BRAIN-BEHAVIOR ADAPTATIONS TO SLEEP LOSS IN THE NOCTURNALLY MIGRATING SWAINSON’S THRUSH (CATHARUS USTULATUS)
Thomas Fuchs
A Dissertation
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
August 2006
Committee:
Verner P. Bingman, Advisor
Donald S. Cooper Graduate Faculty Representative
Kevin Pang
Dale Klopfer
Patricia Sharp © 2006
Thomas Fuchs
All Rights Reserved iii
ABSTRACT
Verner P. Bingman, Advisor
Many typically diurnal songbirds experience dramatic sleep loss during the migratory seasons because of their nocturnal flights. However, nocturnally migrating songbirds continue to function normally with no observable effect of sleep loss on their behavior. To mitigate the effects of sleep loss, nocturnal migrants may engage in daytime sleep, unihemispheric sleep, sleep during migratory flight, or increased quality of what sleep is available. Studying the Swainson’s thrush, a long-distance trans-gulf migrant, I investigated how avian migrants might compensate for sleep loss during the migratory season. Daytime behavior, nighttime behavior and forebrain EEG activity was recorded in thrushes when migratory and non-migratory. Behavioral sleeping postures and their EEG/brain correlates were identified throughout the 24 h light-dark cycle.
Slow wave sleep (SWS) and rapid eye movement (REM) sleep were investigated, and the temporal profile of the two sleep states was analyzed. Brain activity (EEG power) in the delta frequency band (1.5 – 4Hz) was employed as a measure of sleep quality.
Interestingly, the most prominent alterations in sleep and sleep-related behavior in nocturnally active migratory thrushes were found during the day. In contrast to their behavior when non-migratory, migratory Swainson’s thrushes engaged in numerous episodes of daytime sleep, unilateral eye closure, and an intermediate sleep-like state referred to as drowsiness. The electrophysiological recordings demonstrated that the observed behavior was accompanied by reliable sleep like changes in brain activity. In addition, EEG activity during episodes of unilateral iv eye closure was frequently accompanied byinterhemispheric asymmetries characteristic of unihemispheric sleep. The relatively brief but frequent daytime sleep states (“micro naps”) may represent an adaptive balance that enables migratory birds to compensate for extended periods of nocturnal sleep loss during the subsequent day without rendering them entirely vulnerable to environmental challenges like predation and the need to feed and store energy. Our findings also offer the intriguing possibility that avian migrants partially compensate for nocturnal sleep loss by taking lateralized naps during the day.
Non-migratory Swainson’s thrushes exhibit a marked decrease in delta power during the night indicating a parallel decline in sleep quality. Decreasing SWS pressure, possibly in concert with a circadian REM sleep regulating mechanism, may explain increasing amounts of REM sleep during the latter part of the night. Evidence for compensatory changes in nighttime sleep in migratory thrushes is presented.
The present work suggests that birds, like mammals, require a minimum amount of sleep.
The finding that a nocturnal migrant, a species highly adapted to its migratory life style, requires compensatory sleep, strongly suggests that a basic need for sleep is shared by many if not all avian species. Furthermore, the observed sleep characteristics indicate that some aspects of avian and mammalian sleep are similarly regulated. Avian sleep, therefore, may provide further insight into processes involved in the regulation of sleep and resting states that likely generalize to many species, vertebrates and invertebrates alike. v
ACKNOWLEDGMENTS
I would like to express my deepest appreciation to Herr Bingman, my dissertation advisor, for guiding me through the process of conducting and writing this dissertation, and also for providing me with an opportunity to do what cannot be done easily these days (comparative sleep research). My sincere gratitude also goes to Drs Pat Sharp, Kevin Pang, Dale Klopfer and
Donald Cooper, my dissertation committee, for offering invaluable advice and having a lot of patience with my rather erratic time schedule. I am also indebted to the J. P. Scott Center for
Neurosience Brain and Behavior for awarding me a research fellowship during the last year of this project that allowed me to finally get my act together. Finally, I would like to thank my family and newly wed wife Ako for putting up with me during theses last stressful weeks.
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TABLE OF CONTENTS
Page
INTRODUCTION ...... 1
PART I: BEHAVIOR...... 7
Methods ...... 7
Results ...... 12
Discussion ...... 21
PART II: EEG RECORDINGS ...... 25
General EEG Methods ...... 25
Analysis ...... 28
Slow Wave Sleep and REM Sleep ...... 29
Slow Wave Sleep ...... 29
REM Sleep...... 31
Daytime Recordings ...... 32
Analysis ...... 32
Results ...... 36
Discussion ...... 44
Nighttime Recordings...... 47
Analysis ...... 47
Results ...... 52
Non-migratory thrushes ...... 52
Nighttime Sleep during the Migratory Season ...... 64
Discussion...... 67
vii
GENERAL DISCUSSION...... 75
Daytime Sleep ...... 76
Unilateral Eye Closure...... 78
Drowsiness ...... 81
Some Ecological Considerations...... 84
Comparative Aspects...... 88
CONCLUSION ...... 95
APPENDIX I: Mammalian Neocortex and the Avian Wulst Formation (Hyperpallium):
Similarities and Differences ...... 98
APPENDIX II: Sleep in Invertebrates, Fish, Amphibians and Reptiles ...... 102
Defining Sleep...... 102
Invertebrates ...... 104
Fish ...... 106
Amphibians ...... 107
Reptiles ...... 108
APPENDIX III: Neurophysiology of mammalian REM Sleep and Non-REM Sleep.
Comparative Aspects ...... 112
Mammalian Non-REM Sleep...... 114
Comparing Mammalian and Avian Non-REM sleep ...... 118
Mammalian REM sleep ...... 123
Avian REM sleep ...... 126
Conclusions ...... 128
APPENDIX IV: REPLICATION IN THE HISTORY OF PSYCHOLOGY: PATRICK &
GILBERT (1896) ON SLEEP DEPRIVATION ...... 129 viii
The Original Study ...... 131
The Replication Project ...... 136
Comparison of Results...... 139
Historical Questions ...... 143
REFERENCES ...... 152
1
INTRODUCTION
Sleep deprivation and sleep fragmentation pose serious problems to mammals, resulting in substantial deficits of cognition (Horne, 1988; Bonnet & Arand, 2003), physiology (Everson,
1995; Rogers et al., 2001), and vigilance (Horne & Reyner, 1999; Bonnet & Arand, 2003). In today’s fast-paced societies, humans in particular are increasingly forced into lifestyles leading to irregular and deprived sleep patterns with all the negative consequences associated. 150 years of scientific sleep research, accompanied by great technological advances, have generated a wealth of data on the physiological and neuroanatomical substrates of mammalian sleep.
Nevertheless, the “function” of sleep is still unknown and remains the basis of much speculation
(e.g., Horne, 1988). Sleep, its function(s) and the probably related consequences of sleep deprivation have therefore remained a field of considerable interest at the junction of medicine and neuroscience.
To date, most studies on sleep and sleep deprivation have traditionally been conducted in
only a few species; predominantly cats, rats and humans. Much of the sleep deprivation work, for ethical reasons, has been performed in rats and cats while the majority of the findings have been directly related to humans. The methods of instrumental sleep deprivation required to keep animals awake, however, differ radically from those used in human sleep deprivation, resulting in stress and physiological side effects, which are nearly impossible to control for. Interestingly, the consequences of sleep deprivation in humans are generally less dire than those encountered in animal models1, possibly reflecting these differences in methodology. Nevertheless, the finding that instrumental sleep deprivation, under controlled experimental conditions, in at least
1 Death that is.
1 2
one species, the laboratory rat, results in death (Everson, 1995; Rechtschaffen & Bergmann,
1995), has firmly established itself in the public consciousness and was often, unquestioningly,
related to human sleep and sleep in general. In recent years some researchers have suggested
(e.g., Horne, 1988; Horne, 2000) that sleep deprivation may not be as harmful as previously
thought, and that the devastating effect of sleep deprivation on animal models may
predominantly reflect stress confounds caused by the various sleep deprivation procedures.
Obviously, ethical limitations do not allow testing such predictions in human subjects, and the limitations of animal models, to date, do not permit us to test this hypothesis in other species.
An animal model avoiding the limitations encountered in every previous model utilized is
therefore highly desirable and could present us with a substantial opportunity to learn more about
sleep, maybe even help to finally approach the question of sleep’s basic function(s). Avian
migrants may possibly offer such a model.
Twice a year billions of songbirds migrate, over thousands of kilometres, between their
wintering and breeding grounds. The vast majority of migratory passerine birds are diurnal,
feeding by day and sleeping at night. During their migrations, however, this picture changes
radically. Most species migrate at night (Kerlinger & Moore, 1989), yet the energetic demands of
migration ( Moore, 1992; Lindström, 2003) require them to maintain their diurnal feeding
patterns (passerines are rather small, so that a high surface to volume ratio exaggerates these
pressures). As a result, during the migratory seasons, birds lose substantial opportunity for
nighttime sleep, but that loss cannot be quantitatively compensated for during the day. If this rare
form of ‘voluntary’ sleep deprivation in the animal kingdom resulted in impairments similar to
the ones observed in humans and other mammals, migrants should face serious disadvantages at
2 3 stopover sites, where they compete for food with non-migratory species and are subject to predation. However, the resulting sleep deficit seems to have little if any effect on a migrant’s daytime performance.
Migratory birds are known to have developed physiological (Munro, 2003; Jenni &
Schaub, 2003) and morphological (Leisler & Winkler, 2003) adaptations in response to the energetic demands of migration. If sleep deprivation were to limit migratory efficiency, it would not be surprising if migrants had also evolved adaptations in response to the negative consequences of sleep loss. Migrants might change their daily activity patterns and compensate for lost sleep at night by sleeping/resting during the day. However, daytime sleep would likely be associated with an increased risk of predation and limit the time available to replenish depleted energy stores. Consequently, natural selection may have also promoted complementary/alternative mechanisms for sleep-loss compensation such as sleep during migratory flight, a physiological resilience to sleep deprivation (if possible), or increased sleep quality during the little time that remains for nocturnal sleep. Unihemispheric sleep, a phenomenon during which one hemisphere of the brain engages in slow wave sleep while the other hemisphere displays the electrophysiological correlates of wakefulness, has also been proposed to potentially have a compensatory function during avian migration (Rattenborg et al.
2000, Rattenborg et al. 2004). However, to date no scientific evidence is available to confirm this hypothesis. An alternative explanation for a differential effect of sleep deprivation on migrants and mammals may be that sleep and its basic functions in birds and mammals differ on a more fundamental level, a possibility that needs to be explored in more detail.
3 4
In my dissertation, I attempt to behaviorally and electrophysiologically characterize sleep and possible adaptive behavior in response to sleep loss in the nocturnally migrating Swainson’s thrush (Catharus ustulatus). Towards this goal, I recorded the behavior and brain activity of captive Swainson’s thrushes during migratory and non-migratory seasons from fall 2002 until spring 2006.
The Swainson’s thrush, a Nearctic-Neotropical long distance migrant, travels up to 5000
km from its breeding grounds in the boreal forests of northern Canada and Alaska to its
wintering grounds in Central and South America. It is also one of the few species whose
migratory behavior has been studied during migration in the wild by radio telemetry (Cochran,
1987). However, it is prohibitively difficult to study sleep in free-flying migrants. Fortunately,
captive migratory birds readily display the behavioral changes associated with the migratory
season in the form of appropriately oriented nocturnal restlessness or “Zugunruhe” (Berthold et
al. 2000). The changes in nocturnal activity in caged migrants have been studied for many years,
but rarely on a behavioral level. That is to say, activity, not sleep, was the focus of most of these studies (e.g. see Berthold, 1987), and activity, in most cases, was assessed by quantitative not qualitative methods (infrared motion detectors, ink funnels or micro switches). It was not until
2000 that Berthold and colleagues provided the first video recordings of “cage restlessness” and officially distinguished a flight substitute named “wing whirring” from “perch-hopping”
(Berthold et al., 2000).
The activity component of nocturnal migratory restlessness has been fairly well studied.
By contrast, sleep and resting behaviors have not received similar attention (with a single exception, the white crowned sparrow: Rattenborg et al., 2004). Although the quantities of
4 5
migratory cage restlessness are well documented (for review see Berthold 1987), to date, only
one study (Rattenborg et al., 2004) attempted to estimate the amount of sleep and rest in an
active migrant. As mentioned above, sleep has been studied extensively in only a few species,
predominantly mammals. Our knowledge on sleep in other species, especially non-mammalian
species (e.g., reptiles, birds) is quite limited (for a review see Hartse, 1994; Rattenborg, 2002;
appendix II). A comparison of sleep in mammalian species demonstrates that even within this
class, the daily amounts of sleep can differ substantially from species to species (for review see
Zepelin & Rechtschaffen, 1974; Tobler, 1995; Zepelin, 2000). For example, the amount of total
sleeping time (REM sleep + slow wave sleep) over the 24 hour period in mammals can range
from as little as 3 hours in the giraffe (in Tobler, 1995) to as much as 17-20 hours in the Siberian
hamster and the opossum (in Tobler, 1995). In addition, experimental manipulations in a laboratory setting cannot only have a significant impact on these numbers, they can also alter
sleeping postures giving sleep a very different appearance. To date, no study on sleep has been conducted in the Swainson’s thrush or any related new world thrush (the closest studied relative is the non-migratory European blackbird; Szymczak et al., 1996). Therefore, to study changes in sleep quotas and sleeping postures, sleep in the Swainson’s thrush had first to be characterized on a behavioral level. This endeavour comprised the first half of this dissertation during which the thrushes were video recorded in their home cages, during periods of diurnal activity and migratory restlessness. The study led to an index of behavior and a behavioral profile that, especially during daytime, showed some remarkable differences between the migratory and non- migratory state. Following this behavioral study it remained to be demonstrated that the observed sleep and sleep-related behavior was actually accompanied by corresponding changes in brain activity. To address this question 12 Swainson’s thrushes were implanted with EEG, EOG and
5 6
EMG electrodes and electrophysiological recordings were conducted over the course of the last 2 years.
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PART I: BEHAVIORAL EXPERIMENTS
Material and Methods:
Adult Swainson’s thrushes of mixed gender were mist-netted on the Mississippi gulf
coast in the fall of 2002, 2003 and 2004. All necessary federal and state permits were obtained.
Birds were temporarily housed at the University of Southern Mississippi, and then transported to
Bowling Green State University (BGSU). Transport and housing conditions were in accordance with BGSU animal care and use regulations. The thrushes were individually placed in cages (60 x 40 x 40 cm) and housed in the BGSU animal facility. Birds were maintained on an ad libitum diet of meal worms, mixed fruit, moistened monkey biscuits (Mazuri) and a vitamin supplement
(Eight in One Pet Products). Water was available at all times.
The main goal of the behavioral observations was to obtain a quantitative and qualitative
overview of the behavior displayed by captive Swainson’s thrushes in a holding environment
less restrictive than the one required for electrophysiological recordings. Animals were observed
with Sony video cameras (infrared night-shot function) to characterize behavioral states and
sleeping postures. As a result of preliminary observations, seven behavioral states were operationally defined: active wakefulness, alert wakefulness, drowsiness (D), front sleep (FS), back sleep (BS), unilateral eye closure (UEC) and wing whirring.
Active wakefulness: Active wakefulness was characterized by ongoing locomotor activity
(perch-hopping). In experiment 1 (see below) the additional operational distinction of specific
and “non-specific” behavior was employed. Goal-directed behavior (grooming, singing and
feeding), which serves an immediate purpose, was subcategorized as specific behavior. General
7 8
activity, mostly in the form of perch-hopping, does not promote an observable purpose, but can
consume large amounts of time, and was therefore categorized as non-specific behavior.
Alert wakefulness: Round eyes, quick and frequent head movements and an absence of locomotor activity characterized this behavioral state. In addition, the posture of alert
wakefulness was more erect than during drowsiness or front sleep, and feathers were not ruffled
(Fig. 1 d).
Drowsiness: A bird was perched in a sitting position with oval or partially closed eyes.
Feathers were ruffled; head movements were either slow or absent (Fig. 1 c).
Front sleep: The front sleep position resembled that of drowsiness. The state was further
characterized by ruffled feathers, a lack of head movements and closed eyes (Fig. 1 b).
Back sleep: A bird was perched in a sitting position. The head was facing backwards and
was partially buried in the scapula feathers. The bird’s eyes were usually not visible in this
posture (Fig. 1 a).
Unilateral eye closure: In birds unilateral eye closure (UEC) is the most reliable
behavioral indicator of unihemispheric sleep (Kavanau, 1998; Rattenborg et al., 2000).
Therefore, it was of particular interest if the quantities of UEC changed as the experimental
Swainson’s thrushes transitioned between a migratory and a non-migratory state. UEC was
solely defined by eye state. One eye had to be completely closed, while the other eye had to be fully visible and open. Behaviors shorter than two seconds were not included in the analysis.
8 9
Wing Whirring: Characterized by wing flapping, in an erect perched position.
Occasionally the bird would lose contact with its perch but usually birds returned into a perched
position immediately to resume flapping activity.
Fig. 1: Representative examples of the behavioral postures displayed by migratory and non- migratory Swainson’s thrushes. a: Back sleep. The bird’s eyes are covered by its feathers. b:
Front sleep; c: Drowsiness, an intermediate state between wakefulness and sleep, behaviorally characterized by ruffled feathers, oval eyes and inactivity; d: Alert wakefulness; e: Wing whirring; f: Daytime front sleep.
9 10
Data Collection and Analysis
To determine seasonal behavioral state (migratory/non-migratory), activity was recorded
continually with infrared motion detectors, mounted on the ceilings of the holding cages during
the entire observation period (12 months). A bird was classified as migratory if it showed nocturnal activity on every night for at least one week, and non-migratory if it did not show
nocturnal activity for at least seven days. Observations were made during both spring and fall migratory seasons, as well as during summer and winter non-migratory seasons. (Although there are known differences between fall and spring migration in the Swainson’s thrush (Evans Mack
& Yong, 2000) no distinction between seasons was made for the data analysis. For the purpose of this study, it was deemed sufficient to assume that similar amounts of sleep loss during the fall and spring migratory seasons pose a similar challenge to migratory birds. The operational criterion of at least 7 subsequent days of nocturnal activity attempts to ensure comparable amounts of sleep loss.)
Experiment 1 (N=6): One hour daytime video recording sessions were conducted 2 hours
after lights on (early), at midday (middle) and two hours before lights off (late), and manually scored for behavior. Video recordings were stopped at 60-sec intervals and the ongoing behavior
was classified according to one of the behavioral categories outlined above. The same sampling
method was used for the analysis of nocturnal recordings, with the first one hour recording
occurring 2 hours after lights off. All recordings were conducted with infrared camcorders
(Sony) in the animals’ home cages. Each bird was sampled during two 24 hour periods (two continual daytime and night-time recordings) when non-migratory, and two 24 hour periods
10 11
when migratory. The thrushes were kept on a light-dark cycle that varied from 14:10 to 12:12
(Swainson’s thrushes in the wild would rarely experience days shorter than 12 hours).
Experiment 2 (N=6): This experiment was designed to analyze episodes of daytime sleep
(DTS) and unilateral eye closure (UEC), and, therefore, used a different video recording
procedure that enabled the simultaneous viewing of both eyes. Animals were moved into a
recording cage with only one perch and Plexiglas side walls 24 hours prior to recordings.
Recordings of eye state were conducted with two camcorders and a screen splitter. The one-hour
video samples were obtained in the same manner as described for experiment 1. Because DTS
and UEC episodes have a duration of only several seconds, the video recordings were analyzed
continually and not, as in experiment 1, scored at 60-sec intervals (interval sampling for short
duration behaviors like DTS or UEC can result in an under-sampling of the number of behavioral
episodes, while the actual duration of the behavior might be overestimated). Eye closures shorter
than 2 seconds were not included in the analysis. Thrushes in experiment 2 were maintained on a
12:12 light-dark cycle. (Birds were exposed to a long day (14:10) for up to 5 weeks during summer to promote moult, allow entrainment of their circannual rhythm and synchronize the
onset of “fall migration” at the end of August. No recordings were performed during this period.
More than 2 weeks were allowed for re-entrainment to a 12:12 light dark cycle after this time.)
Analysis: The occurrence and duration of the observed behavior during the night and day
of non-migratory and migratory Swainson’s thrushes were subjected to paired comparisons
(paired t tests). The behavior observed in experiment 1 (Fig. 2) was separated into two groups;
nighttime and daytime behavior. Seven comparisons were performed in the nighttime group,
11 12
eight in the daytime group. An additional three comparisons were performed in each of two behavioral categories (“daytime sleep” and “drowsiness”). Multiple comparisons were controlled for Type I error with the Holm Simultaneous Testing Procedure (a step-down procedure derived from the Bonferroni method; Neter et al., 1996). The distribution of the difference scores for
each paired comparison was tested for normality using the Shapiro-Wilk test.
Results
Experiment 1: Swainson’s thrushes adopt two behavioral sleeping postures: back sleep
(BS, Fig 1a) and front sleep (FS, Fig. 1b). In the first experiment carried out with six of the
Swainson’s thrushes, sleep constituted on average X ± SEM = 71.9 ± 3.4 %, N = 6, of the total nighttime video recordings of the birds when non-migratory (Fig. 2 a). The remaining time was almost entirely occupied with drowsiness (26.6 ± 2.9 %; Fig. 1c). By contrast, nocturnal behavior when the birds were in a migratory state consisted of a flight substitute termed “wing whirring” (29 ± 4.1%; Fig. 1e), alert wakefulness (27.0 ± 11%; Fig. 1d), and a relatively small amount of perch-hopping (4.1 ± 0.7 %). The remaining 39 % of the nighttime recordings were occupied with drowsiness (15.0 ± 4.4 %) and sleep (24.4 ± 6 %). Total sleep time was reduced by an average of 66.6% (paired t test: t5 = -7.85, P < 0.05, Holm) in thrushes displaying
migratory restlessness. The frequency of different sleeping postures (FS/BS) was characterized
by considerable inter-individual variability, and differences between migratory restless and non-
migratory states in the relative distribution of front and back sleep (paired t test: t5 = 0.124 , P =
0.906) did not reach significance. Sleeping postures have been associated with differences in depth of sleep, with front sleep possibly indicative of a lighter form of sleep (Amlaner & Ball
12 13
1994). Therefore, if sleeping posture is an indication of depth of sleep, an increase in the relative amount of a deeper, back posture-sleep at night during migration is unlikely to be a major source of sleep loss compensation. It is interesting to note that alert wakefulness comprised a significant portion of the nighttime behavior (27.4 ± 4.5 %) during migratory restlessness, a state that is likely to go undetected by conventional activity monitoring.
a Nighttime Behavior
non- 80% migratory * migratory 60% restless * * 40% * 20% 0% perch singing feeding grooming alert drowsy front back wing sleep hopping wake sleep sleep whirring b Daytime Behavior 60% * * 40%
20% * 0% perch singing feeding grooming alert drowsy front hopping wake sleep
Fig. 2: Quantitative analysis of nighttime (a) and daytime (b) behavior in the thrushes of
Experiment 1 when non-migratory and migratory restless. Y Axis: Percent of one minute sampling episodes a particular behavior was recorded. Asterisks mark significant differences between non-migratory and nocturnally restless states. a: Wing whirring (paired t test: t5 = 7.04,
13 14
P < 0.05, Holm), alert wakefulness (paired t test: t5 = 6.35, P < 0.05, Holm) and perch-hopping
(paired t test: t5 = 5.98, P < 0.05, Holm) were significantly elevated in migratory restless
thrushes. Although neither front sleep nor back sleep changed significantly between conditions,
due to large inter-individual variability, the decrease in total sleep time (sleep: front sleep + back
sleep) was significant in the birds when migratory restless. Error bars: one standard deviation. b:
Non-specific, active daytime behavior (perch-hopping) is significantly reduced in migratory restless thrushes, while specific behaviors like feeding, grooming and singing remain unaltered.
Daytime drowsiness and daytime front sleep were significantly elevated in the birds when migratory restless. The values reported in the figure represent the cumulative data across the three sessions (early, middle, late) which characterize either one nighttime or daytime recording
(X + SEM).
No clear behavioral indicators of REM sleep were found during front or back sleep. Head
drooping, a behavior which is typically associated with REM sleep in many bird species
(Amlaner & Ball, 1994; Rattenborg & Amlaner, 2002)) was absent in the front sleep position, but could be masked by head position during back sleep. The prominent differences in nighttime behavior between the migratory and non-migratory states are an obvious consequence of the presence or absence of migratory restlessness. However, of more interest with respect to the question of sleep deprivation was the daytime behavior of the birds.
The daytime behavior of the six thrushes of Experiment 1 when nocturnally active was
strikingly different from their behavior when non-migratory (Fig. 2b). On average, 57.1 ± 1.3 %
of the daytime video recordings in the non-migratory thrushes was occupied with active
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behaviors such as perch-hopping (34.3 ± 2.7 %), feeding (7.9 ± 2 %), grooming (12.5 ± 3.1 %)
and singing (2.5 ± 1.5 %). The remaining time was occupied with alert wakefulness (27.7 ±
2.9 %) and drowsiness (14% ± 2.9 %). When nocturnally active, the same thrushes significantly
reduced their active daytime behavior by more than 50% to 28.2 ± 4.9 % of the recordings
(paired t-test, t5 = -6.15, P < 0.05, Holm). Interestingly, this change was mainly due to a
reduction in non-specific behavior (paired t test: t5 = -8.99, P < 0.05, Holm; Fig. 2b). By contrast,
the frequency of specific behavior such as feeding (paired t test: t5 = -0.43, P = 0.684), grooming
(paired t test: t5 = -1.13 , P = 0.309) and singing (paired t test: t5 = 1.94, P = 0.11) was
unaffected by behavioral state (Fig. 2b). However, due to the small sample size (N = 6) and low
statistical power, non significant differences must be interpreted with caution. The decrease in
non-specific, active behavior was associated with an increase in sleep-related behavior. The
daytime behavioral observations revealed periods of daytime front sleep (Fig. 1f), which were
substantially more frequent in birds nocturnally active with migration (paired t test: t5 = 4.33, P <
0.05, Holm). Further testing showed that increases in daytime sleep were most pronounced early in the day (paired t test: t5 = 3.43, P < 0.05, Holm; Fig. 3) and at the middle of the day (paired t
test: t5 = 4.84, P < 0.05, Holm), while differences late in the day did not reach statistical significance (paired t test: t5 = 1.51, P = 0.191). Episodes of front sleep were short and typically lasted for no longer than several seconds (see below).
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Occurrence of Daytime Sleep
7 **non- migratory 6 migratory restless 5
4
3 Observations/Hour 2
1
0 early middle late Session
Fig. 3: Distribution of sleep during daytime. Quantitative analysis of the distribution of front sleep across the three daytime recording sessions (early, middle, late) in the thrushes of
Experiment 1 when non-migratory and migratory restless. Y Axis: Percent of one minute sampling epochs/hour of recorded daytime sleep. Asterisks mark significant differences (paired t-tests, P < 0.05, Holm, 3 planned comparisons) in the thrushes when non-migratory and migratory restless. Note the prominent differences early and during the middle of the day, while the difference in the late afternoon did not reach statistical significance (X + SEM).
Daytime drowsiness increased more than threefold to 40.6 ± 6.3 % of the recordings
(paired t test: t5 = 4.07, P < 0.05, Holm). Further analysis showed that, like daytime sleep, the
increase in daytime drowsiness occurred primarily early in the day (paired t test: t5 = 4.58, P <
0.05, Holm) and during the middle of the day (paired t test: t5 = 3.65, P < 0.05, Holm), while
16 17
differences in the late afternoon did not reach statistical significance (paired t test: t5 = 1.79, P =
0.133). Daytime episodes of drowsiness in nocturnally active birds ranged from 1 to 25 minutes
(X ± SEM = 4.7 ± 5 min, N=6, n=209) in duration, and were significantly shorter than episodes of drowsiness in diurnal birds which ranged from 1 to 22 minutes (X ± SEM = 2.5 ± 1 min, N=6, n = 106; paired t test: t5 = -2.87, P = 0.035).
Distribution of Drowsiness during Daytime Non- Migratory 60 **Migratory Restless 50
40
% 30
20
10
0 early middle late Session
Fig. 4: Distribution of drowsiness during daytime. Quantitative analysis of the distribution of drowsiness across the three daytime recording sessions (early, middle, late) in the thrushes of
Experiment 1 when non-migratory and migratory restless. Y Axis: Percent of one minute sampling epochs/hour of recorded daytime drowsiness. Asterisks mark significant differences
(paired t-tests, p<0.05, Holm) in the thrushes when non-migratory and migratory restless. Note
17 18
the prominent differences early and during the middle of the day, while the difference in the late
afternoon did not reach statistical significance (mean + SEM).
Experiment 2: The results of Experiment 1 revealed a notable increase in sleep-related
daytime behavior in thrushes when in a migratory state. However, the video recordings were
taken with only one frontal camera, which made it difficult to detect unilateral eye closure (UEC).
To specifically examine the occurrence of UEC, and to more accurately assess daytime sleep, a
second experiment with 6 different birds was carried out, using two cameras and a different type
of analysis based on continual behavioral sampling. The behavioral analysis of Experiment 2
produced similar daytime differences for front sleep during migratory and non-migratory states as reported for Experiment 1. Daytime front sleep occurred when the birds of Experiment 2 were migratory restless at levels similar to Experiment 1, but was never observed when the same birds were non-migratory (Fig. 6). Importantly, in all 6 birds a considerable amount of daytime unilateral eye closure was detected, which only occurred during periods of migratory restlessness
(Fig. 6). The percent of time spent in daytime UEC approached that of daytime front sleep in the thrushes when migratory restless (X ± SEM , sleep: 2.8 ± 0.9 %; UEC: 2.5 ± 1.3 %, N = 6; Fig.
5). The occurrence of daytime unilateral eye closure and sleep was closely related to drowsiness.
All observed episodes of daytime UEC and sleep were nested within periods of drowsiness.
Episodes of front sleep and unilateral eye closure were short, averaging 8 ± 2.9 s for UEC and
9.2 ± 5 s for front sleep (UEC: Range = 2-53 s, N = 6, n = 214 ; front sleep: Range = 2-138 s, N
= 6, n = 201).
18 19
We also investigated in the captive birds if there was any indication of UEC occurring when the birds were active with migration at night. Because wing whirring is thought to simulate actual migratory flight in captive birds, we hypothesized that if unihemispheric sleep occurs during an
Daytime Sleep & Unilateral Eye Closure
4
3.5
3
2.5
2
1.5 % of recording time 1
0.5
0 Daytime Sleep Unilateral Eye Closure
Fig.5: Daytime sleep and unilateral eye closure were observed at comparable amounts during the
behavioral observations. Together they occupied roughly 5% of the observed behavior (N = 6; X
± SEM).
actual migratory flight, it should reveal itself during captive wing whirring. In the analysis of the complete video material, not a single episode of unilateral or bilateral eye closure was detected during wing whirring. No evidence of sleep or UEC during the nocturnal “flight” of captive migratory Swainson’s thrushes was found.
19 20
Unilateral Eye Closure and Daytime Sleep in Migratory Restless Thrushes 1000 102 UEC
800 Front Sleep 42 600 51
s / 3 Hours s / 79 400 63
12 200 12 12 16 9 11 9 0 B1 B2 B3 B4 B5 B6
Fig. 6: Unilateral eye closure and daytime sleep in migratory restless thrushes. Total time
(seconds/3 hours of observation) spent in daytime unilateral eye closure and front sleep during periods of nocturnal restlessness. Values reported represent the cumulative data across the three daytime sessions (early, middle, late) for each bird of Experiment 2 (B1-B6) over the course of one representative sample day. Data for the non-migratory period is not shown, because unilateral eye closure and front sleep were not observed in any of the experimental birds when non-migratory during experiment 2. Numbers on top of the columns represent the total number of episodes detected during the three-hour observation period for each bird (early, middle, late).
20 21
Discussion
The behavioral observations provide evidence that migratory Swainson’s thrushes,
despite a suite of physiological adaptations in support of migration, are not extraordinarily
resilient to sleep loss. While the thrushes drastically reduce the amount of nocturnal sleep during
the migratory seasons, they compensate for that loss, at least in part, during the day. During the
migratory seasons, when the amount of nighttime sleep is considerably diminished, Swainson’s
thrushes significantly reduce their non-specific daytime activity and engage in numerous brief
episodes of daytime sleep (DTS) and unilateral eye closure (UEC). The episodes occurred during
extended periods of drowsiness, which on average occupied more than 40% of the daytime
recordings compared to 15% when birds were diurnal and slept at night.
To date the relationship between sleep and migration has been studied in only one other
migratory bird species. Rattenborg et al. (2004) provided a detailed electrophysiological account
of nighttime sleep and other behavior in the nocturnally migrating white crowned sparrow
(Zonotrichia leucophrys gambelii), another long-distance migrant that breeds in Alaska and winters in southern California. Despite differences in analysis and sampling method, the observed average reduction in nighttime sleep during the migratory seasons was surprisingly similar (66 % in the Swainson’s thrush and 63 % in the white-crowned-sparrow). Also, a similar increase in daytime drowsiness, but not nighttime drowsiness, was observed in both species when nocturnally active with migration.
21 22
The typically short period lengths of DTS and UEC episodes in the Swainson’s thrush
raise the question of the extent to which the behavior compensates for nocturnal sleep loss. I
believe that both DTS and UEC provide adaptive recuperative opportunities, while at the same
time only marginally increasing the risk of predation and allowing a migrant to forage for a large
portion of the day. The average duration of the nocturnal sleep cycle in birds is less than 2 1/2
minutes, with typical episodes of REM sleep lasting no longer than 9 seconds (Amlaner & Ball,
1994). A normal night of avian sleep is therefore composed of hundreds of successive sleep
cycles, which are interrupted by multiple arousals. Therefore, it follows that the numerous short
bilateral and unilateral episodes of sleep during the day would have the same properties of
nocturnal sleep and compensate, at least partially, for nocturnal sleep loss.
Daytime drowsiness possibly provides Swainson’s thrushes with some of the benefits of
sleep. The behaviorally observed increase in daytime drowsiness in nocturnally active
Swainson’s thrushes may provide a form of daytime rest that, together with daytime sleep and
UEC, can compensate for extensive nocturnal sleep loss. However, it is challenging to reliably distinguish drowsiness from wakefulness when exclusively relying on behavioral criteria, and it is difficult to make a convincing case for drowsiness as a sleep substitute without electrophysiological recordings (see below).
As with white crowned sparrows (Rattenborg et al., 2004), no indications of sleep during
migratory “flight” were observed. However, considering possible limitations of using wing
whirring as a captive equivalent of migratory flight, and the limited amount of sleep research
conducted in avian migrants, it is premature to exclude the possibility of bilateral or
22 23 unihemispheric sleep occurring during flight as a potential adaptation to sleep loss during migration. It is also worth noting that migratory behavior has evolved independently in different bird taxa. Therefore adaptations to sleep loss may also have evolved independently in different species of migrants, and as a consequence, be characterized by group-specific properties.
It should also be noted that no behavioral signs of REM sleep were detected in sleeping
Swainson’s thrushes (see Rattenborg et al., 2004 for a discussion of REM sleep in the white- crowned sparrow (Zonotrichia leucophrys gambelii)). Therefore, it was not possible to estimate the amount of nocturnal REM sleep lost due to migratory activity based on the behavioral observations. If substantial amounts of nocturnal REM sleep are lost during the migratory seasons, it seems unlikely that any of the observed daytime sleep-related behavior would compensate for that loss. The relative brevity of daytime sleep episodes makes it unlikely that they would include REM episodes. In addition, birds, like mammals, have not been reported to exhibit episodes of REM sleep during drowsiness or unihemispheric sleep. However, electrophysiological recordings are necessary to test the claim that REM episodes do not occur during daytime sleep-related behavior, the results of which are discussed below.
The experimental procedures used in the behavioral experiments attempted to simulate as closely as possible ambient conditions during migratory and non-migratory periods. However, it is likely that the behavior of Swainson’s thrushes in the wild would differ somewhat from the behavior we recorded in captive birds. For example, in captive birds unlimited availability of food and a safe holding environment could reduce the need for general activity, and increase sleep-related behavior. Therefore, it is possible that the relatively large amounts of DTS, UEC
23 24
and daytime drowsiness observed in our captive animals during the migratory season will not
occur to the same extent in the wild because of a greater need to forage. Captivity alone,
however, cannot account for the substantial increase in DTS, UEC and drowsiness observed in
the migratory compared to the non-migratory state. The findings demonstrate that captive, non-
migratory thrushes exhibit low levels of DTS, diurnal UEC and drowsiness, under the same
holding conditions in which they display high levels of the same behavior after nocturnal sleep loss. Therefore, it seems reasonable to conclude that the drastic changes in daytime sleep-related behavior are primarily due to differences in migratory state rather than holding conditions.
Avian and mammalian sleep are characterized by both behavioral and
electrophysiological criteria. The behavioral data reported so far, therefore, provide only one
source of information on adaptation to sleep deprivation in migrant birds. The remainder of this
dissertation will examine if the described daytime sleep-related behavior in nocturnally active
Swainson’s thrushes is accompanied by sleep-related brain activity similar to nocturnal sleep.
24 25
PART II: EEG RECORDINGS
General Methods
To obtain EEG recordings, 12 Swainson’s thrushes were bilaterally implanted with screw electrodes over the anterior forebrain (hyperpallium (formerly hyperstriatum; also known as
“Wulst” region). The avian Wulst, the functional avian equivalent of the (anatomically different; see appendix I) mammalian neocortex, showes the most reliable EEG differences between arousal states (Rattenborg et al., 2000) and is, therefore, traditionally targeted in avian sleep studies. The Wulst is also easily identifiable without a brain atlas, which is not available for most bird species (no stereotaxic coordinates are available for the guided implantation of electrodes in the Swainson’s thrush or any other seasonal migrant). After visual identification of the Wulst, electrodes were implanted 3 mm laterally to the midline. The conductive surface of the epidurally implanted electrodes was large, measuring 1 mm in diameter. Therefore, a large portion of the anterior forebrain was sampled by a single electrode and small departures from exactly parallel coordinates should have had only minor effects on the overall recordings. In most animals 4 EEG electrodes were implanted; 2 over the anterior hyperpallium, and 2 over the posterior hyperpallium. In addition, bare wires were threaded under the skin, just above the orbital bones to detect eye movements (EOG). In 4 animals an additional pair of bare wires was implanted into the nuchal muscles to detect arousal-state related changes in muscle tone (EMG; in this case EEG was only recorded from two anterior hyperpallium locations). A reference electrode used for differential recordings was implanted in the posterior midline and a solder- coated ground wire (copper/ stainless steel) was attached to the right posterior portion of the
25 26 skull. All wires were gathered in a small connector board (Microsystems) and the array was fixed to the skull with dental acrylic. Surgical procedures were conducted under Isoflurane anesthesia, in accordance with Bowling Green State University animal care and use regulations.
After surgery, the animals were allowed to recover for 2 weeks. During the second week of recovery, the birds were introduced to the recording environment, a glass terrarium (50x50x30 cm) that allowed camera access from all angles. The recording cage was furnished with a single perch to make a bird’s position in the cage more predictable. The perch was mounted close to ground level to prevent birds from passing below it and getting tangled in the recording cable.
Birds were connected to a movable lightweight cable (Dragonfly Inc.) attached to their head plug/connector board. The cable was mounted on a low-torque commutator (Dragonfly Inc.), to avoid restraint and cable damage. Animals were allowed to adjust to the recording cable and recording environment for several nights while their behavior was recorded on video. After a minimum of two nights, if a bird behaved “normally”, a subjective decision by the experimenter based on the video recordings during training), the first EEG recordings were conducted.
Differentially recorded signals (EEG, EOG, EMG) first passed through a head-stage amplifier constructed of JFETs, were then amplified 2000-4000 times, bandpass filtered between 1 and 50
Hz (Neuralynx, Tucson, AZ), and sampled at 200 (EEG, EOG) or 1000 Hz (EMG; DataWave
Technologies, Longmont, CO). Daytime only, nighttime only and continuous recordings were performed (up to 168 hours). All EEG recordings were accompanied by video recordings, using
4 Sony camcorders and a Screen splitter. While a bird’s behavior was monitored by either 2 or 3 camcorders, one camera recorded the EEG timer to allow for accurate alignment of behavior and
EEG samples. Between daytime and nighttime recordings, birds were housed in their home cages.
26 27
During continuous recordings birds remained in the recording cage for several days, with food
and water provided ad libitum.
The behavior during recordings did not differ markedly between episodic and continuous
recordings. Once birds reached the behavioral criterion for recordings, they resumed “normal”
behavior (posture, movement, grooming, in many cases drinking and feeding) in a matter of
minutes, after the experimenter had left the room. A uniform recording style was not employed
because birds, despite behaving and sleeping ‘normally’ under the restraint of the recording cable,
failed to show migratory restlessness. Consequently, under recording conditions, most birds
failed to show the behavioral daytime changes associated with nocturnal sleep loss, initially
detected in the behavioral observations. To address this problem the author resorted to ‘daytime
only’ recordings. Birds remained in their home cages at night and were moved to the recording
cage by day (recordings in diurnal birds were conducted both ways, continually and episodic,
without any apparent differences). Birds treated in this manner displayed migratory restlessness
at night in their home cages and sleep-related daytime behaviors in the recording cage.
The same problem severely limited the analysis of nighttime sleep in nocturnally active
birds. More than a week in the recording environment was not enough to elicit migratory
restlessness in any bird; however, without the recording cable attached, birds readily displayed
“Zugunruhe” in the same environment. Swainson’s thrushes, during their nocturnal flights, emit contact calls. Capitalizing on this behavior, birds were exposed to contact calls on an audiotape for the first 6 hours of every night. This treatment did not eliminate the problem. However, 3
27 28 birds treated this way eventually yielded the nighttime data discussed below that, in any event, should be interpreted with caution.
Analysis
The main goal of the EEG analysis was to understand relationship between the behavior found in the behavioral experiments and corresponding brain activity. Behavioral postures and eye state are usually quite reliable indicators of sleep (Amlaner & Ball, 1994). However, this reliability has not been established for brief episodic behaviors. In the analysis of daytime behavior in experiment 1 and 2 numerous brief episodes of bilateral and unilateral eye closure were detected. The brevity of these episodes (~10s) termed ‘daytime naps’ raised the question if these events are accompanied by sleep-like brain activity. Also, while behavioral sleeping postures permit one to determine total sleep time (TST), they do not allow oneto reliably distinguish between slow wave sleep (SWS) and REM sleep (see below). In addition, the relationship of “intermediate states” like drowsiness and unihemispheric sleep to sleep/wakefulness is still uncertain, and a combination of behavioral and electrophysiological recordings is best suited to adequately describe that relationship.
.
28 29
Slow Wave Sleep and REM Sleep
Sleep in birds and mammals is not a uniform state. It consists of two different types of sleep, slow wave sleep (SWS; non-REM sleep) and rapid eye movement sleep (REM sleep), both characterized by distinct patterns of neural activity. In most avian species, behavioral markers
(most notably nodding or “head drooping”) accompany REM sleep. However, it is difficult to discriminate between SWS and REM sleep when relying exclusively on behavioral scoring criteria.
Slow Wave Sleep: Avian and mammalian non-REM sleep superficially resemble each other. They are both characterized by low-frequency (1-4 Hz; delta activity), high-amplitude synchronized neuronal activity that dominates the “cortical” EEG (Fig. 7-B). However, the two states differ in potentially important aspects, which at present have not received much attention.
For now (and for the purpose of this study) it is sufficient to note that avian SWS resembles the delta component of mammalian SWS, but lacks clear sleep stages, spindles and the K- complex.
Also, the recently discovered mammalian “cortical slow oscillation” has yet to be detected in birds. For a more detailed discussion of the similarities and differences of avian and mammalian
SWS, and their possible implications, please see appendix III.
The EEG analysis of mammalian SWS, is based on the assumption that the amount/ amplitude of delta activity (EEG activity in the 1-4 Hz frequency range, assessed as “power” in the “delta band” by spectral analysis) accurately reflects sleep quality (i.e. depth of sleep). This assumption rests on observations in mammals, showing that increased delta power corresponds
29 30
to higher arousal thresholds (Borbély & Acherman, 2000 for review). Sleep-deprived mammals and humans respond to sleep deprivation with a “delta rebound,” an increase in delta power during recovery sleep (Borbély & Acherman, 2000; Horne, 1988). In addition, mammals and humans show the most intense SWS at the beginning of their subjective night when “sleep pressure” is presumably highest. Several studies in birds indicate that avian and mammalian
SWS may be comparable in this respect (Van Ljuitlaar at al., 1987; Szymczak et al., 1996;
Rattenborg & Amlaner, 2002). Based on this assumption, “power” in the delta band was
employed as a measure of sleep quality/depth of sleep in the Swainson’s thrush. Delta band
power was calculated by spectral analysis, employing a Fast Fourier Transformation (FFT)
algorithm (Matlab, Neuroexplorer, SciWorks).
A quick introduction to FFT: An FFT algorithm decomposes a complex signal into sine
and cosine components of different frequencies. Each frequency is assigned a power value (mV2)
based on its contribution to the original signal. The frequency range covered is given by the
Nyquist frequency (FN), which is a function of the sampling frequency (FS; FN=FS/2). The
frequency resolution is determined by the number of data-points entered in the equation (i.e. the size of the “window”). A one second window (number of data points = FS ) results in a one Hz
frequency resolution (my typical FS (200 Hz), would therefore result in 100 bins (FN) of 1 Hz). A
bin width of 0.25 Hz (4s window) is considered sufficient in most EEG studies. The final power
values for a certain frequency represents the average of all windows that were applied to the
signal. FFT algorithms have a tendency to create leakage (frequencies that are not contributing to
the actual signal are detected), if the signal amplitude on both ends of the window does not equal
zero. As a consequence windowing functions are applied that artificially reduce the signal to
30 31 zero on its edges while leaving most of the signal unaltered. A variety of different windows
(Hanning, Hamming, Koenig) are available. In this study the most popular, the Hanning window was employed.
REM sleep: Avian REM sleep, similar to mammals, is characterized by a low-amplitude high-frequency activated EEG (Fig. 7C) closely resembling the EEG of wakefulness (Fig. 7A).
Clusters of rapid eye movements (REMs) are reliably observed during avian REM sleep, but unlike mammals, pontine-geniculo-occipital (PGO) spikes are undetected (Amlaner & Ball,
1994). Atonia, the total lack of muscle tone and a prominent feature of mammalian REM sleep, is virtually absent in many avian species (Amlaner & Ball, 1994). However, a decrease in muscle tone (hypotonia), and behavioral signs of decreased neck muscle tone (nodding) have been observed in most species studied (Rattenborg & Amlaner, 2002). Despite considerable effort, hippocampal theta rhythm, another characteristic of mammalian REM sleep, has not been detected during avian REM sleep (Van Twyver & Allison, 1972; Walker & Berger, 1972). The similarities between the electrophysiological correlates of REM sleep and wakefulness make it necessary to employ a combined behavioral and electrophysiological approach to reliably detect episodes of REM sleep. In this study, REM sleep episodes were defined as a combination of a.) bursts of eye movements in concert with b.) wake-like EEG while a bird was c.) behaviorally asleep (Fig.2-C). In addition, for quantitative/statistical purposes, indicators of REM sleep
(clusters of eye movements) were scored as 5s REM sleep episodes.
31 32
A EOG
EEG (L)
EEG (R)
B
C
REM episode
10s
Fig. 7: Electrophysiological correlates of wakefulness (A), SWS (B) and REM sleep (C) in a
homing pigeon (Fuchs et al., 2006). REM sleep is characterized by a low amplitude, high
frequency EEG combined with bursts of eye movements (L: left hemisphere; R: right
hemisphere).
Daytime Recordings
Analysis
Power in the delta frequency band was used as an indicator of depth of sleep.
Unfortunately, the analysis of long passages of daytime EEG in birds is typically confounded by
32 33
artifact. This artifact stems predominantly from locomotor activity, and during spectral analysis,
contributes to the delta band (Fig. 8).
EOG
EEG
Fig. 8: Movement artifact in an active Swainson’s thrush (EOG and EEG). Note how the sharp
artifact in the EOG trace takes the shape of slow waves in the EEG recordings.
The high levels of daytime activity in songbirds make it virtually impossible to entirely
remove artifact from daytime recordings. Also, the observed daytime sleep-related behavior was
short, in the range of seconds (daytime sleep, unilateral eye closure) and minutes (drowsiness),
and usually appeared in clusters. Spectral analysis of long segments of EEG would therefore not
only be confounded by movement artifact, it would also yield mixed results precluding the distinction of individual behavior. Consequently a different approach was chosen. The video recordings were visually scored for the behavior outlined above (daytime sleep, unilateral eye closure, drowsiness), and compared to the time stamped EEG/ EOG/ EMG recordings. Scoring was conducted blind to condition. That is to say, behavioral state was recorded without knowledge of EEG activity. Episodes of daytime sleep (DTS), unilateral eye closure (UEC) and drowsiness (D) were identified behaviorally, cut from the original EEG file and aligned in single
files corresponding to each behavior (Fig. 9). After visual inspection for artifact the processed
33 34
files were subjected to spectral analysis (FFT, 4s window, Hanning) and average EEG power in
the delta band was computed for frequency values between 1.5 and 4 Hz.
A total of 508 episodes was analyzed in this fashion (D: 178, N = 8; FS: 131, N = 7;
UECL: 126, N = 6; UECR: 73, N = 5). A bird was included in the analysis if a minimum of 10
episodes for any behavioral category was observed. This criterion resulted in an EEG sample of
at least 60 seconds for each bird included in the analysis.
In the case of one behavior, unilateral eye closure (UEC), hemispheric differences in
brain activity were of particular interest. If unilateral eye closure is a true equivalent of mammalian unihemispheric sleep, the brain hemisphere contralateral to the open eye should display more wake-like EEG activity, while the hemisphere contralateral to the closed eye would show more sleep-like EEG activity (Mukhametov, 1985; Rattenborg et al., 2000). Episodes of
UEC were selected as described in Fig. 9. Indipendent of behavioral state, asymmetries in hemispheric brain activity can originate from slight differences in electrode impedances and placement, and therefore must be interpreted with caution. Also, unlike mammals, hemispheric differences in brain activity during avian “unihemispheric sleep” are generally subtle, short-lived, and difficult to detect (Rattenborg et al., 2000; Rattenborg & Amlaner, 2002). However, asymmetries due to recording procedures are not state dependent and can therefore be corrected.
This was achieved by normalizing delta power values for unilateral eye closure and other daytime behaviors (DTS, D) to average delta power during alert wakefulness, for each recording electrode. Average power values for D, DTS and UEC were divided by the average power value for wakefulness recorded on the same electrode. The result was multiplied by 100 depicting
34 35 average power values for D, DTS and UEC as percent of wakefulness. Swainson’s thrushes conveniently ‘freeze’ when the experimenter enters the room and generally remain in this posture for some time, while displaying an EEG of alert wakefulness. I exploited this behavior and used the first and the last minute of each recording session for this calculation. Normalized power values were then subjected to paired comparisons (paired t-tests).
Drowsy Alert Alert
EEG left
EEG right 10 s
Daytime Sleep
Spectral Analysis
Fig. 9: Analysis of daytime EEG. In this example episodes of daytime sleep are identified in the behavioral records. The corresponding EEG is selectively removed from the recordings, combined into a single file and subjected to spectral analysis (EEG: Swainson’s thrush).
35 36
Results
Daytime Sleep
EEG left
EEG right 10s
Fig.10: Swainson’s thrush displaying a series of daytime “naps” (shaded areas) interrupted by short arousals (arrows).
Visual inspection and spectral analysis of multiple episodes of drowsiness, daytime sleep and unilateral eye closure in 8 birds clearly showed that all three behavioural states, despite their brevity, were accompanied by reliable changes in corresponding brain activity. Daytime drowsiness, daytime sleep and unilateral eye closure were all characterized by a low-frequency, high-amplitude EEG similar to nocturnal slow wave sleep that was easy to discriminate from alert wakefulness (see examples in Fig. 11). The EEG of daytime drowsiness was characterized by intermittent changes in amplitude, but slow wave activity during drowsiness rarely approached the levels of daytime sleep. Episodes of daytime sleep, albeit short, were characterized by a marked increase in slow wave activity immediately following eye closure
36 37
(and sometimes slightly preceding it; Fig. 10). The observed episodes of daytime sleep did not
contain episodes of REM sleep.
Exemple EEG Tracks of Different Arousal States
Alert Wakefulness EOG
EEG (L)
EEG (R) Daytime Drowsiness
arousal
Daytime Sleep
arousal
8s
Fig.11: EEG amplitude and SW activity progressively increase from alert wakefulness to
drowsiness to daytime sleep (EOG (top) and EEG left/ right hemisphere; arrows mark arousals).
A comparison of delta power (averaged between both hemispheres and normalized to alert wakefulness, Fig.12) revealed a clear statistical difference between all three sleep like behavioural states (D, DTS, UEC) and alert wakefulness (Wilcox against 100%; D: Z = -2.521, P
< 0.05, N = 8; DTS : Z = -2.366, P <0.05, N =7; UEC: Z = -2.521, P < 0.05, N = 8). When compared to each other, drowsiness, showed the smallest power increase in the 1.5-4 Hz range
37 38 while daytime sleep was characterized by the largest increase. Interestingly the power increase during UEC was intermediate between D and SWS. Paired t-tests revealed significant differences between daytime sleep and drowsiness (Paired t test: t6 = -2.944, P < 0.05) and unilateral eye closure and drowsiness (paired t test: t7 = -3.695, P < 0.05), while the difference between unilateral eye closure and daytime sleep only approached significance (paired t test: t6 = -2.308,
P = 0.06).
Slow-wave activity during sleep related daytime behaviour
500% Daytime Sleep
Unilateral 400% Eye Closure
300% Drowsy
200%
100% = Alert Wakefulness 100%
0% N=8, n=178 N=7, n=131 N=8, n=199
Fig.12: Average delta power during drowsiness (N=8), daytime sleep (N=7) and unilateral eye closure (N=7) depicted as a percentage of delta power during alert wakefulness (X ± SEM). All three behavioral states show significantly increased delta power compared to wakefulness. Delta power during both daytime sleep and unilateral eye closure was significantly increased compared
38 39
to drowsiness, while the difference between daytime sleep and unilateral eye closure approached
significance (N = number of animals, n = number of episodes).
To detect if delta activity during daytime sleep is similar to nighttime sleep non-
migratory thrushes average delta power during DTS was compared to an early (2h) and a late
(10h) sample of nocturnal SWS in the same birds when non-migratory. While early nighttime
sleep showed higher average delta power than DTS, late nighttime sleep was characterized by
lower delta power than DTS (Fig.13). However, the statistical differences between DTS and the
two nighttime sleep samples did not reach significance (paired t test; sleep early/DTS t5 = 2.393,
P > 0.05; sleep late/DTS t5 = -1.228, P > 0.05), while a significant difference between the early and the late nighttime sample was found (paired t test: t5 = 5.278, P < 0.01; also see below,
nighttime sleep).
Nighttime Sleep/Daytime Sleep Nighttime 700% Sleep Early
600% Daytime 500% Nighttime Sleep 400% Sleep Late
300%
200%
100%
0%
Fig.13: Comparison of delta power (1.5-4Hz) during DTS with an early (2 h) and a late (10h)
sample of nighttime sleep. Slow-wave activity during daytime sleep did not approach the levels
39 40
of early nighttime sleep but exceeded the levels of late nighttime sleep. While the early nighttime
sample differed significantly from the late sample, the difference between either of the nighttime
samples and DTS did not reach significance. Power values are depicted as a percentage of alert
wakefulness during migratory and non-migratory states (N = 6; X ± SEM).
Hemispheric differences
Episodes of unilateral eye closure were frequently accompanied by visible EEG
asymmetries indicative of unihemispheric sleep (e.g., Fig. 14). However, on other occasions the
EEG of UEC closely resembled brain activity during drowsiness without marked differences between the two hemispheres.
EEG Left Hemisphere/right eye
Unilateral Eye Closure Left
EEG Right Hemisphere/left eye
Daytime Sleep
2 s
Unilateral Eye Closure Left
40 41
Fig.14: EEG asymmetries between the left and the right hemisphere in a Swainson’s thrush
displaying unilateral eye closure, interrupted by a short episode of daytime sleep. A closed left
eye, in this example, co-occurred with a clear increase in slow wave activity in the right
hemisphere, while closure of both eyes led to an immediate increase in SWA in both
hemispheres.
To assess hemispheric power differences in the 1.5-4 Hz range, the EEG samples for
UEC, D and DTS were subjected to spectral analysis and the power values (for each frequency
bin) were normalized to their respective baseline, alert wakefulness, within the same hemisphere
and electrode track (Fig. 15). Interestingly, when analyzed in this way the only significant
hemispheric power difference was observed during unilateral eye closure of the right eye
(UECR), with an expected increase in EEG Power in the left brain hemisphere contralateral to the closed eye (paired t test: t4 = 3.044, P < 0.05). Drowsiness, like unilateral eye closure of the
left eye (UECL) did not show a clear asymmetry between the two hemispheres (paired t test; D:
t7 = -0.009, P > 0.05; UECL: t5 = 0.13, P > 0.05)), while daytime sleep exhibited a trend towards a left right asymmetry similar to UECR that did not reach significance (paired t test: t6 = 2.41, P
> 0.05).
41 42
Daytime Sleep Unilateral Eye Closure 600%
500%
UECL UECR 400%
300% Drowsiness
200%
100% Wakefulness (100 %)
0% left right
N=8, n=178 N=7, n=131 N=6, n=126 N=5, n=73
Fig.15: Delta power (1.5-4 Hz) in the left and right hemisphere during drowsiness, daytime sleep
and unilateral eye closure expressed as percent of alert wakefulness. Only unilateral eye closure
of the right eye showed a significant difference between the left and the right hemisphere (paired
t test: t4 = 3.044, P < 0.05). However, a similar left/right asymmetry during daytime sleep
approached significance (N = number of animals, n = number of episodes; X ± SEM).
If UEC produces a sleep-like state in the contralateral brain hemisphere, the EEG contralateral to the closed eye should be comparable to that of the same hemisphere during SWS,
while the EEG contralateral to the open eye would be expected to be similar to wakefulness or
drowsiness. Therefore, irrespective of inherent left-right asymmetries, the power difference
betweenbihemispheric daytime sleep and UEC in the hemisphere contralateral to the closed eye
(UEC-closed) is expected to be smaller than the difference between daytime sleep and the
hemisphere corresponding to the open eye (UEC-open). When UEC was compared to daytime
42 43
sleep and drowsiness within the same hemisphere (in birds that displayed both, UECL and
UECR, power corresponding to eye state was averaged across hemispheres and compared to the
averaged power during bihemispheric sleep), paired t-tests, as hypothesized, revealed no significant difference between UEC-closed and daytime sleep (paired t test: t6 = 1.11, P > 0.05), while the difference between UEC-open and daytime sleep was significant (paired t test: t6 =
2.799, P < 0.05). Interestingly, when UEC was compared to drowsiness in the same manner, the opposite pattern was observed, with UEC closed showing a significant difference from drowsiness (paired t test: t6 = -3.459, P < 0.05), while the difference between UEC-open and D
did not reach statistical significance (paired t test: t6 = -0.742, P > 0.05; Fig. 16).
500% Daytime Daytime Sleep Sleep
400% UEC closed * UEC Drowsy open 300%
Drowsy * 200% *
* 100%
0% UEC Open Eye UEC Closed Eye Fig.:
Fig.16: Intrahemispheric power differences (1.5-4Hz) during unilateral eye closure with a closed eye corresponding to brain activity similar to DTS (right) and an open eye corresponding to brain
43 44
activity similar to drowsiness (left; N = 7; X ± SEM). While UEC open was significantly
different from Daytime Sleep (paired t test: t6 = 2.799, P = 0.031), UEC closed differed significantly from Drowsiness (paired t test: t6 = -3.459, P = 0.013).
Discussion
As hypothesized for the behavioral data (Fuchs et al., in press), daytime sleep, unilateral
eye closure and drowsiness were all characterized by reliable sleep-like changes in
corresponding brain activity that made them easy to distinguish from alert wakefulness. Each
behavioral category (D, FS, UEC) was accompanied by a low-frequency high-amplitude EEG
supporting the claim that each category provides, at least in part, the same benefits as nocturnal
slow-wave sleep. Assuming that power in the delta frequency band accurately reflects sleep
quality, it appears that DTS, per unit of time, provides the most beneficial form of daytime rest,
followed by unilateral eye closure and drowsiness. Drowsiness, however, occupies more time
than both daytime sleep and unilateral eye closure, and may compensate in quantity for what it
cannot provide in quality.
Daytime sleep, defined as bilateral eye closure for more than 2 seconds, was
accompanied by an EEG resembling SWS that was rapidly reversed at the moment of eye
opening. While the eyes were usually opened quickly accompanied by an EEG of alert
wakefulness, eye closure, almost exclusively embedded in episodes of drowsiness was often a
slow process occupying several seconds. Depending on the speed of eye closure changes in brain
activity were sometimes observable before the eyes were completely closed, indicating that a
loss of visual input is not entirely responsible for the observed changes in brain activity. The
44 45 electrophysiological evidence, together with the known brevity of the avian sleep cycle, strongly supports the conclusion that even short episodes of eye closure are true naps, which not only behaviorally but also physiologically resemble nighttime sleep.
The EEG recordings during episodes of unilateral eye closure provided clear evidence of
EEG asymmetries indicative of unihemispheric sleep. However, on some occasions, the EEG of
UEC closely resembled brain activity during drowsiness without marked differences between the two hemispheres (see also Rattenborg et al., 2001; 2002). It should be noted that even when clear hemispheric differences were present, brain activity in the hemisphere contralateral to the open eye generally resembled an EEG of drowsiness, rather than an EEG of alert wakefulness. This is not surprising, considering that the main argument for drowsiness as a compensatory behavior for sleep loss is that birds (and mammals), displaying a sleep-like EEG remain vigilant to visual stimulation (Ruckebusch, 1972; Rattenborg et al., 1999; Fuchs, personal observation). This observation also explains why delta power during UEC, when averaged over both hemispheres
(Fig. 12), was significantly larger than delta power during drowsiness. An interhemispheric comparison during unilateral eye closure, daytime sleep and drowsiness showed a significant difference in hemispheric brain activity in the expected direction during unilateral eye closure with the right eye closed (UECR) and a similar trend for daytime sleep. Neither unilateral eye closure with the left eye closed (UECL) nor drowsiness showed comparable interhemispheric power asymmetries.
Because interhemispheric comparisons during unilateral eye closure/unihemispheric sleep in birds are comparisons between a “sleeping” and a “drowsy” hemisphere, and not
45 46
between a “sleeping” and an “awake” hemisphere the expected statistical differences are small.
Thus, it seems possible that the observed trend towards a left/right asymmetry during
bihemispheric daytime sleep amplified the interhemispheric differences of UECL, while it obscured the hemispheric differences of UECR, which were expected to be in the opposite direction. Intrahemispheric comparisons are a more sensitive approach to detect small power differences related to eye state during UEC (see Rattenborg et al., 2001). Because most birds showed a preference for closing either the left or the right eye (no clear preference was detected in the behavioral experiments) it was not possible to statistically compare UECL and UECR within the same animal/hemisphere. Alternatively, when UEC was compared to daytime sleep and drowsiness within the same hemisphere, brain activity in the hemisphere corresponding to the closed eye was not significantly different from daytime sleep but differed significantly from drowsiness. The hemisphere corresponding to the open eye showed the reverse pattern, with a significant difference between UEC and DTS, but no significant difference between UEC and D.
Both contrasts indicate a true relationship between eye state and brain state during unilateral eye closure, with a closed eye corresponding to brain activity similar to DTS in the contralateral hemisphere, while an open eye corresponds to an EEG similar to drowsiness in the contralateral
hemisphere. The comparison further indicates that hemispheric brain activity during UEC in
Swainson’s thrushes is comparable to mammalian unihemispheric sleep (Mukhametov, 1985;
Rattenborg et al., 2000), albeit interhemispheric differences in brain activity are of a more subtle nature.
The electrophysiological recordings also support the conclusion that drowsiness provides
Swainson’s thrushes with some of the benefits of sleep. As reported for other species (reviewed
46 47 in Amlaner & Ball, 1994; Rattenborg & Amlaner, 2002), the polygraphic features of drowsiness in the Swainson’s thrush are similar to those of avian SWS. Avian drowsiness can be difficult to distinguish from non-REM sleep based on electrophysiological criteria alone (Tobler & Borbély
1988; Van Twyver & Allison, 1972; Walker & Berger, 1972). However, it is equally challenging to reliably distinguish drowsiness from more alert forms of wakefulness when exclusively relying on behavior. The EEG recordings, which were analyzed according to the same operational definition of drowsiness employed in the behavioral observations, demonstrated that brain activity corresponding to this state was significantly different from alert wakefulness. Thus, the behavioral definition utilized in the earlier observations (experiments 1 and 2) corresponds to a physiological change in brain state and therefore appears to be a legitimate measure of drowsiness in the Swainson’s thrush. The observed increase in behavioral daytime drowsiness in nocturnally active Swainson’s thrushes, together with the electrophysiological data, indicate that drowsiness in migratory birds provides a form of daytime rest that, together with daytime sleep and unilateral eye closure, can compensate for extensive nocturnal sleep loss.
Nighttime recordings
Analysis
Unlike the daytime recordings, the analysis of nighttime sleep was primarily (but not exclusively, see below) directed towards birds in a non-migratory state and an assessment of: a.) total sleep quota during the night; b.) the defining EEG characteristics of SWS and REM sleep during periods of behavioural sleep.
47 48
c.) determining the temporal profile of these two sleep states over the course of the night.
d.) sleep quality defined as power in the delta range (1.5-4 Hz) with greater delta power
presumably corresponding to deeper, high quality sleep.
e.) a comparison of nighttime sleep in animals when migratory and non-migratory.
To address these goals, continuous 12 hour EEG recordings were analyzed employing a
different analysis than during the daytime recordings. The analysis of daytime sleep-like
behavior required the discrimination of short behavioural episodes lasting no more than several seconds. Generally, the behavioural variability during nighttime sleep is not as pronounced as the occurrence of daytime sleep-like states in thrushes active with migration, justifying a less fine grained analysis. In practice the sheer amount of data contained in a single nighttime recording precludes such a detailed approach. The majority of the night in non-migratory thrushes and the sleeping period in migratory restless thrushes (early morning hours) is characterized by behavioral quiescence. Therefore, EEG recordings are generally less confounded by movement artefact, making spectral analysis on long continuous periods of EEG feasible and desirable.
Recording techniques and environment for the nighttime recordings were identical to the ones utilized during the daytime recordings (above). A sample of 6 representative nights in 6 different animals was analyzed for the non-migratory condition. In the migratory condition, for reasons outlined above, only data from three birds were available.
To assess total sleeping time and delta power changes during sleep, 12 hour nighttime
EEG and behavioral video recordings were acquired. A minimum of 3 (no more than 6) full
nighttime recordings was acquired for each of the 6 birds. Then for each bird a representative
48 49
night of good EEG quality was chosen for analysis (the first night of recordings was excluded from analysis). The recordings were divided into 72 ten-minute episodes. Based on the video
recordings, each episode was classified according to the predominant behavior observed (awake
(W), drowsy (D), front sleep (FS), back sleep (BS)). “Predominant” is a subjective measure;
episodes were scored as W, D, FS or BS when at least “80%” of an episode was occupied by this
behavior. If the rater did not feel confident to categorize an episode by a single behavior,
episodes were classified as “mixed” and combinations of the most frequent behavioral states
were listed.
EEG power in the Δ-band was then computed for each EEG/behavior episode, averaging
frequency values between 1.5 and 4 Hz into a single bin. The resulting delta power values were
normalized to average delta power during wakefulness (% of W). 10 minute blocks that were
categorized as “awake” were chosen in each recording. The average delta power value for these
blocks was computed and then the remaining blocks were normalized to this value. The number of “awake” blocks treated in this fashion varied between recordings. This method was mainly employed to make data between animals comparable on a visual scale (e.g., Fig. 19) but also to ensure that recordings of birds in a migratory and a non-migratory state are comparable. Data was only analyzed within subjects (paired t-tests), no between subjects analysis was conducted.
REM sleep: REM episodes were defined as a combination of a.) bursts of eye movements
together with b.) wake-like, “activated” EEG, while a bird was c.) behaviorally asleep and d.)
showed low muscle tone (if recorded). In addition, for quantitative purposes, a more
conventional approach was employed, in which clusters of eye movements were scored as 5 s
49 50
REM episodes (Rattenborg et al., 2004). A “cluster” was defined as 2 or more eye movements
within a 2 s period. If eye movement bouts were separated by at least 2 seconds of eye movement free EOG they were scored as separate clusters.
To acquire an estimate of total REM sleep times and to assess changes in REM sleep
quantities over the course of a night, five of the 72 ten-minute EEG samples were chosen at
different time points during the recordings (at 2, 4, 6, 8 and 10 hours ± 30 min). Only blocks of
behavior that uniformly consisted of behavioral sleep were selected. The selected episodes were
analyzed for REM sleep (clusters of eye movements) blind to condition (recording time).
The presence of a combination of REM sleep and non-REM sleep during behavioral
sleep can potentially affect average observed delta power in EEG blocks if the proportion of
REM sleep and non-REM sleep does not remain constant over the course of the night. To detect
whether the amount of REM sleep influenced the calculation of delta power, two of the 72 10
minute EEG episodes were selected at an early (2h) and a late (10h) time point of each recording.
Only episodes that unequivocally consisted of behavioral sleep throughout were chosen and the
corresponding EEG was inspected for artifact and REM sleep. REM episodes and artifact were
removed and the remaining EEG was subjected to spectral analysis (Fig. 17) and a paired t test.
50 51
a Delta activity over the course of a night
D/F F F B B B B W FFFMIBS BS BS B BBBBB Front Sleep Back Sleep
b EOG
EEG Left
EEG Right
40 s Merge SWS Discard REM episodes
Spectral Analysis
Fig. 17: “EEG block analysis.” Each bar in a stands for one 10-minute block characterized behaviorally (W,D,FS, BS, MIX) and its corresponding power value. Single blocks of uniform behaviour were selected at different time points during the night. To more accurately assess changes in Δ-power over the course of a night (EEG data from pigeon; Fuchs, unpublished),
51 52
REM sleep and artifact were removed from the corresponding EEG tracks (b) and the remaining EEG was then subjected to spectral analysis.
Results
Sleep in non-migratory thrushes
Nocturnal sleep in Swainson’s thrushes while non-migratory was prevailingly
characterized by a synchronized low frequency, high amplitude EEG (SWS) that was frequently
interrupted by the two main phasic components of avian Rapid Eye Movement Sleep (REM
sleep): clusters of eye movements (REMs) and EEG activations (high frequency/low amplitude;
Figs). Total sleeping times (% 12h dark period) under the mild restraint of the recording
environment differed only marginally from the mean values observed during behavioral
experiments (behavior: X ± SEM 71.9 ± 3.4 %, electrophysiology: 67.8 ± 4.2 % ).
SWS. Visual inspection of individual EEG recordings indicated a substantial decline in
SW activity over the course of a night (Fig. 18). Spectral analysis of the 10 minute EEG episodes taken at different times points during the night confirmed this initial assessment (Fig. 19). In the six experimental animals, delta power (1.5-4 Hz) increased during the first 2 hours after lights out, reached an asymiototic peak between hours 2 and 4, and then steadily declined over the rest of the night (Fig. 19).
52 53
Slow wave sleep at different times of the night
A EOG left
EOG right EEG left
EEG right
EMG
B
Fig. 18: Representative EEG tracks demonstrating the decline in delta activity over the course of the night in a single animal, 1.5 hours (A) and 11 hours (B) after lights out. Note the dramatic decrease in slow wave amplitude from the early (A) to the late (B) sample.
53 54
Slow-wave activity decreases over the course of the night
500%
400%
300%
200%
100%
0% 0 1h2h3h4h5h6h7h8h9h10h11h
Fig. 19: Slow-wave activity in the delta band (1.5-4Hz) reached a maximum 2 hours after lights out(0), remained at peak levels until 4 hours, then declined over the rest of the night. Each data point represents a ten-minute block of EEG. Average power values between 1.5 and 4 Hz are depicted as a percentage of average delta power during wakefulness (N = 6; X ± SEM).
A paired comparison revealed a statistically significant decrease of average power between the two hours of 2h through 4h, and the two hours of 8h through 10h (paired t test: t5 =
6.022, P < 0.01; Fig. 20). The initial increase in SW activity coincided with a period of
behavioral variability at the beginning of the night, illustrated by many behavioral blocks scored
as “mixed” (X ± SEM, 54 ± 12%). Behaviorally, these blocks were generally similar to the
periods of daytime sleep-like states in migrating thrushes, characterized by a mixture of
wakefulness, drowsiness and relatively short periods of sleep. Interestingly the behavior between
hours 2 and 4, although accompanied by high power SW activity, proved similarly variable at the
behavioral level (53 ± 14 % of the behavioral blocks were scored as “mixed”). This latter finding
54 55
indicates that relatively short episodes of sleep are sufficient to generate high overall delta power.
Behavior during hours 8 and 10, however, was characterized by little variability (13 ± 7% blocks
were scored as “mixed”) and consisted predominantly of sleep. Behavioral variability, therefore,
seems to be an unlikely explanation for the observed decline in SW activity during the second half of the night.
SWS Early/SWS Late
400% SWS Early 2h-4h 350% SWS 300% Late 8h-10h 250% 200% 150% 100% 50% 0% 12
Fig. 20: Average Δ-Power (1.5-4 Hz) during hours from 2h through 4h differed significantly
from average Δ-Power during hours 8h through 10h. Average power values between 1.5 and 4
Hz are depicted as a percentage of average delta power during wakefulness (N = 6; X ± SEM).
REM sleep. Episodes of electrophysiologically determined REM sleep were generally
brief and rarely accompanied by behavioral signs other than those of SWS. Clusters of eye
movements (REMs) became more “intense” over the course of a night, showing an increase in
amplitude and frequency (Figs. 21-23). Interestingly, when the activity of both eyes was
observed during REM sleep, deflections in the EOG traces indicated unilateral clusters in
55 56
addition to bilateral clusters (Fig. 22). EEG activation similar to wakefulness was observed
during the majority of REMs. Generally, EEG activations accompanying REMs were more
pronounced during the later part of the night. Early in the night EEG changes from SWS to REM
sleep were usually subtle and in some cases difficult to discriminate (e.g., Fig. 21). Consequently
during the early part of the night, a distinction between REM sleep and SWS based solely on
EEG proved difficult. Despite more pronounced REM-EEG activation during the later part of the
night (Figs. 21-23), the above noted general decline in EEG slow wave amplitude attenuated the
contrast between the EEG of SWS and REM sleep. Therefore, for a different reason, the EEG
based distinction of the two sleep states during the later part of the night could also be
challenging. Inspection of electrophysiological data of animals outfitted for EMG recordings
showed that in contrast to wakefulness, the EMG of REM sleep was hypotonic and, unlike
mammals (Siegel, 2000), not interrupted by twitches or brief activations. In most cases the
EMG-determined muscle tone during REM sleep did not differ visibly from the EMG observed
during SWS (e.g. Figs. 21 and 22). Thus, EMG in the Swainson’s thrush is not a suitable
diagnostic tool to discriminate REM episodes from SWS, but it does allow to reliably discriminate REM episodes from brief arousals (e.g. Figs. 21, 23).
It is also worth noting that in a single animal, during a small portion of REM episodes,
the nuchal EMG exhibited intermittent periods of atonia (Fig. 23). The episodes of atonia, in contrast to mammals, were brief, always shorter than the associated cluster of eye movements,
and never exceeded one second.
56 57
“Early” and “Late” REM Episodes
A Early REM sleep
EOG left
EOG right EEG left
EEG right
EMG
B Late REM sleep
C Wakeup from SWS
5 s
57 58
Fig. 21: Representative EEG tracks from a single bird demonstrating differences between
“early” (A) and “late” (B) REM episodes. Note that in A, 1h 50 min after lights out clusters of eye movements were not accompanied by visible EEG activations. By contrast, in B, at 11 hours after lights out and during a cluster of eye movements, the EEG showed activation (highlighted) similar to wakefulness (C). Eye movements are also more “intense” during late night REM sleep illustrated by increased amplitude on the EOG track (B). The EMG during eye movements in A and B remained hypotonic and indistinguishable from the EMG recorded during SWS. In contrast, both EMG (highlighted) and EEG activity change during a wakeup from SWS (C).
Unilateral REMs
EOG left
EOG right
EEG left EEG right
EMG
5s
58 59
Fig. 22: Another example of “late” REM sleep with multiple REM clusters in close succession
accompanied by visible EEG activation. Interestingly one of the EOG clusters (highlighted) only
occurs on the left EOG track indicating unilateral REMs. This phenomenon has been observed in
other subjects on multiple occasions (upper and lower tracks are continual).
Bouts of Atonia during REM sleep
A Atonia
EOG
EEG left EEG right
EMG
B Wakeup
5s Fig. 23: Brief bouts of atonia (A; arrows) on a background of hypotonic EMG were observed in
one thrush. This is another example of “late” REM sleep with multiple REM clusters occurring
59 60
in rapid succession. B illustrates EMG activation during a wakeup after SWS early in the night during the same recording.
Total amount of REM sleep and distribution over the course of the night. Due to the above described properties of REM sleep in the Swainson’s thrush, determining the individual length of each REM episode based on the duration of EEG activation (Fuchs et al., 2006; homing pigeon) was not a viable approach. Therefore, to calculate REM sleep during the course of a night, phasic signs of REM sleep (clusters of eye movements) were scored as 5 second REM episodes (e.g., Rattenborg et al., 2004). This method, applied blind to condition (recording time) to selected blocks of behavior that uniformly consisted of behavioral sleep (at 2,4,6,8,10 h ± 30 min), revealed a robust increase of REM sleep over the course of the night (Fig. 24). A paired comparison of the earliest (2h) and latest (10h) REM sample revealed a significant increase in the number of episodes (paired t test: t5 = -4.243, P < 0.01). The percentage of sleeping time occupied by REM sleep increased from 11.8 ± 2.3 % (X ± SEM) at 2 hours to 22.36 ± 2 % at 10 hours. The average percentage of REM sleep observed over all 5 time points equalled 16.3 ± 2.6
% of total sleeping time. This value translates into 11.2 % of the 12 hour dark period and 5.6% of the 24 hour period and is comparable to REM quantities reported for other avian species
(reviewed in Amlaner & Ball, 1994).
60 61
REM sleep increases over the course of the night
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00% 2h 4h 6h 8h 10h
Fig. 24: Percent of REM sleep as a function of time of night. The average amount of REM sleep
observed at different times of the night (2,4,6,8,10 h) increased two-fold between hours 2 and 10
(N = 6; X ± SEM).
REM sleep, SWS and Delta power. A two-fold increase of REM sleep over the course of the night, in addition to more pronounced high frequency, low amplitude EEG activation during late night, could explain the previously reported decrease in delta power later in the night. This is because a relatively large percentage of the late night EEG is composed of REM sleep, which is characterized by lower delta power than SWS. Spectral analysis generates average power values for the entire EEG episode subjected to analysis. A large proportion of low power EEG (as observed during late night REM sleep) will therefore attenuate average power values.
61 62
Spectral analysis of the 2h and 10h EEG samples, generated to detect REM sleep quantities at different times of the night (Fig. 24), revealed a decline in delta power from the early to the late sample (paired t test: t5 = 3.277, P < 0.05). It remained to be answered if increasing levels of REM sleep are the sole reason for the observed decline in delta power late at night, or if the previously reported decline in slow wave amplitude during non-REM sleep also significantly contributed to the observed decrease in delta power. EEG activations that were temporally associated with clusters of eye movements were selectively removed from the analyzed EEG blocks and the remaining EEG was, again, subjected to spectral analysis.
Consistent with the observed decline in slow wave amplitude over the course of the night (Fig.
19), the “purified” SWS samples still showed a significant decline in delta power from the early night to the late night sample (paired t test: t5 = 3.155, P < 0.05). The observed decline of delta power late at night is therefore only partially related to an increase in REM sleep, reflecting as well a decrease in SWS amplitude over the course of the night.
Front sleep and Back sleep. The present EEG recordings, like the behavioural results, do not suggest that the back sleep posture is associated with a deeper form of sleep. On the contrary, visually inspected SW activity in the back sleep posture generally appeared to be of lower amplitude than in the front sleep posture. This is almost certainly a consequence of BS being the dominant sleeping posture during the second half of the night, at a time when SWS amplitude was already considerably diminished and REM sleep occupied large amounts of time. However a more detailed examination of the EEG recordings, focusing on birds that preferentially showed back sleep or front sleep as their primary sleeping posture, revealed that the sleep EEG between front sleep and back sleep did not differ noticeably. Regardless of sleeping posture, birds showed
62 63
the previously described decline in delta power and increase in REM sleep over the course of the night (Fig. 25)
Sleeping postures and sleep parameters
a Back Sleep b
250 REM Back Sleep
r 200
150 30
100 20
Delta Powe 50 10 0
Number episodes of 0 0 h h 1h 2h 3h 4h 5h 6h 7h 8h 9h 10 11 2h 4h 6h 8h 10h
Front Sleep c d
250 REM Front Sleep 200 60 150 50 100 40 30 50 20 0 10 0 0 h h 1h 2h 3h 4h 5h 6h 7h 8h 9h 10 11 2h 4h 6h 8h 10h
Fig. 25: A comparison of delta power and number of REM episodes in relation to sleeping
posture in two birds that preferentially engaged in front sleep (c,d) or back sleep (a,b).
Independent of sleeping posture, a similar decline in delta power was observed over the course of
the representative night. Also, regardless of sleeping posture, an increase in REM sleep with
recording time was observed. a, c: Delta power (1.5-4Hz), only samples that were uniformly
63 64 composed of back sleep (a) or front sleep (b) are displayed; b, d: Number of REM episodes observed in five 10 minute EEG samples at different times of the night..
Nighttime sleep during the migratory season
The EEG observed in nocturnally active Swainson’s thrushes during bouts of restlessness
(perch-hopping, wing whirring) was always an EEG typical of active wakefulness. In contrast to their behavior in the home cage, birds rarely showed long, continual bouts of wing whirring in the recording environment. Instead, they displayed large amounts of perch-hopping (moving laterally on the single perch). After spending most of the night with active behaviour, birds usually settled down and slept during the final hours of a recording night. Delta power during this time showed an initial peak in two of the three birds, comparable to the delta peak observed early in the night in diurnal birds (Fig. 26, 27). The highest delta power values observed in these two birds exceeded the maximum power observed in the same birds when non-migratory and sleeping at night (Fig. 27). The third bird, however, did not show a similar increase in delta power. Interestingly, the level of REM sleep observed in all three birds after nocturnal restlessness was high and even slightly exceeded the values observed in the same birds at the same time of night when non-migratory. Moreover, REM sleep levels, immediately after sleep onset, were high and did not show the slow increase over time that was observed in non- migratory birds. REM sleep started at levels comparable to those encountered late at night in the same birds when non-migratory, and REM levels remained high until wakeup.
64 65
Delta Power recorded at night in the same bird A when non-migratory and migratory
B5 diurnal
500%
400%
300%
200%
100%
0% 0 1h2h3h4h5h6h7h8h9h10h11h
B Bird 5 restless
600%
500%
400%
300%
200%
100%
0% 0 1h2h3h4h5h6h7h8h9h10h11h
Fig. 26: Average delta power over the course of two nights in a bird when (A) non-migratory and
(B) migratory. Note that delta power in the diurnal condition reaches its highest amplitude during the first half of the night, while in the migratory condition delta power peaks during the second
65 66
half of the night. Average power values between 1.5 and 4 Hz are depicted as a percentage of
average delta power during wakefulness.
A Delta Power (1.5-4 Hz)
Restless 600% Diurnal max 500% max 400%
300%
200%
Corresponding 100% time
0% B1 B2 B3
B Number of REM Phases Restless Diurnal 45 40 35 30 25 20 15 10 5 0 B1 B2 B3
Fig.27: Comparison of delta power (A) and REM sleep (B) in the 3 subject birds when non- migratory and migratory. The first column for each thrush (B1-3) depicts the largest delta power value found for any 10 minute episode of EEG during a non-migratory night (located between
66 67 hours 1 and 4). The third column depicts the largest power value found during a migratory night
(located between hours 8 and 12). The second column depicts the power value in the same bird when non-migratory at a time when the maximum amplitude in delta power was recorded when migratory. In two cases (B2, B3) maximum delta power during restlessness exceeded maximum delta power during the diurnal night, while in the third bird the power value was comparable to what would be expected during a diurnal night at the corresponding time. B depicts the number of REM episodes recorded in the same EEG blocks. Interestingly, in all three birds the number of
REM episodes during restlessness was slightly higher than would be expected for a diurnal night at the same time.
Discussion
The electrophysiological changes observed during nighttime sleep were similar to those observed in earlier studies in other avian species (reviewed in Amlaner & Ball, 1994). Nocturnal sleep in the Swainson’s thrush consisted of SWS and REM sleep. Total sleeping times and the average percentage of nocturnal REM sleep are comparable to existing data in other avian species (reviewed in Amlaner & Ball, 1994).
Slow Wave Sleep. One of the many remaining questions about avian sleep is whether
SWS is characterized by homoeostatic properties similar to its mammalian counterpart. One of the features of mammalian sleep is that the deep stages of non REM sleep (SWS) occupy predominantly the early part of the night, and that sleep during these first hours is associated with higher delta power and arousal thresholds than during the rest of the night (Borbély &
Acherman, 2000). A decrease in SW activity over the course of the night is reflected in a decrease in delta power and arousal thresholds. Sleep pressure presumably builds up during the
67 68 next subjective day, resulting in another peak of SW activity during the early part of the next subjective night (Borbély & Acherman, 2000).
In the exclusively diurnal, non-migratory Swainson’s thrush, examination of the EEG recordings and spectral analysis revealed a steady decline of slow wave activity/power in the delta band over the course of the night, indicating that this aspect of avian and mammalian SWS is similar. The observed decline in SW activity did not disappear when REM episodes were selectively removed from the EEG recordings, demonstrating a true decline in SW activity in addition to increasing amounts of nocturnal REM sleep.
REM sleep. Clusters of rapid eye movements (REMs) accompanied by EEG activity similar to wakefulness while a bird was behaviorally asleep characterized REM sleep in the
Swainson’s thrush. Despite intense clusters of eye movements, the eyelids were always closed when observable (front sleep position), and muscle tone was reduced when compared to wakefulness. In one animal, brief episodes of nuchal atonia were present. Similar to mammals
(reviewed in Horne, 1988) and some avian species (Van Luijtelaar et al. 1987; Szymczak et al.
1996; Rattenborg et al., 2004; but see Rattenborg & Amlaner, 2002), a steep increase in REM activity was observed over the course of the night. Therefore, it may be concluded that REM sleep in the Swainson’s thrush resembles REM sleep in other avian species (except for an absence of head movements), and is similar to its mammalian equivalent. Nevertheless, some methodological obstacles made it difficult to accurately calculate a total amount of nocturnal
REM sleep in the Swainson’s thrush. Clusters of eye movements early in the night were not always accompanied by wake-like EEG activity. In general, EEG “activations” (short for an
EEG similar to wakefulness) during the early night were rarely as pronounced as late at night,
68 69
making it difficult to accurately assess the length of individual REM episodes. The observed decline in SW activity during non-REM sleep also made it difficult to differentiate between
REM and non-REM sleep at late night. The employed scoring technique (5 s of REM sleep for each cluster of eye movements) was intended to be a conservative measure of REM sleep, considering that the average REM episode length in birds is roughly 10 seconds (Amlaner & Ball,
1994). Therefore, it needs to be acknowledged that employing a different definition of REM sleep would likely lead to a slightly different quantitative temporal profile. For example, if REM sleep was primarily defined by EEG activations, the analysis used would likely have resulted in a smaller estimate of REM sleep during the early night and a higher estimate during the late part of the night. The observed differences between early and late REM sleep also raise general questions regarding the definition of REM episodes in mammals and birds. In mammals, REM sleep is typically defined by some combination of eye movements, EEG activations, loss of muscle tone, hippocampal theta, phasic movements (twiches) and eye closure. In practice, only two or three of these characteristics are ever employed in a single sleep study. In contrast to
mammals, muscle tone is not a reliable indicator of REM sleep in birds because it rarely differs
distinctly from SWS (Amlaner & Ball, 1994). Hippocampal theta activity has not been observed
in any bird species to date (Rattenborg & Amlaner, 2002). This leaves eye movements, EEG
activations and phasic head movements as the main criteria for avian REM sleep. Head
movements, however, were not observed in the Swainson’s thrush, and the intensity of EEG
transitions to higher frequency/lower amplitude activity varied over the course of the night. This
raises the question of whether clusters of rapid eye movements alone are sufficient to define a
REM episode. Similar problems have been encountered in studies of mammalian neonatal sleep,
when the EEG is not yet fully developed and therefore does not correlate with arousal states
69 70
(Frank et al., 1997; Frank & Heller, 2003). In the latter case, the scoring criteria for neonatal
sleep (in Frank & Heller, 2003 ), typically rely exclusively on phasic components of REM sleep
(muscle twitches and eye movements). A similar scoring technique has been utilized in several
mammalian species (platypus, beluga; Siegel et al. 1999) where “REM sleep” is not accompanied by EEG changes. Just to illustrate the problem at hand (while not offering a
solution), this scoring technique has elevated the platypus from its humble beginnings as a
monotreme presumably without REM sleep (Allison et al., 1972) to the mammal that shows the
highest amounts of REM sleep (Siegel et al., 1999). Similarly in early avian sleep studies, when mammalian criteria were used and atonia was employed as the main criterion, only 0.5 to 1% of total sleep time were classified as REM sleep in the pigeon and chicken(Tradardi, 1966;
reviewed in Amlaner & Ball 1994). But in later years using different criteria, total REM times
between 5 and 10% have been established for the same species (Walker & Berger, 1972; Van
Twyver & Allison, 1972; Tobler & Borbely, 1988).
REM sleep itself can be approached as either a behavioral state with certain properties or
as a set of distinct parallel processes (REMs, atonia, EEG, PGO spikes) that together comprise
the phenomenon of REM sleep (Siegel, 2000). From a comparative point of view, the latter is an
interesting approach because avian REM sleep is characterized by some of the properties of
mammalian REM sleep while lacking others. Viewing the phasic and tonic components of REM
sleep as separate parallel processes allows one to hypothesize that these processes might, to some
extent, be regulated independently of each other. Such an approach to REM sleep could account
for the early/late differences (REMs, EEG activation) encountered during nocturnal REM sleep
in the present study. While the neurophysiological processes governing avian SWS may be
70 71 strong enough to override the transition to a REM-state EEG early at night, other processes, e.g. the ones governing rapid eye movements, may be fully expressed. Later at night, when the homoeostatic pressure for SWS declines, REM sleep-related EEG activation may be more easily expressed. Similarly, from a developmental perspective, it could be argued that while the brain circuitry for certain tonic REM sleep components (EEG) is not fully developed in utero (Frank et al., 1997; Frank & Heller, 2003), some of the phasic components (twitches, REMs) are already developed and functional.
Another interesting finding is the observation of unilateral clusters of eye movements during REM sleep in the Swainson’s thrush. This observation, in keeping with Siegel’s ‘multi process’ approach to REM sleep, implies that certain aspects of avian REM sleep can be lateralized while others (low amplitude mixed frequency EEG) can only occur bilaterally.
However, the employed EOG recordings are only crude measures for detecting eye movements, and provide no information about direction. Also, it is not possible to distinguish between eye lid and eye movements. Because a single subcutaneous electrode was implanted in the dorsal supraorbital region, it remains possible that certain eye movements that did not involve the dorsal eye muscles escaped detection
Front sleep and Back sleep. Sleeping postures have been proposed as indicators of sleep quality. More precisely, in passerines (Amlaner & Ball, 1994) the back sleep posture (Fig. 1 a) has been hypothesized to represent a deeper (and possibly more beneficial) state of sleep than front sleep (Fig. 1 c). If a discrimination of sleep quality could be made at the behavioral level, sleeping postures could provide a simple, non-invasive tool to assess changes in sleep quality in
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response to sleep loss, stress or altered energetic condition (temperature, body weight). As
mentioned earlier, the initial behavioral results revealed no consistent alterations in nighttime
sleeping postures during the migratory and the non-migratory condition (no back sleep was
observed during the day). In addition, the present EEG recordings do not suggest that the back
sleep posture is associated with a deeper form of sleep if delta power is employed as a measure
of sleep quality. On the contrary, it seems that both the EEG of front sleep and back sleep
undergo the same decrease in delta power and increase in REM sleep over the course of the night.
The results therefore indicate that EEG state is not directly related to sleeping posture. This is not
to say that sleeping postures do not have different or complementary adaptive value. The back
sleep posture, for example, might confer energetic advantages; it would be interesting to know if
sleeping posture preferences can be influenced by ambient temperature changes or fasting. The
back sleep posture may also be advantageous during intense bouts of REM sleep by suppressing phasic head movements, which might alert predators. In addition, birds in this position might
benefit from a small energetic advantage that could help compensate for impaired
thermoregulation during REM sleep (Berger & Phillips, 1994; Parmeggiani, 2000).
Nighttime sleep in migrating Swainson’s thrushes. Only three animals showed nocturnal
restlessness under the restraint of the recording environment. However, the observations yielded
some interesting initial findings worthy of further investigation.
One of the most surprising findings in studies of avian sleep (Berger & Phillips, 1994;
Rattenborg et al. 2004) is the apparent resilience of birds (even non migratory species) to sleep
loss. Sleep deprivation in mammals results in compensatory changes in sleep architecture (the
distribution and duration of sleep stages) during recovery sleep (Borbély & Acherman, 2000).
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Increased amounts of delta activity elevate average delta power to levels that exceed the levels
during a normal night of sleep. This so called “delta rebound” is the result of increased sleep
pressure (Acherman & Borbély, 2000; Feinberg & March, 1995) building up during waking
time. The mechanisms that regulate a sleep rebound have been proposed to be the same
mechanisms that explain the decline in delta power during the night in animals that sleep
normally. Interestingly, while some evidence suggests a nocturnal decrease in delta power in
birds (Van Luijtelaar et al., 1987; Szymczak et al., 1996; Rattenborg et al., 2004; the present
study), a delta rebound after sleep deprivation has yet to be demonstrated.
Delta power at night after the cessation of nocturnal restlessness in the Swainson’s thrush
showed an initial peak in two of the three birds; a peak that exceeded the maximum power observed in the same birds when diurnal. The initial delta power amplitude in these two
individuals could be interpreted as a delta rebound. However, the third bird did not show a
similar pattern. Further research will have to determine if the pattern found in the two animals
generalizes to the species as a whole. In any event, it seems premature to dismiss the possibility
of a delta rebound, similar to mammalian SWS, in birds.
In birds and mammals (Borbely & Acherman, 2000; Rattenborg & Amlaner, 2002) the
evidence for a REM sleep rebound after sleep deprivation is mixed. Short term 24 hour sleep
deprivation, induced by mild handling, resulted in a small REM rebound in the homing pigeon
(Tobler & Borbely, 1988). However, long term sleep deprivation (up to 72 days) induced by
bright light (Berger & Phillips, 1994) did not result in a REM rebound in the same species. A
study in a long-distance migrant, the white crowned sparrow (Zonotrichia leucophrys gambellii;
Rattenborg et al., 2004), detected decreased REM latencies on nights following nights of
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nocturnal restlessness (white crowned sparrows are predominantly active during the second half
of the night). However, the amount of REM sleep displayed did not exceed the amounts of REM
sleep observed in diurnal animals at corresponding times.
Interestingly, the level of REM sleep observed in all three experimental birds after
nocturnal restlessness was high, exceeding the values observed in the same birds at corresponding times when non-migratory. Unlike non-migratory Swainson’s thrushes, a large
portion of early sleep (as soon as a bird assumed a sleeping posture) in restless animals was
occupied by REM sleep. There was no indication of the slow increase over time that was
observed in the same animals when non-migratory. Overall, these observations support the
conclusion that sleep loss in migrating Swainson’s thrush results in a REM rebound. However,
although REM levels in the restless condition exceeded the non-migratory levels at
corresponding times in all 3 animals, a substantial increase was only observed in one animal
(Fig. 27 B, Bird 3). In other words, it is also possible that migratory birds simply engage in the
amount of REM sleep that is typical for that portion of the light-dark cycle. Because migratory
Swainson’s thrushes are predominantly active during the first half of the night, they engage in
large amounts of REM sleep during the second half of the night. White crowned sparrows are
active during the second half of the night, and in line with this interpretation, during the first half
of the night they show REM sleep levels that are comparable to the corresponding (smaller) non- migratory levels. Although the latter interpretation does not qualify as physiological REM rebound, it could potentially explain why Swainson’s thrushes are predominantly active during the first half of the night. Migration concentrated into the first half of the night may prevent excessive loss of REM sleep, which has been implicated in serving important functions in learning and memory (Maquet, 2001; Smith, 1995 but see Siegel, 2001).
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GENERAL DISCUSSION
Swainson’s thrushes undergo substantial sleep deprivation during the migratory season.
However, generally a small portion of the night, usually the early morning hours, is still devoted
to sleep. It seems that SW activity during this time is of an intensity comparable to that observed early in the night in non-migratory birds. Interestingly, almost immediately after sleep onset,
REM sleep in the 3 nocturnally active birds reached levels comparable to or greater than the
levels observed in late night recordings in non-migratory birds. Future research will have to
determine whether the observed REM levels constitute a true rebound in response to loss of
REM sleep. Possibly, REM sleep in this species is predominantly regulated by a circadian
rhythm with REM sleep levels corresponding to certain circadian clock times. A thrush falling
asleep after 10 hours of nocturnal activity would then roughly show the same amount of REM
sleep that a non-migratory thrush would show after ten hours of sleep. This hypothesis could also
account for the lack of REM sleep during the daytime and the observed low levels of nocturnal
REM sleep during the first half of the night in migratory white crowned sparrows (Rattenborg et
al., 2004).
The steep increase of REM sleep during the latter portion of the night in non-migratory
thrushes implies that Swainson’s thrushes, when active with migration (predominantly during the
first half of the night), lose more SWS than REM sleep. The total amounts of REM sleep in mammals can undergo drastic alterations as a function of ambient temperature (Parmeggiani,
2000), core temperature (torpor, Berger & Phillips 1995), stress (Horne, 2000; Körtner & Geiser,
2000) or, in the case of humans, pharmacological manipulation (e.g., antidepressants: Wyatt et
al., 1973; Landolt & de Boer, 2001). Humans in particular can apparently function normally over
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long periods of time with drastically reduced levels of REM sleep (Wyatt et al., 1971; Landolt &
De Boer, 2001; Landolt et al. 2003). Avian REM sleep and its regulation are not well understood.
The available data, however, suggest that avian and mammalian REM sleep are similarly
regulated (Speciale et al., 1976; Karmanova, 1982; Vasconcelos-Duenas & Guerrero, 1983;
Fuchs et al., 2006), and therefore, possibly affected in the same way by environmental factors.
Consequently, it seems plausible that Swainson’s thrushes, even if they are not fully compensating for the loss of REM sleep at night, could function normally without a need for daytime compensation.
The loss of nocturnal SWS during migration, however, seems not to be easily tolerated by
the Swainson’s thrush. Despite high amplitude SWS in the later portion of the night in
nocturnally active birds, large amounts of REM sleep at the same time probably further curtail
the time available for SWS. Based on the data available, it does not seem that, like in humans
(Horne, 1988), a SWS rebound takes precedence over REM sleep in nocturnally active
Swainson’s thrushes. Rather, nocturnally active birds markedly reduce their daytime behavior,
and engage in brief episodes of daytime sleep and unihemispheric sleep embedded in longer
periods of drowsiness.
Daytime Sleep
To the best of the author’s knowledge, the current study is the first to identify brief
episodes of daytime sleep as a distinct behavioral state in an avian species. While equally brief
periods of UEC have received considerable attention in recent years (Kavanau, 1998; Rattenborg
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et al., 1999; Rattenborg et al., 2000), daytime sleep has only been observed in some typically
polyphasic bird species (sleep distributed over the 24 hour period) like ducks (Lendrem, 1983),
owls (in Amlaner & Ball,1994) or in breeding shore birds (Shafferey et al., 1985). However,
sleep in these species can occupy considerable amounts of time and its temporal structure is quite
different from the behavior described in the Swainson’s thrush. Most passerine birds are thought
to adhere to strict diurnal sleeping patterns (Amlaner & Ball, 1994; Rattenborg & Amlaner,
2002). However, brief daytime “naps” of several seconds were not taken into account. In earlier
studies (e.g. Rattenborg et al., 2004), this form of daytime sleep was likely included in an
operational definition of drowsiness and therefore not addressed or noticed as a separate phenomenon. The present work demonstrates that sleep-like EEG activity significantly differs in
intensity between DTS and D, justifying its treatment as a separate behavioral state. Daytime
sleep was accompanied by an EEG of SWS, which was rapidly reversed at the moment of eye
opening. The electrophysiological evidence strongly supports the conclusion that even short eye
closures are true naps, qualifying as physiological sleep. The typically short episode length of
DTS in the Swainson’s thrush raises the question of the extent to which this behavior is capable
of compensating for nocturnal sleep loss. One of the main differences between avian and
mammalian sleep is that avian sleep seems to function on a compressed time scale. REM phases
in birds, on average, last only 10 seconds. A normal night of avian sleep is composed of
relatively short periods of SWS interrupted by hundreds of REM episodes and multiple arousals.
The observed episodes of sleep during the day, therefore, have similar properties as nighttime
sleep. Consequently, DTS provides adaptive, recuperative opportunities that should enable a
migrant to compensate, at least partially, for lost nighttime sleep.
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Unihemispheric sleep
Unihemispheric sleep was originally discovered in marine mammals and describes the ability to selectively sleep with the left or the right brain hemisphere, while the remaining hemisphere shows an EEG similar to wakefulness (for in depth review see: Rattenborg et al.,
2000). Mammalian unihemispheric sleep is usually defined by interhemispheric EEG differences that leave little room for doubt about its authenticity as a physiological brain state.
Unihemispheric sleep is predominantly found in cetaceans (whales) and ottaridae (fur seals), while other marine mammals (e.g., true seals) employ different strategies to prevent drowning during sleep. Unihemispheric sleep in mammals is often accompanied by unilateral eye closure contralateral to the sleeping hemisphere (for review Rattenborg et al. 2000). Cetaceans especially are known for this ability (Lyamin et al., 2001), and it has been proposed that they utilize it to either maintain group contact, or detect predators. Whales in a state of unihemispheric sleep retain the ability for motor activity. Therefore, it seems likely that they also engage in bouts of unihemispheric sleep during their impressive migrations. Migratory behavior is not the only similarity between cetaceans and birds. It has been hypothesized that the degree of connectivity between the two hemispheres is an important prerequisite to develop unihemispheric sleep (Rattenborg, in press). The more isolated the two hemispheres are, the better the conditions for unihemispheric sleep. It is interesting that cetaceans and birds share some of the aspects that according to this hypothesis predispose an animal for unihemispheric sleep. Both whales and birds have lateral eyes with relatively little overlap of their visual field.
Consequently, in both groups the optic nerves almost completely decussate in the optic chiasm and predominantly project to the contralateral hemisphere. The lack of a corpus callosum,
78 79 degenerated in cetaceans and absent in birds, leaves the anterior commissure as the predominant connection between the two hemispheres. In contrast to mammals that do not show unihemispheric sleep, these anatomical differences greatly reduce (but do not abolish) connections between the two hemispheres. The bilateral projections of both eyes in many mammals and the connection of the two hemispheres through the corpus callosum (Horne, 1988) may be responsible for an indiscriminate activation of both hemispheres to visual input in most mammals even if only one eye is open.
Unlike marine mammals, avian unihemispheric sleep is generally characterized by more subtle interhemispehric EEG asymmetries (IA: Rattenborg et al., 2002). These EEG assymetries however persist in darkness and enucleated chicken (as do the general EEG characteristics of sleep and wakefulness; Ookawa & Gotoh, 1965) suggesting that interhemispheric EEG asymmetries are not a consequence of changes in light intensity or visual input (e.g. eye closure).
Episodes of avian unihemispheric sleep are usually short, ranging from seconds to minutes
(Rattenborg et al., 2000).interhemispheric asymmetries are not always visible to the observer and sometimes become only apparent after statistical analysis. In contrast to the striking EEG differences observed in marine mammals, the most prominent feature of avian unihemispheric sleep is unilateral eye closure (UEC). To date UEC has been observed in 50 species of 19 orders
(Ball et al., 1988). However, EEG studies have only been conducted in a few avian species and
EEG asymmetries during episodes of UEC have been observed in the domestic fowl (Ookawa &
Gotoh, 1965, Peters et al., 1965)), glaucous winged gull (Ball et al., 1988), mallard duck
(Rattenborg et al., 1999), pigeon (Rattenborg et al., 2001) and the Swainson’s thrush. EEG asymmetries without monitoring eye state were also reported for the European black bird
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(Szymczak et al., 1996) and zebra finch (Silvain Shank, personal communication). Data from the pigeon, mallard and glaucous winged gull agree with the present finding that not every episode of UEC is accompanied by visible EEG asymmetries. A possible explanation for this observation may be related to the intermediate state of drowsiness (see also below). As described earlier, avian drowsiness is accompanied by EEG activity similar to sleep. Power differences between drowsiness and sleep can be easily demonstrated statistically; power differences that may not be visually detectable during individual episodes. It has been proposed that drowsiness allows a bird to remain vigilant to visual stimulation while providing benefits from sleep like brain activity. In the vast majority of cases, unilateral eye closure in the Swainson’s thrush was displayed during longer bouts of drowsiness. If drowsiness is more valuable than wakefulness as a recuperative state, there would be a possible cost if brain activity during episodes of UECcontralateral to the open eye reverted to an activity pattern resembling alert wakefulness. Therefore, during avian unihemispheric sleep a “sleeping” hemisphere is generally compared to a “drowsy” hemisphere, not an “awake” hemisphere. Consequently, for single episodes of UEC observable hemispheric differences may be subtle or absent. However, by pooling data across episodes of UEC a relationship between eye state and brain activity was demonstrated, with an open eye corresponding to SW activity comparable to drowsiness in the contralateral brain hemisphere and a closed eye corresponding to SW activity comparable to sleep (see also Rattenborg et al., 1999;
2000). Episodes of avian unihemispheric sleep have been predominantly interpreted in a context of predator detection (for review: Rattenborg et al. 2000; Rattenborg & Amlaner 2002; Lima et al., 2005). However, although predator-prey interactions are a likely selective factor promoting the evolution of unilateral eye closure, they represent only one benefit of unihemispheric sleep.
Another benefit is the recuperative opportunity an animal would gain from UEC/unihemispheric
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sleep. Even if the observed differences in brain activity are not as prominent as in marine
mammals, some benefit must outweigh the risk of closing one eye in an unsafe environment. As
long as the precise functions of sleep or slow wave acivity remain unexplained, the range of
benefits associated with UEC will necessarily remain obscure. Aside from its
electrophysiological resemblance to sleep, a substantial increase in daytime UEC in response to
nocturnal sleep loss in the Swainson’s thrush indicates that unilateral eye closure, like daytime
sleep, does provide at least some of the benefits of slow wave sleep.
Drowsiness
Daytime drowsiness, in all likelihood, also provides Swainson’s thrushes with some of
the benefits of sleep. The term drowsiness is unfortunately an ambiguous term. It implies fatigue
and a need for recovery by means of sleep; the opposite of what I am proposing. There are
various other expressions describing this behavioral state; quiet wakefulness, slow-wave sleep
with open eyes, rest or vigilant sleep (Rattenborg & Amlaner, 2002; Lima et al., 2005). These terms are equally unsatisfactory but they allow authors, depending on their orientation, to either
depict drowsiness as a form of sleep or wakefulness. I view drowsiness as a true intermediate
state enabling some behaviour associated with wakefulness, yet also granting some of the
benefits of sleep. Thus, Zepelin & Rechtschaffen’s (1974) definition of drowsiness best describes my own position:
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“Drowsiness, often defined as an intermediate state between wakefulness and sleep,
combines intermittent electrophysiological signs of non-REM sleep with behavioral signs of
wakefulness.”
Drowsiness is leading a solitary existence on the fringe of sleep research and has rarely been the exclusive subject of a scientific investigation (with a single exception: Ruckebusch,
1972). One possible reason is that drowsiness does not lend itself well to analysis. Like avian unihemispheric sleep or short episodes of daytime sleep, it is characterized by rapid, sometimes subtle changes in brain activity that often preclude meaningful analysis by epoch scoring (a
problem that applies to avian sleep in general and avian REM sleep in particular). Depending on
the species studied drowsiness can occupy large amounts of time (birds, ungulates) or be barely
detectable (humans, rodents; Horne, 1988). The electrophysiological correlates of drowsiness
also differ between species, resembling light forms of sleep with sleep spindles in some
mammals (Ruckebush, 1972) and deeper sleep with high amplitude slow waves in others
(Ruckebush, 1972). Interestingly, mammals that exhibit large amounts of drowsiness typically
show reduced quantities of SWS, lending some evidence in support of the hypothesis that it can
partially compensate for non-REM sleep (Horne, 1988). Similarly, in the Swainson’s thrush and
the white crowned sparrow (Rattenborg et al., 2004), the amount of drowsiness recorded during
the day drastically increases during times of nocturnal sleep loss, therefore suggesting benefits
similar to sleep.
Avian drowsiness resembles reduced amplitude SW activity of non-REM sleep, and
therefore, can be difficult to distinguish from non-REM sleep based on electrophysiological
82 83 criteria alone (Tobler & Borbély, 1988;Van Twyver & Allison, 1972; Walker & Berger, 1972).
However, it can be equally challenging to reliably distinguish drowsiness from more alert forms of wakefulness when exclusively relying on behavioral criteria. However, the behaviorally observed increase in daytime drowsiness in nocturnally active Swainson’s thrushes together with the electrophysiological observations, as well as the results of Rattenborg et al. (2004), indicate that drowsiness in migratory birds is drastically elevated and therefore may provide a form of daytime rest that can compensate for extensive nocturnal sleep loss.
During drowsiness, birds display visibly increased slow wave activity while their eyes are partially open and they remain responsive to visual stimuli (Rattenborg et al. 1999; Fuchs personal observation). Slow wave activity has been widely accepted as a measure of sleep quality in mammals, and is therefore tightly associated with the benefits of sleep. However, it is still uncertain why some brain regions generate slow wave activity during behavioral sleep and others do not (for a thoughtful account see Rattenborg, 2006). It is also unclear why an organism would benefit from slow synchronized brain activity (energetically or cognitively). However, sleep research, for the last 50 years has operated on the assumption that power in the slow wave range (1-4 Hz) equals sleep quality. Logically the same assumption applies to drowsiness. If slow wave activity during sleep defines sleep’s recuperative value, we have to entertain the possibility that slow wave activity in the same brain regions during drowsiness also has the potential to provide similar benefits.
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Some Ecological Considerations
What might be the adaptive value of Swainson’s thrushes engaging in the described
fragmented daytime sleep patterns rather than long periods of bihemispheric sleep or a night of
sleep without migration? During their migrations avian migrants are on a restricted time and
energy budget. During fall migration many species (e.g., the Swainson’s thrush) are migrating in
front of an advancing frost line which has the potential to drastically limit food availability
should it reach them. Also low ambient temperatures require increased metabolic activity in
warm-blooded animals, especially those with a high surface to volume ratio like small passerines
(Gill, 2003). It has been suggested that a night of sleep at low ambient temperatures is nearly as
costly as a night of migration (Wikelski et al., 2003). In addition, a night of appropriately
oriented migration would bring a bird further from the frost line and closer to its destination.
In a free adaptive landscape, some migrant species could have evolved the ability to engage in hypothermia or shallow torpor to reduce nightly energy expenditure during cold nights at stopover sites. However, a night spent in shallow torpor, from a sleep researcher’s perspective, may be just as costly as a night of flying. EEG studies in mammals (Daan et al., 1991; Deboer &
Tobler, 2003) and birds (Walker et al., 1983) showed that REM sleep is drastically reduced in shallow torpor, and the EEG of SWS is also greatly reduced in amplitude. In addition, sleep after shallow torpor is characterized by a large delta rebound similar to that found after sleep loss
(Daan et al., 1991; Körtner & Geiser, 2000; Deboer & Tobler, 2003,), suggesting that torpor is actually a physiological state comparable to sleep deprivation (but see Berger, 1998). Periodic torpor-like states would also be costly in slowing the fall migratory progression away from the
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frost line. During spring migration, competition for breeding territories, and the competitive advantage to early arrivals for quality territory , adds an additional incentive for migrants to
travel as fast as possible with a minimum of stopovers. Given these considerations, the
advantages of daytime rest (daytime sleep, unihemisphric sleep, drowsiness) can be considerable.
Intuitively, daytime sleep is a viable strategy as long as enough time remains to feed and
replenish fat deposits. Daytime sleep and unilateral eye closure together constituted only 5 % of
the behavioral daytime observations and, if these values are comparable to values in the field,
should not have a major impact on a migrant’s time budget. Drowsiness, however, occupied
significantly more time (40%) and it remains to be clarified if comparable values occur in the
field. It has been reported that in the late morning crowded stopover sites fall quiet as migrants
seemingly disappear for several hours until they resume foraging in the early afternoon and
evening (Zoltan Nemeth, personal communication). This field observation fits surprisingly well
with the behaviour of caged migrants, which display the largest behavioral changes in the late
morning and at midday. Therefore, field observations of migratory birds indicate that they spend
a significant portion of the day in a state of inactivity, possibly drowsiness, similar to captive,
experimental birds.
Sleeping by day is almost certainly more dangerous than sleeping at night (for review see
Lima et al. 2005), especially at crowded stop over sites teeming with predators that depend on
the biannual migrations as a food source (Fransson & Weber, 1997; Dierschke, 2003; Lank et al.,
2003; Cimprich et al., 2005). Predator detection is the most plausible reason for the unusual
temporal structure of the observed daytime behaviour, and in all likelihood explains part of its
adaptive value. Long episodes of bihemispheric daytime sleep, at high arousal thresholds, would
85 86 render a migrant defenceless to approaching predators. A drowsy bird, however, can respond to visual input from its environment (Rattenborg et al., 1999). Also, during unilateral sleep, at least half of the environment can still be monitored. Daytime sleep is probably the riskiest observed behaviour. Keeping individual sleep episodes short is likely a critical step in reducing the risk associated withbihemispheric sleep. Episodes of daytime sleep in the Swainson’s thrush were no longer than 10 seconds on average and rarely exceeded 30 seconds in duration. Short naps, interrupted by brief arousals, would make it difficult for predators to spot and approach their prey unnoticed.
Lendrem (1983) observed that “peeking” times in sleeping mallards changed as a function of plumage and distance from a river bank. Increased distance from the river bank (a presumably safer environment) corresponded to lower peeking rates. The bright nuptial plumage of males, which increases their visibility to predators, also corresponded to higher peeking rates.
During unilateral eye closure, Rattenborg et al. (1999) demonstrated in laboratory held mallard ducks that they predominantly keep open the eye that is oriented away from the group to monitor their environment. The relevance of unilateral eye closure as a means of predator detection seems clear when animals that live and forage in groups are the target of investigation.
Swainson’s thrushes, however, are solitary migrants unknown to forage in flocks. Therefore, a thrush engaging in unihemispheric sleep can only monitor half of its environment and remains ignorant to whatever happens in the other half. One possible remedy for this problem, a rapid alternation between left and right eye closure was not observed in any of the experimental
Swainson’s thrushes. Rather, it seemed that the experimental thrushes preferentially closed either the left or right eye over extended periods of time (also described for pigeon, Rattenborg et al.
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2001). During the electrophysiological recordings, most birds showed a clear preference for
closing either the left or the right eye, possibly in response to an unintended asymmetry in the recording environment. But again, rapid alternation between UECL and UECR was not observed.
However, episodes of UEC were short, on average as short as episodes of daytime sleep. Like
daytime sleep, they were predominantly separated by periods of drowsiness and short arousals.
Opening both eyes every 10 seconds would be as effective as switching between a closed and
open eye for monitoring the environment. Similar to daytime sleep, it appears essential that the
duration of single UEC episodes does not exceed a critical value, which could depend on the
type of environment (e.g., viewing distance, cover) or predator (e.g., bird or mammal).
Why should a bird close one or both eyes, when drowsiness, with the additional advantage of open eyes, offers similar benefits as sleep? As discussed earlier, the observed state-
related differences in delta power are related to eye state and presumably signal differences in
sleep quality. In other words, a lot more time in drowsiness may be required to obtain the same
benefits gained by a relatively short amount of daytime sleep or unilateral eye closure (for the
contralateral brain hemisphere). The proportion of time spent in each sleep-like state may be the
result of a dynamic equilibrium (similar to Lima et al., 2005) governed by predator pressure (safe
environment), energy balance (fat/lean/temperature) and sleep loss (which could explain the
observed variability between subjects.). SW activity during bi- and unihemispheric sleep is of
higher intensity than during drowsiness. Slow waves, however, are possibly only one easily
detectable aspect of non-REM sleep. Other equally important properties of SWS may not be
reflected in the sleeping EEG. SW activity, for historical reasons, is the hallmark of mammalian
and avian sleep. Based on other criteria (see appendix II) many other animal groups display
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behavior that qualifies as sleep without showing slow synchronized brain activity. In fact, not
every brain area in mammals or birds displays a slow synchronized activity pattern during “slow-
wave” sleep (Sugihara & Gotho, 1973, Hartse & Rechtschaffen, 1974). Therefore, it seems
unwise to assume that only brain regions displaying SW activity benefit from sleep. It would
probably be equally premature to assume that the benefits of sleep, in brain regions displaying a
slow synchronized activity pattern, are exclusively attributable to SW activity. It has been
hypothesized that in mammals the processing of sensory information and memory storage take
place in the same brain regions (Kavanau, 2001). The processing of sensory information may
therefore be in conflict with the dynamic stabilization of memory circuitry (Kavanau, 1998;
Kavanau, 2001). Eye closure and the loss of visual input, independent of increased SW activity,
may therefore create recuperative opportunities for brain regions that integrate sensory
processing and memory functions. The nearly complete decussation of the optic tracts in most
bird species would allow for a similar effect of unilateral eye closure, because visual information
from the open eye has only limited access to theipsilateral hemisphere.
Some “Comparative” Considerations
The Swainson’s thrush’s daytime response to sleep loss, the types of behavior employed
and their temporal organization can aid our understanding of the ecological pressures this species
is exposed to. It also demonstrates some of the extraordinary physiological capabilities of this species (e.g. unihemispheric sleep, drowsiness). From a comparative perspective of sleep research, different questions may be addressed. One potential confound in dealing with a
“natural” model of sleep deprivation is that it seems likely (the premise of this entire study) that
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we deal with a highly adapted species, a species that has evolved mechanisms to cope with the
challenges presented by sleep loss. Therefore, findings regarding performance or an ability to
forgo sleep for extended periods of time in migrant birds do not necessarily generalize to other
animal groups. This is not to imply that an assessment of sleep/sleep deprivation and
compensatory behavior in nocturnal migrant birds has no general information on the relationship
between sleep and sleep deprivation to offer. The observation that an animal like the Swainson’s
thrush, even under severe time and energy constraints, does not simply forgo sleep tells us
something about the importance of sleep as a physiological state (despite its still unexplained
function(s)). It is interesting that every mammalian or avian species studied to date, even under
extreme environmental conditions, showed some form of sleep. The most impressive example is
probably the blind Indus dolphin (reviewed in Horne, 1988) which, forced by its unsafe
environment to be continually on the move in turbid current, resorts to a sleeping pattern not
unlike the Swainson’s thrush; it sleeps only for seconds at a time. In contrast to the Swainson’s
thrush, however, this is the only form of sleep observed in this species. Equally impressive is the ability of whales (cetaceans) to engage in long bouts of unihemispheric SWS, possibly to sustain surfacing and the need to breathe. Fur seals (Ottaridae) that can alternate between unihemispheric sleep in water andbihemispheric sleep on land are another impressive example.
Interestingly, in most of the aforementioned cases REM sleep seems to be either greatly reduced or absent. The enhanced prevalence of SWS over REM sleep under extreme sleeping conditions certainly suggests that SWS in mammals serves more important functions than REM sleep
(Horne, 1988). All avian species studied to date engage in both SWS and REM sleep. It is worth noting that in the Swainson’s thrush despite the described compensatory behaviors no REM sleep was observed during the day while birds were active with nocturnal migration. However, as
89 90 mentioned earlier, the observed high levels of nighttime REM sleep in the migratory condition may enable Swainson’s thrushes to keep the loss of REM sleep at relatively low levels.
Sleep Deprivation: To date all studied avian and mammalian species exhibited some form of sleep, implying a universal need for sleep in both orders. However, artificial (instrumental) sleep deprivation in the laboratory tells a different story. Rats and dogs after prolonged sleep deprivation reach a ‘point of no return’ (de Manacéïne, 1897; Everson, 1995; Rechtschaffen &
Bergmann, 1995) beyond which even recovery sleep cannot prevent death. Less than 3 weeks of total sleep deprivation (enforced by the “moving platform” technique; Rechtschaffen &
Bergmann, 1995) are sufficient to induce irreparable damage in the laboratory rat. In contrast,
Pigeons seem to be extraordinarily resilient to sleep deprivation. 72 days of sleep deprivation in the homing pigeon did not result in any apparent ill effect (Berger & Phillips, 1994). A preliminary study that attempted to sleep deprive pigeons with the moving platform technique
(Amlaner, personal communication) came to similar results. In a more recent study in the migratory white crowned sparrow, Rattenborg et al. (2004) came to a similar conclusion for birds while migratory active. Nocturnal sleep loss had no impact on daytime performance in a repeated acquisition task in restless animals while artificial sleep deprivation outside the migratory season was detrimental to performance.
Compensatory sleep rebound. Sleep deprived mammals show reliable changes in sleep architecture during recovery sleep to compensate for sleep lost (review in Borbély & Acherman,
2000; Horne, 1988). The changes correspond to an increase in delta power, a so-called delta rebound that is especially pronounced at the early stages of recovery sleep. Compensatory REM
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sleep changes in mammals have been observed, but they occur less reliably than the delta
rebound (Borbély & Acherman, 2000). Unlike mammals, a delta rebound has yet to be demonstrated in birds. None of the three sleep deprivation studies in birds (Berger & Phillips,
1994, Tobler & Borbély, 1988, Rattenborg et al., 2004) reported significant changes in delta power during recovery sleep. A compensatory increase in REM sleep was found in the homing pigeon after short term sleep deprivation (24h, Tobler & Borbély 1988), but was absent after long term sleep deprivation (Berger & Phillips, 1994). Decreased REM sleep latencies (albeit no
change in the amount of REM sleep) during the migratory season were reported by Rattenborg et
al. (2004) for the white crowned sparrow. The available data could be interpreted in a way
suggesting that birds as a group may be more resilient to sleep deprivation than mammals. This,
in turn, would imply that sleep in the two groups could differ on a fundamental level.
The apparent absence of a delta rebound in sleep deprived birds is surprising because a
decline in delta activity during normal nights of sleep has been observed in several avian species
(Van Luijtelaar et al., 1987, chicken; Szymczak et al., 1996, European black bird; Rattenborg et
al. 2004, white crowned sparrow; Swainson’s thrush). These observations imply that avian SWS,
like mammalian sleep, is regulated by a homeostatic process. In mammals, a decline in delta
power during a normal night of sleep and the prominent increase in delta activity after sleep
deprivation likely reflect two aspects of the same homeostatic process (Borbély and Acherman,
2000). It is therefore unexpected, assuming avian SWS is regulated similarly, that some birds
display one kind of homeostatic response while lacking the other.
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A possible explanation for the difficulties in detecting a delta rebound in avian species is
provided by differences in sleep architecture between birds and mammals. While avian non-
REM sleep is characterized by continuous slow wave activity, slow waves are not present
throughout the entire duration of mammalian non-REM sleep. Consequently mammalian non-
REM sleep can be divided into sleep stages that are defined by the proportion of slow waves
contained in the sleeping EEG (among other criteria; for review see Borbély and Acherman,
2000). In humans, sleep stages 1 and 2, which occupy the largest proportion of total sleep time, contain only little slow wave activity (< 20%) while stages 3 and 4 are characterized by larger amounts of delta activity (20 – 50 % and > 50 % respectively; Rechtschaffen & Kales, 1968).
Therefore, only stages 3 and 4 of non-REM sleep are termed “slow wave sleep” in humans, while in birds the terms non-REM sleep and SWS are used interchangeably. A delta rebound in mammals is primarily characterized by an increase in the number of individual delta waves
(although delta waves during a rebound can also show increased amplitude; for review see
Borbély and Acherman, 2000). This, given the relatively low percentage of slow waves in most sleep stages, allows for robust changes in delta power after sleep deprivation. The rather uniform composition of avian SWS on the contrary may not allow for a substantial increase in the number of delta waves in response to sleep deprivation. In agreement with this hypothesis, the present study suggests that a decrease in delta power during a normal night of sleep likely reflects a decrease in SW amplitude in the Swainson’s thrush. At sleep onset, even during a normal night
SW amplitude in birds may already be high due to a build up of homeostatic sleep pressure during the day. If this is the case, sleep deprivation in birds would likely not induce a large delta
rebound at the onset of recovery sleep because SW activity at this time is already close to its
maximum value.
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A behavioral study of the barbary dove (Streptopelia risoria) demonstrated a decrease in
eye closure when animals were exposed to a predator that was followed by a compensatory
increase in eye closure when the predator was removed (Lendrem, 1982). The results of the
present study offer a similar perspective on the resilience of birds to sleep deprivation.
Considerable sleep loss during the migratory season was not simply endured without changes in
behavior, as would be expected for a species completely insensitive to changes in sleep quotas.
On the contrary, during the migratory season, Swainson’s thrushes significantly changed their
daytime behavior by spending substantial time in a variety of sleep-related behavioral states.
However, the observed behavior was either so short (daytime sleep, unilateral eye closure) or
superficially ambiguous (drowsiness) that it may have gone undetected or unappreciated in
earlier studies (e.g. Berger & Phillips, 1994; Rattenborg et al., 2004).
The temporal structure of avian nighttime sleep (multiple arousals, and especially late at
night multiple short sleep cycles) is probably well suited to make avian sleep resilient to sleep
deprivation under laboratory conditions. Sleep deprivation techniques usually rely on arousing an
animal either after a certain EEG amplitude threshold is exceeded or when a sleeping posture is
assumed. The ability of birds (migratory and non-migratory) to engage in relatively brief bouts
of sleep makes it very difficult to effectively sleep deprive them. In addition, states like
drowsiness and unihemispheric sleep are nearly impossible to prevent. These behaviors probably
evolved in response to ecological pressures (predation) but they are equally effective in a
laboratory setting. It is extremely difficult for an experimenter to even detect, let alone prevent,
unilateral eye closure without the use of mirrors or cameras. In a recent experiment Niels
Rattenborg demonstrated (unpublished data; Charls Amlaner, personal communication) that even
93 94 automated sleep deprivation techniques are probably insufficient to completely eliminate SWS in birds. The same technique that proved lethal to laboratory rats (Rechtschaffen & Bergmann,
1995) was tolerated well by homing pigeons. Interestingly, in pigeons the “moving platform” technique was not sufficient to eliminate short ‘naps’ similar to the ones observed in the
Swainson’s thrush.
In summary, the present work suggests that birds, like mammals, require a minimum amount of sleep. Even, the Swainson’s thrush, a nocturnal long distance migrant, and a species highly adapted to its migratory life style, cannot completely forgo sleep, and engages in daytime compensatory behavior. Although sleep in birds is likely regulated by a homeostatic process, the architecture of avian SWS may prevent a delta rebound. The extraordinary resilience of birds to sleep deprivation under laboratory conditions may be a result of their natural sleeping patterns with relatively short bouts of SWS and REM sleep. In addition, the shallow sleep state of drowsiness that, in birds (migratory and non-migratory), can occupy significant portions of time
(Amlaner & Ball, 1994) and episodes of unihemispheric sleep, may escape detection during manual sleep deprivation.
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CONCLUSION
The present work provides evidence that Swainsons thrushes (and possibly migratory
birds) despite their remarkable physiological adaptations to migration are not extraordinarily
resilient to sleep loss. While Swainson’s thrushes drastically reduce the amount of nocturnal
sleep during the migratory seasons, they do compensate for that loss during the day. The data
(behavior and EEG) support the hypothesis that migratory Swainson’s thrushes adapt to
nocturnal sleep loss by engaging in numerous, short daytime episodes of bilateral and unilateral
sleep, and by increasing the amount of time spent in daytime drowsiness. The numerous, short- duration episodes of daytime sleep are sufficiently long to allow for sleep-like changes in the
EEG and are therefore hypothesized to provide recuperative opportunities for sleep deprived migrants. The observed daytime sleep-related behaviour was associated with an EEG that resembled nocturnal slow wave sleep, but no compensatory REM sleep was observed during daytime.
Evidence for compensatory changes in nighttime sleep in migratory thrushes is discussed,
but a comparison to non migratory thrushes is limited by the small amount of data. However,
high amounts of REM sleep were consistently encountered. This suggests that nocturnally active
Swainson’s thrushes may either compensate for lost REM sleep during the night, or, due to a
primarily circadian regulation of this sleep state, large amounts of REM sleep are predominantly
displayed during the second half of a night. A consistent increase in REM sleep over the course
of the night observed in non-migratory Swainson’s thrushes supports the latter view. At any rate,
the observations support the notion that loss of REM sleep in migratory birds may not be as
substantial as initially thought, and therefore may not require daytime compensation.
95 96
Non-migratory Swainson’s thrushes, exhibit a marked decrease in delta power over the course of the night indicating a parallel decline in homeostatic slow wave sleep pressure. This effect, for the first time, was demonstrated to partially reflect a decline in the amplitude of slow wave activity during non-REM sleep. Decreasing SWS pressure, possibly in concert with a circadian REM sleep regulating mechanism, may allow for increased amounts of REM sleep during the later part of the night, further diminishing average delta power.
In concert, the quantitative increase in daytime sleep behaviour after sleep loss and the
qualitative decrease of delta power during nighttime sleep in non-migratory thrushes strongly
suggest the participation of a homeostatic component in avian sleep regulation. The homeostatic
control of avian sleep is not well studied, and conflicting data (Tobler & Borbély, 1988; Berger
& Phillips, 1994; Szymczak, 1996; Van Luijtelaar et al., 1987; Rattenborg et al., 2004) hinders interpretation. This work is only the third study to observe consistent behavioral changes in response to sleep loss in an avian species (Lendrem, 1982; Rattenborg, 2004), and it is the first to
demonstrate that brief episodes of bilateral eye closure during the day are characterized by EEG properties similar to nocturnal slow wave sleep.
The present work suggests that birds, like mammals, require a minimum amount of sleep.
The finding that a nocturnal migrant, a species highly adapted to its migratory life style, requires
compensatory sleep, strongly suggests that a basic need for sleep is shared by many if not all
avian species. Furthermore, the observed sleep characteristics indicate that avian and mammalian
sleep are similarly regulated. Most importantly a strong homeostatic component is likely
involved in the regulation of both, avian and mammalian non-REM sleep. Avian models of sleep
deprivation may therefore aid our understanding of fundamental homeostatic processes while
96 97 avoiding some of the confounds associated with sleep deprivation in mammals. Avian sleep may provide further insight into processes involved in the regulation of sleep and resting states are likely shared by many species, vertebrates and invertebrates alike (Tobler, 2000; Borbély &
Acherman, 2000).
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APPENDIX I
Mammalian Neocortex and the Avian Wulst Formation (Hyperpallium): Similarities and
Differences.
The evolutionary relationship of mammalianneocortex and functionally similar regions of
the avian telencephalon has been the subject of considerable controversy (Medina & Reiner,
2000). The dorsomedial part of the avian pallium (hyperpallium or “Wulst”; German for
“bulge”) is functionally similar to the sensory, motor and visual areas of the mammalianneocortex (Medina & Reiner, 2000). Morphological comparisons with recent reptiles
(morphology, cytology, connectivity) support the claim that a primary visual, somatosensory and
somatomotor area in stem amniotes gave rise to the mammalian neocortex, the avian Wulst and the reptilian dorsal cortex (reviewed in Butler & Hodos 2005; Medina & Reiner, 2000) . In addition, embryological evidence supports the view that the superior part of the mammalian neocortex and the avian Wulst arise from a homologous area in the developing brain (reviewed in Medina & Reiner, 2000). Finally, recent genetic data underscores the developmental similarities between mammalian neocortex, the avian Wulst, and reptilian dorsal cortex, demonstrating that the same combination of homeobox genes is expressed in these brain regions
(Emx1, Emx2,Tbr1 and Pax6; typical for mammalianneocortex, hippocampus, claustrum, lateral cortex and endopiriform cortex; for review see Medina & Reiner, 2000; Butler & Hodos 2005).
Despite the similarities, almost 300 million years of separate evolution have led to considerable differences in the morphology and cytology of the mammalian neocortex and the
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avian Wulst formation. Most notably, the “layered” structure of theneocortex and Wulst, despite
their superficial resemblance, is strikingly different (morphologically and developmentally)
(Medina & Reiner, 2000). The EEG correlates of sleep and wakefulness generated by these areas,
however, are surprisingly similar.
The mammalianneocortex is characterized by a typical 6 layered structure. Each horizontal layer contains specialized cell types and the apical dendrites of neocortex neurons
extend perpendicularly through the layers to form vertical columns. Every column therefore
contains all cell types specific to a certain cortex area.
The avian hyperpallium, in contrast, is organized in what has been termed “pseudo-
layers” or “false-layers” (Medina & Reiner, 2000). Individual pseudo layers are separated by cell
free laminae (Medina & Reiner, 2000). Each pseudo-layer, similar to neocortex, contains specialized cell types. Unlike mammals, however, the “multipolar” (in contrast to “apical” in
mammals) arborizations of Wulst neurons are confined to the pseudo-layer of their origin, thus
precluding a “cortical” organization in vertical columns. Axons however can cross “pseudo-
layers” (Medina & Reiner, 2000).
Embryological studies show that the avian Wulst and mammalianneocortex develop from
the same embryonic region (reviewed in Medina & Reiner, 2000), indicating that cell types in
the two structures may have similar electrophysiological properties. However, the orientation of
radial glia cells in the Wulst andneocortex reveals substantial developmental differences between
the two brain structures. The orientation of radial glia determines the direction of neuron
99 100
migration in the developing pallium. Radial glia cells in mammals are oriented perpendicular to the cortical layers. Cell types, migrating along the glial fibers, are deposited in the cortical layers in an inside-out gradient, with later generated neurons being deposited closer to the pallial surface (reviewed in Medina & Reiner, 2000). Radial glia cells in the avian wulst run parallel to the Wulst pseudo-layers. Cells that travel along the glia cells are therefore deposited only in a single pseudo layer (with neurons generated earlier ending up closer to the pallial surface). An interesting consequence of this migration pattern is that cell types in different pseudo layers originate in different proliferation zones, while in the neocortex all cell types originate in the same proliferation zone (Medina & Reiner, 2000). Comparative studies (reviewed in Butler &
Hodos 2005) revealed morphological and cytochemical differences between avian Wulst cells and mammalianneocortex cells (e.g. “pyramidal cells”). It was also concluded that other similar cell types evolved convergently in birds and mammals (e.g. granule cells; reviewed in Butler &
Hodos 2005) based on their absence in reptilian cortex.
Different Wulst areas corresponding to different pseudo layers serve different functions
in the avian Wulst. The most medial pseudo layer (hyperpallium apicale) has properties similar to somatosensory and motor areas in mammalian cortex and is therefore also called the somatosensory-motor Wulst. The three remaining pseudo-layers (from medial to lateral: interstitial part of the hyperpallium apicale, hyperpallim intercalatum, hyperpallium densocellulare) are the projection target of the lemnothalamic pathway (primary projection areas: hyperpallium densocellulare and interstitial part of the hyperpallium apicale) and are hence referred to as the “visual Wulst” (Butler & Hodos, 2005). Similar to the mammalian neocortex the somatosensory-motor Wulst is organized somatotopically and gives rise to a motor projection
100 101
similar to the mammalian pyramidal tract (Butler & Hodos, 2005). Importantly, the reciprocal
connections between the avian pallium (including the Wulst) and thalamus resemble the
mammalian thalamocortical loop and are therefore remarkably similar in birds and mammals
(reviewed in Butler & Hodos, 2005; Medina et al., 1997).
In summary, the available evidence suggests that the mammalianneocortex, the avian
Wulst and reptilian dorsal cortex can be traced back to a common stem amniote ancestor and are
derived from the same embryological tissue. However, much of the complexity of the neocortex
and Wulst developed independently (on a homologous basis) in response to similar evolutionary
pressures. The organization of the avian and mammalian dorsal pallium could therefore be
viewed as two different strategies towards the same end (information processing). Interestingly both pathways, despite differences in morphology and cytoarchitecture, show similar EEG properties during similar arousal states.
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APPENDIX II:
Sleep in Invertebrates, Fish, Amphibians and Reptiles
The aim of this chapter is to give a brief description of definitions of sleep and to provide
a brief introduction to the sleep work that has been conducted in non mammalian species (except for birds which are addressed in appendix III). The chapter also aims to touch lightly on some of the comparative aspects that result from some of these studies.
Defining Sleep
Sleep can be defined in two ways, behaviorally and electrophysiologically. The definition
of behavioral sleep, perhaps not surprisingly, relies exclusively on behavioral criteria to define a
state of sleep. Electrophysiological or “polygraphic” sleep on the other hand is determined by
electrophysiological recordings of brain activity (EEG), eye movements (EOG), muscle tone,
respiration and heart rate (ECG).
Behavioral sleep. A scientific definition of behavioral sleep was advanced as early as
1913 when Pieron proposed his behavioral sleep criteria (in Nicolau et al., 2000). Sleep was
defined as a state of (1) behavioral quiescence that was (2) characterized by elevated arousal
thresholds, and (3) could be rapidly reversed (arousal). In later years other criteria have been
added to this initial definition; (4) stereotypical sleeping postures, (5) specific resting sites, (6)
circadian organization, (7) homeostatic regulation (sleep deprivation and satiety effects), and (8)
eye closure (review in Nicolau et al., 2000). Not all of these criteria have to be fulfilled to
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determine a state of behavioral sleep nor have, to the best of my knowledge, all of them ever been employed in a single study. Behavioral studies of sleep are frequently conducted in the field and especially ‘homeostatic regulation’ and ‘circadian organization’ cannot be easily addressed outside the laboratory. Usually a combination of several easily accessible criteria is utilized. The most frequently employed sleep criteria in behavioral studies are probably ‘behavioral quiescence’, ‘eye closure’, ‘sleeping postures’ and ‘rapid state reversibility’.
Several of the behavioral criteria, are satisfied in most species studied to date; including
insects, fish, amphibians, reptiles, birds, mammals and mollusks (reviewed in Hartse, 1994;
Tobler, 2000; Rattenborg & Amlaner, 2002; Lesku et al., 2006). When relying exclusively on
behavioral criteria, it can be concluded that most animals sleep. A species that sleeps
behaviorally, however, does not necessarily meet the criteria for polygraphic sleep. Animals that
meet the electrophysiological criteria for sleep, however, generally also meet the criteria for
behavioral sleep (exceptions drowsiness, unihemispheric sleep, sleep walking).
Polygraphic sleep. Hans Berger, a German physiologist, coined the term
“electroencephalogram” (EEG) and, in 1928, was the first researcher to demonstrate differences
in the electrical brain activity associated with sleep and wakefulness in human subjects (in
Dement, 2000). Since then two distinct electrophysiological sleep states have been described in
mammals, slow wave sleep (SWS) and rapid eye movement sleep (REM sleep, paradoxical sleep
or activated sleep; in Dement, 2000). Slow wave sleep is primarily characterized by a high
voltage low frequency EEG. Essentially the same methods that are used to record brain activity
can also be used to record the electric activity of contracting muscles. Depending on the
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placement of the electrodes a single method therefore allows for the recording of muscle
electrical activity (EMG), eye movements (EOG), heart rate (ECG), and breathing, and it is
usually a combination of these measures that is referred to as polygraphic sleep. Paradoxical
sleep is characterized by a number of tonic and phasic components, most notably by (1) a low
amplitude mixed frequency EEG, (2) rapid eye movements (REMs), and (3) electrical
quiescence of the skeletal muscles. Mammalian sleep defined through its electrophysiological
properties rapidly became the “gold standard” for non-mammalian sleep (Lesku et al., 2006).
Birds are the only non mammalian group that meets this standard (REM and non-REM sleep).
Arousal state related changes in brain activity (and changes in other polygraphic measures like
ECG, respiration rate, EOG and muscle tone) have also been demonstrated in reptiles,
amphibians, fish and insects (reviewed in Hartse, 1994; Rattenborg & Amlaner, 2002; Lesku et
al., 2006 ). These changes, however, generally do not resemble the changes observed in
mammals and birds.
Invertebrates. Only few studies on sleep have been conducted in invertebrates. This is
unfortunate, because data on invertebrate sleep could provide us with information on general
mechanisms of sleep regulation and perhaps the initial function of sleep. Most sleep studies in
invertebrates have been performed on arthropods. Two behavioral studies have been conducted
in mollusks (Aplysia, Octopus vulgaris; in Rattenborg & Amlaner, 2002). Because of the
difficulties involved in obtaining electrophysiological recordings from small invertebrates, the
majority of these studies relied on behavioral sleep criteria. The studied species include
Scorpions (Tobler, 2000), fruit flies (Shaw et al., 2000), bees (Apis melifera, in Hartse, 1994) moths (Anaghasta kuehniella, in Rattenborg & Amlaner, 2002) and crayfish (Ramon et al., 2004).
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Electrophysiological recordings have been obtained from the honey bee, the fruit fly (Nitz et al.,
2002) and the crayfish (Ramon et al., 2004). All of the studied species periodically assumed
species specific sleep postures. Sleeping postures were accompanied by increased arousal thresholds, and were rapidly reversible. In the cockroach, scorpion, honey bee, fruit fly and
crayfish deprivation of behavioral sleep resulted in a behavioral sleep rebound with increased arousal thresholds to stimulation (reviewed in Rattenborg & Amlaner, 2002). This finding suggests that homoeostatic regulation of sleep is an evolutionary old trait.
Electrophysiological studies in honey bees revealed an increase in spiking activity in the
mushroom bodies (a brain area associated with learning and memory in insects) when in a state
of behavioral sleep. The reverse pattern was found in the fruit fly, where local field potentials,
recorded from the medial brain, showed spiking activity during wakfullnes, and no spikes during
behavioral inactivity. Polygraphic sleep in the crayfish was accompanied by continuous slow
wave activity, while wakefulness was characterized by a high voltage spiking EEG (Ramon et al.,
2004).
The fruit fly (Drosophila melanogaster) is the invertebrate species in which the circadian and homeostatic regulation of sleep has been studied in most detail (reviewed in Shaw et al.,
2000; Lesku et al., 2006). Observations in arhythmic per1 mutants showed that the total amount
of behavioral sleep during the 24 h period is not regulated by a circadian component. Like
mammals, fruit flies show age dependent changes in total sleep time. Total sleep time declined
with age and sleep became more fragmented in older animals. Manual and automated stimulation
during the dark period (= sleep period) resulted in increased time spent in behavioral sleep
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during the following light period, indicating that sleep is homeostatically regulated. (Stimulation during the light (wake) period did not induce a sleep rebound.) Interestingly roughly 70 hours of sleep deprivation are sufficient to kill a fruit fly. Stimulants (e.g. caffeine) and hypnotics exert similar effects on drosophila sleep, as has been observed in mammals. Also, drosophila, like mammals, shows arousal state related changes in gene expression. Genes that are involved in monoamine catabolism are expressed at an increased rate during the wake period and after sleep deprivation, indicating that, like vertebrates, monoamines are involved in invertebrate sleep regulation (in Lesku et al., 2006).
In sum, the data available suggests that behavioral sleep is widespread among
invertebrates, but also, that, behavioral sleep, when examined, was characterized by
neurophysiological correlates. Taken together the limited data in invertebrates suggest that sleep
is an evolutionarily ancient behavior that may, in its essence, have the same molecular basis
throughout the animal kingdom.
Fish. Similar to invertebrates only a handful of systematic studies have been conducted in
fish. Behavioral reports suggest that, like invertebrates, most fish show behavioral sleep, with
stereotypical sleeping postures, accompanied by elevated arousal thresholds (Hartse, 1994). In
two studies behavioral sleep deprivation (either by light or current) induced a sleep rebound and
in one report sleep promoting peptides induced a decrease in locomotor activity (reviewed in
Hartse, 1994). These findings suggest that sleep in fish is regulated by a homeostatic component.
Some behavioral evidence suggests that sleep in some species may be accompanied by non-
conjugate eye movements as observed during mammalian REM sleep (reviewed in Hartse, 1994).
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Electrophysiological recordings in catfish (Ictalurus nebulosus, Karmanova, 1982) were
characterized by arousal state specific EEG correlates. While wakefulness was accompanied by a
“polymorph” mixed frequency EEG, behavioral inactivity was accompanied by a mixed slow
wave and spiking EEG (recorded from the midbrain and forebrain). The number of spikes
increased with the duration of inactivity and spikes disappeared upon arousal (Karmanova, 1982).
Behavioral sleep in the catfish was characterized by increased arousal threshold and a cataleptic
state of muscle plasticity.
In contrast, sleep in the tench (Tinca tinca; reviewed in Hartse, 1994; Lesku et al., 2006)
was not accompanied by electrophysiological correlates (in the optic lobes), but behavioral
quiescence was accompanied by a decrease in muscle activity, and respiratory rate, while heart
rate remained unaltered.
In summary, the presence of behavioral sleep in most fish species is undisputed. Based on
the available data it seems also reasonable to conclude that sleep in fish, as in invertebrates, is
homeostatically regulated. The limited amount of electrophysiological recordings does not allow one to come to definite conclusions about the electrophysiological correlates of sleep in fish. One report (Karmanova, 1982) concludes that polygraphic sleep essentially resembles reptilian sleep
(see below), while another report, in a different species did not detect EEG correlates of
behavioral sleep.
Amphibians. There are 3 recent amphibian orders, including frogs and toads (Anura),
newts and salamanders (Urodela) and limbless amphibians (Apoda). With one exception
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(salamander, in Hartse 1994), all studies on amphibian sleep have been conducted in frogs and toads. Behavioral sleep has been reported for all anurans studied, except for the bull frog (Rana catesbiana; Hobson 1967), which does not exhibit changes in EEG or behavior and thus far constitutes the only species that has not been shown to engage in some form of rest.
Electrophysiological recordings in amphibians have yielded conflicting results. Spiking activity during behavioral sleep has been reported for frogs and toads (Karmanova, 1982; review in
Hartse, 1994). However, there are also reports that did not find distinctive EEG changes in frogs
and toads during behavioral sleep (e.g. Hobson 1968, review in Hartse, 1994). In the tiger
salamander (Ambystoma tigrinium, reviewed in Hartse, 1994) a 4h behavioral sleep-wake cycle
was detected but, again, no distinct changes in EEG activity between behavioral sleep and
wakefulness have been reported.
Reptiles. The class of reptiles encompasses 4 living orders, Crocodilia (alligators and
crocodiles), Testudinata (turtles and tortoises), Squamata (snakes and lizards) and
Rhynchocephalia (tuataras). With the exeption of Rhynchocephalia sleep studies have been
performed in species from all orders (reviewed in Hartse, 1994; Rattenborg & Amlaner, 2002;
Lesku et al., 2006). Reptilian sleep is of particular interest because it may represent a basic form
of sleep that evolved into avian and mammalian sleep. Crocodilia are the group that is cladistically most closely related to birds. Testudinata are of special interest because they are an
evolutionarily ancient group that, at least anatomically, has not changed much over evolutionary
time. Behavioral sleep has been observed in all three orders. Reports of polygraphic sleep
however are often conflicting and require a separate discussion of each order.
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Crocodilians. Only Caiman sclerops has been studied extensively in this group. Flanigan et al. (1974) detected high voltage sharp waves in the forebrain EEG during behavioral quiescence. This spiking activity was reduced during behavioral arousal, and increased after periods of enforced wakefulness, indicating a rebound comparable to mammalian sleep. Van
Twyver (reviewed in Hartse, 1994) also recorded spiking activity in Kaiman but did not observe
a relationship of spiking and behavioral quiescence. On the contrary, the spiking activity was
temperature dependent. High temperatures accelerated spiking and low temperatures abolished
spiking. Huggins and coworkers reported slow wave activity in young alligators (in Hartse,
1994). Hartse and Rechtschaffen recorded from two young alligators, but failed to find any signs
of SWS. Instead they observed state related spiking activity as reported by Flanigan (Hartse,
1994).
Peyreton (in Hartse, 1994) reported paradoxical sleep in the caiman, characterized by
rapid eye movements, a relaxation of muscle tone and a low-voltage mixed frequency EEG.
However, these results have not been replicated to date.
In summary, state and temperature related spiking activity was detected in the majority of studies, while slow wave activity was only reported once and has not been confirmed since then.
Likewise, a single report of paradoxical (REM) sleep has not been replicated to date.
Testudinata. Behavioral signs of sleep were observed in all species studied to date. The sea turtle, Caretta carreta, however, does not show changes in brain activity accompanying
109 110 behavioral sleep. A study in the tortoise (Testudo marginata) revealed polymorphous high amplitude slow waves together with reduced muscle tone during behavioral sleep. In the
American box turtle (Flanigan et al., 1974 ) and the South American red-footed tortoise
(Flanigan, 1974), the EEG during behavioral quiescence resembled the EEG recorded in caiman, with high amplitude spiking activity occurring against a background of low amplitude mixed frequency EEG. The spikes disappeared with behavioral arousal, and showed a rebound after enforced wakefulness. In the box turtle spiking was temperature dependent, and disappeared at temperatures lower than 24° (Eiland et al., 2001).
There are also three reports of paradoxical sleep in Testudinata (reviewed in Hartse,
1994). One report of SWS and REM sleep in the European pond turtle, and high voltage spiking activity and REMs in the desert tortoise (Gopherus flavomarginatus); another, reporting SWS,
REM sleep and high voltage spiking activity during behavioral quiescence in a different species of tortoise (Kinosteron, reviewed in Karmanova, 1982). Spiking activity without elevated arousal thresholds was reported in yet another tortoise (Testudo denticulate) by Walker and Berger
(1973).
In summary all studied chelonians presented signs of behavioral sleep that were either accompanied by changes in the sleeping EEG, by state and temperature dependent spiking activity, or by slow wave activity. These findings are not necessarily conflicting because different species were studied. Paradoxical sleep, in chelonians, has been reported in three studies.
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Squamata. There is general agreement that lizards exhibit behavioral sleep. Behavioral
quiescence is accompanied by high voltage sharp waves and spikes contrasting with a low
voltage background. There are no reports of high amplitude slow wave activity during behavioral
sleep. Evidence on paradoxical sleep is again conflicting. Signs of paradoxical sleep were
detected in the iguana, the chameleon, the black iguana, desert iguana and Ctenosaurus similis
while 5 other studies failed to detect REM sleep in Squuamata (reviewed in Hartse ,1994). All
but one of the positive REM sleep reports (Huntley, 1987) were solely based on the analysis of
eye movements.
A single pharmacological study (Ayala Guerrero et al., 1992) suggests that, similar to
mammals, monoaminergic mechanisms are involved in reptilian sleep regulation.
The common feature that generalizes to all the reptile work is a spiking stage related to
behavioral quiescence. In the few cases investigated, a spiking rebound occurred after enforced
wakefulness, suggesting homeostatic regulation. Interestingly the observed spiking activity
superficially resembles the pontine geniculate occipital (PGO) spikes of mammalian REM sleep.
The idea that reptilian sleep may present a precursor state of mammalian REM sleep has been
advanced by several authors (e.g., Nicolau et al., 2000). However, spiking activity in reptiles also
resembles a subsurface component of mammalian non-REM sleep, the limbic, or anterior
hippocampal spike (Hartse & Rechtschaffen,1974). Consequently spiking activity during
reptilian behavioral sleep may represent an ancestral form of mammalian non-REM sleep.
Pharmacological evidence (Hartse & Rechtschaffen,1974) supports this point of view.
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APPENDIX III
Neurophysiology of mammalian REM Sleep and Non-REM Sleep.
Comparative Aspects.
Many theories regarding the evolutionary origin of mammalian sleep have been proposed
(Lesku et al., 2006 for review). Avian sleep and its relation to mammalian sleep sometimes
figure prominently in these theories. The definitions of homology, analogy and their derivates
homoplasy, syngeny, allogeny, (Butler & Hodos, 2005) and “functional homology” are a
permanent source of scholarly debate and neither is this the place, nor am I qualified to discuss
the often subtle differences between them. However, as I hope experts would agree that the most important prerequisite when approaching the issue of homology or analogy is a clear definition of the elements that are going to be discussed. A homology can become an analogy, or an analogy may turn into a homology depending on scale and focus of the comparison at hand; this
problem is best illustrated by a comparison favored by many textbooks, an evolutionary
comparison of avian and mammalian wings. The wings of bats and birds are homologous, or
analogous, depending on the nature of the comparison anticipated. Because both types of wings
can be traced back to the forelimbs of tetrapode vertebrates, avian and mammalian wings are
homologous as forelimb derivates. However, as “wings” they represent a good example of an analogy because no common winged ancestor of birds and mammals has ever existed. In the case of sleep we have to face a similar problem.
Behavioral sleep, in all likelihood, is an evolutionary old state that is widespread in the
animal kingdom (see appendix II) and closely tied to circadian organization (Tobler, 2000). This
112 113 form of sleep has been observed in all recent reptiles studied, and there is no reason to believe that behavioral sleep evolved independently in birds and mammals. Consequently, from this point of view, avian and mammalian sleep are most likely homologous. The definitions of polygraphic and behavioral sleep are useful tools to point out some of the more obvious differences between mammalian/avian sleep and sleep in ‘lower’ vertebrates and invertebrates.
However, this separation is of course an artificial one and not to imply that changes in neural activity do not occur during “behavioral” sleep. The EEG patterns observed during behavioral sleep in “lower” vertebrates, however, are different from the ones observed in birds and mammals. Considering the differences in brain organization in distantly related vertebrate and invertebrate species such variation is to be expected. In some species, during behavioral sleep, the sleeping EEG does not show any observable differences to an EEG of wakefulness, but of course, neural activity is still required for the timing and execution of behavioral sleep (postures, eye closure, arousal thresholds). Also, the brain stem and hypothalamus of vertebrates have been remarkably conserved over evolutionary time (Butler & Hodos, 2005), and sleep mechanisms in mammals involve cell groups in these structures (midbrain, pons, hypothalamus). Referring to the aforementioned continuity in vertebrate behavioral sleep, it seems likely that similar midbrain, pons and hypothalamic brain areas responsible for arousal states and circadian timing are involved in behavioral sleep across vertebrates. Therefore it seems justifiable to view avian and mammalian sleep as processes that are based on the same ancient brain areas that are also involved in the regulation of behavioral sleep in “lower” vertebrates. In addition avian and mammalian sleep involve new pallial brain areas with distinct connectivity and electrophysiological properties that are not shared in their entirety by a common ancestor (e.g., appendix I). Consequently, avian and mammalian sleep are probably based on homologous
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brainstem, midbrain and basal forebrain structures, yet, some of the EEG properties of SWS and
REM sleep, involving pallial regions likely developed in parallel (homoplasy). In such a scenario
questions of homology or analogy need to be limited to the process or brain structure of concern.
The brain areas participating in mammalian sleep and its regulation have been studied in
much detail in selected mammalian models such as the cat and laboratory rat. Although many
details on the interactions of different cell groups remain to be resolved, there is little doubt that
the brain areas described below are in fact involved in the regulation of mammalian sleep.
Unfortunately the same cannot be said about any non-mammalian species. Future research on the neurophysiological and neuroanatomical foundations of nonmammalian sleep will likely prove
pivotal for evolutionary comparisons and for the claim of homologous deep brain sleep systems
advanced here. In the following I will give a brief summary of the brain areas that are thought to
regulate mammalian SWS and REM sleep. I will then draw comparisons to the (limited) data
available on avian sleep regulation. I believe that some of the EEG differences observed during
avian and mammalian sleep, in the light of recent mammalian data on the regulation of SWS,
have the potential to generate some useful hypotheses about avian SWS and its neural substrates.
Mammalian Non-REM sleep
The key areas regulating mammalian SWS are situated in the hypothalamus. Areas in the
anterior hypothalamus promote sleep, while areas in the posterior hypothalamus promote
wakefulness (the histamine system in the tuberomammilary nucleus, orexin/hypocretin system in
the lateral hypothalamus (reviewed in Pace-Schott & Hobson , 2002)). Micro infusion, c-fos and
114 115 tracing studies have narrowed down the sleep-promoting region in the anterior hypothalamus to an area less than 300 μm in diameter (reviewed in Fort et al., 2005), the ventrolateral preoptic nucleus (VLPO). Cells in this region show strong reciprocal connections with the supra chiasmatic nucleus (SCN), the “internal clock” of mammals, and, like the SCN receive input from retinal ganglion cells. VLPO cells are also sensitive to changes in adenosine concentrations responding with changes in firing rate to adenosine micro infusions (reviewed in Fort et al.,
2005). Adenosine is one of the prime candidates to be involved in homoeostatic sleep regulation.
Thus, the VLPO in all likelihood is the brain area where circadian and homoeostatic information is integrated, and the amount and intensity of SWS is dynamically regulated. The VLPO sends inhibitory projections to the anterior hypothalamus and brainstem arousal systems (laterodorsal tegmental nuclei LDT, locus coeruleus LC, mesencephalic raphe nuclei RN) and, in turn, receives inhibitory input from these structures (reviewed in Fort et al., 2005).
The two main projections through which brainstem and midbrain arousal systems exert their modulating influence onneocortex are a.) a thalamic projection, terminating in the intralaminary and nonspecific thalamic nuclei and b.) a basal forebrain projection terminating on cholinergic and GABAergic neurons in the substantia innominata (SI) both of which project to neocortex (review in Lee & Jones, 2005). The brainstem - basal forebrain –neocortex pathway in mammals is sufficient to activate the cortical EEG even in the absence of thalamic input
(Steriade, 2005). This excitatory effect is mediated through the cholinergic population of basal forebrain neurons which, in turn, is activated by acetylcholine, glutamine and noradrenalin input from the brainstem/midbrain arousal systems. An inhibition of these systems through the VLPO is, in all likelihood, responsible for sleep onset (reviewed in Fort et al., 2005). More specifically,
115 116 decreased input from brain stem arousal systems to the basal forebrain have been hypothesized to decrease activity of the cholinergic basal forebrain population and to increase activity of
GABAergic basal forebrain neurons. The GABAergic basal forebrain popullation, by means of local interneurons further inhibits cholinergic basal forebrain cells; through projections to posterior hypothalamus and the brain stem inhibits ascending arousal systems and, by means of a cortical projection, contributes to the hyperpolarization of cortical cells during non REM sleep
(review in Lee & Jones, 2005).
The inhibition of midbrain and brainstem arousal systems concurrently results in decreased mono-aminergic input to non specific thalamic nuclei (including the reticular nucleus of the thalamus) thus altering neural activity in the thalamocortical network (Steriade, 2003). The reciprocal interactions between mammalianneocortex and the thalamus are essential in generating some of the electrophysiological properties of mammalian SWS (Jones, 1998;
Steriade, 2003). Some recent work on the thalamocortical loop has led to a better understanding of mammalian SWS, sleep spindles and the K-komplex (reviewed in Steriade 2003; 2005).
Sleep spindles and the K-komplex. Sleep spindles are initiated by GABAergic cells in the reticular nucleus of the thalamus. At sleep onset, reticular nucleus cells are hyperpolarized
(decreased brainstem and cortical input) and alter their activity from a tonic to a burst firing pattern. These bursts initially occur at spindle frequency (7-15 Hz) and are transferred to thalamocortical neurons through a reciprocal loop. Thalamocortical neurons, in response to the inhibitiory volleys from reticular nucleus cells, fire rebound spike bursts to cortex where they elicit rhythmic excitatory postsynaptic potentials (EPSPs) that are recorded as sleep spindles.
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Eventually the EPSPs cause tonic depolarization and firing in corticothalamic cells that feed
back on thalamocortical cells and terminate the sequence. An alternative way to generate sleep spindles is through rhythmic hyperpolarizing input from corticothalamic neurons to reticular nucleus cells. This takes place during the depolarizing phase of the cortical slow oscillation, resulting in a depth-negative deflection of the cortical EEG also known as the K-complex which is often followed by a sleep spindle (reviewed in Steriade, 2003).
Slow oscillation and delta activity. While the relatively recently discovered slow
oscillation (0.6-1.0 Hz) is generated in cortex, slow wave or delta activity (1.5- 4Hz) can be of
thalamic or cortical origin. How cortical and thalamic delta interact is still poorly understood
(Steriade, 2003).
The cortical slow oscillation is characterized by a depolarizing phase that is accompanied
by the rapid firing of cortical cells and a hyperpolarizing phase of quiescence. The cortical slow
oscillation greatly influences the appearance of the cortical sleep EEG, exerting a synchronizing
effect on thalamic delta oscillations; grouping cortical delta waves, sleep spindles and the K-
complex into complex sequences (Steriade, 2003; Steriade, 2005). Entrained by the rhythmic
hyper and depolarizations of the cortical slow oscillation, groups of delta waves surface on the
cortical EEG in 20 to 30 second intervals, while sleep spindles are observed with a 0.25 Hz
periodicity.
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Thalamic delta activity is generated by clocklike intrinsic oscillations in thalamocortical neurons, elicited by membrane potentials more negative than 65 or 70 mV (Steriade, 2003; 2005) which are typically recorded during stages of deep sleep. Delta oscillations in thalamocortical cells are intrinsic and usually not synchronized among cells. However, the cortical slow oscillation in its depolarizing (firing) phase exerts a synchronizing effect on thalamic delta activity through the gabbaergic cells of the reticular nucleus (Steriade, 2003). This synchronizing effect allows thalamus generated delta waves to become visible on the level of the cortical EEG; phase locked to the slow oscillation.
However, even without the thalamus (Steriade 2005) cortex is still capable of producing delta activity. Cortical delta activity and its interactions with the cortical slow oscillation and thalamic delta rhythm are still poorly understood. Cortical delta waves are generated by vertical dipoles between cortical layers II-III and layer V (reviewed in Steriade, 2003). They are probably the consequence of long-lasting after-hyperpolarizations governed by potassium currents in pyramidal cells, which are activated by the prolonged firing during deep sleep (slow oscillation,
Steriade, 2003).
Comparing avian and mammalian non-REM sleep
At present, only little is known about the brain regions involved in the control of avian
SWS. The available data (reviewed in Amlaner & Ball, 1994) supports the preliminary conclusion that avian non-REM sleep, like mammalian non-REM sleep, is under the control of sub-pallial structures, involving the hypothalamus and the thalamus. This conclusion rests
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predominantly on stimulation and lesion studies that found that a.) stimulation at certain basal
forebrain areas (preoptic, supraoptic and “hypothalamic” areas; reviewed in Amlaner & Ball,
1994) induced sleep and that b.) lesions in the anterior thalamus (nucleus rotundus) markedly reduced the amplitude of hyperpallial slow waves, reduced sleep and increased wakefulness
(reviewed in Karmanova, 1982). Interestingly electric stimulation near the anterior point of the nucleus rotundus (anterior dorsolateral thalamus) induced sleep-related behaviors (eye closure, sleeping posture, ruffled feathers; reviewed in Amlaner & Ball, 1994).
On the pallial level, a slow synchronized EEG characterizes both, avian and mammalian non-REM sleep. Unlike only several years ago, sleep spindles the K-complex, delta waves and the cortical slow oscillation are no longer abstract EEG phenomena. In mammals, there is now an anatomical and physiological basis to describe the generation of these EEG components (see above) allowing for the generation of comparative hypotheses. Consequently, the presence or absence of these EEG components in birds in combination with anatomical and physiological similarities and differences to mammals may increase our knowledge on both, avian and mammalian sleep.
Slow oscillation. The EEG of avian non-REM sleep lacks sleep spindles or the K-
complex (Amlaner & Ball, 1994; Rattenborg & Amlaner, 2002), two of the defining features of
mammalian non-REM sleep. The absence of these two EEG components calls into question the
existence of yet another, the mammalian slow oscillation. Slow oscillations (0.6-1 Hz), to date,
have not been studied in birds, and their presence or absence needs to be confirmed by intra and
extracellular recordings in anesthetized and behaving animals. However, one interesting feature
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of the mammalian slow oscillation is its grouping effect on other EEG components, namely delta
waves, spindles and the K-complex. Such a grouping effect has, to date, not been observed (nor
was it systematically examined) in the avian sleep EEG. The absence of spindle activity in birds
precludes an evaluation of a grouping effect on this EEG component. Delta waves, however, do
not appear to show a comparable periodicity in birds. On the contrary, the EEG of avian SWS is
typically characterized by continuous slow wave activity at a peak frequency of 2 Hz (e.g.,
Rattenborg et al., 2001; 2004). Also, the K-complex, an EEG component that in mammals has
been hypothesized to reflect the depolarizing phase of the slow oscillation (Steriade, 2003) has
not been observed in birds.
A key feature of the physiological model for the cortical slow oscillation (Steriade, 2005)
is the property of cortical cells to alter their firing patterns (from tonic to rhythmic burst)
depending on their level of hyperpolarization (Steriade, 2003; 2005). In all likelihood certain cell
types with certain neurophysiological properties are required to fulfill this condition and their
existence has yet to be established in the avian pallium. Also, to synchronize the firing of large
populations of cells over large areas a considerable amount of intrapallial connectivity is
probably required (Rattenborg, in press). The specifics of these connections are still subject to speculation. It is therefore premature to conclude that such features can only be provided by a neuronal organization similar to mammalianneocortex. It is however worth noting that the avian wulst (hyperpallium), the primary recording site of avian EEG and the region of the avian dorsal pallium that shares some functional and developmental similarities with the mammalian neocortex (Medina & Reiner, 2000) also markedly differs from neocortex in neuronal organization (Medina & Reiner 2000, review in Butler and Hodos, 2005).
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Delta activity. In the absence of other indicators, avian slow wave activity itself could be
interpreted as an equivalent to the mammalian slow oscillation. Several observations speak
against this possibility: 1.) The low frequency activity peak of the mammalian slow oscillation
(0.6 Hz, compared to 2 Hz for avian slow waves). 2.) Nucleus rotundus lesions diminished EEG
SW-activty in chickens (reviewed in Karmanova, 1982). This would not be expected for a self- sustained pallial oscillation. 3.) The mammalian slow oscillation does not show a homeostatic decline over the course of the night (Borbely, 2000); unlike delta activity in at least some avian species (reviewed in Amlaner & Ball, 1994; Rattenborg & Amlaner, 2002; present study). In addition the earlier described differences in the local organization of the avian and mammalian pallium (pseudolayers; appendix I) make it less likely that avian slow waves and mammalian slow oscillation represent identical phenomena.
If the mammalian slow oscillation is eliminated as a possible equivalent to avian slow
wave activity the only remaining candidates are either the thalamic or the cortical component of
mammalian delta sleep. The frequency peak (2-3 Hz) of mammalian delta activity is comparable
to avian slow waves (2 Hz). But cortical delta activity in mammals requires the columnar organization of mammalian neocortex (involving layers II, III and V), while the avian pallium is not organized in columns. Therefore, it seems more likely that avian slow wave activity is comparable to the thalamic component of mammalian delta sleep. This hypothesis is supported by the earlier stated effect of nucleus rotundus lesions on the avian sleep EEG and by a remarkable similarity in reciprocal connectivity between pallium and thalamus in mammals and birds (reviewed in Butler & Hodos, 2005).
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According to this argumentation, regions of the thalamus, a brain structure that shares
many similarities across mammals, reptiles and birds (Butler & Hodos, 2005), are most likely
responsible for the EEG similarities in the avian and mammalian pallium. A thalamic region that
seems to play a major role in the generation of mammalian EEG components (Spindles, delta
waves; for review Steriade, 2003) is the reticular nucleus of the thalamus. The reticular nucleus
actually represents the most rostral region of the brainstem/midbrain reticular formation. The
thalamic components of the reticular formation are thought to be a phylogenetically old system and can be found in mammals, reptiles and birds (Butler & Hodos, 2005). The reticular nucleus consists of a thin layer of cells that covers the rostral lateral and ventral aspects of the dorsal thalamus. It projects into most regions of the thalamus and is therefore in a position to modulate and synchronize the output of large assemblies of thalamo - cortical/ thalamo - hyperpallial neurons. While the mammalian and avian reticular nuclei are exclusively GABAergic the reptilian reticular nucleus contains multiple cell types and its projections to other thalamic nuclei are not GABAergic (reviewed in Butler & Hodos, 2005). This is an interesting difference considering that the reptilian pallium (medial, dorsomedia, dorsal and lateral cortex) does not show slow synchronized delta activity during behavioral sleep (review in Hartse, 1994). The uniform composition of the avian and mammalian reticular nuclei may be the functional basis of the similarities between avian and mammalian sleep. The similarities in reticular nucleus organization likely developed independently in birds and mammals and therefore present another example of parallel evolution (Butler & Hodos, 2005).
However, the clocklike delta oscillation in the mammalian thalamus requires the
synchronizing input of the cortical slow oscillation to become visible on the EEG level (Steriade,
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2003; 2005), an EEG component that may or may not be present during avian sleep.
Alternatively, the synchrony of avian thalamic slow waves could be governed by other brain areas, but it could also represent a unique property of the avian thalamus that sets it apart from its mammalian counterpart. It should also be noted that according to current theories (Steriade,
2003) mammalian reticular nucleus cells are able to synchronize thalamic output (and cortical
EEG) in the form of sleep spindles. It is therefore also thinkable that, if sufficiently depolarized, they synchronize delta activity in birds. Sleep spindles are eventually terminated by the firing of cortico-thalamic cells. It could be hypothesized that otherwise the spindle sequence would not be terminated and the cortical EEG would be characterized by a continuous alpha rhythm (7-14 Hz) dictated by oscillations in the reticular nucleus. Further hyperpolarization would force reticular nucleus cells into a slower burst mode (1-3 Hz, Steriade, 2003) and, similar to birds, the cortical
EEG would be one of continuous delta activity. If avian delta, in contrast to mammalian delta would not elicit cell firing in the avian hyperpallium, the observed EEG differences between mammals and birds could be explained by the same thalamo-pallial connectivity.
Mammalian REM sleep
Transsection and lesion studies have demonstrated that the caudal midbrain and pons are crucial for the expression of the various phasic and tonic components of REM sleep (reviewed in
Siegel, 2000). Subsequent lesion and unit studies identified the peri-locus coeruleus α (cat: peri-
LC α; rat: locus sub-coeruleus LSC or sublaterodorsal nucleus, SLD), a small region in the dorsal nucleus reticularis pontis oralis (RPO) and caudalis (RPC), as the crucial brain area for the
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initiation and maintainance of mammalian REM sleep (review in Luppi et al., 2005). A portion
of cholinergic and cholinoreceptive cells in this region drastically increases its firing rate during
paradoxical sleep (PS-on cells) and shows reduced or no activity during wakefulness and SWS
(Siegel, 2000 for review).
EEG activation. A cholinergic and presumably a glutamatergic projection from the SLD
to the intralaminar nuclei of the thalamus together with cholinergic and cholinoreceptive cells
from other PS-on regions (laterodorsal tegmentum, LDT; pedunculopontine tegmentum, PPT)
and the basal forebrain is likely responsible for the EEG activation during mammalian REM
sleep (Luppi et al., 2005). However, an alternative view (Siegel, 2000) suggests that two distinct
mechanisms, one cholinergic and one non-cholinergic, are responsible for EEG activation during
paradoxical sleep. This idea is based on the observation that tonic cortical EEG activation is
absent during REM episodes in atropine-treated rats and cats. However, phasic events like muscle twitches and eye movements are still accompanied by brief EEG activations, presumably mediated by a second non-cholinergic mechanism.
Atonia. A descending glutamatergic projection from the SLD probably generates muscle atonia through excitatory input on glycinergic premotorneurons situated in the parvocellular and magnocellular reticular nuclei (Siegel, 2000; Luppi et al., 2005).
PGO Spikes. The brain regions responsible for the generation of PGO spikes have been
localized in the LDT and PPT (reviewed in Siegel, 2000; Datta et al., 1998). Lesions in the LDT or PPT result in REM sleep without PGO spikes or eye movements.
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To date it is not clear how the REM-on population of the SLD is activated at the beginning of paradoxical sleep. The finding that monoaminergic cells in the locus coeruleus (LC) and the midbrain raphe nuclei (RN) show a typical REM-off firing pattern (reviewed in Siegel,
2000; Pace Schott & Hobson, 2002) led to the idea that RPO cells initiate REM sleep after release from monoaminergic inhibition (reciprocal interaction model; review in Pace- Schott &
Hobson, 2002). LC and RN cells exhibit high tonic firing rates during wakefulness, reduce their discharge rate slightly during SWS and slow down dramatically at the transition to REM sleep.
The decreased firing rate correlates with an increase in GABAergic transmission in both the LC and RN suggesting that REM-off cells are actively inhibited during REM sleep. However, the direct application of noradrenalin and serotonin into the SLD did not yield consistent results (in
Luppi et al., 2005)) and more recent evidence (reviewed in Luppi et al., 2005) led to the proposition of a new model (Luppi et al., 2005). According to this theory, during wakefulness
SLD PS-on neurons are inhibited by a population of GABAergic PS-off neurons in the SLD, pontine and mesencephalic reticular nuclei. In addition, inhibitory input from the LC and RN would contribute to the inhibition of the SLD. At the onset of REM sleep a GABAergic PS-on population (presumably in the periaqueductal grey, mesencephalic and pontine reticular nuclei and parvocellular reticular nucleus) actively inhibits LC and RN neurons. The same PS-on population would also inhibit the SLD PS-off population, releasing the SLD PS-on population from its inhibition and initiating paradoxical sleep.
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Avian REM sleep
Few experiments have examined REM sleep regulation in birds (Speciale et al., 1976;
Karmanova, 1982; Vasconcelos-Duenas & Guerrero, 1983; Fuchs et al., 2006). Cholinergic and monoaminergic cell groups in the mesopontine tegmentum (see above) play an essential role in the regulation and execution of the different phenomena that constitute mammalian REM sleep
(Siegel, 2000). The results available for birds support the notion that cholinergic and monoaminergic systems are also involved in the regulation of avian REM sleep. The systemic administration of the cholinomimetic drug arecholine increased the number and duration of REM episodes in adult Hens (reviewed in Karmanova, 1982) consistent with the mammalian finding that acetylcholine plays a major role in the regulation of mammalian REM sleep. Nialamide, a monoamine oxidase inhibitor, injected into chick embryos reduced the amount of REM sleep post-hatching (Speciale et al., 1976), supporting the idea that monoaminergic cell groups (locus coeruleus, raphe nuclei) are similarly involved in the control of avian REM sleep. The systemic application of the selective serotonin reuptake inhibitor Zimelidine resulted in a comparable suppression of REM sleep in pigeons (Fuchs et al., 2006) and rats (Reyes et al., 1983; Reyes et al., 1986) indicating a similar role of serotonin in the regulation of avian and mammalian REM sleep.
All of the above studies relied on the systemic application of more (Karmanova, 1982;
Vasconcelos-Duenas & Guerrero, 1983; Fuchs et al., 2006) or less (Speciale et al., 1976) specific drugs, and consequently, can only provide a crude outline of neurotransmitter systems that may be involved in the regulation of avian REM sleep. To date no attempt has been made to localize
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REM sleep regulating regions in the avian brain. However, the brainstem and midbrain arousal
systems are phylogenetically old brain areas (Butler & Hodos, 2005) and the monoaminergic and
cholinergic systems in birds and mammals are similarly organized (Dube & Parent, 1981; Challet
et al. 1996, Metzger et al., 2002; Butler & Hodos, 2005). It seems therefore plausible to assume
that the avian midbrain raphe nuclei (nucleus raphe superior and medialis), nucleus coeruleus
and areas in the nucleus pontis oralis/caudalis of the pontine reticular formation are similarly
involved in the regulation of avian and mammalian REM sleep.
The most striking difference between avian and mammalian REM sleep is the short
duration of individual REM episodes in birds. While the longer duration of mammalian REM
sleep allows to categorize the different REM sleep phenomena into tonic (active EEG, atonia)
and phasic (REMs, muscle twitches, PGO spikes) components avian REM episodes are generally too short to allow for such a discrimination. As described earlier, one hypothesis suggests that in mammals two independent mechanisms are responsible for the wake like “activated” EEG during REM sleep (reviewed in Siegel, 2000). An acetylcholine blocker (atropine) inhibits tonic cortical EEG activation during REM sleep in cats and rats, but phasic twitches and eye movements are still accompanied by brief EEG activations, probably mediated by a second non- cholinergic mechanism (Siegel, 2000). EEG changes during avian REM sleep are tightly coupled with clusters of eye movements. Avian REM sleep, therefore, is of similar appearance to REM sleep in atropinized mammals. This surface resemblance could lead one to entertain the idea that avian REM sleep employs only one mechanism for EEG activation, a mechanism similar to the one responsible for phasic EEG activations in mammals.
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Conclusion
Avian sleep lends itself especially well to a group comparison with mammals, because it
shares some of the electrophysiological properties of mammalian sleep while lacking others.
Some of the similar electrophysiological features in birds are expressed in brain regions that have
evolved independently of their respective mammalian counterparts. However, any comparative
analysis, and an evolutionary reconstruction of sleep based on that analysis, will suffer because of the relative dearth of knowledge regarding sleep regulation in animals other than mammals. A
comparison between avian and mammalian sleep, to date, is too often limited to quantitative (e.g.
sleep times) and qualitative (delta power) comparisons of EEG components of similar appearance. However, it needs to be clarified if this visual resemblance is the result of similar neurophysiological processes mediated by the same midbrain, pons, hypothalamus and thalamus regions and governed by the same neurotransmitter systems. It will be essential for future work to focus on the neurophysiological and neuroanatomical foundations of avian sleep in order to fully exploit the full potential of avian sleep as a contributing model for comparative sleep research.
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APPENDIX IV:
Replication in the History of Psychology: Patrick & Gilbert (1896) on Sleep Deprivation.
THOMAS FUCHS & JEFFREY BURGDORF
We report an attempted replication of G. T. W. Patrick’s and J. A. Gilbert’s pioneering
sleep deprivation experiment “Studies from the psychological laboratory of the University of
Iowa. On the effects of loss of sleep”, conducted in 1895/96. Patrick and Gilbert’s study was the
first sleep deprivation experiment of its kind, performed by some of the first formally trained
psychologists. We attempted to recreate the original experience in two subjects, using similar
apparatus and methodology, and drawing direct comparisons to the original study whenever
possible. We argue for a strong influence of an ‘Americanized’ Wundtian psychology on Patrick
& Gilbert, a claim supported biographically by their education and by their experimental
methods. The replication thus opens interesting new perspectives, which are unlikely to be
generated by any other historical approach.
G. T. W. Patrick’s and J. A. Gilbert’s sleep deprivation study, published in the
Psychological Review in 1896, was the first study conducted on human subjects. It was
performed by some of the first formally trained psychologists, and not as might be expected, by
physicians or physiologists. The study relied on a Wundtian approach, which had many things in common with the measuring techniques of physiology but in addition drew on introspective techniques, thereby adding an emphasis on mind and consciousness. However, the study was isolated within the newly emerging discipline of psychology, and was also quite different from
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the rest of Patrick and Gilbert’s other work. Finally, unlike another famous sleep deprivation
study of the time (de Manacéïne, 1894), Patrick and Gilbert did not inspire any further work for
many years to come.
Although sleep research in the late 19th century already had a scientific basis grounded in
medicine and physiology, the study of the effects of enforced wakefulness did not make an appearance before the early 1890’s. Earlier theories on the ability to function with little or no sleep were largely based on medical case reports of insomniacs and the mentally ill, on the reports of military commanders and soldiers under the strain of battle, and on historical reports of sleep deprivation as a means of torture.
This changed when, in 1894, the Russian physician and physiologist Maria Michailovna
Manasseina2 reported the results of her seminal sleep deprivation study on young puppies at the
International Medical Congress at Rome. Manasseina subjected 10 young dogs to sleep
deprivation, keeping them awake by constant walking and handling. The animals invariably died
within 96 to 120 hours, presenting a variety of symptoms. The rapid decline of her experimental
animals led Manasseina to comment that:
As a rule, the puppy deprived of sleep for three or four days presents a more pitiful
appearance than one which has passed ten or fifteen days without food. (De Manacéïne,
1897, p.66)
2 ‘Marie De Manacéïne in the authorship of her publication, following the practice of the time of translating names into French for an international audience’ (Bentivoglio & Grassi-Zucconi 1997, p. 570)
130 131
It is interesting to note that, in contrast to food deprivation experiments, it was not possible to
counteract the effects of sleep deprivation and that, after several days of enforced wakefulness
the animals were irreversibly lost. Dissections of the dead animals revealed small hemorrhages
all over the cerebral cortex, which led Manasseina to conclude, “In deprivation of sleep it seems
to be the brain which suffers most …” (De Manacéïne, 1897, p.66). Manasseina’s research received considerable attention, inspired a series of replications (Bentivoglio& Grassi-Zucconi,
1997) and ultimately gave rise to a continuous stream of animal research on sleep deprivation within the field of physiology and medicine.
Patrick and Gilbert’s “On the effects of loss of sleep”, published in the Psychological
Review in 1896, was the first experimental contribution to the study of sleep deprivation using
human subjects. Although clearly inspired by Manasseina’s seminal work, the study expanded on
it in several important aspects, which, in our opinion, justify interest in a replication.
Manasseina’s findings, in an animal model, were in line with earlier reports that associated lack
of sleep in humans with mental disease (MacNish, 1839; Hammond, 1873; de Manacéïne, 1897)
and with the misguided believe that, in China, sleep deprivation was a form of capital
punishment (Kleitman, 1939, p.300). However, not before Patrick & Gilbert had anybody
observed the effects of sleep deprivation in an experimental setting in healthy human subjects.
The original study (Patrick & Gilbert 1896)
Patrick and Gilbert sought to assess not only the physiological but also the psychological effects
of “enforced abstinence from sleep” (Patrick & Gilbert, 1896, p.469). Their method was to keep
131 132
three subjects awake for 90 hours and to conduct a series of physiological, psychological and biochemical tests at regular intervals. Some of the measurements were purely physiological:
temperature, pulse, body weight, grip and pull strength, and visual acuity. Others were designed
to assess reaction- and discrimination times, sensibility to pain, voluntary motor ability and
fatigue (Fig. 28).
Fig. 28: “Reaction Board” used in Gilbert’s 1894 study “Researches on the Mental and Physical
Development of School Children.” The apparatus was designed to measure “voluntary motor
ability” and “fatigue”, reaction times, “reaction with discrimination and choice” and “time-
memory”(Gilbert, 1894, p. 46-52). The board consists of a tuning fork setup A, stimulating
apparatus C, reaction time key E, tapping-apparatus F and an Ewald Chronoscope H (Gilbert,
1894). The device was obviously designed to be portable, which was not a requirement in the
sleep deprivation study. However, the overall mechanics and setup are probably comparable. For
detailed information on the setup please refer to Gilbert (1894).
132 133
A subset of tests was generated to determine the effects of loss of sleep on cognition and
memory. In addition, urine samples were collected to detect metabolic changes. Gilbert himself participated as a subject and went through the procedure first in November 1895. Two observers, whose most important function was to keep him awake, and to administer a series of tests every
6 hours, permanently watched him. Each test session was approximately 2 hours long, therefore testing alone took up one third of the total sleep deprivation time. All of the tests were practiced
for several days before the study to avoid learning effects.
Gilbert, managed to stay awake for the full 90 hours, passing time with his “usual daily
occupations” (p.470), and some exercise in fresh air when fatigue became overwhelming,
especially during the early morning hours. About 50 hours of voluntary wakefulness had to be
supplemented with 40 hours of mildly enforced wakefulness. According to the experimenters,
Gilbert “had to be watched closely and could not be allowed to sit down unoccupied, as he
showed a tendency to fall asleep immediately, his own will to keep awake being of no avail”
(p.470). After 2 nights of sleep deprivation Gilbert started to experience visual hallucinations.
The subject complained that the floor was covered with a greasy-looking, molecular layer
of rapidly moving or oscillating particles… Later the air was full of these dancing
particles which developed into swarms of bodies like gnats…The subject would climb
upon a chair to brush them from about the gas jet or stealthily try to touch an imaginary
fly on the table with his finger. (Patrick & Gilbert, 1896, p.470f)
133 134
These visual distortions apparently persisted for the remainder of the experiment, until, on
Saturday, November 31, at midnight, Gilbert was finally allowed to go to sleep. To assess the
depth of recovery sleep Gilbert was equipped with an electric bracelet on his ankle. Every hour
an adjustable current was applied to wake the sleeper, who had to signal his awakening by the
push of an electric button next to his bed. Compared to Kohlschuetter’s (1862) and Michelson’s
(1891, as cited in Patrick & Gilbert 1896) depth of sleep curves in non-deprived subjects, sleep
was found to be extraordinarily deep during the first 3 hours of recovery sleep. Unfortunately,
Gilbert’s sleep was so deep that an adjustable “resistance tube” (Patrick & Gilbert, 1896, p. 481)
failed to produce the necessary current to awake him, precluding a more detailed comparison.
When the tube was removed and the current was applied directly for an estimated time,’…the
subject could not be aroused sufficiently to ring the bell, but responded by a cry of pain’ (Patrick
& Gilbert, 1896, p.481).
A few months later, in March 1896 two more subjects underwent the same procedure.
Both were employed as instructors at the University of Iowa and were young men, 27 and 24
years old, to us only known as A. G. S. and G. N. B. This time the depth of recovery sleep was
not determined and some of the other tests were altered because of the previous experiences with
Gilbert. Gilbert himself did not show marked deficits on cognitive measures. Consequently, the
Ebbinghouse-nonsense-syllable-task, which was used to assess memory, was replaced by a digit
memorization task in later trials. In addition, a “reading letter” task was added (the tests
employed in our replication were derived from this adjusted version of the experiment).
134 135
Both subjects completed the experiment successfully and stayed awake for the entire
period of 88 hours. In the case of A. G. S., a sudden drop in body temperature on the last evening of the experiment gave rise to concern,3 but A. G. S. was nevertheless able to complete the
experiment. While A. G. S.,”… became very sleepy during the last 24 hours and had to be
watched constantly” (Patrick& Gilbert, 1896, p 474), G. N. B., outwardly showed few signs of
sleepiness throughout the experiment. However, G. N. B. showed considerable deficits during
some of the later test sessions.
In summary, the outcome of Patrick and Gilbert’s study was characterized by
considerable inter-individual variability. The most consistent impairments of sleep deprivation
were found in the cognitive tasks, especially in the digit-memorization task, which G. N. B.
could not complete during two of the last three test sessions. The clearest findings in the
physiological category were a steady decline of reaction times in J. A. G. and A. G. S., and
consistent weight gain during sleep deprivation in all three subjects, followed by weight loss
during recovery sleep. Sensibility to pain decreased in all subjects throughout the experiment,
and, again in all subjects, acuity of vision showed a seemingly counterintuitive increase.
The diurnal nature of sleepiness was experienced by all subjects, as were episodes of
‘semi-waking dreaming’ (Patrick &Gilbert, 1896, p. 482), which most likely correspond to
today’s ‘micro-sleeps’ (Horne, 1988). Neither G. N. B, nor A. G. S. experienced visual
3 After a walk in the cold evening air A. G. S.’ temperature fell to 35.33 C but quickly regained its normal value. The researchers were probably especially sensitive to drops in body temperature because these were among the major pathological symptoms described in Manasseina’s sleep deprived dogs. However A. G. S.’ drop in body temperature was not nearly as drastic as that described in Manasseina’s monograph (De Manacéïne 1897, p. 68).
135 136
hallucinations. The amount of extra recovery sleep taken was surprisingly small and varied
between 15% (A. G. S.) and 35% (G. N. B.) of the total sleep lost.
The replication project
Research reports are always aimed at specialized groups of readers that are familiar with certain types of procedures, apparatus and theories. In the case of historical research reports, this connection to the reader can be lost. Apparatus may be obsolete, procedures are likely to have
changed, and established theories may have been discarded and forgotten. One promising
approach to overcome historical hurdles of that kind is the replication of historical experiments
(Tweney, 2004). Moreover, research reports can only serve as second hand accounts, and in no
case, past or present, can they completely substitute for first hand personal experience. This is
especially true for sleep deprivation and the subjective experience of its effects, but even more so
for a study of sleep deprivation conducted at the end of the 19th century. Subjective experience
served an integral function in late 19th century psychology, and for many researchers it was a
form of scientific measurement. Therefore, in order to gain an understanding of the study in its
historical context, we found it necessary to attempt to recreate the actual experience of being
sleep deprived in two subjects, using similar apparatus and methodology.
We started our replication project on Sunday, March 7, at 12 pm. The two participating
subjects were the authors, J. B. and T. F. Unlike the subjects in Patrick and Gilbert’s study we
did not attempt to stay awake for 90 hours. Our more moderate goal was to stay awake for 72
hours, or three full days. Like Patrick and Gilbert we took a series of tests every 6 hours. The
136 137 testing schedule followed the original (3am, 9am, 3pm and 9pm), but only 11 of the 15 original tests were replicated. Consequently, one testing session took approximately one hour. The biochemical category of tests was entirely omitted. Biochemical testing would have required a considerable amount of organization and collaboration while not significantly contributing to the experience4. The same cannot be said for the electroshock/depth-of-sleep measure (in the original study this test was only conducted on Gilbert), or the pain-threshold tests, which were also omitted. Of the 11 measurements, pulse, temperature, weight, grip and pull are self- explanatory. Weight, grip and pull were all determined with a mechanical scale, while in the original study grip and pull were determined using a hand dynamometer. “Digit memorization” consisted of the latency to memorize a series of single digit numbers (1-9) which were arranged in a sequence of 18 key cards. The experimenter, between each trial, rearranged the sequence and
3 trials constituted one test session. As in the original study, obvious number combinations were avoided. “Reaction time” consisted of the latency to press a response key after hearing a tone
(15 trials/session). “Naming letter” consisted of the number of letters that could be read backwards in a typed passage in 60 sec. “Voluntary motor ability” and “fatigue” were tested by tapping on a reaction time response key as quickly as possible for one minute. The number of taps during the first 5 seconds was used as a measure of voluntary motor ability, while the difference between the number of tabs during the first and the last 5 seconds was used as a measure of fatigue. We conducted this task with a reaction time response key /impulse counter setup, which promptly broke in the middle of the experiment. Therefore, no direct comparisons could be drawn between the original study and the replication on these two measures. Patrick and
Gilbert most likely relied on a setup similar to the one depicted in Fig.1, which was published in one of Gilbert’s earlier studies (Gilbert, 1894). Finally, “acuteness of vision” consisted of the
4 Patrick and Gilbert did not analyze the samples but had them analyzed by the Chemistry department.
137 138
maximum distance at which a participant could read a passage of printed text by the light of a
candle. Although we did not assess depth of recovery sleep, we did estimate the amount of extra
sleep taken by T. F. during several days following the replication experiment. The tasks were trained in one long training session one day before the study.
The first 24 hours went by without difficulty and were passed with our usual daily
activities. After 30 hours, during the morning hours of the second day, J. B. started to experience
considerable difficulties staying awake. These problems were reflected in large deficits across
the board in the 9 am test session, and appeared to be explained by a total inability to concentrate.
During the tests, drowsiness became so intense that it became necessary for J. B. to retire from
the experiment. After hour 33, therefore, all tests were solely conducted on T. F., who also
experienced substantial difficulty staying awake during morning hours, but showed only little
effect on the test parameters. As in the original study T. F. experienced the marked diurnal
rhythm of sleepiness, which was least pronounced in the afternoons and evenings, when,
according to observers, it was hard to tell a difference between T. F. and a non-deprived person.
Unlike the original study T. F., was able to sit down without falling asleep during most of the
experiment except for the early morning hours (5 am- 8 am) when he resorted to taking walks.
On one of these walks, the subject experienced a visual hallucination (daydreaming), which left
him disoriented for several minutes. After 72 hours, T. F. was allowed to retire. During the first
recovery night he slept for 14 hours, and for 11 hours during the second night. On the third night
following the experiment, T. F returned to his normal amount of 8 hours of sleep. T.F. thus
required 9 hours of extra sleep during the first two recovery nights, corresponding to roughly
38 % of the sleep lost during the experiment.
138 139
Throughout the experiment neither subject experienced an unusual drop in body temperature. In addition, no marked changes in bodyweight were detected. The cognitive deficits in the digit memorization task were minor. In the case of “naming letters” both subjects, despite sleep deprivation, showed an increase in performance. One notable exception was J. B.’s final test session, where large deficits on most non-physiological measures were encountered.
Comparison of Results
Some of the most consistent findings of Patrick and Gilbert had the character of general
observations that were not reflected in the results of their systematic testing (e.g. diurnal nature
of sleepiness, micro sleeps). These effects of sleep deprivation were readily encountered in our
replication.
Two of the three subjects in the original study showed a marked linear decline in reaction
times, a finding that received some attention in later reports (De Manacéïne, 1897; Horne, 1988)
and is also important in placing the study into its historical context (see below). T. F. and J. B.
also showed this trend, albeit in a more moderate form. During the last test session of our
replication we noticed that even in the late phase of the experiment T. F. was capable of reaction
times similar to baseline. Further analysis revealed that a small number of extreme outliers was
responsible for the observed increase in average reaction times over the course of the experiment
(Fig. 29). This happened in our opinion because of momentary lapses of attention, which became
more pronounced during sleep deprivation and could go as far as to miss entire trials. Patrick and
Gilbert provided averages of their reaction times, therefore it was impossible to re-analyze their
139 140 single trial data. However, they also provided a measure of variability with their data, and, for the two subjects that showed a decrease in reaction times, this measure was remarkably similar to the variability in T. F.’s reaction time data (Fig. 30). Comparing their data with ours, we find that there is enough variability in Patrick & Gilbert’s data to allow for similar outliers5, suggesting that the decline in reaction times could be an averaging “artifact” due to an increased number of outliers as opposed to an overall decline in reaction times.
30
25 early trials late trials 20
15
10 Number of Trials
5
0
.2 0.1 11 12 .13 .14 17 18 19 0 .21 24 25 .26 27 .28 .29 0.3 32 33 .34 0. 0. 0 0 0.15 0.16 0. 0. 0. 0 0.22 0.23 0. 0. 0 0. 0 0 0.31 0. 0. 0 Reaction Time (seconds)
5 In addition, G. N. B. who did not show an increase in reaction times showed considerably larger variability during the first half of the experiment, which may have masked the effect observed in the other two subjects.
140 141
Fig. 29: Reaction time data plot for T. F. Comparison of the first 36 hours of sleep deprivation
(black) with the second 36 hours (white). Note that although the mode of reaction times hardly changes during the second half of the experiment, the range increases considerably due to a number of outliers.
160
140 J.A.G 120 A.G.S. ) 100 T.F.
80
60 Time (ms
40
20
0 Mean Reaction Time Mean Variation
Fig. 30: A comparison of mean reaction times and variance between J.A.G., A.G.S. (1896) and T.
F. (2004). The mean variance is remarkably similar between the three individuals, suggesting that similar outliers may be contained in their raw data (error bars: standard error of the mean).
Two of Patrick and Gilbert’s measures apparently relied more on the participants’ subjective impression of when they completed a task, rather than on a more objective measure of the outcome. In the digit memory task, ‘the watch [was] stopped when the subject announced his readiness to recite the list’ (Patrick & Gilbert, 1896, p 476), however, no mention was made in the report whether the subject was actually required to recite the list without error whether the subject had to repeat the task until no error was made, how many errors were made on each trial,
141 142 nor if the stop watch was started again. Similarly, in the reading distance task, the participants chose the farthest distance at which they believed they could accurately read a passage from
Wundt’s “Studien”. Again, Patrick and Gilbert’s study did not include statements on the accuracy of the participants’ subjective impressions. The reliance on the subjective judgment of a trained “subject” seems to resemble the common practice at the turn of the 19th century, using introspection as it was employed by Wilhelm Wundt and his followers (Danziger, 1990).
To see if our “subjective” judgments were indeed accurate in the digit memory task, we wrote down our answers immediately after each trial. In the case of J. B. and T. F. latency to memorize the digits was not correlated with accuracy. Both subjects showed a minor increase in latency to memorize digits, as a function of sleep deprivation, but not in the percentage of correct recalls, which were close to 100 % for both participants, comparing the last sleep deprivation trial to the trial after recovery sleep. There appeared to be no relationship between latency to memorize digits and percentage of digits recalled correctly, indicating that in the memory task self-report may be an adequate measure to determine the effects of sleep loss.
We were unable to replicate several of the effects encountered in the systematic measures of the original study like weight gain/loss, or marked deficits in the cognitive tasks. To Patrick and Gilbert’s credit, it must be mentioned that cognitive deficits and, to some extent, weight changes, have been replicated numerous times over the last 50 years (for review see Horne,
1988) and our failure to replicate them, is most likely due to shortcomings on our part. In our experiment, we only attempted to stay awake for 72 hours. This may have contributed to the
142 143 difficulties replicating some of the original results, because in all likelihood some of the effects of sleep deprivation are less pronounced at shorter durations.6
There are considerable individual differences regarding the effects of sleep loss, which may be best illustrated by J.B. who showed severe deficits after only 33 hours without sleep. The number of subjects was small in the original study and even smaller in our replication. Several of the effects in the original study were not observed across all subjects. In addition, some of the cognitive tests were altered after Gilbert’s pilot trial further reducing the number of subjects tested on identical tasks.
Historical Questions
In addition to the direct comparisons, a number of historical questions began to form, as we became involved in our project. Interestingly, both, G. T. W. Patrick and J. Allen Gilbert were psychologists, while sleep research in the second half of the 19th century was mainly a domain of medicine and physiology. From today’s point of view, it is therefore somewhat surprising that two young American psychologists would carry out one of the first studies on sleep deprivation, a study that leaned heavily towards physiological measurement. The physiological methods required for the study of sleep, however, do lend themselves to a psychological approach characterized as “physiological psychology”, a concept that differs
6 However, according to Kleitman, a period of 62-65 hours of prolonged abstinence from sleep is sufficient to produce maximal effects of sleep deprivation (Kleitman, p 303). In our replication, only T. F. was awake for this period of time, but he did not show any marked effect on the cognitive or physiological measures derived from the original study.
143 144 considerably from today’s conception of psychology. Founded and propagated by the German psychologist Wilhelm Wundt, this form of psychology gained some influence in the United
States (Seashore, 1942; Patrick, 1947; Rieber, 2001) during the last quarter of the 19th century, at a time when Patrick and Gilbert received their education. We thus argue for a strong influence of Wundtian psychology on Patrick and Gilbert, a claim supported biographically by their education, but also by their experimental methods. Patrick and Gilbert were both educated in the
United States, Patrick in a more philosophical tradition at Yale and John’s Hopkins, while
Gilbert, also at Yale, was a member of the first generation to graduate from American psychology laboratories.
Patrick (1857-1949, Fig. 4) began his academic career at the University of Iowa in 1874.
After receiving his Bachelor of Arts in 1878 he spent several years in Colorado before he went on to Yale (1882-1885). At Yale Patrick had the opportunity to hear George Trumbull Ladd, one of the founding fathers of American psychology and a great sponsor of experimental psychology.
It was also during these years that Patrick got a first taste of the writings of G. Stanley Hall, arguably Wundt’s strongest advocate in the United States:
…Hall’s Aspects of German Culture, describing all the new movements in psychology
and philosophy in Germany at that time, greatly influenced me. (Patrick, 1947, p. 69)
In the fall of 1885 Patrick enrolled at Johns Hopkins where Hall was head of the department of philosophy and psychology:
144 145
Fig. 4: George Thomas White Patrick (1857-1949 and J. Allen Gilbert (1867-???)
“1897 Hawkeye annual” University of Iowa Archives.
Throughout the department at Johns Hopkins there was at this time especial interest in
psychology and the psychological laboratory. A new method of approach to the science of
the mind was coming to America. The old armchair psychology[]7 was giving place to the
new experimental and physiological approach. G. Stanley Hall was taking the lead in this
new movement. (Patrick 1947, p. 75)
However, Patrick, who majored in philosophy, did not earn his Ph.D. with experimental work, but with a translation of the fragments of Heraclitus of Ephesos (Patrick, 1889). In 1887, some
7 a term coined by E. W. Scripture (Sokal 1980)
145 146
time before he actually graduated from Johns Hopkins, Patrick received the call from his old
Alma Mater, the University of Iowa and assumed the position of “Professor of Mental and Moral
Science”(Patrick, 1947). Patrick founded the psychology laboratory at the University of Iowa around 1890 and it reflected the strong influence of Hall and German psychology (Seashore,
1942; Patrick, 1947):
Some of this influence I brought to Iowa. Almost from the beginning, I gave courses in
German psychology and philosophy… The first instruments I used in my laboratory for
class demonstrations were those I had seen Wundt use before his classes in Leipzig.
(Patrick 1947, p. 89)
Furthermore, like almost every self respecting American psychologist at the turn of the 19th
century (Sokal, 1981), Patrick made several trips to Germany, notably one extended trip in 1894
(Patrick, 1947), the year before the sleep deprivation study. He was registered at the University
of Leipzig and attended several of Wundt’s and Lotze’s lectures (Patrick, 1947). Interestingly
some 30 years earlier at the same University one of Fechner’s students, Ernst Kohlschütter,
performed what is now considered by some to be the first scientific experiment on sleep8. More
8 Kohlschütter, a student of medicine measured the depth of sleep in human subjects using Fechner’s “Schallpendel”, a pendulum that would strike against a slate slab to produce varying sound intensities depending on the length of the pendulum’s arc. With this instrument, Kohlschütter produced a depth of sleep curve while observing sleepers over several nights and recording the sound intensities necessary to awaken them. 33 years later the same curve served as a baseline comparison in Patrick and Gilbert’s recovery sleep experiment. Although Kohlschütter’s curve was essentially flawed this should not make a difference in respect to Patrick and Gilbert’s observations. The curve, which made its way into almost every textbook on sleep (Kleitman 1939), was flawed by Kohlschütters preconceptions. Kohlschütter, a firm adherer to “Weber’s law” (Fechner’s biophysical law), regarded every value after the first two hours that was larger than the preceding value as error, which led him to reject 45 % of his original data (Swan 1929). This data would have radically changed the appearance of the curve during the later part of the night, an error that was only discovered 67 years later (Swan 1929). For a detailed discussion, see Swan (1929) and Kleitman (1939).
146 147
than one generation later the results of this experiment served as a baseline comparison in Patrick
and Gilbert’s study.
Although fascinated by psychology as an experimental science, Patrick had no formal
training in the use of experimental techniques. Probably as a consequence, in 1895, J. Allen
Gilbert (Fig. 3) was hired as a laboratory assistant. Gilbert was trained as an experimental
psychologist at E. W. Scripture’s laboratory at Yale. Scripture had received his Ph.D. from
Wundt himself and relied heavily on physiological measurement in his research. Scripture’s
approach to physiological psychology although clearly inspired by German psychology, differed
from Wundt’s in a way that appears to be very important in an attempt to place Patrick and
Gilbert’s study in its historical context. Reaction time measurements were an integral part of
both Wundt’s and Scripture’s psychology. But, in contrast to Wundt who attempted to measure
the duration of mental processes under controlled conditions (Sokal, 1980), Scripture and many of his American colleagues, “… measured the reaction times of different people under different conditions of fatigue and stress” (Sokal, 1980, p. 264). Patrick and Gilbert’s study appears to be perfectly in line with this Americanized version of physiological psychology as it measured the effects of sleep deprivation on various physiological and mental processes.
Instead of measuring “the time it takes to think” the Americans wished to test the abilities
of different people to perform different mental operations under different conditions.
(Sokal, 1980, p. 264)
147 148
Scripture’s influence is also widely reflected in Gilbert’s other work. “Researches on the Mental
and Physical Development of School Children” (Gilbert,1894) conducted under Scripture’s supervision (Scripture, 1894 a), relied to a large extent on physiological measurement. In a follow up study that was started prior to the sleep deprivation experiment, Gilbert introduced this
approach to the University of Iowa (Gilbert, 1897), and, not surprisingly, most of the measures
employed in “On the effects of loss of sleep” (Patrick & Gilbert, 1896) were directly derived
from these two older studies (notably reaction times, pain threshold, voluntary motor ability and
fatigue). It is also worth noting that the analysis of Patrick and Gilbert’s data, which relies
mainly on the presentation of means and variances, closely resembles Scripture’s approach to
statistics, although it does not nearly reach the same level of sophistication (e.g. see Scripture,
1894 b). In summary, it seems quite obvious that Wundtian psychology and its American derivatives had a major influence on both researchers, as they had on so many of the first psychologists at the turn of the 19th century (Seashore, 1942).
Interestingly, Patrick and Gilbert conducted only one study on the subject of sleep. The
main reason for this was most likely the problematic relationship between the two researchers.
According to Carl Seashore (1942), Gilbert,”… fully aware of his superior equipment as an
experimental psychologist. […] immediately began to chafe under the idea of being an assistant,
regardless of his actual academic title” (Seashore, 1942, p. 7). Carl Seashore, a fellow student of
Gilbert at Yale, was also the one to succeed Gilbert at the University of Iowa although not as a
lab assistant, but under the more prestigious title of an assistant professor of philosophy
(Seashore, 1942). Unable to cope with either his low status, nor with Patrick as his superior,
148 149
Gilbert left the University of Iowa in 1897 after only two years. According to Seashore (1942), he went on to study medicine and became a successful psychiatrist.
Patrick remained at the University of Iowa for the rest of his career but soon withdrew from experimental work.9 In 1900 he was “named head of the department of philosophy and psychology” (Seashore, 1942, p. 9). Only 5 years later however, he withdrew from this position
“on account of frail health” (Seashore, 1942, p. 13) to be replaced by Seashore. During the 31 years between the sleep deprivation study and his retirement in 1927, Patrick published, to our knowledge, only one experimental study (Patrick, 1899) but remained a prolific writer on various topics of philosophy and psychology (Patrick, 1947).
We do not know for sure if Patrick or his newly hired assistant Gilbert was the driving force behind the sleep deprivation experiment. However, it seems relevant that Patrick’s interest in sleep research started with Gilbert’s arrival, and ended with his departure. Also, although the choice of physiological and cognitive measures may have been limited by the methods used in physiological psychology as well as by the symptoms encountered in Manasseina’s animal study,
Patrick and Gilbert used some of the same experimental procedures that were already employed in one of Gilbert’s previous studies (Gilbert, 1894). This, taken together with the fact that Gilbert actually went through the experimental procedures as a subject, suggests that Gilbert’s role in this project was a rather active one, exceeding the responsibilities of a lab assistant.
9 Seashore’s talent as an experimental psychologist, his energy and his research interests may have played a role.
149 150
While the experimenter’s personal histories may explain why this study was their only
contribution to sleep research, it is not clear why it was not until 1922 that similar experiments
were performed on humans (Robinson & Herman,1922; Robinson & Richardson-Robinson,
1922; Kleitman, 1923). Thus, Patrick and Gilbert’s study stands alone in the field of psychology
at the turn of the 19th century. Why this is the case is not entirely clear. But, within the field of
psychology, the strict Wundtian approach lost ground almost as rapidly as it had gained it several
years earlier (Rieber, 2001). Therefore the interest in experiments that relied heavily on
physiological measures may have been limited only a few years after Patrick and Gilbert’s study
was conducted. The fact that the study was published in the Psychological Review may also have
diminished its impact in other disciplines like physiology or medicine. However, Patrick and
Gilbert’s study was not forgotten nor was it ignored at the time it was published. Maria
Manasseina gives a detailed account of the study in her monograph on sleep (de Manacéïne,
1897), and so does H. Addington Bruce (1915). Kleitman (1939) refered to Patrick and Gilbert’s
study as “the first study on man” (p. 300) and Horne gave a detailed review of the study in 1988,
to name but a few. However, the study did not inspire any replications or similar experiments on
humans for the next 26 years to come.
Perhaps the question is not why there was a lack of interest in human sleep deprivation
studies, but rather why this interest suddenly returned after more than one generation. The
achievements of single historical figures like Nathaniel Kleitman can probably not be
underestimated in this respect. Also, without doubt, the whole field of sleep research greatly
benefited from the availability of electroencephalographic recording techniques after 1927
(Kleitman, 1939). However, the first sleep deprivation studies after Patrick and Gilbert were
150 151 conducted in 1922 (Robinson & Herman; Robinson & Richardson-Robinson) and 1923
(Kleitman), several years before the arrival of electroencephalographic methods. Therefore, the question remains whether the availability of new technologies after the turn of the century rekindled an interest in sleep deprivation. It seems just as likely that a renewed interest in fatigue and the borders of human resilience, perhaps facilitated by World War I, paved the way for the use of new technologies and experimental sleep deprivation alike.
151 152
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