and in synapses potentiated during prior wakefulness. Thus, the extensive usage of the visual area during wakefulness may result in SWA increase in the visual area during the subsequent sleep in the first sleep session.

In the present study, we tested whether the SWA strength is changed in the visual area in the first compared to the second session. If the FNE impacts the brain globally, the strength of SWA may be decreased in the visual area in the first compared to the second sleep session. On the other hand, if the impact of the FNE is local, then the strength of SWA may not be decreased in the visual area due to the FNE; in this case there would be two scenarios. In one, the strength of SWA may be increased in a visual area if the synaptic strength were greatly increased in the visual area (Tononi and Cirelli, 2003). In the other, there would be no change in SWA of a visual area in the first sleep session compared to the second, if neither the FNE nor visual plasticity affect the visual area, or if both affect the visual area and cancel each other out. To test these possibilities, we compared the strength of SWA between the first and second sleep sessions in three visual areas: the ventral and dorsal early visual areas, and the object area of the higher visual area. Furthermore, to compute SWA originating in these visual areas, we employed a source-localization technique (Ahveninen et al., 2007; Lin et al., 2004) with a combination of magnetoencephalography (MEG), PSG and magnetic resonance imaging (MRI). We used this method because the advanced source-localization technique would allow us to measure the SWA strength in the sub-regions within the visual area with high spatial resolution (Ahveninen, et al., 2007; Dale et al., 2000; Dale and Sereno, 1993; Lin, et al., 2004).

2. Material and Methods

The present paper is based on a new analysis of our past projects that utilized the MEG and MRI source localization technique on sleeping brain. The past projects had different purposes, thus, the original experimental designs were slightly different. This section describes both the original designs and how we conducted the analysis of the present paper.

2.1. Subjects

After experimenters thoroughly described the purpose and procedure of experiments to the subjects, potential subjects completed questionnaires regarding their sleep-wake habits; usual sleep and wake time, regularity of their sleep-wake habits and lifestyle, habits of nap-taking, and information regarding their physical and psychiatric health including sleep complaints. Anyone with physical or psychiatric disease, currently receiving medical treatment, or suspected of having a sleep disorder was excluded. People who had the habit of taking a nap, consuming alcoholic beverages before sleep, or smoking were also excluded. Only people who had regular sleep-wake cycles were included, i.e., differences between average bedtimes, sleep durations and wake-up times on weekdays and weekends were less than 2 hrs. The average sleep duration for each potential subject ranged from 6 to 9 hours regularly. The studies were approved by the institutional review board of the Massachusetts General Hospital where the data were collected and of Brown University where the data were analyzed. All subjects gave written informed consent for their participation in experiments.

A total of 10 subjects data sets (5 females and 5 males, mean age 26.5 ± 0.99 years) were analyzed in the present paper. These subjects were chosen because they had MEG recordings for both the first and second night sleep sessions.

2.2 Experimental procedures

In the original experiments, three to four sleep sessions were conducted in total, depending on the project, followed by one MRI session. In all sessions, MEG and PSG were measured during sleep (for detail, see 2.3 Data acquisition below). In the original experiments, taking into account individual circadian rhythm variations, each subject’s sleep time was set to their habitual sleep time for both sessions instead of enforcing a uniform time. On average, the lights were turned off around midnight, and the termination of recording was determined depending on the purpose of each project. In one project, the recording was terminated either when the subject showed REM sleep or when 90 min had elapsed. In another project, the recording was terminated when 180 min had elapsed. After the recording, the subjects were allowed to sleep for the rest of the night. The first and second nights were not consecutive in any of the projects. Starting three days before the onset of the experiment, subjects were instructed to maintain their sleep-wake habits, (i.e., their daily wake/sleep time and sleep duration). One day before each experiment, they were instructed to refrain from alcohol consumption, unusual physical exercise, and nap taking. Their sleep-wake habits were monitored by a sleep log to ensure the success of this procedure.

2.3 Data acquisition

MEG and PSG were simultaneously recorded in a magnetically shielded room. PSG consisted of EEG, electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG). EEG was recorded at 4–7 scalp sites (including C3, C4, O1, O2, Fz, Cz, Pz) according to the 10–20-electrode system referenced to the nasion. EOG was recorded from two electrodes placed at the outer canthi of both eyes (horizontal EOG). EMG was recorded bipolar from the mentum. ECG was recorded from two electrodes placed at the right clavicle and the left rib bone. Electrode impedance was kept below 5kΩ. MEG data were collected using a 306-channel whole-head Vectorview system (Elekta Neuromag, Helsinki, Finland) with 204 planar gradiometers and 102 magnetometers. Both MEG and EEG data were recorded at a sampling rate of 600 Hz. The data was filtered between 0.1 and 99 Hz, and was re-sampled at 198Hz. All epochs with changes that exceeded 3000 fT/cm at any MEG channel or that were contaminated by artificial noises were discarded. The positions of all scalp electrodes, anatomical landmarks including the nasion and two auricular landmarks, and four head-position indicator coils were measured using a FastTrack 3D digitizer (Polhemus, Colchester, VT). Head position within the MEG sensor array was measured at the beginning of the session. Five-minute empty room MEG recordings were also made immediately prior to each experiment for the purpose of estimating the noise covariance matrix (Ahveninen, et al., 2007).

MRI anatomical data was used for determining the conductor geometry for the boundary element model (BEM) of the head (Hamalainen and Ilmoniemi, 1984; Hamalainen and Sarvas, 1989), and for registering the MEG sensors’ locations with the individual subject’s anatomy (Dale, Fischl, and Sereno, 1999, Fischl, Sereno, and Dale, 1999). Subjects were scanned in a 3 T MR scanner (Trio, Siemens); a head coil was used in all experiments. Three T1-weighted MR images (MPRAGE; TR = 2.531 s, TE = 3.28 ms, flip angle = 7°, TI = 1100 ms, 256 slices, voxel size = 1.3 × 1.3 × 1.0 mm) were acquired. Data was inflated for each participant for brain parcellation to localize individual gyri and sulci (Destrieux et al., 2010; Fischl et al., 2004). This information was used for the region-of-interest (ROI) analysis described later.

2.4 Sleep-stage scoring procedure and the definition of the FNE

EEGs were scored and classified into sleep stages for every 30-s epoch according to standard criteria (Rechtschaffen and Kales, 1968). EEG recordings from the C3 electrode were used for this scoring; if the C3 recordings were contaminated by artifacts, C4 recordings were used instead. The same scalp area was used for scoring across individuals and across nights. The following terminology was used for the stages of sleep: stage W (wakefulness), stage 1 (NREM sleep stage 1), stage 2 (NREM sleep stage 2), slow-wave sleep (NREM sleep stages 3 + 4), stage REM (sleep stage REM). Sleep onset was defined as the time taken to reach the first epoch of stage 2 after lights off. The following variables were calculated for each subject for assessment of basic sleep structure (sleep variables): latency to each sleep stage (min), the time spent in each sleep stage (min), and percent sleep efficiency [(Total sleep time / Total recording time) × 100].

While there could be various measures to indicate the presence of the FNE, we decided to use sleep onset latency, which was the interval between the lights-off to the onset of stage 2, since this has been shown to be indicative as the measurement of the FNE (Tamaki, Nittono, and Hori, 2005). Although the latency to stage REM may also be useful to quantify the FNE, this could not be used because data of stage REM was not necessarily measured due to the experimental procedure adopted in a past project (see section 2.2 Experimental procedures).

2.5 Data entered into the present analysis

In the present study, we decided to analyze the data that were collected from the first two sleep sessions for the following two reasons. First, the experimental intervention to sleep took place between the end of the second sleep session and the start of the third sleep session in the majority of subjects. The task performed before the sleep session may have affected the subsequent sleep (Gais et al., 2000; Karni, et al., 1994; Mascetti et al., 2013; Stickgold, James, and Hobson, 2000; Tamaki et al., 2013; Yotsumoto, et al., 2009). Second, we were particularly interested in the difference between the first and second sleep sessions, because the FNE is largely reduced in the second session with regard to NREM sleep variables (Agnew, Webb, and Williams, 1966).

To compare the strength of SWA between the first sleep session and the second, we used the first sleep cycle of NREM sleep that occurred within 90 minutes from lights out, because all of the subjects had this segment.

2.6 MEG wavelet and source localization analysis

A Morlet wavelet analysis (Ahveninen, et al., 2007; Lin, et al., 2004) was applied to MEG raw data every 30-sec epoch to obtain spectral power from 1–4 Hz that corresponds to sleep SWA frequency range (Huber, et al., 2004). To localize the current sources underlying the MEG signals, we employed the cortically constrained minimum-norm estimate (MNE) using individual anatomical MRI and constrained the current locations to the cortical mantle (Ahveninen, et al., 2007). Information of the MEG sensors’ locations and the structural MRI segmentation were used to compute the forward solutions for all source locations using a single-compartment boundary element method (BEM; Hamalainen and Sarvas, 1989). The individual forward solutions constituted the rows of the gain (lead-field) matrix. For inverse computations, the cortical surface was decimated to ~20,000 vertices per hemisphere. The noise covariance matrix was computed from the empty-room MEG data. These two matrices were used to calculate the inverse operator to yield the estimated source activity, as a function of time, on a cortical surface (Ahveninen, et al., 2007). We did not use EEG data for the source localization, because the number of EEG channels was very small (4–7 channels) compared to MEG (306 channels).

We computed the average strength of SWA (1–4 Hz) on the first (Night 1) and the second (Night 2) sleep sessions in each of the ROIs (see section 2.7 ROI below). As the strength of SWA changes with elapsed time from sleep onset as well as with sleep stage, we examined the time-dependent and sleep-stage dependent components separately. First, to examine the time-dependent component of SWA, the mean of the MEG currents were calculated every 1 minute for each ROI (see section 2.7 ROI below). Then, the time-course data was further divided into three epochs to test whether the FNE had an impact on a specific time window. Each epoch corresponded to a specific phase of the development of SWA, that is, initiation (epoch 1), development (epoch 2), and maintenance / decline (epoch 3) of SWA. Second, to examine the differential impact of the FNE among the sleep stages, a sleep-stage dependent component was computed. To compute the sleep-stage dependent component, the strength of SWA for every 30-s epoch that corresponded to each sleep-stage epoch, was averaged separately for each sleep stage. Since we defined sleep onset as the onset of stage 2, SWA during stage 2 and slow-wave sleep were computed as SWA during sleep. In addition, SWA during stage W was calculated as SWA during wakefulness.

2.7 ROI

We defined three ROIs: the ventral and dorsal early visual areas and the higher visual area, to test whether there are differences in the impact of the FNE among the regions. The procedure to localize these ROIs was as follows. We first identified the ventral and dorsal part of V1, V2, and V3 for each subject by a standard fMRI retinotopic mapping technique (Engel et al., 1994; Fize et al., 2003; Sereno, Mcdonald, and Allman, 1994; Yotsumoto, et al., 2009; Yotsumoto, Watanabe, and Sasaki, 2008). The higher visual area was defined anatomically using an automated parcellation system (Fischl, et al., 2004), as the posterior part of the inferior temporal sulcus that roughly corresponds to higher visual areas specializing in object processing (Kourtzi and Huberle, 2005).

For the retinotopic mapping, blood oxygen level dependent (BOLD) signals were acquired using a gradient echo EPI sequence (TR = 2 s, TE = 30 ms, Flip Angle = 90°). Twenty-five contiguous slices (3 × 3 × 3.5 mm) orientated orthogonal to the calcarine sulcus were acquired covering the occipital to parieto-temporal cortices. Data were analyzed with FSFAST and FreeSurfer (http://surfer.nmr.mgh.harvard.edu) software. All functional images were motion corrected (Cox and Jesmanowicz, 1999), spatially smoothed with a Gaussian kernel of 5.0 mm (FWHM), and normalized individually across scans. Functional data were registered to the individual reconstructed brain (Dale, Fischl, and Sereno, 1999; Fischl, Sereno, and Dale, 1999). The individually reconstructed brain was also used for an automated parcellation (Destrieux, et al., 2010; Fischl, et al., 2004) for objective anatomical segregation to localize higher visual area in this study.

3. Results

3.1 Sleep variables

First, we compared sleep structures on Night 1 and Night 2 to confirm that the FNE had occurred (Table 1 and Fig. 1). Sleep stages kept changing back and forth between different sleep stages on Night 1. However, the sleep stage transition became smoother on Night 2 (Fig. 1). The sleep onset latency was significantly longer on Night 1 than on Night 2 (t (9) = 2.438, p =.038; two-tailed paired t test), which confirmed the presence of the FNE in the current study. In addition, latency to stage 1 was significantly different (t (9) = 2.428, p =.038). The time spent in stage W (Table 1, t (9) = 2.190, p =.056) tended to increase, the time spent in slow-wave sleep decreased (t (9) = 2.389, p =.041), and percentage sleep efficiency was significantly lower (t (9) = 2.247, p =.049;) on Night 1 compared to Night 2.

Table 1.

Basic sleep structure on Night 1 and Night 2.

Sleep variables Night 1
(m ± sem)		 Night 2
(m ± sem)		 p-value*
Latency to sleep stage (min)
  Stage 1 7.7 2.36 3.0 0.65 0.038
  Stage 2 15.2 3.98 7.4 1.91 0.038
  SWS 31.1 5.60 21.1 3.09 0.112
Time in sleep stage (min)
  Stage W 15.2 5.32 5.0 1.55 0.056
  Stage 1 7.5 1.67 5.2 1.04 0.128
  Stage 2 29.4 2.96 33.6 3.37 0.390
  SWS 25.9 5.39 37.9 3.64 0.041
Percentage sleep efficiency (%) 82.1 5.94 93.9 2.04 0.049
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Since the total recording time varied depending on study protocol, all the sleep variables were calculated from the first sleep cycle of NREM sleep. Percentage sleep efficiency was calculated by [(Total sleep time / Total recording time) × 100]. SWS, slow-wave sleep.

*

Two-tailed paired t test (alpha = 0.05)

Figure 1.

Figure 1

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Sleep hypnogram of a representative participant. Data were recorded for 90 min from lights out. X-axis corresponds to the elapsed time from lights out (min). Y-axis corresponds to each sleep stage (stage W, stage 1, stage 2, and slow-wave sleep). The red line indicates the stage transition during Night 1, and the blue line indicates the stage transition during Night 2. SWS, slow-wave sleep.

3.2 Time-course changes of SWA in the visual areas

We next investigated the time course of SWA on Nights 1 and 2. In both sessions, SWA increased over time; however, there was a notable difference between sessions (Fig. 2). The result showed that SWA was profoundly decreased on Night 1 throughout the epochs. A 3-way repeated measures ANOVA was conducted to see whether the following factors impacted the strength of SWA; the factors were ROI (ventral and dorsal part of early visual areas, and higher visual area), epoch (1–3), and session (Night 1 and 2). There was a significant main effect for all three factors (ROI, F (2, 18) = 53.642, p <.001, ε = 1; epoch, F (2, 18) = 14.307, p <.001, ε = 1; session, F (1, 9) = 5.156, p =.049, ε = 1). In addition, the ROI × epoch interaction was significant (F (4, 81) = 9.707, p <.001, ε = .57). However, no significant interaction was found for ROI × session, epoch × session, or ROI × epoch × session. This analysis indicates that the impact of FNE was present in all ROIs.

Figure 2.

Figure 2

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The time-dependent modification of SWA in the early and higher visual areas on Night 1 and Night 2. a, c, e, The time course of SWA in the ventral part of the early visual area (a), the dorsal part of the early visual area (c), and the higher visual area (e). Red line shows the mean SWA strength and shaded light orange area shows standard error of Night 1. Blue line shows the mean SWA strength and shaded light blue area shows standard error of Night 2. X-axis shows time from lights out (min). Y-axis shows the strength of SWA (nAm). b, d, f, The time course activity (shown in a, c, e) was further divided into three epochs in the ventral part of the early visual area (b), the dorsal part of the early visual area (d), and the higher visual area (f). Orange bars are the mean SWA on Night 1, and blue bars are the mean SWA on Night 2. X-axis shows epoch. Y-axis shows the strength of SWA (nAm). Error bar, s.e.m.

3.3 Decreased SWA during slow-wave sleep in the visual areas

Next, SWA was calculated for each sleep stage (stage W, stage 2, and slow-wave sleep) to examine the influence of the FNE on different sleep stages (Fig. 3). First, a 3-way repeated measures ANOVA was conducted to test whether the following factors impact on the strength of SWA; the factors were ROI (ventral and dorsal part of early visual area, and higher visual area), sleep stage (stage W, stage 2, and slow-wave sleep), and session (Night 1 and 2). There were significant main effects for ROI (F (2, 18) = 64.866, p <.001, ε = 1) and sleep stage (F (2, 18) = 106.667, p <.001, ε = 1), but not for session. In addition, significant interactions were found between ROI × sleep stage (F (4, 81) = 44.873, p <.001, ε = .37), sleep stage × session (F (2, 54) = 6.328, p < .01, ε = .53), and ROI × sleep stage × session (F (4, 162) = 5.496, p <.05, ε = 40). No significant interaction was found in ROI × session. These results suggest that the impact of FNE differed depending on the sleep stage and on the ROI.

Figure 3.

Figure 3

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The sleep-stage dependent modification of SWA during stage W, stage 2, and slow-wave sleep in the ventral part of the early visual area (a), the dorsal part of the early visual area (b), and the higher visual area (c). Orange bars show SWA on Night 1, blue bars show SWA on Night 2. X-axis is sleep stage, and Y-axis is SWA strength (nAm). SWS, slow-wave sleep. Error bar, s.e.m. *p < .05 (two-tailed pared t test).

Since a significant interaction associated with the ROI factor was found, a 2-way repeated measures ANOVA was conducted subsequently for each ROI to test whether the following factors impacted the strength of SWA; the factors were sleep stage (stage W, stage 2, and slow-wave sleep) and session (Night 1 and 2). First, in the ventral part of the early visual area, there was a significant main effect for sleep stage (F (1, 9) = 149.478, p <.001, ε =.60). Moreover, a sleep stage × session interaction was significant (F (2, 18) = 6.554, p <.001, ε =.97). Subsequently, post-hoc paired t-tests were conducted for each sleep stage to examine the difference in the impact of the FNE among sleep stages. There was a significant difference between sessions in slow-wave sleep (t (9) = −2.225, p =.027; two-tailed paired t test), but not in stage W or stage 2 (ps >.05). This analysis suggests that the strength of SWA in the ventral part of the early visual area was significantly reduced in the first-night sleep, especially during slow-wave sleep. In the dorsal part of the early visual area, a 2-way repeated measures ANOVA (factors = sleep stage and sessions) showed that SWA was significantly different among sleep stages (F (2, 18) = 80.059, p <.001, ε = .56). No significant main effect for session or sleep stage × session interaction was found. The analysis suggests that the FNE on the strength of SWA in the dorsal part of the early visual area is limited. For the higher visual area, a 2-way repeated measures ANOVA (factors = sleep stage and sessions) showed a significant main effect for sleep stage (F (1, 9) = 112.429, p <.001, ε = .40). A sleep stage × session interaction was also significant (F (2, 18) = 6.950, p = .006, ε = .79). Post-hoc paired t tests conducted for each sleep stage revealed that there was a significant difference between sessions in slow-wave sleep (t (9) = −1.997, p =.039; two-tailed paired t test), but not in stage W or stage 2. This suggests that the strength of SWA in the higher visual area was significantly reduced in the first-night sleep, especially during slow-wave sleep.

The analyses so far showed that the impact of the FNE on SWA was significant during slow-wave sleep, especially in the ventral part of the early visual areas and the higher visual area, but not in the dorsal part of the early visual area.

3.4 Individual differences and the gender effect

We also examined individual differences and the gender effect on SWA. We subtracted the average SWA strength of Night 2 from that of Night 1 during slow-wave sleep for each visual area individually, and the FNE was determined to be present if the difference was smaller than zero. The results showed that nine out of ten subjects showed the FNE in at least one visual area (Table 2). When we divide the result for each visual area, seven (dorsal part of early visual area) or eight (ventral part of early visual area and higher visual area) out of ten subjects displayed the FNE during slow-wave sleep in the visual areas (Table 2). Next, we sorted the frequency of the FNE by the gender. There seems to be no significant gender differences in the frequency of the FNE in any of the visual areas (all p > .05, chi-square test for each visual area, Table 2).

Table 2.

Frequency of the FNE for each visual area and the gender effect.

FNE
presence		 Visual areas
Ventral Dorsal Higher
Female (N=5) 5 4 3 4
Male (N=5) 4 4 4 4
Total # of FNE (N=10) 9 8 7 8
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Each numerical value shows the number of subjects that showed the FNE in each visual area during slow-wave sleep. The FNE presence shows the total number of subjects who showed the FNE in at least one of the visual areas. Ventral, ventral part of early visual area; Dorsal, dorsal part of early visual area, Higher, higher visual area.

4. Discussion

The present study examined whether the FNE has any impacts on the strength of SWA originating in the visual area. Because of limitations in experimental procedures, we could not compare all the sleep variables obtained here with those in previous studies that were computed from full-night recording. There is evidence, however, that the FNE that we documented in the present study may be comparable to previous studies. For instance, the change in sleep onset latency from Nights 1 to 2 (Table 1) were similar to those in previous studies (e.g., sleep onset latency was 14 min on Night 1, 7 min on Night 2, in Tamaki, et al., 2005a). Duration of stage W tended to be larger on Night 1 compared to Night 2. Percent sleep efficiency was significantly lower on Night 1. Although the changes in some of the parameters did not reach significance, the alteration of the sleep structure recognized in previous research (Agnew, Webb, and Williams, 1966; Curcio, et al., 2004; Toussaint, et al., 1997) has also been indicated in our results.

Our results indicated that the impacts of the FNE were slightly different among the visual areas. First, we conducted the time-course analysis of SWA (3.2 Time-course changes of SWA in the visual areas, Fig. 2), which showed a reduction of SWA in Night 1 in comparison to Night 2 in all of three visual areas. This seems to support the view that the FNE has a global impact across brain regions when the analysis does not consider the sleep stages. However, the second analysis by sleep stages illustrated the possibility that the impact of the FNE may not be ubiquitous in the visual system. The analyses on sleep stages (3.3 Decreased SWA during slow-wave sleep in the visual areas, Fig. 3) showed a significant main effect of ROI and a significant ROI × sleep stage × session interaction, among others. This suggests that the impact of the FNE is different in different ROIs. Furthermore, the post-hoc tests demonstrated a significant suppression of SWA, especially in the ventral part of the early visual area and the object area of the higher visual area, whereas we did not find a significant suppression in SWA in the dorsal part of the early visual area. These results may be a piece of counterevidence to the claim that the impact of the FNE is homogeneous across brain regions.

Interestingly, there was little suppression in SWA in the dorsal part of the early visual area. This rejects the assumption that FNE should reduce the strength of SWA uniformly in the brain. The result may suggest that the impact of the FNE was negligible in this region, or that the influence of the FNE may have been cancelled out by a local increase of SWA due to the synaptic homeostatic regulation (Tononi and Cirelli, 2003) in the dorsal stream (Goodale, 1993; Mishkin and Ungerleider, 1982). It is known that the lower visual field, which is projected to the dorsal part of the visual area, has an advantage in perception of particular stimuli (Previc and Mullen, 1990; Rubin, Nakayama, and Shapley, 1996). Furthermore, asymmetry in attentional resolution between the upper and lower visual field has also been suggested (He, Cavanagh, and Intriligator, 1996). The dorsal part of the visual area might have been stimulated in the first sleep session where novel objects and equipment are exposed to the subject. This may result in a local need of synaptic homeostatic regulation during sleep and nullify the reduction of SWA caused by the FNE.

We included only younger and healthy participants in the present study. Nine out of ten participants showed suppression of SWA during slow-wave sleep in at least one of the three visual areas. Thus, the FNE was present in a majority of the participants. Furthermore, there was no significant difference between genders in the frequency of the FNE. However, the impact of the FNE on SWA or individual and gender differences may vary with populations. Previous research suggests that the impact of the FNE differs by age (Webb and Campbell, 1979; Wauquier et al., 1991) or physical conditions such as respiratory disorders (Levendowski et al., 2009; Le Bon et al., 2000), chronic fatigue syndrome (Le Bon et al., 2003), depression (Rotenberg et al., 1997; Toussaint et al., 2000), or posttraumatic stress disorder (Herbst et al., 2010). Some groups are suggested to require more days for adaptation (Herbst, et al., 2010; Rotenberg, et al., 1997). Thus, greater SWA suppression may be shown in particular groups.

There are several limitations in the present study. Because we used the data collected from our past projects that had different purposes and employed different procedures, the data analyzed here was restricted to the first sleep cycle that occurred in the initial 90 minutes of nightly sleep. Moreover, it is still unclear which brain regions are affected by the FNE and which are not, because we restricted the regions of interest in the visual area. Whereas the present study shows that the visual areas are affected by the FNE through reduction of SWA during slow-wave sleep, the FNE in other cortical regions has yet to be studied in detail. Also, which spontaneous oscillations other than SWA, such as alpha or theta oscillations, are affected by the FNE in the visual area is unclear. Stage REM parameters should be also tested, because these may be sensitive to adaptation as well (Lorenzo and Barbanoj, 2002). Moreover, the relationship between neural plasticity and the FNE needs to be clarified.

In summary, our study is the first to show that the strength of SWA originating in the visual areas is largely suppressed by the FNE, although the impact of the FNE may be slightly different among the visual areas. As our results showed that the strength of SWA in the visual areas was reduced by the FNE, we suggest that it is crucial to reduce the FNE in order to study the roles of SWA in visual learning. Incorporating at least one adaptation session would be necessary.

HIGHLIGHTS.

  • -

    We examined the impact of the first-night effect on the visual areas during sleep.

  • -

    We compared the strength of MEG slow-wave activity between two sleep sessions.

  • -

    Slow-wave activity was reduced in the visual areas in the first sleep session.

  • -

    The first-night effect modulates the visual system during sleep.

ACKNOWLEDGEMENTS

This work was supported by grants to TW and YS (NIH R01AG031941, R01MH091801, R01EY015980, R01EY019466, R01EY018334 and NSF BCS-1261765). This work also involved the use of instrumentation supported by the NCRR Shared Instrumentation Grant Program and High-End Instrumentation Grant Program; specifically, grant numbers S10RR014978, S10RR021110 and S10RR023401. This research was carried out in part at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, using resources provided by the Center for Functional Neuroimaging Technologies, NCRR P41RR14075. Part of this research was also conducted using computational resources and services at the Center for Computation and Visualization, Brown University. We thank Fa-Hsuan Lin, Matti Hämäläinen, Omid Khalilzadeh, Daniel G. Wakeman, Erika Scilipoti for technical assistance and Matthew S. Cain for comments on the manuscript.

Footnotes

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Contributor Information

Masako Tamaki, Email: tamaki@brown.edu.

Ji Won Bang, Email: ji_won_bang@brown.edu.

Takeo Watanabe, Email: takeo_watanabe@brown.edu.

Yuka Sasaki, Email: yuka_sasaki@brown.edu.

References

  1. Aeschbach D, Cutler AJ, Ronda JM. A role for non-rapid-eye-movement sleep homeostasis in perceptual learning. J Neurosci. 2008;28:2766–2772. doi: 10.1523/JNEUROSCI.5548-07.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Agnew HW, Jr, Webb WB, Williams RL. The first night effect: an EEG study of sleep. Psychophysiology. 1966;2:263–266. doi: 10.1111/j.1469-8986.1966.tb02650.x. [DOI] [PubMed] [Google Scholar]
  3. Ahveninen J, Lin FH, Kivisaari R, Autti T, Hamalainen M, Stufflebeam S, Belliveau JW, Kahkonen S. MRI-constrained spectral imaging of benzodiazepine modulation of spontaneous neuromagnetic activity in human cortex. Neuroimage. 2007;35:577–582. doi: 10.1016/j.neuroimage.2006.12.033. [DOI] [PubMed] [Google Scholar]
  4. Aton SJ, Broussard C, Dumoulin M, Seibt J, Watson A, Coleman T, Frank MG. Visual experience and subsequent sleep induce sequential plastic changes in putative inhibitory and excitatory cortical neurons. Proc Natl Acad Sci U S A. 2013;110:3101–3106. doi: 10.1073/pnas.1208093110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Aton SJ, Seibt J, Dumoulin M, Jha SK, Steinmetz N, Coleman T, Naidoo N, Frank MG. Mechanisms of sleep-dependent consolidation of cortical plasticity. Neuron. 2009;61:454–466. doi: 10.1016/j.neuron.2009.01.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Born J, Wilhelm I. System consolidation of memory during sleep. Psychol Res. 2012;76:192–203. doi: 10.1007/s00426-011-0335-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Browman CP, Cartwright RD. The first-night effect on sleep and dreams. Biol Psychiatry. 1980;15:809–812. [PubMed] [Google Scholar]
  8. Cajochen C, Munch M, Knoblauch V, Blatter K, Wirz-Justice A. Age-related changes in the circadian and homeostatic regulation of human sleep. Chronobiol Int. 2006;23:461–474. doi: 10.1080/07420520500545813. [DOI] [PubMed] [Google Scholar]
  9. Carrier J, Viens I, Poirier G, Robillard R, Lafortune M, Vandewalle G, Martin N, Barakat M, Paquet J, Filipini D. Sleep slow wave changes during the middle years of life. Eur J Neurosci. 2011;33:758–766. doi: 10.1111/j.1460-9568.2010.07543.x. [DOI] [PubMed] [Google Scholar]
  10. Coble P, McPartland RJ, Silva WJ, Kupfer DJ. Is there a first night effect? (a revisit) Biol Psychiatry. 1974;9:215–219. [PubMed] [Google Scholar]
  11. Cox RW, Jesmanowicz A. Real-time 3D image registration for functional MRI. Magn Reson Med. 1999;42:1014–1018. doi: 10.1002/(sici)1522-2594(199912)42:6<1014::aid-mrm4>3.0.co;2-f. [DOI] [PubMed] [Google Scholar]
  12. Curcio G, Ferrara M, Piergianni A, Fratello F, De Gennaro L. Paradoxes of the first-night effect: a quantitative analysis of antero-posterior EEG topography. Clin Neurophysiol. 2004;115:1178–1188. doi: 10.1016/j.clinph.2003.12.018. [DOI] [PubMed] [Google Scholar]
  13. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysisISegmentation and surface reconstruction. Neuroimage. 1999;9:179–194. doi: 10.1006/nimg.1998.0395. [DOI] [PubMed] [Google Scholar]
  14. Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW, Lewine JD, Halgren E. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron. 2000;26:55–67. doi: 10.1016/s0896-6273(00)81138-1. [DOI] [PubMed] [Google Scholar]
  15. Dale AM, Sereno MI. Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach. J Cogn Neurosci. 1993;5:162–176. doi: 10.1162/jocn.1993.5.2.162. [DOI] [PubMed] [Google Scholar]
  16. Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53:1–15. doi: 10.1016/j.neuroimage.2010.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Dresler M, Kluge M, Genzel L, Schussler P, Steiger A. Impaired off-line memory consolidation in depression. Eur Neuropsychopharmacol. 2010;20:553–561. doi: 10.1016/j.euroneuro.2010.02.002. [DOI] [PubMed] [Google Scholar]
  18. Engel SA, Rumelhart DE, Wandell BA, Lee AT, Glover GH, Chichilnisky EJ, Shadlen MN. fMRI of human visual cortex. Nature. 1994;369:525. doi: 10.1038/369525a0. [DOI] [PubMed] [Google Scholar]
  19. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207. doi: 10.1006/nimg.1998.0396. [DOI] [PubMed] [Google Scholar]
  20. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14:11–22. doi: 10.1093/cercor/bhg087. [DOI] [PubMed] [Google Scholar]
  21. Fize D, Vanduffel W, Nelissen K, Denys K, Chef d'Hotel C, Faugeras O, Orban GA. The retinotopic organization of primate dorsal V4 and surrounding areas: A functional magnetic resonance imaging study in awake monkeys. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2003;23:7395–7406. doi: 10.1523/JNEUROSCI.23-19-07395.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Frank MG, Issa NP, Stryker MP. Sleep enhances plasticity in the developing visual cortex. Neuron. 2001;30:275–287. doi: 10.1016/s0896-6273(01)00279-3. [DOI] [PubMed] [Google Scholar]
  23. Gais S, Molle M, Helms K, Born J. Learning-dependent increases in sleep spindle density. J Neurosci. 2002;22:6830–6834. doi: 10.1523/JNEUROSCI.22-15-06830.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Gais S, Plihal W, Wagner U, Born J. Early sleep triggers memory for early visual discrimination skills. Nat Neurosci. 2000;3:1335–1339. doi: 10.1038/81881. [DOI] [PubMed] [Google Scholar]
  25. Goodale MA. Visual pathways supporting perception and action in the primate cerebral cortex. Curr Opin Neurobiol. 1993;3:578–585. doi: 10.1016/0959-4388(93)90059-8. [DOI] [PubMed] [Google Scholar]
  26. Hamalainen M, Ilmoniemi R. Technical Report TKK-F-A559 Helsinki University of Technology. Finland: 1984. Interpreting measured magnetic fields of the brain: estimates of current distributions. [Google Scholar]
  27. Hamalainen MS, Sarvas J. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Trans Biomed Eng. 1989;36:165–171. doi: 10.1109/10.16463. [DOI] [PubMed] [Google Scholar]
  28. He S, Cavanagh P, Intriligator J. Attentional resolution and the locus of visual awareness. Nature. 1996;383:334–337. doi: 10.1038/383334a0. [DOI] [PubMed] [Google Scholar]
  29. Hensch TK. Critical period plasticity in local cortical circuits. Nat Rev Neurosci. 2005;6:877–888. doi: 10.1038/nrn1787. [DOI] [PubMed] [Google Scholar]
  30. Herbst E, Metzler TJ, Lenoci M, McCaslin SE, Inslicht S, Marmar CR, Neylan TC. Adaptation effects to sleep studies in participants with and without chronic posttraumatic stress disorder. Psychophysiology. 2010;47:1127–1133. doi: 10.1111/j.1469-8986.2010.01030.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Huber R, Ghilardi MF, Massimini M, Tononi G. Local sleep and learning. Nature. 2004;430:78–81. doi: 10.1038/nature02663. [DOI] [PubMed] [Google Scholar]
  32. Kajimura N, Kato M, Sekimoto M, Watanabe T, Takahashi K, Okuma T, Mizuki Y, Yamada M. A polysomnographic study of sleep patterns in normal humans with low- or high-anxiety personality traits. Psychiatry Clin Neurosci. 1998;52:317–320. doi: 10.1046/j.1440-1819.1998.00401.x. [DOI] [PubMed] [Google Scholar]
  33. Karni A, Tanne D, Rubenstein BS, Askenasy JJ, Sagi D. Dependence on REM sleep of overnight improvement of a perceptual skill. Science. 1994;265:679–682. doi: 10.1126/science.8036518. [DOI] [PubMed] [Google Scholar]
  34. Kourtzi Z, Huberle E. Spatiotemporal characteristics of form analysis in the human visual cortex revealed by rapid event-related fMRI adaptation. Neuroimage. 2005;28:440–452. doi: 10.1016/j.neuroimage.2005.06.017. [DOI] [PubMed] [Google Scholar]
  35. Le Bon O, Hoffmann G, Tecco J, Staner L, Noseda A, Pelc I, Linkowski P. Mild to moderate sleep respiratory events: one negative night may not be enough. Chest. 2000;118:353–359. doi: 10.1378/chest.118.2.353. [DOI] [PubMed] [Google Scholar]
  36. Le Bon O, Minner P, Van Moorsel C, Hoffmann G, Gallego S, Lambrecht L, Pelc I, Linkowski P. First-night effect in the chronic fatigue syndrome. Psychiatry research. 2003;120:191–199. doi: 10.1016/s0165-1781(03)00185-9. [DOI] [PubMed] [Google Scholar]
  37. Levendowski DJ, Zack N, Rao S, Wong K, Gendreau M, Kranzler J, Zavora T, Westbrook PR. Assessment of the test-retest reliability of laboratory polysomnography. Sleep Breath. 2009;13:163–167. doi: 10.1007/s11325-008-0214-6. [DOI] [PubMed] [Google Scholar]
  38. Lin FH, Witzel T, Hamalainen MS, Dale AM, Belliveau JW, Stufflebeam SM. Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain. Neuroimage. 2004;23:582–595. doi: 10.1016/j.neuroimage.2004.04.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lorenzo JL, Barbanoj MJ. Variability of sleep parameters across multiple laboratory sessions in healthy young subjects: the "very first night effect". Psychophysiology. 2002;39:409–413. doi: 10.1017.S0048577202394010. [DOI] [PubMed] [Google Scholar]
  40. Manoach DS, Stickgold R. Does abnormal sleep impair memory consolidation in schizophrenia? Front Hum Neurosci. 2009;3:21. doi: 10.3389/neuro.09.021.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Mascetti L, Muto V, Matarazzo L, Foret A, Ziegler E, Albouy G, Sterpenich V, Schmidt C, Degueldre C, Leclercq Y, Phillips C, Luxen A, Vandewalle G, Vogels R, Maquet P, Balteau E. The impact of visual perceptual learning on sleep and local slow-wave initiation. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2013;33:3323–3331. doi: 10.1523/JNEUROSCI.0763-12.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Massimini M, Huber R, Ferrarelli F, Hill S, Tononi G. The sleep slow oscillation as a traveling wave. J Neurosci. 2004;24:6862–6870. doi: 10.1523/JNEUROSCI.1318-04.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Mishkin M, Ungerleider LG. Contribution of striate inputs to the visuospatial functions of parieto-preoccipital cortex in monkeys. Behav Brain Res. 1982;6:57–77. doi: 10.1016/0166-4328(82)90081-x. [DOI] [PubMed] [Google Scholar]
  44. Nir Y, Staba RJ, Andrillon T, Vyazovskiy VV, Cirelli C, Fried I, Tononi G. Regional slow waves and spindles in human sleep. Neuron. 2011;70:153–169. doi: 10.1016/j.neuron.2011.02.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Previc FH, Mullen TJ. A comparison of the latencies of visually induced postural change and self-motion perception. J Vestib Res. 1990;1:317–323. [PubMed] [Google Scholar]
  46. Rechtschaffen A, Kales A. A Manual of standardized terminology, techniques, and scoring system for sleep stages of human subjects. Washington DC: Public Health Service, U.S. Government Printing Office; 1968. [Google Scholar]
  47. Rechtschaffen A, Verdone P. Amount of Dreaming: Effect of Incentive, Adaptation to Laboratory, and Individual Differences. Perceptual and motor skills. 1964;19:947–958. doi: 10.2466/pms.1964.19.3.947. [DOI] [PubMed] [Google Scholar]
  48. Rosadini G, Consoli D, Ferrillo F, Rodriguez G, Sannita WG, Silvestro C. Correlates of adaptation to the sleep laboratory. Behavior, sleep organization, quantitative EEG. Neuropsychobiology. 1983;10:178–182. doi: 10.1159/000118006. [DOI] [PubMed] [Google Scholar]
  49. Rotenberg VS, Hadjez J, Kimhi R, Indurski P, Sirota P, Mosheva T, Benatov R, Elizur A. First night effect in depression: new data and a new approach. Biol Psychiatry. 1997;42:267–274. doi: 10.1016/S0006-3223(96)00343-5. [DOI] [PubMed] [Google Scholar]
  50. Rubin N, Nakayama K, Shapley R. Enhanced perception of illusory contours in the lower versus upper visual hemifields. Science. 1996;271:651–653. doi: 10.1126/science.271.5249.651. [DOI] [PubMed] [Google Scholar]
  51. Sereno MI, Mcdonald CT, Allman JM. Analysis of Retinotopic Maps in Extrastriate Cortex. Cerebral Cortex. 1994;4:601–620. doi: 10.1093/cercor/4.6.601. [DOI] [PubMed] [Google Scholar]
  52. Sharpley AL, Solomon RA, Cowen PJ. Evaluation of first night effect using ambulatory monitoring and automatic sleep stage analysis. Sleep. 1988;11:273–276. doi: 10.1093/sleep/11.3.273. [DOI] [PubMed] [Google Scholar]
  53. Stickgold R, James L, Hobson JA. Visual discrimination learning requires sleep after training. Nat Neurosci. 2000;3:1237–1238. doi: 10.1038/81756. [DOI] [PubMed] [Google Scholar]
  54. Suetsugi M, Mizuki Y, Yamamoto K, Uchida S, Watanabe Y. The effect of placebo administration on the first-night effect in healthy young volunteers. Prog Neuropsychopharmacol Biol Psychiatry. 2007;31:839–847. doi: 10.1016/j.pnpbp.2007.01.019. [DOI] [PubMed] [Google Scholar]
  55. Tamaki M, Huang TR, Yotsumoto Y, Hamalainen M, Lin FH, Nanez JE, Sr, Watanabe T, Sasaki Y. Enhanced spontaneous oscillations in the supplementary motor area are associated with sleep-dependent offline learning of finger-tapping motor-sequence task. J Neurosci. 2013;33:13894–13902. doi: 10.1523/JNEUROSCI.1198-13.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Tamaki M, Nittono H, Hayashi M, Hori T. Examination of the first-night effect during the sleep-onset period. Sleep. 2005a;28:195–202. doi: 10.1093/sleep/28.2.195. [DOI] [PubMed] [Google Scholar]
  57. Tamaki M, Nittono H, Hayashi M, Hori T. Spectral analysis of the first-night effect on the sleep-onset period. Sleep and Biological Rhythms. 2005b;3:122–129. [Google Scholar]
  58. Tamaki M, Nittono H, Hori T. The first-night effect occurs at the sleep-onset period regardless of the temporal anxiety level in healthy students. Sleep and Biological Rhythms. 2005;3:92–94. [Google Scholar]
  59. Tononi G, Cirelli C. Sleep and synaptic homeostasis: a hypothesis. Brain Res Bull. 2003;62:143–150. doi: 10.1016/j.brainresbull.2003.09.004. [DOI] [PubMed] [Google Scholar]
  60. Torasdotter M, Metsis M, Henriksson BG, Winblad B, Mohammed AH. Expression of neurotrophin-3 mRNA in the rat visual cortex and hippocampus is influenced by environmental conditions. Neurosci Lett. 1996;218:107–110. doi: 10.1016/s0304-3940(96)13127-x. [DOI] [PubMed] [Google Scholar]
  61. Torasdotter M, Metsis M, Henriksson BG, Winblad B, Mohammed AH. Environmental enrichment results in higher levels of nerve growth factor mRNA in the rat visual cortex and hippocampus. Behav Brain Res. 1998;93:83–90. doi: 10.1016/s0166-4328(97)00142-3. [DOI] [PubMed] [Google Scholar]
  62. Toussaint M, Luthringer R, Schaltenbrand N, Nicolas A, Jacqmin A, Carelli G, Gresser J, Muzet A, Macher JP. Changes in EEG power density during sleep laboratory adaptation. Sleep. 1997;20:1201–1207. doi: 10.1093/sleep/20.12.1201. [DOI] [PubMed] [Google Scholar]
  63. Toussaint M, Luthringer R, Staner L, Muzet A, Macher J. Changes in EEG power density during sleep laboratory adaptation in depressed inpatients. Biol Psychiatry. 2000;47:626–633. doi: 10.1016/s0006-3223(99)00161-4. [DOI] [PubMed] [Google Scholar]
  64. Vyazovskiy VV, Olcese U, Hanlon EC, Nir Y, Cirelli C, Tononi G. Local sleep in awake rats. Nature. 2011;472:443–447. doi: 10.1038/nature10009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Walsh JK. Enhancement of slow wave sleep: implications for insomnia. J Clin Sleep Med. 2009;5:S27–S32. [PMC free article] [PubMed] [Google Scholar]
  66. Wauquier A, van Sweden B, Kerkhof GA, Kamphuisen HA. Ambulatory first night sleep effect recording in the elderly. Behav Brain Res. 1991;42:7–11. doi: 10.1016/s0166-4328(05)80034-8. [DOI] [PubMed] [Google Scholar]
  67. Webb WB, Campbell SS. The first night effect revisited with age as a variable. Waking Sleeping. 1979;3:319–324. [PubMed] [Google Scholar]
  68. Westerberg CE, Mander BA, Florczak SM, Weintraub S, Mesulam MM, Zee PC, Paller KA. Concurrent impairments in sleep and memory in amnestic mild cognitive impairment. J Int Neuropsychol Soc. 2012;18:490–500. doi: 10.1017/S135561771200001X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Yotsumoto Y, Sasaki Y, Chan P, Vasios CE, Bonmassar G, Ito N, Nanez JE, Sr, Shimojo S, Watanabe T. Location-specific cortical activation changes during sleep after training for perceptual learning. Curr Biol. 2009;19:1278–1282. doi: 10.1016/j.cub.2009.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Yotsumoto Y, Watanabe T, Sasaki Y. Different dynamics of performance and brain activation in the time course of perceptual learning. Neuron. 2008;57:827–833. doi: 10.1016/j.neuron.2008.02.034. [DOI] [PMC free article] [PubMed] [Google Scholar]

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