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Daytime micro-naps in a nocturnal migrant: An EEG analysis.
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Fuchs, T., Maury, D., Moore, F. R. & Bingman, V. P.
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Supplemental methods, references and figure captions
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Animals
Adult Swainson’s thrushes (Catharus ustulatus) of mixed gender were mist-netted
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on the Mississippi gulf coast in the fall of 2002 and 2003. Birds were temporarily housed
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at the University of Southern Mississippi, and later transported to Bowling Green State
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University (BGSU). Transport and housing conditions were in accordance with BGSU
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animal care and use regulations. The thrushes were individually housed in wire cages (60
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x 40 x 40 cm) containing 2 perches. All animals were maintained on a 12:12 light-dark
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cycle throughout the experiments. Birds were provided with an ad libitum diet of meal
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worms, mixed fruit, moistened monkey biscuits (Mazuri) and a vitamin supplement
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(Eight in One Pet Products) with water available at all times.
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Electrophysiology
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Thrushes were bilaterally implanted with stainless steel screw electrodes over the
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hyperpallium accessorium (dorsal “Wulst” region; 3 mm lateral to the midline) to record
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Electro-Encephalogram (EEG). Bare wires (100μm stainless steel) were threaded under
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the skin, above the orbital bones, to detect eye movements (EOG). A reference electrode
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used for differential recordings was implanted along the posterior midline. Wires were
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gathered into a small connector board (Microsystems) and the array was fixed to the skull
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with dental acrylic. Surgical procedures were conducted under isoflurane anesthesia, in
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accordance with Bowling Green State University animal care and use regulations.
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After surgery animals were allowed to recover for 2 weeks. During the second
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week of recovery, birds were introduced to the recording environment; a glass terrarium
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(50x50x30 cm) that allowed camera access from every angle. The recording cage was
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furnished with a single perch to make a bird’s position in the cage more predictable.
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Birds were connected to a movable lightweight cable (Dragonfly Inc.) through their head
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plug/connector board. Animals were allowed to adjust to the recording cable and
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recording environment for a minimum of 3 nights before any recordings were conducted.
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The presented data are based on daytime recordings during episodes of migratory
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restlessness and nighttime recordings in the same birds when non-migratory. Continuous
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daytime and nighttime recordings during the migratory season were not employed
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because the birds, while otherwise behaving normally (posture, movement, grooming,
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drinking and feeding), failed to show signs of nocturnal migratory restlessness under the
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restraint of the recording cable. Consequently, under recording conditions, birds initially
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failed to show the sleep-like, daytime behaviour, initially detected during behavioural
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observations (Fuchs et al. 2006), presumably because of an absence of nocturnal sleep
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loss. To address this problem, we resorted to ‘daytime only’ recordings. Birds remained
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in their home cages at night and were moved to the recording cage by day. Birds treated
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in this manner displayed migratory restlessness at night in their home cages and sleep-
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related, daytime behaviour during EEG recordings.
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Differentially recorded signals (EEG, EOG) first passed through a head-stage
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amplifier constructed of JFETs, were then amplified 2000-4000 times, bandpass filtered
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between 1 and 50 Hz (Neuralynx, Tucson, AZ), and sampled at 200 Hz (DataWave
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Technologies, Longmont, CO). All EEG recordings were accompanied by video
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recordings using 4 Sony camcorders and a screen splitter. While a bird’s behaviour was
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monitored with up to 3 camcorders, one camera recorded the EEG-timer to allow for
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accurate temporal alignment of behaviour and EEG samples. Between recording sessions
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birds were housed in their home cages.
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Behaviour
Captive migratory birds display the behavioural changes associated with the
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migratory season in the form of nocturnal restlessness or “zugunruhe” (Berthold et al.
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2000). Home cage activity was recorded continually with infrared motion detectors. A
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bird was classified as migratory if it showed nocturnal activity on every night for at least
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one week, and non-migratory if it did not show nocturnal activity for at least seven days.
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Daytime behaviour was categorized according to supplemental Tab. 1 employing the
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same behavioural criteria as in Fuchs et al. (2006). Behaviour shorter than 4 seconds was
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not included in the analysis.
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Analysis
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The EEG analysis of mammalian sleep rests on the assumption that the intensity
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of Δ-activity (average EEG slow-wave activity typically in the 1-4 Hz frequency range,
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assessed as ‘power’ in the ‘Δ-band’ by power spectrum analysis; see below) accurately
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reflects sleep quality (i.e. depth of sleep). The validity of this assumption is based on
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observations in mammals showing that increased delta power corresponds to higher
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arousal thresholds (Neckelmann & Ursin 1993) and that sleep deprivation results in
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increased Δ-power ( the so-called ‘Δ-rebound’) during recovery sleep (reviewed in
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Borbely & Acherman 2000). Mammals, including humans, show the most intense slow
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wave activity at the beginning of their subjective night when ‘sleep pressure’ is
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presumably highest. Several studies in birds indicate that this aspect of slow-wave sleep
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is shared with mammals (Martinez-Gonzalez et al. 2008, Rattenborg et al. 2004,
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Szymczak et al.1996, Van Luijtelaar et al. 1987). Consequently, spectral power in the Δ-
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band was employed as a measure of sleep quality in the present study.
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The video recordings were manually scored for behaviour (daytime sleep,
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unilateral eye closure, drowsiness, alert wakefulness) according to the criteria outlined in
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Tab. 1 and Fig. 1 of the electronic supplemental material and the corresponding sections
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of the time stamped EEG recordings were subjected to further analysis (also see Fig. 2 of
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the electronic supplemental material). All scoring was conducted blind to condition; i.e.,
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behavioural state was recorded without knowledge of EEG activity. Episodes of daytime
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drowsiness typically last several minutes, with episodes of daytime sleep and unilateral
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eye closure nested within (Fuchs et al. 2006). Consequently the analyzed EEG samples of
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drowsiness, unlike the samples of unilateral eye closure and daytime sleep, do not
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correspond in length to entire bouts of drowsiness (10-20s samples, frequently repeated
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samples within a single period of drowsiness, were taken). Nocturnal slow-wave sleep
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samples consisted of 5 min episodes of uninterrupted sleep behaviourally identified on
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the video recordings. Swainson’s thrushes display 2 nocturnal sleeping postures, front
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sleep and back sleep (Fig. 1; Tab. 1 of the electronic supplemental material). 3 of the
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tested birds exclusively displayed a back sleep posture during the nighttime sleep
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samples. 2 animals exclusively showed front sleep and one animal showed front sleep
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during the early (2h) sample and switched to a back sleep posture for the late (10 h)
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sample (1 of the 7 animals had to be eliminated from this comparison due to a corrupted
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nighttime video file). A similar preference of birds for either sleeping posture was also
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observed in Swainson’s thrushes that were not implanted or restrained by recording
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equipment (Fuchs et al. 2006).
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EEG files were visually inspected for artifact (movement, cutting/mending) and
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episodes of rapid eye movement (REM) sleep were removed from the night-time sleep
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samples (characterized by clusters of eye movements on the EOG traces and a decline of
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slow wave activity in the EEG while birds remained behaviourally asleep). The
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processed EEG was then subjected to power spectrum analysis (Fast Fourier Transform
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(FFT), Hanning window, 0.2 Hz frequency resolution) and average EEG power was
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computed for frequency values between 1.4 and 4 Hz (Δ-power).
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The power values corresponding to the analyzed behaviour (daytime sleep,
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unilateral eye closure, drowsiness) were standardized and expressed as a percentage of Δ-
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power during alert wakefulness for each electrode/recording session, before they were
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subjected to statistical analysis. The alert wakefulness EEG consisted of a one minute
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sample of alert wakefulness from the beginning and a one minute sample from the end of
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each recording session.
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Fast Fourier Transform (FFT) and Power Spectrum
FFT is a mathematical procedure that allows to describe a complex signal (an
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EEG trace for example) by breaking it down into sine and cosine components of discrete
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frequencies. A Power spectrum assigns a power value to each frequency component
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(Armitage et al. 1995) which is a measure of how much energy a certain frequency
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component contributes to the original signal or how prominent a certain frequency
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component is in the signal. The frequency resolution of an FFT is determined by the
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number of data-points entered into the equation (i.e. the size of the “window”) and the
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sampling frequency of the analyzed signal. In this study a 1024 point window at a
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sampling frequency of 200 Hz resulted in a 0.2 Hz resolution. The final power value for a
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certain frequency represents the average of all windows applied to a given signal for that
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frequency. FFT algorithms have a tendency to generate leakage if the signal amplitude on
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both ends of the applied window does not equal zero (frequencies that are not
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contributing to the original signal are detected). Consequently, windowing functions are
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used to artificially reduce the signal amplitude to zero at a window’s edges. A variety of
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different windows (e.g. Hanning, Hamming, Koenig) are available. In the present study a
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Hanning window was employed. The analysis was conducted with DataWave, Neuro
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Explorer and EEG Lab (Delorme & Makeig 2004) software. For an introduction to FFT
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see Ramirez (1985).
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Statistics
The data were tested for normality using the Shapiro-Wilk test. Normaly
distributed data were subjected to paired t-tests or within subjects repeated measures
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analysis of variance (ANOVA/GLM/univariate). The distribution of the difference scores
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for paired comparisons was also tested for normality with the Shapiro-Wilk test.
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Violations of sphericity were compensated with the Greenhouse-Geyser correction. If
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significant main effects were found with ANOVAs, paired t-tests were used for follow up
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comparisons. Multiple comparisons were controlled for Type I error with the Holm
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Simultaneous Testing Procedure (a step down procedure derived from the Bonferroni
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method; Netter et al. 1996). Wilcoxon signed ranks tests were used to compare sleep-like
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daytime behaviour to alert wakefulness (the 100% line in Fig. 2 A of the manuscript
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proper). Because of the conservative nature and low power of non-parametric tests, these
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3 comparisons were not corrected for type 1 error.
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Supplemental References
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Armitage, R., Hoffmann, R., Fitch, T., Morel, C.,& Bonato, R. 1995 A comparison of
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period amplitude and power spectral analysisof sleep EEG in normal adults and
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depressed outpatients. Psychiat. Res. 56, 245-256.
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Berthold, P., Fiedler, W. & Querner, U. 2000 Migratory restlessness or zugunruhe in
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birds: A description based on video recordings under infrared illumination. J. Ornithol.
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141, 285-299.
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Borbély, A. A. & Acherman, P. A. 2000 Sleep homeostasis and models of sleep
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regulation. In Principles and practice of sleep medicine (eds M. H. Kryger, T. Roth,
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W. C. Dement), pp 377 -390. Philadelphia: Saunders.
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Delorme, A., & Makeig, S. 2004 EEGLAB: an open source toolbox for analysis of
single-trial EEG dynamics. J. Neurosci. Meth. 134, 9-21.
Neckelmann, D., Ursin, R. 1993 Sleep stages and EEG power spectrum in relation to
acoustical stimulus arousal threshold in the rat. Sleep 16, 467-77.
Netter, J., Kutner, M. H., Nachtsheim, C. J., Wasserman, W. 1996 Applied Linear
Statistical Models. USA: Irwin.
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Ramirez, R. W. 1985 The FFT. Fundamentals and Concepts. USA: Prentice-Hall.
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Van Luijtelaar, E. L. J. M.,van der Grinten, C. P. M., Blokhuis, H. J., & Coenen, A. M. L.
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1987 Sleep in the domestic hen (Gallus domesticus). Physiol. Behav. 41, 409-414.
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Supplemental Figure Captions
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Fig. 1: Representative examples of behavioural postures. Daytime sleep and night-time
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front sleep are behaviourally identical. Note that in the night-time back sleep posture the
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bird turns its head and buries its beak and eyes in the scapula feathers. The back sleep
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posture was not observed at daytime and not every bird studied showed this posture at
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night.
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Fig. 2: Analysis of sleep-like daytime behaviour. Episodes of each behaviour (here:
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daytime sleep) were identified behaviourally in the video recordings. The corresponding
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EEG was then selectively removed from the recordings, combined into a single file for
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each recording session and subjected to spectral analysis (images do not correspond to
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the EEG sample and are only intended to illustrate behavioural state).
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