TRANSIENT HYPOXIA APPLlED DURlNG SLEEP DISRUPTS SLEEP-WAKE REGULATION

IN FREELY BEHAVING RATS

Hedieh Hamrahi

A thesis submitted in conformity with the requirements for the degree of Master of Science (M.Sc.),

Graduate Department of Zoology, University of Toronto

O Copyright by Hedieh Hamrahi (2001)

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ABSTRACT

TRANSIENT HYPOXIA APPLIED DURING SLEEP DISRUPTS SLEEP-WAKE REGULATION IN FREELY

BEHAVING RATS

Hedieh Hamrahi, Department of Zooiogy, University of Toronto, 2007

Obstructive sleep apnea (OSA) is a cumrnon sleep-related breathing disorder and

is associated with repeated hypoxic episodes and sleep disturbance. The present

study tested the hypothesis that application of hypoxia exclusively in sleep disrupts

sleep-wake regulation, using rats as an experimental model. Accordingly, we first

developed and validated an on-line computerised system to detect sleep and

wakefulness. The algorithm was robust with detection accuracies of 94.5%+1 .O for

wakefulness, 96.2%&0.8 for non-rapid-eye-movement sleep (NREM) and

92.3%t1.6 for rapid-eye-movement sleep (REM), compared to human judgement.

Hypoxia was then applied exclusively during sleep over a 3-hr period producing

significant decreases in REM sleep and increases in wakefulness compared with

room air (p=0.0004 and 0.003). Following the removal of sleep-related hypoxia

there were compensatory increases in REM sleep and decreases in wakefulness

(p=0.009 and 0.02, respectively). These data dernonstrate that hypoxia applied

exclusively during sleep, as occurs in OSA, results in significant disturbances in

sleep regulation.

This thesis is dedicated to rny family

iii

The completion of this thesis would not have been possible without the scientific guidance and continuous support and encouragement of many people. My sincere gratitude goes to:

Dr. Richard L. Horner, for giving me the opportunity to pursue this degree in his laboratory. His dedication to his students' progress, and enthusiasm for research have proven invaluable to me. Through his unique approach to research and compelling work ethics he has taught me the importance of scientific research. He has been an inspiring teacher to me.

Dr. Richard Stephenson, for reading my thesis and providing invaluable suggestions in the course of my study.

Dr.'s Dîna Brooks, Les Buck and Martin Wojtowicz for being a part of my defence and 1 or graduate cornmittee. Their time and useful insights are much appreciated.

Sandeep Sood and Kiong Sen Liao for their genuine support and encouragement when 1 needed it most. The late night coffee breaks and the inspiring discussions by the fume hood as well as their witty sense of humour will be remembered fondly. 1 will always value their friendship. I would like to thank thern both for making this journey a mernorable one.

Beverley Chan, for her helpful contributions to the analysis of the data.

Hattie Liu, Lucy Liu and Janna Momson for their helpful comments in my thesis.

Safraaz Mahamed for designing the valve to apply hypoxia in sleep (Chapter 3).

On a personal note, 1 would like to truly thank my mom and dad, Fatemeh Jalaeikhoo and Manouchehr Hamrahi for instilling in me the belief that I can do almost anything, my brother Bugzy (Hormoz Hamrahi) the best brother in the world who has always been there for me and my very best friend Shakhie (Sunny Pak) for her invaluable friendship. It is because of their endless love and continued encouragement that I have been able to complete this work.

LIST OF ABBREVIATIONS

ANOVA

AP

ATP

AVP

CBF

CSN

EEG

EMG

GABA

i.p.

LDT

NA

nCPAP

NREM

OSA

PO2

PPT

REM

s a 0 2

SCN

SD

SE

5-HT

a

Pl P2

8 1

82

0

analysis of variance

action potential

adenine tri-p hosphate

arg inine vasopressin

cerebral blood flow

carotid sinus newe

electroencephalogram

electromyog ram

y-amino butyric acid

intra peritoneal

laterodorsal tegmental nuclei

noradrenaline

nasal continuous positive ainvay pressure

non-rapid-eye-rnovement sleep

obstructive sleep apnea

partial pressure of oxygen

pedunculopontine tegrnental nuclei

rapid-eye-movement sleep

arterial oxygen saturation

suprachiasmatic nucleus

standard deviation

standard error

serotonin

alpha frequency (7.5 - 13.5 Hz)

beta 1 frequency (1 3.5 - 20 Hz)

beta 2 frequency (20 - 30 Hz)

delta 1 frequency (2 - 4 Hz)

delta 2 frequency (0.5 - 2 Hz)

theta frequency (4 - 7.5 Hz)

TABLE OF CONTENTS

CONTENTS PAGE #

Abstract

Chapter 1

Introduction

1.1 Generation of sleep and wakefulness 1.1.1 Neuronal basis of EEG patterns in wakefulness, NREM

and REM sleep 1.1.2 Cellular mechanisms involved in wakefulness, NREM and

REM sleep 1.1.3 Postural muscle activity during wakefulness, NREM and

REM sleep

1.2 Sleep regulation 1.2.1 Circadian process 1.2.2 Homeostatic process

1.3 Effects of hypoxia on sleep-wake rnechanisms 1.3.1 Neuronal responses to hypoxia 1.3.2 Effects of hypoxia on sleep-wake states 1.3.3 Mechanisms of sleep disturbance via hypoxia 1.3.4 Other mechanisms responsible in the hypoxia arousal

from sleep

1.4 Aim of studies

Chapter 2

2.1 Introduction

2.2 Methods 2.2.1 Surgical procedures

2.2.1.1 Placement of EEG and EMG electrodes 2.2.2 Recording procedures 2.2.3 Computensed analysis of EEG and EMG signals 2.2.4 Accuracy of cornputer-detected signals 2.2.5 Protocol 2.2.6 Analysis

2.3 Results 2.3.1 Sleep-wake detection 2.3.2 Changes in EEG frequencies across sleep-wake states 2.3.3 Changes in EEG amplitude across sleep-wake states 2.3.4 Changes in neck EMG amplitude across sleep-wake states 2.3.5 Computer algorithm 2.3.6 Overall accuracy of algorithm in detecting sleep and

wa kefu I ness

2.4 Discussion 2.4.1 Reliability of the computer algorithm to detect sleep-wake

states

Chapter 3

Introduction

3.2 Methods 3.2.1 Surgical preparation and procedures

3.2.1.1 Preparation of the telernetry unit 3.2.1.2 Calibration of EEG and EMG signals 3.2.1.3 Calibration of the temperature signal 3.2.1.4 Sterilisation procedures 3.2.1.5 Surgical procedures 3.2.1.6 Implantation of the telemetry unit 3.2.1.7 Placement of EEG and EMG electrodes

3.2.1 Experirnental protocol 3.2.3 Rationale for applying 10% hypoxia 3.2.4 Application of the hypoxic stimuli 3.2.5 Removal of the telemetry unit 3.2.6 Analysis 3.2.7 Statistical analysis

3.3 Results 3.3.1 Accuracy of the solenoid-valve triggenng system 3.3.2 Overall effects of hypoxia applied during sleep on

sleepwake patterns 3.3.3 Changes in percentages of sleep and wa kefulness 3.3.4 Changes in du ration of sleep-wake episodes 3.3.5 Changes on frequency of sleep-wake episodes 3.3.6 Changes in the number of arousals 3.3.7 Changes in EEG frequencies 3.3.8 Effects of control experiments on sleep-wa ke states 3.3.9 Sleep latencies in the control experiments 3.3.1 0 Effects of continuous hypoxia on sleep-wake patterns 3.3.1 1 Effects of hypoxia on core body temperature

3.4 Discussion 3.4.1 Effects of hypoxia during sleep on sleep-wake patterns

3.4.1.1 Stimulus phase 3.4.1.2 Recovery phase

3.4.2 Effects of sleep-related hypoxia on EEG parameters 3.4.3 Effects of hypoxia on core body temperature

Chapter 4

4.1 Conclusions 4.2 Technical Limitations 4.3 Future Directions References Appendix

viii

LIST OF FIGURES

FIGURES AND TITLES PAGE #

Cha pter 1

1.1: Typical cortical EEG and muscle EMG across sleep-wake states

1.2: Differential activities of thalamocortical neurons in wakefulness and NREM sleep

1.3: Regions involved in cortical activation in wakefulness

Chapter 2

2.2.1 : Layout of the experimental set-up to record sleep-wake states in the rat

2.2.2: Accuracy of cornputer-detected signais

2.3.1 : Distribution of EEG frequencies in wake. NREM and REM sleep

2.3.2: EEG and €MG frequency and amplitude distribution across sleep-wa ke states

2.3.3: Changes in EEG and EMG activities across sleep-wake states

2.3.4: Cornputer algodthm for detection of sleep and wakefulness

2.3.5: Sleep-wake patterns across the Wenty-four hour cycle

2.3.6: The mis-scored wake and NREM epochs occurred around PZ 1 6 , threshold values

Chapter 3

3.2.1 : Schematic of the experimental set-up used to detect sleep-wake states and apply 10% O2 exclusively in sleep

3.2.2: Temperature vs. Voltage output

FIGURES AND TITLES PAGE #

3.2.3: X-ray of rat with implanted telemetry unit

3.2.4: Experimental protocol

3.2.5: Flow rate of 17.0 Umin required the lowest lag and response times

3.2.6: Schema of hypoxic stimuli applied exclusively during sleep

3.3.1: Application of hypoxia during natural sleep in a freely behaving rat

3.3.2: Changes in sleep-wake states with application of hypoxia

3.3.3: Application of hypoxia during sleep disnipts sleep-wake regulation

3.3.4: Application of hypoxia in sleep disrupts maintenance of sleep-wa ke states

3.3.5: Frequency of sleep-wake cycle is diswpted by application of hypoxia during sieep

3.3.6: Application of hypoxia during sleep does not effect the nurnber of arousals

3.3.7: Effects of hypoxia on EEG frequencies in wakefulness, NREM and REM sleep

3.3.8: Total time spent in sleep and wakefulness are similar in the three control conditions

3.3.9: Sleep latency in the three control conditions

3.3.1 0: Application of chronic hypoxia disrupts sleep-wake reg ulation

3.3.1 1 : Core body temperature decreases with application of hypoxia

3.3.12: Application of intermittent and chronic hypoxia decrease core body temperature

CHAPTER 1

INTRODUCTION

Obstructive sleep apnea (OSA) is a sleep related breathing disorder,

described by repeated sleep-related apneas. which lead to hypoxia, hypercapnia,

and recurrent arousals from sleep. This disorder which affects approximately 4%

of adults is associated with debilitating day-time sleepiness (Sauter et al.. 2000:

Bennett et al., 1997), impaired work performance (Findley et al., 1992; George et

al., 1987). hypertension (Brooks et al., 1997) and decreased ventilatory and

arousal responses to changes in blood gases and airway occlusion (Brooks et al.,

1997; Kimoff et al., 1997). It is believed that the excessive daytime sleepiness

associated with OSA is the cause of psychosocial deterioration and cognitive

functions also evident in patients suffenng from OSA (Marrone et al., 1998; Borak

et al., 1996).

The frequent awakenings from sleep due to repeated sleep-related apneas

are usually associated with daytime sleepiness (Phillipson et al., 1993). The

treatment of OSA with nasal continuous positive aiway pressure (nCPAP)

obliterates the sleep-related hypoxia and hypercapnia by eliminating the apneas.

As a result, the consequential sleep disturbances and impaired daytime functions

are also abolished (Lamphere et al., 1989; Findley et al., 1989; Sullivan and

Gninstein. 1994). It has also been shown that significant increases in "deep" non-

rapid-eye-movement sleep (NREM) and rapid-eye-movement sleep (REM) occur

during the first night of treatment with nCPAP (Issa et al., 1986), indicating the

disturbance of these sleep parameters in patients suffenng from OSA. The extent

of the sleep disturbances associated with OSA has not been clearly understood.

In a novel study that modeled the OSA syndrome closely in dogs, Horner et al.

(1 998) investigated sleep-wake organisation before and during experimentally-

induced OSA as well as after the recovery from OSA. They found an increase in

REM sleep in the recovery period, which could not be attributed to a REM sleep

deficit during the OSA period. It is therefore conceivable that the compensatory

increase in REM sleep may result from REM sleep fragmentation as a result of

hypoxia. hypercapnia andlor arousals from sleep. rather than a decrease in the

total amount of REM sleep experienced before the treatment. Since the theta

rhythm associated with REM sleep arise from the hippocarnpus. the region of the

brain intimately involved in memory consolidation and cognitive processes (Nadel

et al., 2000). REM sleep disturbance in OSA may contribute to the impaired work

function observed in OSA patients.

It is known that the repeated narrowing and closure of the pharyngeal aiway

in OSA impairs lung ventilation and gas exchange. leading to hypoxia,

hypercapnia and an increase in inspiratory effort against an obstructed airway, al1

of which ultimately lead to arousal frorn sleep and sleep disturbance. Accordingly.

each of these stimuli acting alone or together may be responsible for the adverse

effects of OSA on sleep mechanisms. However, it was not feasible to study each

mechanism separately for the purposes of this thesis. As such. hypoxia was

chosen as it has been established from studies in chronic hypoxia that such

stimuli can affect sleep patterns. Therefore, this study investigated the

independent effects of hypoxia on sleep regulation. Hypercapnia was not added

to the hypoxic stimuli to fully mimic the clinical condition because we aimed to

determine the independent effects of hypoxia without the complication of

determining which effects were due to hypoxia, hypercapnia or both.

In the following sections of this chapter, a summary of background knowledge

pertaining to experiments undertaken in this study are presented. Firstly, an

overview of the neuronal mechanisms involved in sleep and wakefulness is

introduced. A description of the circadian and homeostatic processes which

regulate sleep and wakefulness are presented. This is followed by an overview of

the effect of hypoxia on sleep-wake mechanisms.

1.1 Generation of sleep and wakefulness

In general, mammals and birds exhibit three distinct behavioural states

associated with daily phases of activity and rest: state of wakefulness, non-rapid-

eye-movement sleep (NREM) and rapid-eye-movement sleep (REM).

These states can be differentiated visually on an electroencephalograph

(EEG) trace using electrodes placed on the surface of the skull to measure the

cumulative cortical activity of the underiying cortex. Wakefulness and REM sleep

are associated with a low voltage, fast frequency EEG activity and NREM sleep is

associated with a high voltage, slow frequency EEG pattern. Muscle activity as

rneasured by the electromyogram (EMG) also changes with sleep-wake states.

Du ring wakefulness, muscle activity is hig h, it gradually decreases with transition

to NREM sleep (hypotonia) and is absent during REM sleep (atonia. Figure 1 A ) .

Figure 1.1 Typical EEG and EMG activities seen in wakefulness, NREM and REM sleep.

Typical cortical EEG and muscle EMG activity across sleep-wake states - - ..

Wake NREM REM

EEG 5orv 1

EMG sorvl

5 Sec

1.1.1 Neuronal basis of EEG patterns in wakefulness, NREM and REM sleep

Wakefulness and REM sleep are both associated with a sirnilar low voltage.

high frequency EEG pattern whereas NREM sleep is a state of high voltage. low

frequency activity. It has been shown that the neurons in the cerebral cortex and

the thalamus exhibit two different patterns of activity: single spike activity

accompanied by a short after-hyperpolarisation leading to EEG desynchrony , as

observed in wakefulness and REM sleep; and burst spike activity followed by a

long after-hyperpolarisation causing EEG synchrony (Steriade et al.. 1993).

evident in NREM sleep.

The neurons of the thalamocortical cells (i.e. thalamic cells that project to the

cortex) possess a pacemaker potential, and their membrane potential oscillates

between 4 0 and -60 mV (Figure 1.2, top). The slow frequency oscillatory

pattern present during NREM sleep is produced by the inward movernent of the

low threshold Ca" ion current (It), which occurs only when the membrane

potential is depolarised. The It current deactivates as soon as Ca'+ moves in.

The inward movement of Ca" in turn causes the production of bursts of action

potentials as well as the activation of a Ca"-dependent K+ efflux. This outward

movernent of K' causes a long after-hyperpolarisation following each burst of

action potentials. Hyperpolarisation of these neurons activates a

hyperpolarisation-activated cation current (Ih), consisting of mixed Na' and K'

currents that depolarise the neurons and activate the It current. This pattern of

depolarisation followed by a long after-hyperpolarisation leads to the synchronised

oscillation in these cells indicative of NREM sleep (Steriade et al.. 1991 ;

Figure 1.2 Activity of thalamocortical neurons. Note the two distinct bursting patterns of these cells (top panel) observed in NREM and wakefulnesslREM. respectively. The bottom panel shows the expanded trace of the oscillatory activity (refer to text for more detail). (Frorn McCormick and Pape. 1990a).

Differential activities of thalamocortical neurons in wakefulness and NREM sleep

Burst Spike Single Spike Burst Spike

2 s e c vvVVV

McCormick, 1992, Figure 1.2, bottom). This pattern of activity of a population of

thalamocortical cells is synchronised in such a way that causes these cortical cells

to oscillate in phase with each other. leading to a resultant activity pattern of high

amplitude oscillations reminiscent of NREM sleep (Vandewolf, 1988).

The pattem of activity in wakefulness and REM sleep is produced by the

inhibition of the K' efflux during these states. causing the membrane potential to

remain depolarised and the thalamocortical cells to fire tonically and produce a

regular train of action potentials. The EEG becomes desynchronised in both

wakefulness and REM sleep as a result of the tonic firing of the thalamocortical

cells. Acetylcholine (Ach) is an example of a neurotransmitter that is released

rnaximally during both wakefulness and REM sleep and has been shown to cause

the inhibition of the NREM-related K' efnux during sleep and the subsequent EEG

desynchrony during wakefulness and REM sleep (McCormick. 1990a).

1.1.2 Cellular mechanisms involved in wakefulness, NREM and REM sleep

Wakefulness is actively generated by the projection of an ascending activating

systern in specific regions in the brainstem reticular formation to the cerebral

cortex. Specifically the cells of the ventral medullary reticular formation. the

central pontine reticular formation and midbrain reticular formation are involved in

maintenance of an activated cortex and fire maximally du ring wa kefulness (Figure

1.3). In order to rnaintain an activated cortex. the participation of certain chernical

neurotransrnitters is necessary. Several such neurotransmitters have been

Figure 1.3 Mechanisms involved in the generation and maintenance of wakefulness. The cell of the reticular activating system cause cortical activation secondary to the activation of the 1) thalamus and 2) posterior subthalamus/hypothalamus causing activation of the basal forebrain. AD. Anterior commissure; CB. cerebellum; CC, corpus callosum; F, fornix; Hi, hippocampus; OB, olfactory bulb; OT, optic tract; SC, spinal cord. Schernatic diagram drawn with reference to Jones B, 1994.

Regions involved in cortical activation in wakefulness

identified in the cells of the reticular formation in different segments of the

brainstem of which four will be mentioned specifically: nor-adrenaline (NA)

released from the locus coeruleus cell of the pons (Ahlsen and Lo, 1982; De Lima

and Singer, 1978; Hughes and Mullikin, 1984; Kromer and Moore, 1980; Leger et

al.. 1975). Serotonin (5-HT) released from pontine dorsal raphe and rnedullary

neurons (Ahlsen and Lo. 1982; Hughes and Mullikin. 1984; Leger et al.. 1975).

histamine from the tuberomamrnillary nucleus of the hypothalamus (Airrksinen

and Panula. 1988; Panula et al., 1989, 1990; Wada et al.. 1991 ) and acetylcholine

(Ach) from the pontine laterodorsal tegmental nuclei (LDT) and pedunculopontine

tegrnental nuciei (PPT) as well from the nucleus basalis of Meynert in the basal

forebrain (Hallanger et al., 1988; Levey et al., 1987a; Steriade et al., 1988; Smith

et al.. 1988; Sofroniew et al., 1985; Woolf and Butcher. 1986). The activity of

these wakefulness-related cells is maximal during wakefulness and progressively

decreases with the onset of NREM sleep, except for the LDT and PPT cells,

which are also maximally active durhg REM sleep. The increase in LDT and PPT

activity in REM sleep produces the EEG patterns in REM sleep that are sirnilar to

wakefulness via their excitatory effects on thalamocortical cells (Figure 1.2).

NREM sleep generation is also actively controlled by the cells of the anterior

hypothalamus and pre-optic area. which inhibit cortical activation. y -aminobutyric

acid (GABA). an inhibitory neurotransmitter, is released in high concentrations

from the hypothalamus and basai forebrain (Asanuma, 1989; Asanuma and

Porter, 1990) during NREM sleep. GABA may be involved in the inhibition of the

wakefulness-related cells of the reticular activating system, via GABAA mediated

inhibitory post synaptic potentials (IPSP1s) inhibiting the neuronal activity in

thalamic relay neurons (convey neuronal input to the thalamus) and causing a

decrease in cortical activation. GABAA mediated lPSPs have also been shown to

be involved in generation of spindle waves during NREM sleep (Steriade and

Deschenes. 1984). GABA causes an increase in K* conductance (CruneIli et al..

1988; Hirsch and Burnod, 1987; Thompson, l988), causing the hyperpolarisation

of the thalamocortical cells, which may lead to the slow oscillation of these cells

observed in NREM sleep (Figure 1.2).

Adenosine is another candidate believed to be involved in the control of

NREM sleep by inhibiting the LDT and PPT cells involved in activation of the

cortex during wakefulness and increasing K* conductance in the thalamocortical

cells (discussed later).

The cells of the oral pontine reticular formation are involved in the generation

of REM sleep (Shiromani et al.. 1995). Ach is the neurotransmitter involved in

REM sleep genesis. It is released from a different subset of pontine LOT and ?PT

cell groups than those involved in wakefulness and acts via M2 muscarinic

receptor on the oral pontine reticular nucleus causing EEG activation, as well as

the muscle atonia characteristic of REM sleep via depolarisation of medullary

reticular formation neurons and the subsequent inhibition of motoneruons via

glycine and GABA. During wakefulness, 5-HT and NA have inhibitory effects on

the REM-related LDT and PPT cells (Monti and Monti, 2000; Leonard and Llinas,

1994). but this inhibition is subsequently removed with progression into REM

sleep (Homer et al., 1997).

1 .l.3 Postural muscle activity during wakefulness, NREM and REM sleep

The transition from wakefulness to NREM sleep is associated with a decrease

in muscle activity. Progression of NREM to REM sleep is accompanied by a

further decrease in action potential finng frequency leading to the loss of postural

muscle tone during this state. The progressive decrease in muscle activity with

progression into sleep is associated with a decrease in action potential production

in the motoneuron innewating the muscle. Since cellular depolarisation I

excitability is required for A? production, the membrane potential of the

motoneuron plays an important roie in controlling muscle activity during sleep-

wake states.

Suppression of muscle tone can therefore be achieved by either a decrease in

motoneuron excitability, also referred to as dis-facilitation, or by post-synaptic

inhibition of the motoneuron. For instance, a progressive hyperpoiarisation of u

motoneurons (neurons which project to skeletal muscle cells) is observed from

wakefulness to sleep explaining the decrease in AP firing frequency observed in

sleep (Rechtschaffen and Siegel, 2000).

1.2 Sleep Regulation

Sleep regulation is achieved by the participation of three processes: circadian,

homeostatic and ultradian processes. In short, the circadian mechanism causes

the consolidation of sleep during the rest phase and wakefulness during the

activity phase. The ultradian mechanism causes the regular oscillations in NREM

and REM sleep cycles (approximately every 90 min in humans and every 10 min

rats), within the specific circadian phases. Finally, the homeostatic mechanism

involved in preservation of sleep and wakefulness, such that the loss of either

parameter will lead to its immediate, compensatory increase. These three

systems work concurrently to maintain a consistent oscillation between sleep and

wa kefulness.

Although it is widely accepted that wakefulness. NREM and REM sleep are

regulated by the interaction of the three mechanisms mentioned above, there is

evidence that the circadian processes closely regulate REM sleep, whereas

NREM sleep is homeostatically controlled.

1.2.1 Circadian Process

The circadian pacemaker is located in the suprachiasmatic nucleus (SCN)

of the hypothalamus. This region of the brain is also referred to as the biological

or intemal dock, and accordingly utilises extemal cues such as light, to entrain

and synchronise many processes in the body to a circadian rhythm (a rhythm that

repeats about every 24 hrs) including the oscillation of the sleep-wake cycles. It

has been shown that in hurnans, rats and squirrels, the SCN causes the

consolidation of wakefulness du ring the activity phase by counteracting the drive

to sleep (Edgar et al., 1993), and consequently the consolidation of sleep in the

rest phase (Dijk and Czeisler. 1 994.1 995).

A number of key observations have been made which demonstrate the

importance of circadian mechanisms in sleep-wake regulation. Firstly, lesioning of

the SCN removes the regular oscillations in sleep and wakefulness. such that they

are uniformly distributed across the 24-hr day and no longer consolidated into a

specific time period (Cohen and Albers. 1991; Edgar et al.. 1993; Wurts and

Edgar. 2000). This shows the influence of the SCN on sleep-wake consolidation.

Secondly, in the absence of light cues. which are very powerful synchronisers of

the sleep-wake rhythms (Moore, 1997), the timing of REM sleep cycles is

disrupted (Czeisler et al.. 1980a). implying the importance of the circadian

mechanisms in promoting specific sleep states. Thirdly, in the absence of light

cues. the augmentation of REM sleep intensity. which normally occurs towards

the end of the sleep cycles (Feinberg, 1974). does not occur (Weitzman et al..

1980; Zulley and Schulz. 1980). In contrast. the percentage of high amplitude.

low frequency 6 waves (prominent in stages 3 and 4 of NREM sleep) which is

generally high at the beginning of the sleep cycle (Dement and Kleitmarn. 1957;

Williams et al.. l964b; Webb. 1971 ) and progressively declines thereafter (Dijk

and Daan. 1989; Lancel and Kerkhof, 1989; Tobler and Borbely. 1986; Tobler and

Jaggi. 1987; Trachsel et al., l988), remain undisturbed in the absence of light

cues, suggesting a strong influence of circadian mechanisms on REM sleep

regulation.

Furthermore. sleep normally occurs during the declining phase of the body

temperature rhythm just after its maximum level and wakefulness occurs just after

the body temperature minimum. In the absence of light cues. sleep rhythm

changes its phase relationship with temperature rhythm such that sleep onset

occurs just after the trough of body temperature rhythm (Czeisler et al., 1980).

More importantly, REM sleep and body temperature are closely linked and share

a similar rhythm over the 24-hour cycle, being closely regulated by circadian

mechanisms (Carman et al., 1984). In the absence of light cues. the maximum

REM sleep propensity which norrnally occurs towards the end of the rest phase

just after the start of the rise in body temperature, occurs at the beginning of the

sleep cycle, but still coincides with the minimum body temperature (Czeisler et al.,

1980a). This relationship is also evident in spontaneous dissociation of sleep-

wake timing and body temperature (Czeisler et al., 1980b; Weitzman et al.. 1980;

Zuliey, 1980) as well as in forced desynchrony protocols (Carskadon and

Dement. 1977; Lavie, 1987; Dantz et al.. 1994; Dijk and Czeisler, 1995).

Although in the absence of time cues REM sleep timing is altered but NREM

sleep rhythm remains intact (Dijk et al.. 1989), indicating the independence of

NREM sleep regulation on circadian mechanisms and the dependence of REM

sleep on these mechanisms. The change in the light-dark schedule does not

affect the pattern of NREM sleep (Borbely, 1982; Zulley 1980; Weitzman et al.,

1980) further suggesting that NREM sleep is not closely regulated by circadian

processes. Moreover, even when the daily timing of sleep episodes by the

circadian mechanism is disrupted by lesioning of the SCN, NREM and REM sleep

states are still evident (Edgar et al.. 1993). illustrating the influence of another

regulatory mechanism on NREM and REM sleep generation.

1.2.2 Homeostatic process

The homeostatic model of sleep regulation is based on observations that in

humans sleep episodes (both NREM and REM sleep) are very tightly regulated.

such that the prevention of any of these states will lead to the compensatory

enhancement of that particular state during the recovery period to maintain the

total amount of each state constant during the daily 24-hr cycles (Dement. 1960;

Dernent 1965; Agnew et al., 1967; Moses et al., 1975a; Borbely, 1982) and vice

versa, such that its enhancement will lead to its attenuation (Karacan et al.. 1970;

Feinberg et al.. 1980).

Under normal light-dark cycles, NREM sleep propensity is high at the

beginning of the resting phase and progressively declines thereafter. REM sleep

propensity on the other hand, increases towards the end of the resting phase.

These relationships are evident in studies in which naps taken towards the end of

the activity phase were shown to contain more slow waves than those taken at the

beginning of the activity phase (Maron and Rachtschaffen. 1964). As well.

shortening of sleep episodes during the resting phase leads to an enhancement of

slow wave activity at the beginning of the activity phase (Akerstedt and Gillberg,

1986; Gillberg and Akerstedt, 1991). Conversely, day-time naps have been shown

to cause a decrease in the amount of sleep during the rest period (Karacan et al..

1970; Werth et al., 1997), showing that the overall amount of sleep is tightly

regulated by the homeostatic mechanisms.

In support of a homeostatic model of NREM sleep regulation, it has been

shown that in rats and humans, NREM sleep increases as a result of prior waking,

and is associated with a concomitant augmentation of slow wave activity

(predominance of 6 frequency band) (8orbely and Neuhaus, 1979; Pappenheimer

et al.. 1975; Tobler et al., 1990; Friedman et al., 1979; Borbely et al.. 1981;

Borbely and Achemann, 1992; Webb and Agnew, 1 971 , Dijk et al.. 1991 ). The

amount of sleep recovered after total sleep deprivation experiments, however

have been inconsistent with the amount of sleep loss with an average sleep

recovery of about 20% (Patrick et al., 1896). Thus, it has been suggested that the

notable increase in slow wave activity during the recovery phase rnay account for

this loss (Feinberg. 1974; Borbely. 1982). In general, in humans the period of

hig h intensity NREM sleep (stages 3 and 4) occurring almost immediately

following total sleep deprivation is confined to the first recovery night (Williams et

al., 1964a; Moses et al., 1975a; Agnew et al., 1967), and as such has linked the

level of NREM sleep intensity to prior waking (Borbely, 1982). In contrast, REM

sleep rebound has been shown to either remain elevated for several nights

following sleep deprivation (Dement. 1960,1965; Agnew et al., 1967; Moses et al.,

1975a) or be delayed until the second or third recovery night (Berger and

Oswald, 1962; Williams et al., 1964a; Agnew et al., 1967; Kales et al.. 1970;

Benoit et al., 1980). As well, partial sleep deprivations sufîcient to cause a

significant alteration in NREM sleep during the recovery phase, have produced

little or no change in REM sleep recovery (Dement and Greenberg, 1966; Jones

and Oswald, 1968; Webb and Friel, 1970; Johnson and MacLeod, 1973; Webb

and Agnew. 1974a). Thus, REM sleep recovery seems to be secondary to NREM

sleep since the recovery of REM sleep norrnally occurs when NREM sleep

pressure is low (Beersrna et al., 1990; Bninner et al.. 1990).

The above obsewations indicate that NREM sleep rnechanisms rnay be more

prone to extemallintemal disturbances, making it a suitable candidate for the

homeostatic model, where as REM sleep appears to be more modulated by

circadian influences.

Selective NREM (stages 3 and 4) and REM sleep deprivation experiments

have also shown increases in the number of attempts to enter both NREM and

REM sleep during the deprivation phase and an increase in the total amount of

the lost sleep state during the recovery phase (Dement et al.. 1960; Agnew et al..

1967; Modern et al., 1967; Beersma et al., 1990; Benington et al., 1994; Endo et

al., 1997; Wurts and Edgar. 2000). Similar observations have been made in SCN-

lesioned rats, where the circadian influence on sleep and wakefulness was

removed (Tobler et al., 1983; Wurts and Edgar, 2000). suggesting that

homeostatic mechanisrns influence both NREM and REM sleep. but to different

degrees.

1.3 Effects of hypoxia on sleep-wake mechanisrns

1.3.1 Neuronal responses to hypoxia

Most neurons in the brain, including those of the hippocampus (involved in

REM-related theta frequency generation) and locus coeruleus (involved in wake-

related cortical activation) exhibit a triphasic response to a decline in O2 levels

(Fujiwara et al.. 1987;Nieber et a., 1995; Watts et al., 1995). The first response to

a depletion in O2 levels is hypoxic depolarisation. which is believed to be due to

the build up of Na' ions in the intracellular space, which in turn are a result of

Na+/K' ATPase pump blockade due to a decrease in intracellular ATP levels.

Hyperpolarisation follows the efflux of K*, due to the opening of the KATP channels

as a result of ATP depletion (Ashcroft. 1990). Upon reoxygenation, a second.

more pronounced hyperpolarisation occurs (Fujiwara et al., 1997; Leblond and

Krnjevic. 1989).

The changes that occur in sleep-wake patterns as a result of changes in

inspired O2 levels may be mediated through different mechanisms. since different

types of neurons even in the same regions of the bain exhibit different sensitivity

to the same levels of hypoxia (Hochachka et al., 1993; Krnjevic et al., 1993;

Martin et al.. 1997). For instance, application of brief episodes of hypoxia

decreases synaptic transmission in the hippocampus (Leblond and Krnjevic,

1989; Krnjevic. 1993), a brain region associated with a high degree of theta

activity prominent in REM sleep. The decrease in synaptic transmission brought

about by hypoxia has been linked to an increase in K' efflux arising from either a

decrease in intracellular ATP (Ben-Ari, 1990b; Fujimura et al.. 1997) or an

increase in intracellular Ca" (Belousov et al., 1995). The increase in extracellular

K' resuits in cellular hyperpolarisation and causes the delay or complete

prevention of excitotoxicity (Fujiwara et al.. 1987; Kmjevic and Leblond. 1989;

Leblond and Kmjevic, 1989). In contrast to hippocampal neurons, neurons in the

brain stem depolarise in response to a decrease in O2 levels via an increase in

intracellular Ca" (Hansen, 1985; Kass and Lipton, 1986; Du binsky and Rothman,

1991; Kaplin et al., 1996). This response is considered to be a protective

mechanisrn to ensure that autonomic and cardiovascular functions are maintained

(Haddad and Donnelly, 1990; Cowan and Martin, 1992; Haddad and Jiang, 1993;

Nolan and Waldrop, 1996).

1.3.2 Effects of hypoxia on sleep-wake states

The effects of hypoxia as a result of sleep apnea (Le. sleep-related

hypoxia) on sleep-wake mechanisms have not been investigated. However, the

effects of continuous hypoxia (i.e. of the type experienced at high altitude) have

been examined by many investigators. Pappenheimer (1977) was amongst the

first to investigate the effects of hypoxia (10% inspired 02) to simulate that

experienced at high altitude. on sleep-wake mechanisms in rats. He showed that

continuous hypoxia caused an increase in wakefulness, a decrease in NREM

sleep and abolished REM sleep. Since then, many investigators have examined

the effects of various levels of inspired O2 on sleep-wake mechanisms (Ryan and

Megirian, 1982; Laszy and Sarkadi. 1990; Hale et al., 1984). Application of

continuous hypoxia has been shown to have multiple effects on EEG and sleep-

wake mechanisms depending on its severity. It has been shown that changes in

sleep-wake patterns appear once the inspired O2 levels faIl below 15% (Laszy

and Sarkadi. 1990). These changes include frequent awakenings from sleep,

increased light NREM sleep (stages 1 and 2) and suppression of deep NREM

sleep (stages 3 and 4). The hypoxia-related changes in sleep-wake organisation

are intensified with the increase in the degree of hypoxia. Laszy and Sarkadi

(1 990) and Pappenheimer (1 977) found that in the presence of 12.5% inspired 02,

the proportion of wakefulness significantly increased, while NREM and REM sleep

significantly decreased. With a further reduction of inspired O2 levels to about

I O % , these changes were exaggerated, such that REM sleep was almost

cornpletely abolished. The results of these studies suggest that deeper stages of

NREM sleep are replaced by lighter stages (stage 1 and 2) with hypoxia,

signifying a depression of deep NREM sleep due to a decrease in sleep duration.

The changes observed in sleep-wake patterns can be examined by the effects of

hypoxia on the u nderlying sleep-wake centres in the corresponding brain regions.

1.3.3 Mechanisms of sleep disturbance via hypoxia

Arousal from sleep in response to hypoxia, is likely to be a protective

response, which prevents an asphyxic death in the OSA syndrome (Henderson-

Smart and Read, 1979b). The frequent awakenings from sleep in tum lead to the

disruptions observed in NREM and REM sleep. The activation of the peripheral

chemoreceptors as well as the increase in ventilatory response may contribute to

the arousal response to hypoxia.

Hypoxic ventilatory response decreases with progression from NREM sleep

to REM sleep (Phillipson et al., 1978), such that it is highly diminished in REM

sleep in both humans and dogs. Accordingly, the responsiveness to a hypoxic

stimulus is different in NREM and REM sleep. In dogs, arousal from NREM sleep

in response to isocapnic hypoxia occurs when artenal Oxygen saturation (SaO,)

declines to about 83%, whereas in REM sleep it is achieved once the SaO,

decreases to about 70% (Phillipson et al., 1978a). Gleeson et al. (1990) have

shown that in humans, arousal from NREM sleep occurs at the same level of

ventilation in response to both hypoxia and hypercapnia, suggesting the

importance of the level of ventilatory effort in the arousal response cascade.

The importance of the peripheral chemoreceptors in mediating the arousal

response is shown by the findings of Ryan and Megirian (1982) who showed that

sectioning of the carotid sinus nerves in rats exposed to 10% inspired O2 caused a

significant decrease in the number of arousals as well as the duration of

wakefulness episodes compared to the carotid sinus nerve in intact rats.

Accordingly, in chemo-denervated dogs, Bowes (1984) observed a failure in the

arousal response to either progressive hypoxia or airway occlusion despite arterial

desaturation of 60% in NREM and 50% in REM sleep. The peripheral

chemoreceptors may be involved in the hypoxic arousals by sending direct

stimulatory projections to areas responsible for arousals such as the reticular

activating system.

1.3.4 Other mechanisms responsible in the hypoxic amusa1 from sleep

Hypoxia may also act directly on the cells involved in EEG arousals such as

the LDTIPPT, locus coenileus and the cells of the dorsal raphe. Guyenet et al.

(1993), has reported that the noradrenergic cells of the locus coenileus in the

pons which are nomally active during wakefulness are also activated by the

stimulation of the pedpheral chemoreceptors during hypoxia (1 2% inspired O*)

and do not respond to such levels of hypoxia after the sectioning of the carotid

sinus nerve (CSN).

The loweflng of inspired O2 levels has also been shown to cause an increase

in the low frequency waves in the EEG signal. Kraaier et al. (1988) and Van der

Worp et al. (1991) observed that changes in the EEG frequencies occurred once

SaO2 dropped below 70%. As well, Meyer et al. (1962) observed that in monkeys,

a decline in the EEG frequency occurs once the brain partial pressure of 0 2 (PO2)

falls below 10 mm Hg, corresponding to about 2% inspired 02. A further decrease

in the PO2 leveis to 6 mm Hg causes the amplitude of the EEG to decrease and to

eventually become isoelectric at a PO2 of about 4 mm Hg. The decrease in

inspired O2 levels tends to stimulate anerobic metabolism. which only provides

temporary sustenance for tissues, yieiding insuffÏcient energy to maintain ionic

balance by active sodium transport, and as a result, leads to EEG slowing.

The decrease in the EEG frequency may also be due to the release of

adenosine in the cortex. Adenosine is a neuromodulator released under almost

any instance of cerebral trauma such as hypoxia. hypoglycemia. hyperthermia

andlor mechanical damage or seizures (White and Hoehn. 1991). Since

adenosine is a by-product of ATP metabolism and because ATP concentration is

100 times greater than that of AMP, a small decrease in ATP leads to a profound

increase in adenosine. making adenosine a sensitive marker of cellular energy

change (Arch and Newsholme, 1978; Benington and Heller. 1995). Adenosine is

known to block mesopontine cholinergie neurons (LDTIPPT neurons) involved in

the EEG arousal cascade, and as such, is thought to promote sleep (Materi et al.,

2000). Stimulation of A l adenosine receptors has been shown to increase K'

conductance through the action of a membrane delimited G-protein (Trussell et

al., 1987; Greene and Hans, 1991). The increased K' conductance of the

thalamo-cortical cells via &"-dependent K' channels (Ka++). in tum cause their

hyperpolarisation and cause these cells to oscillate in the delta frequency range

(Benington and Heller, 1995). as observed in the above-mentioned studies. Meyer

et al. (1962) found that the increase in low frequency activity in the EEG was

accompanied by an increase in cortical K* and a decrease in cortical Na'

concentrations. The increase in K' efflux out of the cortical cells may also be due

to insufficient O2 levels causing anaerobic metabolism. which provides only a

temporary sustenance for tissues. The energy provided by anaerobic metabolism

is inadequate for maintenance of the ATP-dependent Na+lK+ purnp, leading to

hyperpolarisation of the thalamocortical cells an increase in the iow frequency

bandwidths in the EEG. However, it has been shown that the increase in

cerebral blood flow (CBF) via vasodilation and a decrease in cerebrovascular

resistance in response to cortical hypoxia. are sufficient to provide oxygenated

blood to these tissues. Hamer et a1.(1978), observed a 20% increase in CBF

without any changes in cortical ATP, ADP, AMP or phosphocreatine levels, in

response to PO2 of 45 mm Hg. However a marked increase in cerebral lactate

production suggested an increase in anaerobic glycolysis. In contrast. hypoxia

has been shown to decrease ATP levels in cortical and hippocampal tissues

(Paschen and Djuricic, 1995; Milusheva et al., 1996; Pissarek et al.. 1998). The

above observations suggest that adenosine may be involved in cortical protection

against hypoxia by stimulation of K+ emux. resulting in hyperpolarisation, leading

to diminished neuronal excitability and decreasing synaptic transmission

(Katchman and Hershkowitz, 1993; Phillis et al., 1997). These effects are

mediated via KATP or Kea++ channels. To date. pharmacological studies have

established the involvement of KATp channels in hypoxia-related hyperpolarisation

(Tromba et al., 1992; Paschan and Djuricic, 1995; Milusheva et al., 1996; Neiber

et al., 1999). The involvernent of Km++ channels in this phenomenon is still under

investigation.

A decrease in metabolism is also observed in response to hypoxia and as

such it is termed hypoxic hypometabolism (Mortola and Reuonico, 1988). This

decline in the metabolic rate leads to a delayed decrease in body temperature

(Laszy and Sarkadi, 1990; Frappell et al., 1991; Mortola and Reuonico, 1988).

which is found to cause disruptions in sleep-wake states (Hale et al., 1984). Hale

et al. (1984) observed that 15% hypoxia at neutral ambient temperature of 2 9 ' ~

disrupted the sleep-wake organisation in the same manner as normoxia (21%) at

1 5 ' ~ in the rat. These changes were manifested by an increase in the number of

arousals and a decrease in REM sleep. Parmeggiani (1987) and Buguet et al.

(1979) also observed severe diminution of REM sleep in response to a decrease

in body temperature.

1.4 Aim of the studies

Application of chronic hypoxia (Le., during wakefulness and sleep) has been

shown to cause increased wakefulness and decreased NREM and REM sleep,

however to our knowledge the effects of sleep-related hypoxia, as occurs in

obstructive sleep apnea, have not been examined. Thus, the aim of the present

study was to test the hypotheses that the application of hypoxia exclusively in

sleep disrupts sleep mechanisms, manifested by an increase in wakefulness and

decreases in sleep (NREM, REM or both), and that following the rernoval of the

hypoxic stimuli, sleep will be regulated by the appropriate compensatory

increases in both NREM and REM sleep and a decrease in wakefulness. In order

to perforrn these studies. it was necessary to first develop and validate a system

capable of detecting sleep and wake onsets so that hypoxia could be applied

exclusively in sleep and removed upon arousal from sleep. The need for an

automated sleep-detection system is of paramount importance, as visual scoring

of sleep records as well as rnanual application of hypoxic stimuli are impractical in

chronic studies lasting several hours or even days. As such we hypothesised that

the algorithm based on the frequency and amplitude analyses of the EEG and

EMG can distinguish sleep-wake states on-line irrespective of the light-dark cycle.

This methodology and validation are described in the next chapter (chapter 2).

CHAPTER 2

In a sleep-related breathing disorder, such as OSA, episodic pharyngeal

closures during sleep in humans, cause repeated sleep-related asphyxia, leading

to hypoxia, hypercapnea and repeated arousals frorn sleep. Excessive day-time

sleepiness, increased trafic accidents and irnpaired memory and work

performance are some of the adverse clinical consequences associated with this

disorder (Findley et al., 1992; George et al., 1987). Currently, the role of repetitive

intermittent hypoxia as an underlying cause of the clinical consequences of OSA

has been investigated in a number of animal studies. Rats have been commonly

used for such studies due to their suitability for sleep-wake recordings under

freely behaving conditions. However, thus far, the effects of repetitive intermittent

hypoxia on sleep-wake mechanisms have not been fully established. since

hypoxia has been applied without any reference to sleep-wake states (Laszy and

Sarkadi. 1990; Pappenheirner, 1977; Ryan and Meginan, 1982). To investigate

the specific role of repetitive intermittent hypoxia and to understand its

consequences on sleep-wake mechanisms, hypoxia needs to be applied

exclusively in sleep. As such, a reliable and practical method of sleep detection is

required. A simple and accurate two-step algorithm utilising typical changes in

EEG and EMG activities across al1 sleep-wake states has been previously used in

dogs to detect sleep and wakefulness (Homer et al., 1998). This study was

designed to detenine if the changes in the EEG frequencies and EMG activities

that occur across sleep-wake states observed in dogs also apply to rats and if so,

if the algorithm is equally applicable to rats. In addition, to investigate the impact

of hypoxia on sleep mechanisms, methods were developed to apply hypoxia

exclusively during sleep. This study is in press in the Journal of Applied

P hysiolog y.

2.2 METHODS

Studies were performed on twelve male Sprague-Dawley rats (mean body

weight = 315 g; range. 290 to 420 g, Charles River Laboratories). The rats were

housed individually, maintained on a 12:12 hr light-dark cycle. and had access to

food and water ad libitum. Rats were separated into two groups and studied under

two different light : dark scheduies. The first group (six rats) were maintained on a

1100-2300 hrs light : 2300-1100 hrs dark cycle, to which they had been

habituated for an average of 10.0 days (range. 6 to 14 days). The second group

(six rats) were studied on a reverse schedule to which they had been habituated

for an average of 13.5 days (range, 12 to 21 days). We chose to study the rats

during both their activity phase and their rest phase. to ensure that the developed

algorithm was applicable in detecting sleep-wake states under both lighting

conditions.

2.2.1 Surgical Procedures

The animals were anesthetised by intraperitoneal injection of ketamine (85

mg1Kg) and xylazine (12 mglKg). Anesthesia was confirmed by the absence of

- the pedal withdrawal reflex. Rats were then pre-medicated with atropine (1 mg/Kg,

i.p.) to reduce tracheal secretions and with a long-acting barbituate,

buprenorphine (0.03 mglKg , i.p.) to minimise post-operative discomfort. Sterile

saline was also injected (3 ml. 0.9%. i.p.) to maintain body Ruids. The head and

neck area were shaved with an electric razor and scrubbed with betadine and

90% ethanol. Body temperature was measured throughout surgery with a rectal

probe and maintained between 36 and 38 O C with a heating pad (BAS Inc.. West

Lafayette, IN). The animais were maintained in hyperoxia (50% O2 1 50% air)

throughout the experiments. if additional anaesthesia was required du ring

surgery, (e.g., the pedal withdrawal reflex typically returned proximately 1.5 hrs

after start of surgery), the animais were administered Halothane anesthesia

(-1.5%) through a gas anaesthesia mask (Freedman, 1992) and maintained at

this level for the remainder of the surgery.

To fix body position. the rats were placed in a stereotaxic frame (Kopf.

mode1 962) with blunt ear bars in a prone position. The scalp and the first layer of

neck muscles (the rhomboids) were revealed via a four-centimetre midline

incision. To obtain a clear view of the skull, the skin flaps were pulled away and

temporarily fixed with silk sutures to their adjacent ear bars. The skull was

cleaned and dried with 3% hydrogen peroxide to reveal the skull sutures.

2.2.1.1 Placement of EEG and €MG electrodes

Two insulated wires stripped at the ends and coiled around two stainless

screws (0-80 X 0.062, Plastics One. Raonoala, VA, USA) were used as EEG

electrodes. Two holes on contralateral sides of the fronto-parietal skull were drilled

for placement of the EEG electrodes: one at approximately 2mm anterior and 2 mm

to the right of bregma, and the other approximately 3 mm posterior and 2 mm to the

left of bregma. A second pair of EEG electrodes were used as spares in case the

first pair of electrodes did not give clear signals. These electrodes were placed

across from the other pair (Le. 2 mm posterior and 2 mm to the left of bregma and

3 mm anterior and 2 mm to the right of bregma. respectively). The EEG

electrodes were then screwed in place. It was estimated that the tip of the screws

were positioned just above the dura. A third hole was drilled for the placement of

the ground electrode (prepared like the EEG electrodes) in the frontal region of

the skull about 2 mm left and 4 mm anterior to Bregma. Two additional screws

were placed about 4 mm posterior and 2 mm to the left and right of Bregma.

res pectivel y, which served as anchoring screws to further secu re the electrodes to

the scull (see below).

Two insulated multi-stranded stainless steel wires stripped and looped at

the ends were sutured (silk, 3-0, Ethicon) to contralateral sides of the splenius

muscles of the neck to record neck EMG activity.

The free ends of the EEG and EMG electrodes were then connected to

amphenol pins and inserted into a miniature plug (STC-89PI-220ABS, Carlton

University, Ottawa). The plug as well as the skull screws were then covered by

dental acrylic (Plastics One, cranioplastic powder and je1 acrylic liquid (ratio 2:l).

further securing them to the skull. The muscle as well as the skin incisions

around the plug were then sutured closed (dexon 11, 3-0). The incision sites were

cleaned with betadine and the animals were placed in a dry cage under a heating

lamp and covered with a light-weight surgical drape to keep warm. The animals

were closely monitored until they were fully awake and were then transferred to

their home cage. The animals recovered in their home cage for an average of

14.1 r 3.7 days (range, 7 to 20 days) prior to the commencement of the

experiments and habituated to their respective light : dark schedule (see below).

2.2.2 Recording procedures

The EEG and EMG signals were routed to a preamplifier via a lightweight

shielded cable connected to the miniature plug on the rats' skull. The cable was

connected to a swivel to support the weight of the cable and allow for easy

movement of the animal. These eiectrical connections were used to direct the

EEG and EMG signais to a Grass Model 79D polygraph with 7P511 amplifiers.

The EEG and EMG signals were then filtered between 1-1 00 Hz and 1 O-! 000 Hz

respectively and recorded on chart paper at 5 mm.sec-' For subsequent

amplitude and frequency analyses, the EEG and the €MG signals were also sent

to a personal cornputer (IBM-compatible, 386, 16 MHz) after analog-to-digital

conversion at a sampling rate of 300 Hz (Lab Master DMA. Arrow Electronics,

Techmar. OH). The experimental set up is shown in Figure 2.2.1.

2.2.3 Computerized Analysis of EEG and EMG Signals

The EEG and EMG signals received by the personal wmputer were then

measured in 6-second intervals. The EEG frequency analysis was performed using

a modification of the intewal histogram method (Kuwahara et ai., 1988), utilising an

Figure 2.2.1. Schematic of the experimental set-up used to record sleep-wake states in the rat.

Layout of the experimental set-up to record sleep-wake states in the rat

Analog to digital converter

EEG and EMG analysis

I Am DI ifier

't Pre-Amplifier

-F swivel

EEG

EMG

State

EEG frequency analysis software similar to that previously used in dogs (Kimoff et

al., 1994; Homer et al.. 1998). In short. the amplitude of the EEG signal was divided

into 32 equally spaced horizontal slice lines. A period was measured as the time

interval between two points at which the same slice line crossed consecutive

positive going slopes of the EEG signal. A histogram was then constructed for these

intemals, and from this histogram the percent distribution of frequencies was

calculated. The EEG frequencies were then separated into six different frequency

bands: h2 (0.5 - 2 Hz). 61 (2 - 4 Hz), 8 (4 - 7.5 Hz). a (7.5 - 13.5 Hz). Pi (13.5 - 20

Hz), P2 (20 - 30 Hz), and the percent of the signal in each of six bandwidths was

measured by the computer in 5-second intewals. The ratio of high to low EEG

frequencies (P2 1 8, ratio - see below), the peak-to-peak EEG amplitude, and the

moving average of the EMG signal were also detemined by the computer.

2.2.4 Accuracy of corn puterdetected signals

To determine the accuracy for detecting frequencies in EEG signal and

amplitudes in the EEG and EMG signals, sine waves equivalent to inputs of 10-500

pV. and frequencies of 1-25 Hz (similar to those encountered in the rat) were

produced by a fundion generator (Wavetek. San Diego. CA) and sent to the

cornputer. For a 100 pV peak-to-peak sine wave the average signal detection

accuracy for al1 EEG frequency bands (62 through to p2) was 99.3 r 0.7% (Figure

2.2.2 A). The magnitude of the detected EEG signal was also linearly related to the

input voltage (r = 1.000, p < 0.0001, n = 8, range of inputs 10 - 500 pV, Figure 2.2.2

Figure 2.2.2. Accuracy of computer-detected signais. Note that the EEG signal detection was accurate across al1 frequency bands ranging from 0-30 Hz (A). There was a linear relationship between input voltage and recorded EEG amplitudes (6). There was also a direct relationship behveen voltage input and recorded EMG amplitude below voltage input of about 100 pV. after which the signal saturated.

EMG amplitude (arb. units) A 4

N P O , O D O N 0 0 0 0 0 0 0

Recorded EEG amplitude (UV)

- r N W P U 1 < 3 ) 0 0 0 0 0 0

0 0 0 0 0 0 0

% detection

B). The average coefficient of variation (Le., 100 * standard deviation / mean) of the

measured EEG signal for a given input was < 0.6 %. For the EMG, the magnitude of

the detected signal was also linearly related to the input voltage (r = 0.999. p = 0.03)

with the average coefficient of variation < 0.2 % (Figure 2.2.2 C).

2.2.5 Protocol

For the purpose of habituation, the rats were connected to the cabie and

swivel apparatus at least one day before the onset of experiments. On the day of the

experiment the EEG and EMG amplitude signals were calibrated using 100 1tV

signals produced by the polygraph. The detected signal was then used by the

computer as a reference for the incoming signals from the rats. Except for brief

periods to download and back-up the data, the signals were sampled continuously

by the computer for twenty-four hours and recorded on the chart paper for six hours,

throughout the experiment. The threshold values for the P21 6, as well as the €MG

activities were determined by visual comparison of the behavioural state of the rats

and the corresponding cornputer-generated numerical values of the EEG and €MG

signais one day before the onset of experiments. These values were then used on

the subsequent day and their validity was tested.

2.2.6 Analysis

Wakefulness, NREM and REM sleep were visually scored from the chart

record using standard EEG and EMG criteria (Homer et al., 1997). Sleep eficiency

(sleep timelrecording tirne), the percentage (%) of wakefulness, non-REM and REM

sleep, and the number of arousalslhr were calculated for both paper and computer

records for both light : dark studies. In order to detemine the accuracy of the

computer in detecting sleep-wake states. the computer and paper records were

time-aiigned and direct comparisons of the judgements of wakefulness. non-REM

and REM sleep were perfoned. The EEG frequencies and EEG and €MG

amplitudes were detemined by the computer. Also, in six rats chosen randomiy

(three studied in the dark phase and three studied in the light phase), the */O of

wakefulness, non-REM and REM sleep were also detemiined by a second

independent human scorer without reference to the results of the first scorer. For

these rats, comparisons between scorers were perfoned for the data collected over

the whole of the 6 hr experimental period in each rat.

Al1 comparisons were made using analysis of variance with repeated

measures (ANOVA) and considered statistically significant at ~ ~ 0 . 0 5 . Bonferroni's

corrected p values were used as post-hoc analysis to confirrn specific statistical

changes. For two-way ANOVA, the two factors were light-dark cycles and sleep-

wake states (i.e., wakefulness, NREM and REM sleep). Sigmastat software (Jandel

Scientific, San Rafael, CA) was used for the analyses. All Data are presented as

means I SEM unless othemvise indicated.

2.3 RESULTS

To ensure that the computer software previously utilised in dogs (Horner et al.,

1998) was also reliable in detecting sleep-wake states in rats, the constituents of its

algorithm as well as its efFicacy for detection of sleep-wake states were validated for

* rats. The reliability of the system to differentiate sleep and wakefulness was

particularly important. since this system in conjunction with a valve system was later

used (Chapter 3) to administer 1 0% inspired O2 exclusively during sleep.

2.3.1 Sleep-wake detection

Two groups of rats were studied under two different light : dark conditions

(section 2.2). Each rat was studied for two days and their sleep-wake cycles were

continuously recorded for an average of 5hr and 48 min _+ 13 min on chart paper. and

for 24 hrs on the cornputer each day.

2.3.2 Changes in EEG frequencies across sleep-wake states

Sleep-wake states affect the distribution of EEG frequencies. The EEG

signai is similar in wakefulness and REM sleep and is comprised of fast-frequency

bands, whereas in NREM sleep. slow-frequency bands dominate the signal

(Figure 1.1). Once the individual frequency bands were analysed, it was also

found that EEG frequencies were similar in wakefulness and REM sleep, but there

were noticeable increases in slow-frequency bands in NREM sleep compared to

wakefulness and REM sleep (&, &, and 0) and an increase in a fast-frequency

band in both wakefulness and REM sleep (Pz), (Figure 2.3.1). There was also an

increase in the a band (7.5-13.5 Hz) in REM sleep. The group data showed that 67

and 0 frequencies were different across sleep-wake states (F(2,20) = 137.6, p <

0.0001 and (F(2,20) = 90.2, p c 0.0001, respectively). Post-hoc analyses

confirmed that 6 , was significantly increased in NREM sleep compared to

wakefulness and REM (t(11) = 11.7 and 16.0 respectively, both p < 0.001). 0

Figure 2.3.1. Changes in EEG frequencies across wakefulness (black), NREM (white) and REM sleep (red). Note the increases in slow EEG frequencies in NREM sleep compared to wakefulness and REM sleep ( 6 : and 3 bandwidths) and the decrease in fast frequencies (P2).

Distribution of EEG frequencies in wake non-REM and REM sleep

Wake n non-REM

REM

frequency bands ( 0.5-30 Hz)

activity was also significantly increased in NREM sleep compared to wakefulness

and REM sleep (t(1l) = 11.4 and 11.9 respectively, p < 0.001). However, the

observed increase in 6, activity in NREM sleep was much more profound than 0

frequency, being almost dou bled in NREM sleep (Figure 2.3.1 , bottom). Fast-

frequency P2 activity was also significantly affected by sleep-wake states. (F(2.20)

= 263.5, p < 0.0001 ), being significantly decreased in NREM sleep compared to

wakefulness and REM sleep (t(l1) = 20.76 and 18.87 respectively, both pc

0.001). The most important observation made after post-hoc analyses was that

the effects of sleep-wake states on EEG frequencies. althoug h sig nificantl y

different in NREM sleep as compared to wakefulness and REM sleep. were

independent of the time of day in which the animals were studied (p > 0.309). i.e.

the increases in Si and 0 activity and the decrease in p2 activity occurred

consistently in NREM sleep and were not affected by the light : dark cycles.

Individual analysis of 6i,O and P2 activity across sleep-wake states in each

rat, showed that although they consistently changed in NREM sleep, there was a

marked ovedap in these frequenciss in sleep and wakefulness (Figure 2.3.2A.B

and C). According to figure 2.3.2 B, the most noticeable overlap was obsewed in

the 0 frequency band. The overlap was less visible in the individual Pz and 61

frequency bands and was rnarkedly reduced in the ratio of P2/ij1 activity (Figure

2.3.2D). The large effect of sleep-wake states on P2/& activity was also seen in

the group mean data (F(2,20) = 75.66, p c 0.0001. Figure 2.3.3A). Post-hoc

analysis confirmed that P2/6, activity significantly decreased in NREM sleep

compared to wakefulness and REM sleep (t(l1) = 9.5 and 1 1.5 respectively, both

Figure 2.3.2. Box and whisker plot to show the EEG frequencies (A-0) and EEG and EMG amplitudes distribution (E and F) across sleep-wake states in one rat. In each box the median. lom, 25". 75m, and 9om percentiles and range of values (black circles) are shown. Note the increase in slow EEG frequencies in NREM sleep compared to wakefulness and REM sleep (A) and the decrease in fast frequencies (C), although clear overiaps are seen in the frequencies. NREM sleep is better distinguished from REM sleep and wakefulness by the ratio of p2/81 frequencies (D). Panel F also shows the distinct decrease of EMG activity from wakefulness to REM sleep. See text for more details.

EEG and EMG frequency and amplitude distribution across sleep-wake states

. 6: 0 EEG freguencies C: P2 EEG frequencies

O WAKE non-REM REM

O WAKE non-REM REM

O WAKE non-REM REM

700 . E: EEG Amplitude > 600 .

A ,'O0 F: EMG Amplitude - 5 80

' D: higMow EEG freqwnciss 10 .

4 . - - P -

2 . r - -- P u

0 WAKE non-REM REM

O WAKE non-REM REM

O WAKE non-REM REM

p < 0.001). Most importantly, the consistent decrease in P2/61 activity in NREM

sleep was independent of the time of day in which the animals were studied (p =

2.3.3 Changes in EEG amplitude across sleep-wake states

The amplitude of the EEG signal was increased in NREM sleep compared

to wakefulness and REM sleep (Figure 2.3.2E). Statistical analysis of the grouped

data showed that sleep-wake states affect the amplitude of the EEG signal

(F(2,20) = 123.0, p c 0.0001). Post-hoc analysis confirmed that the EEG

amplitude increased significantly in NREM sleep compared to wakefulness and

REM sleep (t(11) = 14.2 and 12.0 respectively, both p < 0.001. Figure 2.3.38).

EEG amplitudes were similar in wakefulness and REM sleep (p > 0.1). The

overall amplitude of the EEG signal was increased in the dark as cornpared to the

light phase (189.5 t 17.3 pV and 253.1t17.3 pV respectively, p < 0.05). Although

post-hoc analysis showed that the lighting condition had an effect on the overall

EEG amplitude (F(1 , IO) = 6.78, p = 0.026), no interaction was seen between the

sleep-wake states and the lighting conditions on EEG amplitude (F (2,20) = 2.29.

p = 0.128), suggesting that the change in the EEG amplitude was consistent

across the sleep-wake states in both light and dark phases.

2.3.4 Changes in neck €MG amplitude across sleep-wake states

Although EEG frequencies and amplitude were similar in wakefulness and

REM sleep, distinct decreases in the amplitude of the €MG signal were observed

Figure 2.3.3. Group mean data 2 SEM for al1 rats (n=12) to show changes in P2/6, EEG frequencies (A), EEG amplitude (B) and EMG amplitude (C) across wakefulness, NREM and REM sleep. As can be seen there was a significant decrease in P2/& ratio and an increase in EEG amplitude in NREM sleep compared to wakefulness and REM sleep. There was also significant gradua1 decreases in €MG amplitude with progression into sleep.

Changes in EEG and EMG activities across sleep-wake states

A: Ratio of high i low Frequencies 6 -

Wake non-REM REM

- 500 - B: EEG Amplitude Y

Wake non-REM REM

cn " t 80 - C: EMG Amplitude

Wake non-REM REM

with progression from wakefulness to sleep (Figure 2.3.2F). Statistical analysis

of the grouped data showed that sleep-wake states produced significant

alterations in the amplitude of the €MG signal (F(2,20) = 153.3. p < 0.0001, Figure

2.3.1 and 2.3.3C). The EMG activity was significantly reduced from wakefulness

to NREM sleep (mean decrease = 62.6%, t(l1) = 25.8, p c 0.001) and to REM

sleep (mean decrease = 83.7%, t(l1) = 34.46, p c 0.001). This decrease in €MG

activity across sleep-wake cycles was independent of the lighting condition

(F(1 JO) = 3.44. p = 0.09).

2.2.5 Cornputer algorithm

Based on the observations that the EEG frequencies in NREM sleep are

low compared to wakefulness and REM sleep and that the muscle activity

decreases from wakefulness to NREM sleep and is alrnost absent in REM sleep,

an algorithm was developed to detect sleep-wake states. In short, three threshold

values were incorporated into the algorithm: an EEG threshold to distinguish

NREM sleep from wakefulness and REM sleep (a ratio of high to low frequencies.

p2/S1), and two EMG thresholds: a high threshold to distinguish wakefulness from

NREM and a low threshold to distinguish REM sleep frorn wakefulness (Figure

2.3.4).

2.2.6 Overall accuracy of algorithm in detecting sleep and Wakefulness

Dunng the light phase, the animals spent a large portion of time in sleep.

while in the dark phase they were mostly awake (Figure 2.3.5). Statistical analysis

Figure 2.3.4. Flow chart showing the different constituents of the cornputer algorithm to detect sleep-wake states. If the &/G1 and neck EMG are both below the set thresholds, then the rat is in NREM sleep. High P216r depicts either wakefulness or REM sleep; if EMG activity is high and above the set threshold. then the rat is awake and if it is low and below the set threshold, then the rat is in REM sleep.

Cornputer algorithm for detection of sleep and wakefulness

EEG: Frequency analysis (82, 61, 0, a, BI $2)

EMG: Amplitude analysis

EEG B2 1 61 Threshold

NREM Wake Wake REM

. T~A Th

NREM or Mov ment artifact

High €MG Threshold Low €MG Threshold

also showed that the rats studied in their light phase, spent 26.9 2 2.7 %, 66.3 +

2.5 % and 6.7 f. 1.3 % of the total recording time in wakefulness, NREM and REM

sleep, respectively. Whereas, the rats studied in the dark phase spent 69.7 t 2.3 O h ,

23.5 + 1.8 % and 6.7 + 0.7 % of the total recording time in wakefulness, NREM and

REM sleep. respectively. Statistical analysis showed that light : dark cycles

significantly affected sleep-wake states (F(1 , I O ) = 7.68, p = 0.02). with post-hoc

analyses showing that the total time spent in wakefulness and NREM sleep was

significantly different in the two group of rats (t(10) = 14.84 and 14.04 respectively.

both pc 0.001). In contrast, REM sleep was similar in both conditions (t(10) = 0.01,

p= 0.99). Figure 2.3.5 shows the typical sleep-wake patterns under both lighting

conditions in one rat.

In order to determine the accuracy of the algorithm in distinguishing sleep-

wake states, the computer records were time-rnatched to the paper records.

Detection of wakefulness by the cornputer was not statistically different from the

blinded visual analysis (mean difference = 0.58 c 4.42 (SD) %, t( l1) = 0.457. p =

0.657, paired t-test). The percentages of non-REM sleep were also not statistically

different from blinded visual analysis (mean difference in the overall percentage

between the two methods = 0.58 + 4.88 (SD) %. t(l1) = 0.414, p = 0.687).

However, REM sleep was slightly over-scored by the computer algorithm (mean

difference = 2.81 f 3.20 (SD) %, t(l1) = 3.038, p = 0.01). Sleep efficiencies

determined by the computer were also not statistically different from visually

analysed records (rnean difference = 0.58 + 4.42 (SD) %, t(l1) = 0.457, p = 0.657,

Figure 2.3.5. Line plot to show the typical sleep-wake organisation across the 24- hr cycle. Note that during the dark phase (rat's active phase). the amount of wakefulness is high compared to NREM and REM sleep, whereas in the light phase (rat's rest phase) the amount of wakekilness decreases and is replaced by NREM and REM sleep.

Sleep-wake patterns across the twenty-four hour cycle

I - - Wake I NREM

-- REM

. . 8

I . . . - 1

1 - . . l . . . 1 . . 1 !

. - 8 . . . . -

--• , . a . .

l . - - .

*- Y-

-2-7 + 1-1-2 * rx--J- L. -- - --

Time (hrs)

paired t-test). However, the number of arousals determined by the computer. was

significantly higher than detenined by visual analysis. The computer detected an

average of 1 13.7 + 13.6 bief arousals lasting 3 to 6 sec (i.e. one cornputer epoch)

per hour. whereas only 34.3 rt 8.0 were detected visually (t(l1) = 10.654. p

<0.001. paired t-test). For longer arousals, lasting 7 to 12 sec (i.e. two computer

epochs), an average of 45.3 k 10.5 arousals per hour were detected by computer

and only 20.5 + 3.0 were detected visually (t(l1) = 2.806, p =0.017, paired t-test).

For clear periods of wakefulness, NREM and REM sleep, the accuracy of the

computer algorithm was 94.5 r 1.0 %, 96.2 -c 0.8 % sleep and 92.3 + 1.6 %.

respectively compared to blinded human visual analysis. For al1 periods across

sleep-wake states, including hard to score periods such as transitional and

drowsy periods. the accuracy of the algorithm was reduced to 88.0 + 2.4 % for

wakefulness. 86.3 + 2.1 % for non-REM sleep and 89.6 t 1.5 Oh for REM sleep.

Although the accuracy of sleep-wake detection was decreased in these hard to

score instances, further analysis showed that in the mis-scored epochs, the P2 / 6,

values were gathered around the PÎ 18, threshold values (Figure 2.3.6).

In order to remove bias and gain confidence in the visual analysis. a

second scorer also blindly analysed the complete 6-hour records of sleep-wake

states in each of the six randomly chosen rats (3 from each phase). The sleep-

wake analyses of the two human scores were comparable: 96.2 i_ 1.7%. 93.2 r

1.9% and 98.3 ir 1.6% for wakefulness, NREM and REM sleep, respectively. The

differences in scoring arose mainly in distinguishing quiet wakefulness from non-

REM sleep. and distinguishing bief arousals in between sleep episodes.

Figure 2.3.6. Errors in cornputerized detection as a function of P2 I 6, . EEG frequencies were clustered around P2 16 , set threshold values, used to distinguish wakefulness from NREM sleep. Note that the errors in computerized detection are gathered close to the values of B2 I l i i frequencies close to the set threshold value. Group mean (t SEM) data from twelve rats are presented.

The mis-scored wake and NREM epochs occurred around B2 18, threshold values

Visually scored wake Visually scored non-REM but called non-REM but called wake by

by algorithm T T algorithm

t threshold

Percent of p, 1 5, threshold

2.4 DISCUSSION

2.4.1 Reliability of the computer algorithm to detect sleep-wake states

Analysis of the EEG signal in wakefulness and REM sleep showed that the

EEG signals were similar across al1 frequency bands. However, the slow-

frequency & and 8 bands increased in non-REM sleep and the fast-frequency P2

band decreased in NREM sleep cornpared with wakefulness and REM sleep (Figure

2.3.land 2.3.24-C). Although a number of frequencies were shown to be altered

during NREM sleep compared with wakefulness and REM sleep, the frequencies

that were more significantly affected in NREM sleep were 61 and p2 frequency

bands, showing significant increase and decrease in NREM sleep. respectively

(Figure 2.3.1). The 8 frequency also increased in NREM sleep compared to both

wake and REM sleep, however the percent increase was smaller in comparison to

6, and pz frequencies. Figure 2.3.2 shows the overlap in population value of 61 and

P2 in one rat, with the overiap reduced by taking their ratio (Figure 2.3.2 D).

Therefore, this increase in slow EEG frequencies and decrease in fast frequencies

in NREM sleep was found to be better reflected in the ratio of these two frequencies.

namely P2 / & ratio (Figure 2.3.2 and 2.3.3A). A criterion for a reliable algorithm to

continuously detect sleep-wake states throughout the 24-hour day, is its consistency

across the sleep-wake states. This criterion was achieved as the ratio of high 1 low

frequencies was shown to be independent of the lightdark cycle as the mean

threshold in the rats studied in the dark phase were similar to those studied in the

light phase. The independence of the p2 I SI criferia on the lightdark cycles was

observed in spite of the increase in 6 , activity in the early rest period and its

decrease in the light phase. These observations make this criterion a robust

parameter for distinguishing NREM sleep from wakefulness and REM sleep across

the light-dark phases.

A consistent increase in the amplitude of the EEG signal was also observed in

NREM sleep compared to wakefulness and REM sleep. The increase in the EEG

amplitude was surprising in light of the increase in wakefulness in the dark phase.

associated with a low EEG amplitude. It is possible that the rats in the dark phase

exhibited high amplitude EEG activity as a result of the prolonged wakefulness, which

rnay have led to the increased EEG amplitude in the dark phase. However. the EEG

amplitude was found to be dependent on the lighting condition. increasing

significantly in the dark phase. This dependence of the EEG amplitude on light-dark

cycles rendered it unsuitable for on-line detection of sleep-wake states, as the

threshold value would change over time.

As shown in Figure 2.3.3, EEG frequencies and amplitude were not sufficiently

different between wake and REM sleep to distinguish between these states. There

was however a significant decrease in the neck €MG activity with progression from

wakefulness to sleep (Figure 2.3.3), which was also independent of the light-dark

phases and this parameter was therefore chosen to separate wake from REM sleep.

Since the p2 16, significantly decreased from wakefulness to NREM sleep. and

the neck €MG activity significantly decreased from wakefulness to REM sleep and

because these changes were independent of light-dark conditions. they were

incorporated into a simple algorithm to distinguish behnreen wakefulness. NREM and

REM sleep.

The algorithm based on P;, / 8, ratio and the neck EMG activity utilised in our

study showed to be effective in distinguishing sleep and wakefulness. Its accuracy

for detection of clearly established periods of wakefulness, NREM sleep and REM

sleep was 94.5%, 96.2% and 92.3%, respectively. This accuracy was relative1 y

lowered after including al1 sleep-wake states that were also difficult to score visually.

such as transitional and drowsy periods (88.0% for wakefulness, 86.3% for NREM

and 89.6% for REM sleep). Other studies do not include such transitional periods,

which poses a problem since any on-line sleep-detection system will encounter such

EEG patterns. As such, the reported accuracies for other systems is artificially

inflated. However, despite d i fb l t ies in detecting transitional periods. Chapter 3

shows that the algonthm is accurate enough to detect sleep and apply hypoxic stimuli

exclusively in sleep (Chapter 3). The accuracy of detecting NREM and REM sleep

exceeded the accuracy of other systems designed to detect sleep-wake states which

are on average about 90% accurate for NREM and 88% accurate for REM sieep (Van

Gelder et a1.,1991; Grant et a1.,1995; Neuhaus and Borbely, 1978. Chouvet et al..

1980).

Comparison of computer-judged and blinded human visual analysis of al1

sleep-wake states, showed a high degree of similarity in detecting wakefulness and

NREM sleep confirming the validation of the computer judgement. REM sleep and

brief awakenings from sleep were over-detected by the computer. This over-

detection of arousals has been shown in other studies (De Carli et al., 1999). It is not

clear whether the difference in the number of arousals detected by the computer is

due to the algorithm's ability to pick out subtle EEG changes not observable by visual

judgement or sirnply the over-sensitivity of the cornputer algorithm. Since the over-

detection of arousals may have caused

sleep in the second study (Chapter 3),

premature termination of hypoxia during

this potential problem was reduced by

adjusting the computer algorithm specifically for the following study so that at least

two consecutive epochs (12 sec) of wakefulness were required to identify an arousal

episode before terminating the hypoxia.

In summary the p2 1 61 ratio showed to be an effective parameter to

distinguish NREM from wakefulness and REM sleep, whereas, the neck EMG

activity proved to be very effective in distinguishing REM sleep from wakefulness.

These parameters remained very stable over prolonged light-dark cycles in rats

and as such were used as a part of a system to apply hypoxia during sleep

(Chapter 3). Since Chapter 3 also relied on this computer algorithm to detect

sleep and deliver stimuli in rats, a discussion of other sleep-detection systems in rats

compared to Our system is included in Chapter 4. This discussion is included in

Chapter 4 because it is only then that the ability of this algorithm to address the

hypotheses and experiments of this thesis can be properly assessed.

3.1 INTRODUCTION

The underlying effects of the repetitive intermittent hypoxia on sleep-wake

mechanisms have not been fully established. since in such studies. hypoxia has

been applied either continuously or intermittently, without reference to sleep-wake

states (Bekehe et al.. 1995; Fletcher and Boa, 1996; Fletcher et al.. 1992; Kraiczi

et al., 1999; Pappenheimer 1977; Greenberg et al., 1999; Laszy and Sarkadi.

1990; Ryan and Megirian, 1982). Application of 15-1 0 % hypoxic stimuli has been

shown to cause prolonged wakefulness (Laszy and Sarkadi, 1990;

Pappenheimer, 1977; Ryan and Megirian, 1982) and to shift or suppress circadian

rhythms (Jarsky and Stephenson, 1999; Mortola and Seifert, 2000). Since hypoxia

acts as an arousing stimulus and in most studies is applied for several hours

during the day, it is possible that the rats may also shift their sleep episodes to a

period when the stimuli are not applied. In order to study the specific effects of

sleep-related hypoxia associated with obstructive sleep apnea, on sleep-wake

mechanisms, hypoxia needs to be applied exclusively in sleep and subsequently

removed upon arousal from sleep. As such in this study we have utilised a

cornputer algorithm, developed and validated for rats to detect sleep-wake states

(Chapter 2) to apply hypoxia exclusively in sleep. This study is the first to deliver

stimuli in such a way to mimic the intermittent hypoxia experienced in sleep in

OSA patients. This study has been submitted for publication to the Journal of

Applied Physiology.

3.2 METHODS

Studies were perfonned on ten male Sprague-Dawley rats (mean body

weight = 275.3 f 41.3 g; range, 228.2 to 334.5 g, Charles River Laboratories).

The rats were housed individually, maintained on a 12:12 hr light-dark cycle. and

had access to food and water ad libifum. The rats were habituated to 0700:f 900

light and 1900:0700 dark cycle for at least 7 days (range. 7 to 21 days) before the

commencement of the studies.

3.2.1 Surgical Preparation and Procedures

3.2.1.1 Preparation of the Telemetry Unit:

The telernetry system consists of two main components: a transmitter

(consisting of two bipolar EEG and EMG electrodes. one ground electrode and a

temperature sensor) and a receiver. The EEG, EMG and body temperature

signals were transmitted (Data Sciences, TL1 OM3-F50-€ET) via radio-telernetry to

a receiver (Data Science, Model RPC-1). These digital signals were then sent to a

digital-to-analog converter (Data Science, UAl O Physio Tel Multiplus Analog

Adaptor), which amplified the signals 1000 times. The resultant analog EEG and

EMG signals were filtered between 0.1-100 Hz and 3-100 Hz, amplified (x 6.25.

DC-936 Buffer. CWE Inc.) and recorded on chart paper (Grass Model 78D). For

subsequent analyses, the EEG and the EMG signals were also sent to a personal

computer (IBM-compatible, 386, 16 MHz) after analog-to-digital conversion at a

rate of 300 Hz. as previously described (section 2.2.3). The experimental set up is

shown in Figure 3.2.1.

Figure 3.2.1. Schematic of the experimental set-up used to record sleep-wake states and apply 10 % O2 exclusively during sleep.

Layout of the experimental set-up to apply hypoxia in sleep

1 Analog to digital

Wake Soienoid vaIve

EEG

EMG

O2 Terrp

3.2.1.2 Calibration of EEG and EMG Signals:

Due to differences in the initial gains of the EEG and EMG signals from the

telemetry units, each unit had to be separately calibrated. Before implantation.

the EEG and EMG frequency and amplitude signals received from the telernetry

unit by the computer, were calibrated using a 10 Hz. 100 pV peak-to-peak signal

produced by a signal generator (custom-made) and sent to the computer after

amplification and filtering as described in the previous section. The computer

then produced a calibration value for the EEG and EMG signals, which was used

by the software to determine the EEG and EMG amplitudes. The accuracy of the

frequency signal was verified by its close examination on the chart recorder and

its appropriate detection as an alpha frequency band (10 Hz) by the computer.

The EEG and €MG calibration data produced by the computer were unique to

each telemetry system due to the differences in the manufacturer's calibrations.

Accordingly. each rat's implanted telemetry unit calibration value was matched to

its own calibration data for each study.

3.2.1.3 Calibration of the Temperature Signal

The telemetry unit was also equipped with a thermistor sensor, which

produced a voltage signal in response to changes in temperature. In order to

calibrate the temperature signal. the transmitter was placed in a water bath

(Branson, Model 251 0) at four different temperatures normally encountered in the

rat ranging from 36 to 40°C. Resultant transmitted signals were sent to a voltage

meter (Yu Fong, YF-3110) and also recorded on the Grass chart recorder. The

voltage signal corresponding to each temperature level was allowed to stabilise

for approximately 5 minutes on the voltage meter as well as on the chart recorder

and the relationship between temperature (OC) and voltage output was then

plotted (Figure 3.2.2). Once the telemetry unit was irnplanted in the rat. it was not

possible to access the unit to calibrate the temperature signal. However, since

the relationship between the voltage output and temperature was known. a

voltage calibrator (World Precision Instruments. Model 2010-A) was used to

simulate the voltages and calibrate the temperature signais from the rats for the

chart recorder,

3.2.1.4 Sterilisation Procedures

The transmitter as well as the EMG electrode tip covers (placed at the end

of the EMG electrodes to reduce damage to the neck muscles) were sterilised in

2% active glutaraldehyde for at least 12 hours before surgery and rinsed two

times in 0.9% sterile saline to ensure complete removal of the sterilising agent.

The transmitter and the EMG electrode tip covers were then kept in a covered

dish in 0.9% sterile saline until their implantation in the rats (approximately two

hours). All surgical materials and instruments were autoclaved before surgery.

Tools not suitable for autoclaving were sterilised in 90% ethanol over night. The

surgical area was steriiised with 90% ethanol prior to surgery.

Figure 3.2.2. The relationship between temperature and voltage output of the thenosensor in one transmitter. Each symbol represents the mean of three separate trials. The linear regression formula was consistent in each trial. See text for further details.

Temperature Vs. Voltage Output

3.2.1.5 Surgical Procedures

The animals were anaesthetised and prepared for surgery as described

previously (section 2.2.1 . Pg . 30).

3.2.1.6 Implantation of the Telemetry Unit

The two-channel telemetry unit, consisting of EEG and €MG electrodes

was implanted to record skull EEG and neck EMG activities. An anterior-posterior

mid-line incision was made extending from the xiphoid process down to the pelvic

region. The exposed extemal oblique muscle was then cut along the linea alba

(about 4 cm) expose the peritoneal cavity. The unit was then placed in the

peritoneal cavity just below the liver, and lightly held in place with four suture

attachments (silk. 3-0) to the underside of the external oblique muscle layer

overlying the intestines. The EEG and €MG electrodes as well as the ground

electrode were tunnelled subcutanously to a midline incision made on the back of

the skull for subsequent implantation (Figure 3.2.3). The external oblique muscle

and the skin directly above it were then closed using standard ninning and

interrupted sutures (Dexon 11, 3-0).

3.2.1.7 Placement of EEG and EMG Electrodes

The cortical EEG and neck EMG electrodes were implanted as previously

described (section 2.2.1.1, Pg. 31). However. in contrast to previous technique.

after the skull screws were covered by dental acrylic (Plastics One. cranioplastic

Figure 3.2.3. An X-ray of a rat with the implanted 3channel telemetry unit.

X-ray of rat with implanted telemetry unit

powder and je1 acrylic Iiquid (ratio 211). the incision over the electrodes was

completely sutured closed. The EMG electrodes were also sutured (silk, 3-0,

Ethicon) to contralateral sides of the splenius muscles of the neck as previously

described (chapter 2). Since the electrodes were not flexible enough to loop. a

pair of EMG tip covers was placed at the sharp ends of the electrodes to prevent

damage to the muscles. The muscle as well as the skin incisions were then

sutured closed (Dexon 1 1 , 3-0).

The incision sites were cieaned with betadine and the animals were placed

in a dry cage covered with a light-weight surgical drape under a heating lamp to

maintain body temperature. Animals were closely monitored until they regained

consciousness and were moving freely, after which tirne they were transferred to

their home cage. Anirnals remained in their home cages until they were fully

recovered (1 1.2 + 6.0 days) prior to commencement of experiments. After the

animals recovered from surgery, they were handled daily and habituated to the

experimental chamber (see below).

3.2.2 Experimental Protocol

The objective of this study was to investigate the effects of hypoxia (10%

inspired O,) applied exclusively during sleep on sleep-wake organisation. Rats

were placed in a smaller chamber (3.26 L) than their home cage (20 L) during

experimentation (, 6 hm). The smaller volume of the experimental chamber

hastened equilibration of the chamber air to desired O, level after switches from

Figure 3.2.4. Experimental protocol for application of hypoxia during sleep. Conditions 1-3 represent the control experiments performed on three separate days, in random order. In condition 1, the rats were placed in their home cage exposed to continuous room air during both stimulus and recovery phases. In condition 2, the rats were placed in the experirnental chamber and exposed to room air during both stimulus and recovery phases. In condition 3, the rats were placed in the experimental chamber. Room air was applied via the solenoid valves which were triggered at the onset of sleep or wakefulness in both stimulus and recovery phases. In condition 4, the rats were placed in the small experimental chamber. In stimulus phase, hypoxia was applied during sleep via solenoid valves. In the recovery phase, room air was applied via the solenoid valves. In condition 5, the rats were placed in the experimental chamber. They were exposed to hypoxia via the solenoid valves during both sleep and wakefulness in the stimulus phase. and room air via the solenoid valves in the recovery phase.

Experimental Protocol

Stimulus Phase Recovery Phase Condition placed

Experimental chamber Room Air 10 Room Air

1

1

Experimental chamber Room Air to Room Air

Rat placed in chamber l I

1

Experimental chamber l

Room Air Rat placed in chamber

Experimental chamber Room Air to Hypoxia

Home cage Room Air in homecage

Experimental chamber Room Air

5

Time

Home cage Room Air

Experimental ctiamber Room Air to Room Air

Rat placed in chamber

Experimental charn ber Hypoxia to Hypoxia

Experimental chamber Room Air to Room Air

1 1

room air (see below). Rats were retumed to their home cage following the

experiment.

In al1 experiments. the sleep-wake patterns were analysed continuously for

a period of six hours, starting at 1130 hrs and ending at 1730 hrs. This 3-hr

stimulus phase followed by a 3-hr recovery phase was chosen, since previous

studies have shown that application of continuous hypoxia for periods as short as

2 hours is sufkient to produce significant sleep disturbances in rats (Laszy and

Sarkadi. 1990).

To distinguish the changes that occurred in sleep-wake cycles due to the

application of hypoxia exclusively during sleep from other potential factors

involved in the experiment, several factors were controlled (Figure 3.2.4). The

effects of size of the experimental chamber, noise disturbance by triggering of the

solenoid valves and of hypoxic exposure during both wakefulness and sleep were

controlled for as follows:

Conditions l(home cage - room air) vs. Condition 2 (chamber - room air),

(Figure 3.2.4) tested whether the size of the experirnental chamber per se

affected sleep-wa ke patterns. Du ring these experiment the rats breathed room air

(21 % O*) during both sleep and wakefulness.

There is also a slight noise disturbance associated with the triggering of the

solenoid valves to apply the hypoxic stimuli. Conditions 1 and 2 vs. condition 3

(chamber - room air to room air) tested whether this noise noise per se affected

sleep-wake patterns. In condition 3, rats were placed in the srnall experimental

chamber, and exposed to room air- to-room air switches at sleep onset and again

at wake onset. The sleep-wake patterns attained under this condition were then

compared to those of conditions 1 and 2 in which the rats had been exposed to

continuous room air (Le. without valve triggers). Condition 3 also served as our

sham intervention, since it only differed from our main study in that room air and

not hypoxia. was applied during sleep. Condition 4 was the main study where

hypoxia was only applied in sleep and room air in wakefulness (see below).

Condition 5 (chamber -hypoxia I hypoxia) compared the effects of

continuous hypoxia (Le. during both sleep and wakefulness) on sleep-wake

patterns gained in this study to previous studies. During this study. the rats were

placed in the small experimental chamber and exposed to 10% inspired Oz during

both wakefulness and sleep (i.e. valves switched from hypoxia-to-hypoxia at the

transition of wakefulness to sleep and vice versa) .

The order of the control experiments in home cage. experimental chamber

with continuous room air and in the experimental chamber with room air-to-room

air switches (conditions 1-3, Figure 3.2.4) were deterrnined using a random

number generator and were performed in three consecutive days. The study

involving hypoxia application only during sleep (condition 4) was always

perforrned on day 4 after the control studies had been performed. Condition 4 vs.

3 represented the main focus of the study, mainly to investigate the effects of

application of hypoxia in sleep vs. sham intervention). The continuous hypoxia

study (condition 5) was always conducted approxirnately 7 days after condition 4.

since it has been shown that continuous hypoxia over a long period of time may

profoundly affect sieep-wake patterns, even several days after removal of hypoxia

(Pappenheimer, 1977).

To habituate rats to their experimental chamber, they were housed in their

appropriate experimental chamber for 1-2 hour periods for at least 2 consecutive

days prior to the start of each study. Al1 studies were conducted in a noise-

attenuated Faraday Cage (Model GPC-010, BRSILVE Inc. MD, USA) for about 8

hours during the resting phase (light phase) of the rats (0900 hrs to 1700 hrs).

The ambient temperature and humidity (thermohygrometer, Cole-Parmer Model

37950-10) as well as the sleep-wake states and core body temperature were

measured continuously for the duration of each experiment. The EEG and EMG

signais were calibrated on both the computer and the chart recorder and the core

body temperature was calibrated on the chart recorder. The EEG and €MG

criteria for the on-line system used to detect sleep-wake states were determined

before the start of each study (section 2.2).

In the control experiments (conditions 1-3) rats were exposed to room air

for the duration of the experiments. In condition 4, hypoxia was applied whenever

the rats fell asleep for a three-hour period. In condition 5. hypoxia was applied

continuously, regardless of sleep-wake states for a three-hour period. Following

the bhour studies in Conditions 4 and 5, the rats were exposed to room air to

room air switches to investigate the sieep-wake patterns after a penod of sleep

distu rbance.

3.2.3 Rationale for applying 10% inspired Oz

In this study we chose to apply 10% hypoxia exclusively in sleep, since the

effects of application of 10% continuous hypoxia on sleep-wake mechanisms

have been established (Laszy and Sarkadi, 1990; Ryan and Megirian, 1982;

Pappenheimer, 1977) we wished to use these studies for comparison. Also.

Lewis et al. (1 973) have shown that breathing 10% inspired O2 for a period of 10

minutes, causes the arterial PO2 to decline to about 37 mm Hg, which

corresponds to about 65% arterial O2 saturation, at a pH of 7.55 (as a result of

hypoxia-induced hyperventilation). In our study however, it is unli kely that arterial

O2 saturation would decrease to such levels, since hypoxia is only applied during

sleep and on average a typical sleep episode is just over one minute.

Furthermore, it has also been established that during the apneaic episodes in

OSA, arterial 0 2 saturation decreases to about 80-90% (Olson et al.. 1999; Noda

et al., 1998; Fletcher et al., 1991), which also relates Our choice of hypoxia to the

level experienced in OSA.

3.2.4 Application of the hypoxic stimuli

A solenoid valve triggerhg systern was designed (S. Mahamed. 1999) to

apply the hypoxic stimulus (10% inspired O*) during sleep and room air (21%

inspired 04 during wakefuiness (Appendix 1). The rats were studied in an airtight

chamber continually Rushed with gases of desired O2 concentration at a flow rate of

17 Umin. This fiow rate was chosen because it was the fastest fiow rate at which Cl2

levels in the chamber could be changed without causing the rats to arouse from

Figure 3.2.5. Relationship between flow rate, lag time through the chamber and response time (040% and 0-90% of total response time) of the oxygen analyser. Flow rate of 17 Urnin required the lowest lag and response times to apply hypoxia in sleep and room air in wakefulness. Mean t SEM in three trials.

Flow rate of 17.0 Umin required the lowest lag and response times

7.5 10 17 Flow Rate (Umin)

7.5 1 O 17 Flow Rate (Umin )

Flow Rate (Umin) Flow Rate (Urnin)

sleep (Figure 3.2.5). The O2 content of the animal chamber was continually

measured using an O2 analyser (Taylor Servomex. OA 272). Upon detection of two

consecutive epochs of sleep by the cornputer (-12 seconds). a voltage pulse was

generated (4.4 V) that triggered a solenoid valve to switch the chamber air from

room air to 100 % N2, causing the ambient O2 levels to decrease to 10% a short

time after sleep onset (lag time - 15 sec). Once this desired level was reached.

ambient Oz level was maintained by continuous administration of 10% O*.

Similady. upon detection of two consecutive epochs of wakefulness following

arousal from sleep, the voltage retumed to O V. which increased the ambient O2

level transiently to 100 %, causing the O2 levels to rise quickly to 21 %.

This level was maintained by continuous administration of 21% O2 unfil the next

sleep episode (Figure 3.2.6). See Appendix 1 for detailed set up of the circuit

diagrams of the valve system, which controlled the triggering of the solenoid

valves and attainment of desired 0 2 levels.

3.2.5 Rernoval of the Telemetry Unit

After completion of studies in each rat, the telemetry unit was explanted to

be re-used in the next animal. The animals were sacrificed using a lethal dose of

sodium pentobarbital (- 20 mg, i.p.). After the unit was removed. it was rinsed

with distilled water and placed in an enzyme-active powdered detergent (Terg-A-

Zyme, 1%) for about 2 hours. The unit was then thoroughly rinsed in distilled

water, left to dry and stored.

Figure 3.2.6. Schernatic diagram of hypoxia application in sleep. A pair of solenoid valves were designed to trigger the application of 100% N2 at sleep onset detected by the cornputer to decrease the 0 2 level quickly to 10% (threshold 1 = 12.5 % 02) and to maintain at this level until wake onset. A second pair of solenoid valves controlled the return to room air upon arousal from sleep. At wake onset. 100% 0 2 was administered to increase the 0 2 level quickly to 21 % and then this was maintained with room air during wakefulness (threshold 2 = 15.0% 02).

Schema of hypoxic stimuli applied exclusively during sleep

Wake

Sfeep

Hypoxia On Hypoxia Off

Room Air --------------------- -------------------4------________________________________________________________

threshold 2 --__-------_--_------ ---------------------------- O% '2 lhresh~ld 1

3.2.6 Anaiysis

The overall sleep-wake pattern under al1 of the experimental conditions was

determined from visual analyses of the polygraph records to facilitate later

corn parisons with the computer records. Wakefulness, non-rapid-eye-movement

(non-REM) and REM sleep were determined visually from the chart record using

standard EEG and €MG criteria (Chapter 2). From the chart records, sleep

efkiency (sleep time Irecording time) was determined in al1 rats for the five

different experimental conditions mentioned above. The total time spent in

wakefulness, NREM and REM sleep, the duration and frequency of wakefulness.

NREM and REM sleep periods, sleep latency (time required to fall asleep). the

number of arousals were determined for the main experimental conditions.

namely the room air-to- room air, hypoxia in sleep, and continuous hypoxia as

well as their respective recovery phases. The main frequencies of the EEG signal

(range: 0.5 to 30 Hz) as well as the amplitude of both the EEG and EMG activity

were also determined in these rats. The mean duration of hypoxia application in

both the sleep-reîated and continuous hypoxia conditions as well as the accuracy

of application during sleep were also determined. Finally, body temperature was

determined in IO-minute epochs throughout the studies. The average body

temperature in each phase of the study was also detemined.

3.2.7 Statistical Analysis

Al1 cornparisons were made using analysis of variance with repeated

measures (ANOVA) and considered statistically significant at pc0.05.

Bonferroni's corrected p values were used as post-hoc analysis to confirm specific

statistical changes. For two-way ANOVA, the h o factors were phase (stimulus

phase, 11 30-1430hrs and recovery phase 1430-1730 hrs) and condition (room air

- room air, room air - hypoxia or hypoxia - hypoxia). Sigmastat software (Jandel

Scientific, San Rafael, CA) was used for analysis. Ali Data are presented as

means 2 SEM unless otherwise indicated.

3.3 RESULTS

3.3.1 Accuracy of the solenoid-valve triggering system

Figure 3.3.1 shows a raw trace for application of hypoxia during sleep in a

freely behaving rat. A total of 689 stimuli were applied during sleep in al1 10 rats

over the 3-hour stimulus phase. On average, 69.0 k 6.9 hypoxic stimuli were

applied in each rat. Out of these stimuli, 95.0 + 1.6% were successfully applied

during sleep (81.6 + 2.0% in NREM and 13.3 + 2.3% in REM sleep), 2.4 r 1 .O %

were incorrectly applied in wakefulness and 2.8 2 1.1 % were incorrectly applied in

drowsiness (an intermediate state between light NREM sleep and wakefulness).

Finally, 94.9 ~r 1.9% of these stimuli were successfully removed upon arousal from

sleep, whereas 4.9 k 1.9% were incorrectly removed in sleep (3.8 r 2.0% in

NREM and 1 .O I 0.3% in REM) and 0.3 r 0.2% were removed in drowsiness. The

Figure 3.3.1. An example of application of hypoxia exclusively in sleep in a freely behaving rat. The first arrow shows the onset of NREM sleep as detected by the computer. After detection of two, six-second epochs of sleep. the solenoid valves are triggered by a voltage signal from the computer to decrease and maintain the O2 level at 10%. The second arrow shows the onset of wakefulness as detected by the computer (state panel). ARer detection of h o epochs of wakefulness. the solenoid valves are triggered to apply room air.

tirne spent in 10% O2 following the tnggering of the stimuli averaged 45.1 + 1.9

sec for NREM sleep and 58.8 t 4.8 sec for REM sleep.

3.3.2 Overall effects of hypoxia applied during sleep on sleep-Wake patterns

Figure 3.3.2 shows an example of the sleep-wake patterns in room air and

in hypoxia. during the stimulus (1 l3Ohrs-1430hrs) and recovery phases (1430hrs-

1730hrs) in one rat. Visual analysis of the data shows that during the application

of hypoxia there is an increase in the number of transitions to wakefulness and a

decrease in that of REM sleep. In the recovery phase. an observable increase in

the number of REM sleep episodes is observed.

3.3.3 Changes in Percentages of Sleep and Wakefulness

In order to distinguish the changes that occurred by application of hypoxia

in sleep, al1 measured variables were compared to room air to room air control

experiments (Condition 3) for each rat. Statistical analysis showed that there was

a significant effect of experimental condition (i.e. room air to room air Vs. room air

to hypoxia) on sleep-wake patterns. Post-hoc analyses (Bonferroni's method)

confined that the mean percentage of wakefulness was significantly increased

(t(9) = 4.0 p< 0.003) and REM sleep was significantly decreased (t(9) = 5.4. p<

0.0004) by application of hypoxia during sleep. However NREM sleep was less

affected (t(9)= 2.3, p< 0.046) and the effect was of bordedine statistical

significance (Figure 3.3.3A). During the recovery phase when the hypoxic stimuli

were removed, and the animals were exposed to room air to room air conditions.

Figure 3.3.2. An example of typical changes in sleep-wake states with application of room air (top panel) and hypoxia (bottom panel) in sleep in one rat. Note the decrease in the duration of NREM sleep and the decrease in the transitions to REM sleep during the application of hypoxia (stimulus phase). In the recovery phase after removal of hypoxia, note the noticeable decrease in the amount of wakefulness.

there as a significant decrease in wakefulness (t(9) = 2.9. p< 0.02) and a

significant increase in REM sleep (t(9) = 3.3, p< 0.009, Figure 3.3.3 B).

3.3.4 Changes in duration of sleep-wake episodes

To establish which parameters of the sleep-wake architecture were

affected by application of hypoxia during sleep, the duration of each episode, the

frequency of sleep-wake episodes as well as the total number of arousals (Le.

periods of wakefulness lasting between 3 tolO seconds) were determined in each

rat during exposure to both hypoxia and room air stimuli during sleep.

Statistical analysis showed that there was a significant effect of

experimental condition (i.e. room air to room air vs. room air to hypoxia) on sleep-

wake durations. Post-hoc analysis confirmed that the median du ration of

wakefulness episodes increased (t(9) = 3.5, p = 0.007) and the median duration of

NREM and REM sleep episodes decreased (t(9) = 6.3 and 4.3 respectively.

both p< 0.002) with application of hypoxia in sleep (Figure 3.3.4A). In the recovery

phase, there was no significant change in the duration of the sleep-wake states

(All p > 0.08. Figure 3.3.48).

Figure 3.3.3. Group mean (_+ SEM) data from 10 rats to show overall changes in wakefulness, NREM and NREM sleep as a result of hypoxia in sleep. The stars (') on top of bar graphs depict statistical significance. Note that the amount of wakefulness is significantly increased and REM sleep decreased with hypoxia (A). In the recovery phase (B), note that the amount of wakefulness is significantly decreased and REM increased. Changes observed in NREM sleep were of borderline statistical significance.

Application of Hypoxia During Sleep Disrupts Sleep-Wake Regulation

A: Stimulus Phase (1 730-1430 hrs) *

Wake

Room air during sleep 1-1 Hypoxia during sleep

6: Recovery Phase (1430-1730 hrs)

REM

Wake NREM REM

Figure 3.3.4. Group mean (t SEM) data from 10 rats to show changes in median duration of wakefulness, NREM and NREM sleep episodes as a result of hypoxia in sleep. The stars (') on top of bar graphs depict statistical significance. Note that the duration of wakefulness is significantly increased and NREM and REM sleep decreased with hypoxia (A). In the recovery phase (B), no significant changes were observed in sleep-wake states.

Mean duration of states (sec) Mean duration of states (sec)

3.3.5 Changes in frequency of sleep-wake episodes

Statistical analysis showed that there was a significant effect of experimental

condition (i.e. room air to room air vs. room air to hypoxia) on sleep-wake

frequency. Post-hoc analysis confirmed that application of hypoxia during sleep

increased the transitions to both wake and NREM sleep episodes (t(9) = 6.5 and

6.8 respectively, both p ~0.0001) and decreased transitions to REM sleep (t(9) =

3.1. p = 0.01, Figure 3.3.5A). Post-hoc analysis showed that in the recovery

phase, changes observed in the nurnber of sleep-wake episodes were of

bordedine significance (ail p > 0.04, Fig. 3.3.5 B).

3.3.6 Changes in the number of arousals

Statistical analysis revealed that the number of arousals were not effected

by application of hypoxia during sleep (p = 0.8, Figure 3.3.6).

Figure 3.3.5. Group mean (I SEM) data from 10 rats to show changes frequency of sleep-wake episodes as a result of hypoxia in sleep. The stars (*) on top of bar graphs depict statistical significance confirmed by Bonferroni's method. Note that the transitions to wakefulness and NREM sleep significantly increased and transitions to REM sleep decreased with hypoxia (A). In the recovery phase (B). no significant changes were observed in sleep-wa ke states.

Frequency of sleep-wake cycle is disrupted by application of hypoxia during sleep

30 1 A: Stimulus Phase

WAKE NREM

30 1 B: Recovery Phase

Room air during sleep [-1 Hypoxia during sleep

REM

WAKE NREM REM

3.3.7 Changes in EEG frequencies

Post-hoc analysis showed that application of hypoxia caused an increase

in the percentage of the EEG signal in the fast frequency bands, namely bl and

b2 (t(9) = 5.2 and 7.0 respectively. both p < 0.0005) and a decrease in the

percentage of the EEG signal in the slow frequency bands, namely d2 and d l ( t (9)

= 3.4 and 8.6 respectively, both p < 0.007) in NREM sleep. In REM sleep d l was

decreased (t(9) = 3.57. p = 0.006) and b2 was increased (t(9) = 4.4, p = 0.002.

Figure 3.3.7A). In the recovery phase, changes observed were of borderline

statistical significance( al1 p > 0.03, Figure 3.3.78).

3.3.8 Effects of control experiments on sleep-wake states

In order to ensure that the alterations in sleep architecture were due to t h e

application of hypoxia perse and not other variables. such as cage size and noise

disturbance produced by the triggering of the valves, the sleep-wake pattern

under those conditions were compared to room air to room air condition (sham

intervention) for both stimulus (1 130 hrs-1430 hrs) and recovery phase (1430 hrs-

1730 hrs) for each rat. Statistical analysis showed that there were no significant

differences in the percentage of sleep and wakefulness between the control

conditions during both the stimulus and recovery phase (al1 p > 0.250). Le. t h e

rats did not exhibit a change in their sleep-wake pattern with changes in their

environment (Figure 3.3.8).

Figure 3.3.6.. Group mean (I SEM) data from 10 rats to show effect of hypoxia on the number of arousals (i.e. wakefulness periods > 12 seconds). No significant changes were observed in the number of brief wake periods with hypoxia.

Application of hypoxia during sleep does not effect the number of arousals

(Wakefulness periods < 12 sec)

Room air in sleep f

L j Hypoxia in sleep

Stimulus Phase Recovery Phase

Figure 3.3.7. Group mean (k SEM) data from 10 rats to show effect of hypoxia during sleep on EEG frequencies. The stars (*) on top of bar graphs depict statistical significance. During the stimulus phase (A) in NREM sleep, significant decreases were observed in the low-frequency bandwidths, namely 6,, h2 and U and an increase was seen in the fast-frequency bandwidths, PI and p2. In REM sleep increases in 6, and a and an increase in Pz was obsewed. NO noticeable changes were observed in EEG frequencies in the recovery phase (B).

Effects of Hypoxia on EEG Frequencies in Wakefulness, non-REM and REM Sleep

WAKE NREM REM - ~ o o m air dunng sleep - . Hypoxia dunng sieep

* 40 A: Stimulus Phase - 35 .

* * * *

10 * * m -

40 : B: Recovery phase - 35

*

"J 15 ' 0 10 .

* O a - 1

Frequency Bands (0.5-30 Hz)

3.3.9 Sleep latencies in the control experiments

Sleep latencies defined as the period of time taken for the rats to fall asleep

(NREM) were measured after placement in experimental chamber. after start of

the stimulus phase and after start of the recovery phase in each rat for each

control condition. Statistical analyses showed that the sleep latencies were similar

in the control experiments when compared to sham experiment in each rat (AH

p>0.09).

3.3.10 Effects of continuous hypoxia on sleep-wake patterns

Application of hypoxia during sleep as well as wakefulness caused very

large changes in both sleep and wakefulness. Statistical analysis showed that

there was a significant effect of experimental condition (i.e. room air to room air

vs. chronic hypoxia) on sleep-wake patterns (p c 0.0001). Post-hoc analysis

confirmed that total percentage of wake episodes was significantly increased (t(9)

= 9.3, p = 0.00001) , NREM sleep was significantly decreased (t(9) =7.28 , p =

0.00005), and REM sleep was effectively abolished during application of

continuous hypoxia (t(9) = 8.1, p= 0.00002, Figure 3.3.1 OA). In the recovery

phase, the percentage of wakefulness was decreased and those of non-REM and

REM sleep were increased in cornparison to the stimulus phase, but no significant

change was observed in the sleep-wake pattern when cornpared to sham

experiments in the recovery phase (al1 p > 0.2, Figure 3.3.1 OB).

Figure 3.3.8. Group mean (+ SEM) data from 10 rats to show total percentage of sleep and wake episodes in the control experiments. The stars (*) on top of bar graphs depict statistical significance. No changes in sleep-wake states were observed between the control experiments, suggesting that cage size and the noise produced by tnggering of the valves do not disrupt sleep-wake states.

Total time spent in sleep and wakefulness are simillar in the three control conditions

50 ] A: Stimulus Phase

45 1 -r T

Wake

50 1 B: Recovery Phase

Home cage, room air 1-1 Chamber, room air

Charnber, room air

NREM REM

Wake NREM REM

State

Figure 3.3.9. Group mean (I SEM) data from 10 rats to show sleep latencies in the control experiments. Latency #1: time to fail a sleep after placement in charnber. Latency #2: time to fall asleep in stimulus phase. Latency #3: time to faIl asleep in recovery phase. Sleep latencies were similar across al1 control experiments. further suggesting that cage size and the noise produced by triggering of the valves do not disrupt sleep-wake states.

Sleep latency in the three control conditions

Homecage, room air 11 Charnber, room air

Charnber, switches to room air

Latency #1 Latency #2 Latency #3

3.3.1 1 Effects of hypoxia on core body temperature

There was a significant effect of experimental conditions (i.e. room air to

room air vs. room air to hypoxia vs.. hypoxia to hypoxia) on sleep-wake patterns

(F(1 , I O ) = 62.7, p < 0.0001). with post-hoc analysis showing that hypoxia applied

during sleep significantly decreased core body temperature (t(9) = 5. 8, p =

0.0003) but the decline in body temperature was more pronounced with

continuous hypoxia (t(6) = 10.3, p = 0.00005) . In the recovery phase, the body

temperature increased to normal levels observed in room air breathing (al1 p >

0.06, Figure 3.3.1 1 ). In hypoxia to hypoxia experiments, no temperature data was

recorded in three rats and as such the data presented for those experiments are

for 7 rats only.

A gradua! decrease in body temperature was observed as a result of both

intermittent and chronic hypoxia, which returned to baseline levels in the recovery

phase (figure 3.12). The retum to baseline levels in the recovery phase was more

rapid afier the room air to hypoxia conditions ( - 5 min) whereas, following

continuous hypoxia, it took approximately 75 min for body temperature to reach

baseline levels (Figure 3.3.1 2).

Figure 3.3.10. Group mean (f SEM) data from 10 rats to show the effects of continuous application of hypoxia on sleep-wake states. The stars (*) on top of bar graphs depict statistical significance. During the application of hypoxia (A), the percentage of wakefulness was significantly increased and NREM and REM were decreased. In the recovery phase (B), no change in sleep-wake states was observed.

Application of Chronic Hypoxia Disrupts Sleep-Wake Reg ulation

'O0 1 A: Stimulus Phase (1 l3O-'l43O hrç)

Wake

- Room air during wakefulness and sleep 1-1 Hypoxia during wakefulness and sleep

NREM REM

1 B: Recovery Phase (1430-1 730 hrs)

Wake NREM REM

Figure 3.3.1 1. Group mean (k SEM) data to show the effects of sleep-related and continuous hypoxia on core body temperature. The stars (*) on top of bar graphs depict statistical significance. Both sleep-related and chronic hypoxia significantly decreased core body temperature. However, continuous hypoxia caused a more profound decrease in core body temperature. In the recovery phase. core body temperature retumed to baseline conditions and no significant change in between the conditions was observed.

Core body temperature decreases with application of hypoxia

Room Air during sleep and wakefulness i Hypoxia during sleep

Hypoxia during sleep and wakefulness

Stimulus Recovery

Figure 3.3.12. Group data to show the gradua1 effects of sleep-related and continuous hypoxia on core body temperature across the stimulus and recovery phases. Each point on the graph represents the mean (I SEM) at 10-minute intervals. Both sleep-related and chronic hypoxia significantly decreased core body temperature. However, continuous hypoxia caused a more profound decrease in core body temperature. In the recovery phase, core body temperature retu med to baseline almost immediately aft er removal of sleep- related hypoxia. Aft er removal of continuous hypoxia body temperature began to increase immediately however due to the drastic decrease in core temperature. it took longer to reach baseline levels.

Application of intermittent and chronic hypoxia decrease core body temperature

1

38 1 Stimulus Phase 1 Recovery Phase

Time (hours)

+ Room air during wake and sfeep + Hypoxia during sleep t Hypoxia during wake and sleep

3.4 DISCUSSION

This study investigated the effects of hypoxia applied exclusively during sleep

on sleep-wake regulatian. The uniqueness of the present study was two-fold:

Firstly. by applying hypoxia only during sleep. it provided a novel model to

investigate the disru ptive effects of sleep-related h ypoxemia that is normally

associated with sleep-disordered breathing on sleep-wake patterns. In previous

studies, the disrupting effects of hypoxia on sleep-wake states have been

investigated without controlling for the duration of the stimuli or the state during

which the hypoxia is applied. Our approach therefore offers a more accurate and

clinically relevant model in which to study the effects of sleeprelated hypoxia on

sleep-wake patterns. Secondly, the study provides new insights into sleep-wake

homeostasis by examining the sleep-wake organisation in the recovery period,

following the complete removal of the hypoxic stimuli.

3.4.1 Effects of hypoxia during sleep on sleep-wake patterns

3.4.1 .l Stimulus Phase

To date the effects of hypoxia applied only during sleep. as occurs in

disorders such as OSA have not been examined. As such. it is difficult to fully

compare our results to previous findings in which hypoxia has been applied

without controlling for sleep-wake states.

Results from Our experiments showed that both sleep-related (i.e. hypoxia

applied only during sleep) and chronic (i.e., hypoxia applied during sleep as well

as wakefulness) application of 10% O2 caused an increase in wakefulness and a

decrease in REM sleep. These results are in good agreement with eariier studies

which showed that application of chronic inspired 12.5% O2 and 10% O2 hypoxia

suppresses REM sleep and increases wakefulness (Laszy et al.. 1990;

Pappenheirner, 1977; Megirian et al., 1980; Ryan et al.. 1982; Baker et al., 1984;

Hale et al.. 1984; Pollard et al., 1987). In some of these studies reductions in

NREM sleep were also observed under the condition of chronic inspired 10% O2

(Pappenheimer, 1977; Megirian et al., 1980; Ryan et al., 1982). In Our study.

application of chronic hypoxia also profoundly decreased NREM sleep.

Once alterations in the overall sleep-wake organisation were observed in

the present study, the different components of the sleep-wake cycle were

examined to determine which parameter(s) contributed to the overall change (i.e.

the duration of each episode or frequency of each state).

Although the change observed in the overall percentage of NREM sleep

during the application of sleep-related hypoxia were of borderline statistical

significance, further investigation of frequency and duration of NREM sleep

episodes revealed large changes in NREM organisation (Figures 3.3.4 and 3.3.5).

We found that sleep-related hypoxia caused the duration of wakefulness episodes

to increase (40%) and NREM and REM sleep episodes to decrease (-4O0/0).

These results are in accordance with those of previous studies in continuous 12.5

to 10% Cl2 (Pappenheimer. 1977; Ryan et al., 1982; Laszy et al., 1990). Ryan et

al. (1 982.1 983) observed that bilateral sectioning of the carotid sinus nerve

restored the duration of the sleep-wake episodes to normal levels despite severe

hypoxia. Since peripheral chemoreceptors have been shown to project to arousal

centers in the brain (e.g.. reticular activating systems), it is therefore possible that

hypoxia sensed by the carotid bodies may act as an arousing stimulus and

promote wakefulness.

In our study, administration of 10% hypoxia during sleep also caused the

frequency of wakefulness and NREM sleep episodes to increase by about 28%

and REM sleep to decrease by 40%. Ryan et al. (1982), Laszy et aL(1990). and

Pappenheimer (1977) observed similar changes in the sleep-wake frequency

during continuous exposure to 12.5 to 10% hypoxia. Likewise. Hale et al. (1 984)

and Pollard et al. (1987) found that continuous administration of 15.0% hypoxia

caused observable alterations in sleep-wake patterns due to changes in the

frequency of sleep-wake states rather than their duration. In our own study (10%

02), the duration of al1 sleep-wake episodes was more subject to change than was

the frequency of these episodes. it is therefore, likely that mild levels of hypoxia

(15%) tend to disrupt only the frequency of state transitions, whereas severe

levels of hypoxia (10%). as utilised in Our study, have an additional profound

impact on the duration of sleep-wake states (Laszy et al., 1990).

The increase in the duration and frequency of wakefulness episodes rnay

account for the changes observed in the duration and frequency of NREM and

REM sleep episodes. Firstly, according to the homeostatic model of sleep

regulation, (Benington and Heller, 1 994), NREM sleep du ration is positively

correlated with previous REM sleep episode duration. indicating that brief REM

sleep episodes lead to brief NREM episodes, as a result of premature incomplete

discharge of REM sleep propensity. Following this reasoning, if REM sleep

episodes are interrupted prematurely by awakenings from sleep due to hypoxia,

then brief episodes of NREM sleep will follow. as was observed in Our study

(Figure 3.3.5). For example, REM duration was decreased by 4O0/0 and NREM

duration was also decreased by 40%. However, if REM sleep is disrupted

prematurely by arousals. transitions to REM sleep also increase in order to cause

complete discharge of REM sleep expression, which was not observed in Our

study. On the other hand. if NREM sleep episodes are terminated prematurely

due to awakenings from sleep. the duration of NREM sleep episodes decrease

and as well REM sleep is not able to fully accumulate and as such the transitions

into REM sleep also decrease. The latter reasoning is more consistent with Our

findings. suggesting that the disniptions seen in REM sleep organization may be

as a result of and secondary to NREM sleep disniptions.

We found no statistically significant increase in the number of arousals (Le.

waking episodes lasting between 3 to 12 seconds) with sleep-related hypoxia.

Continuous hypoxia has been shown to increase the number of arousals (Laszy et

al., IWO. Pappenheimer 1976. Megirian et al., 1982). However, in Our study the

total percentage of wakefulness was increased compared to normoxic conditions.

It is reasonable to conclude that sleep-related hypoxia caused prolonged

awakenings from sleep (Le., once awake, the rats remained awake). It has been

suggested that the peripheral chemoreceptors sensing the decrease in O2 levels

send signals to areas responsible for arousals such as the reticular activating

system thereby causing awakenings from sleep. Guyenet et al. (1 993). reported

that the noradrenergic cells of the locus coeruleus in the pons that are normally

active during wakefulness are also activated by the stimulation of the peripheral

chemoreceptors via hypoxia (1 2% Oz) and are not activated after the sectioning of

the carotid sinus nerve. Medullary centers from which the ventilatory rhythm is

generated including the rostral ventrolateral medulla, may also send signals to

arousal centers in the brain, in response to hypoxia-induced hypetventilation. To

date, the general consensus has been that arousal from sleep is dependent on

the level of inspiratory effort, suggesting that arousal from sleep requires a rnulti-

component mechanism, utilising both the chemoreceptors as well as the

mechanoreceptor drives (Berry et al.. 1997). In our study, it is possible that

hypoxia promoted awakenings from sleep as well as prevention of falling asleep

by causing prolonged awakenings from sleep.

3.4.1.2 Recovery Phase

In the context of this study, recovery phase refers to the sleep-wake

organisation in the period following the complete removal of the hypoxic stimuli.

The recovery phase is particulariy important in the investigation of sleep

homeostasis. As discussed previously, chronic as well as sleep-related hypoxia

decreases the overall amount of sleep. If sleep is homeostatically controlled there

should be a compensatory increase in the amount of sleep following the removal

of hypoxia. however to Our knowledge this has not been investigated following

sleep-related hypoxia. Day-tirne sleepiness has been well documented in

individuals suffering from OSA. Thus, it is of great importance to investigate the

sleep-wake structures after complete removal of hypoxic stimuli (chronic or

intermittent). We found that following intermittent hypoxia, in the recovery phase,

there was a significant decrease in the total percentage of wakefulness (30%) and

a significant increase in that of REM sleep (25%), i.e., in the opposite direction to

that observed in the stimulus phase. These results were in accordance with

results from sleep deprivation studies after the removal of sleep deprivation

(Dement. 1960; Sampson. 1965; Dement et al., 1966; Agnew et al., 1964; Agnew

et al., 1967). Our results were also in agreement with Benington et al. (1994).

who showed that selective REM sleep deprivation, leads to a rebound increase in

REM sleep during the recovery phase. We did not observe an increase in NREM

sleep in the recovery phase, although as described above, there were changes in

both frequency and duration of this state during the stimulus phase. Borbely et al.

(1 982), and Feinberg et al. (1 987) have both shown inhibitory interaction between

NREM and REM sleep pressures, such that the deprivation of one state leads to

the increase in drive of that particular state which in turn suppresses the other

state. In out= study, REM sleep was affected far more than NREM sleep. and it is

therefore plausible that the drive for REM sleep by far exceeded that of NREM

sleep and as such led to the suppression of NREM sleep expression in the

recovery phase. In accordance to our findings, Benington et al. (1 994) found that

selective REM sleep deprivation lead to dramatic and progressive increase in the

frequency of attempts to enter REM sleep and suppression of NREM sleep, which

was followed by a cornpensatory increase in REM sleep in the recovery phase.

It has been well documented that "sleep need" or NREM sleep intensity is

high at the beginning of the sleep cycle and gradually decreases over the

subsequent cycles. As well, it has been shown that NREM drive is gradually built

up during prior wake episodes (Borbely et al., 1981 ; Webb et al., 1971 ; Blake et

al., 1937; Williams et al., 1964; Dernent et al., 1957) and that it is discharged over

the periods of subsequent NREM sleep. As such, if NREM sleep is prevented in

any way, the pressure accumulates over the deprivation cycle and is hence

released after the removal of the deprivation (Borbely et al., 1992; Dijk et al.,

1991). Although significant changes in both frequency and duration of NREM

sleep episodes were observed dunng the stimulus phase of Our study, we did not

observe any significant changes in those parameters during the recovery phase.

This again may be explained by the greater effect of hypoxia on REM sleep. as

compared to NREM sleep. which led to the ovewhelming increase seen in REM

sleep drive causing suppression of NREM sleep.

According to Benington et al. (1994), REM sleep timing, or the frequency of

REM sleep episodes, is controlled by the accumulation of REM sleep propensity

in NREM sleep, and since NREM sleep was disrupted by hypoxia due to

awakenings (as seen by an increase in repeated short episodes of NREM sleep in

the stimulus phase) REM sleep propensity was not allowed to accumulate in

NREM sleep and was not fully expressed, and as such REM sleep was greatly

enhanced in the recovery phase.

Although we observed increases in both frequency and duration of wake

episodes and decreases in those of REM sleep in the stimulus phase. no obvious

changes were observed in those parameters in the recovery phase, although the

overall percentage of wakefulness decreased and that of REM increased. as

mentioned previously. This was an unexpected result, since the two parameters

should describe the overall change. It is possible that the changes that occurred

in frequency and duration did not follow the same pattern in al1 the rats and as a

result evened out the changes, so that no consistent alteration in frequency or

du ration could be seen.

In the experiments in which continuous hypoxia was applied, no

compensatory changes were observed in any sleep-wake parameters upon

removal of continuous hypoxia, despite a significant increase in total percentage

of wakefulness and decreases in both NREM and REM sleep during the

application of hypoxia. It may be possible that hypoxia causes phase delays in

sleep and wake cycles in the rats, which extends through the rebound phase,

causing sleep to be shifted to another phase of the circadian rhythm. Jarsky and

Stephenson (2000) have shown that application of hypoxic pulses (8% 0 2 ) during

the resting phase in hamsters, cause a phase delay corresponding to a decrease

in activity as well as body temperature and metabolism. Since these investigators

have not measured sleep-wake states, it not possible to directly infer Our results

frorn their study. However, in Our study, it rnay be likely that rebound sleep was

shifted to a period following the 3-hr interval allocated to measure the recovery

sleep and as such we were not able to detect the changes in sleep. if any.

3.4.2 Effects of sleep-related hypoxia on EEG parameters

Quantitative EEG analysis conducted in human subjects have shown that

hypobaric hypoxia causes slow activity (0.1-7.7 Hz) to increase and fast alpha

activity (7.7-12.4 Hz) to decrease during wakefulness, (Berger, 1934; Davis et al..

1942; Gibbs et al., 1942; Mayer et al., 1960; Kraaier et al., 1988b; Van der Worp

et al., 1991). This increase in the delta and theta activity as well as the decrease

in the alpha activity has also been demonstrated in visually-scored EEG traces in

studies conducted in low oxygen I low pressure chambers, and rebreathing

experiments (Davis et al., 1938; Theibauld et al., 1983; Zhonguyan et al., 1983;

Ginsberg, 1977). These studies were mainly conducted at O2 levels that

decreased the Sa02 ta 70% or lower. Kraaier et al. (1987) and Van der Worp et

al. (1991) observed that changes in the EEG frequencies occurred once the Sa02

dropped below 70%. In our study, where Oz saturation was thought to decrease to

about 80-90% with the application of 10% O2 (see Methods), we found a decrease

in slow frequencies and an increase in fast frequencies during the application of

the hypoxic stimuli. Our results are within reason, since it is possible that the

application of hypoxia during sleep disturbs sleep maintenance by acting as an

arousing stimulus, leading to a decrease in the amount of sleep-related low

frequency slow wave sleep and increasing the wake-related high-frequency

activity.

3.4.3 Effects of hypoxia on core body temperature

Hypoxia has been shown to decrease core body temperature, which may

considerably contribute to the disruption of sleep-wake organisation (Sakaguchi et

al., 1979; Schmidek et al., 1973; Valatx et al., 1973; Hale et al., 1984; Laszy et al..

1990; Anholm et al., 1992; Mortola et al.. 2000). In our study, we also found a

statistically significant

sleep-related hypoxia

gradua1 decline

of about 0.5OC.

in core body temperature in

Within minutes after retum

response to

to complete

normoxia, body temperature increased to normoxic levels. The decrease in body

temperature was consistently beyond that of normoxia across the stimulus phase.

Mortola et al. (2000) showed that hypoxia does not affect the circadian period but

instead acts at the hypothalamic centres for thermoregulation. Therefore, the

decrease in core body temperature rnay be due to alterations in the

thermoregulatory centre in the pre-optic area of the hypothalamus (Refinetti et al..

1992).

It has been shown that hypoxia also depresses thermogenesis via both

shivering and non-shivering mechanisms (Alexander. 1979; Gautier et al.. 1989;

Gautier et al.. 1991) by decreasing the set point of thermoregulation (Mortola et

al.. 1995). However, shivering was not considered to be a contributor to the

thermogenesis in this study. since ambient temperature was kept above the

threshold for shivering in normoxia around 22OC (Bnick et al., 1966).

The circadian phase of the temperature rhythm follows that of REM sleep.

As such. it is low at the beginning of sleep phase and increases over the sleep

period so that it is at its highest by the end of the sleep period, as is the case with

REM sleep. Thus, changes in core body temperature have been closely linked to

modulations in REM sleep structure (Szymusiak and Satinoff. 1981). In our study.

we found a profound decrease in total REM sleep coinciding with a similar

decrease in core body temperature. In agreement with previous data.

(Parmeggiani, 1987; Buguet et. al., 1979), we suggest that REM sleep may be

to hypoxia. Therefore. the decrease obseived in REM sleep.

to a decrease in body temperature as a result of hypoxia.

particularly sensitive

may in part be due

!

However, NREM and REM sleep interruptions have also been shown to decrease

body temperature in humans (Sasaki et al., 1993). Thus it seems that hypoxia-

related sleep disturbance and body temperature may be interrelated. An increase

in body temperature has been shown to enhance slow wave activity in NREM

sleep (Home, 1988; Shapiro, 1989; Horne, 1991; Home. 1992). In Our study.

during the recovery phase, no increase in body temperature above the baseline.

and no increase in NREM sleep were obsewed. These results provide supportive

evidence that changes in sleep organisation and body temperature as a result of

hypoxia are complementary to one another. However, further experiments need to

be performed to explore these relationships.

4.1 CONCLUSIONS

We have explored the effects of sleep-related hypoxia (e.g.. as

encountered in OSA) on sleep-wake organisation, by applying hypoxia exclusively

during sleep in rats.

In order to accurately apply hypoxia in sleep, we have developed and

validated a computerized sleep-detection system for the rat. Our automated on-

line method of sleep-wake detection proved to be highly reliable in detecting

sleep-wake states and in many ways superior to other such systems. Firstly, it

uses a simple algorithm utilising parameters of EEG and EMG activities that are

easily measurable. Secondly, the algorithm is robust in detecting sleep-wake

states continuously and remains stable for at least two days. Thirdly, the

accuracy of sleep-wake detection is independent of the lighting condition. Finally,

the system proved to be highly accurate in detecting equivocal periods of

wakefulness, NREM and REM sleep, and as such was utilised as an integral part

of a system to apply hypoxia exclusively in sleep (chapter 3). These attributes

make this system applicable in distinguishing sleep and wakefulness. Other

systems have had limitations due to their lack of long-term validation (> 8 hrs) for

detection of sleep-wake states (Riugt et al., 1989; Witting et al., 1996; Grant et al..

1995; Van Gelder et al., 1991), suitability of detection during different lighting

conditions (Ruigt et al., 1989; Grant et al., 1995; Van Gelder et al., 1991), and

inter-subject stability of algorithm (Ruigt et al., 1989; Witting et al., 1996; Grant et

al., 1995). Furthemore, other algorithms have not always been developed as a

result of systematic construction based on a range of different bandwidths

encountered in the EEG frequencies and the related EEG and EMG amplitudes,

but by measuring only a few pre-selected parameters such as delta and beta as

well as EMG activities (Roncagliolo and Vivaldi, 1990; Morrow and Casey. 1986;

Kohn et al., 1974; Winson et al., 1976; Johns et al., 1977; Neuhaus and Borbely,

1978; Mendelson et al., 1980; Bergmann et al., 1987). Finally, many such

previous systems were less accurate in the detection of al1 equivocal periods of

sleep-wake states as compared with our system (Ruigt et al., 1989; Witting et al..

1996; Wauquier et al., 1978; Grant et al., 1995; Van Gelder et al., 1991 ; Van

Gelder et al., 1991; Neuhaus and Borbely, 1978; Van Luijtelaar and Coenen.

1984; Bergman et al., 1987; Gandolfo et al., 1988; Goeller and Sinton, 1989;

Neckelmann et al., 1994; Witting et al., 1996). Our findings support the hypothesis

that parameters detected by frequency and amplitude analysis of the EEG and

EMG can distinguish sleep-wake states on-line irrespective of light-dark cycle.

To our knowledge this study is the first to examine the independent effects

of sleep-related hypoxia on sleep-regulation that approximates sleep-related

hypoxia in OSA before and after treatment. Hypoxia as an important contributor to

fragmented sleep encountered in OSA has been demonstrated in our study.

Application of hypoxia causes disruption of sleep-wake organisation due to

repeated and prolonged awakenings, which is thought to be the initial defense

mechanism in response to a potential life-threatening stimulus. Application of

transient, sleeprelated hypoxia resulted in significant decreases in REM sleep

and increases in wakefulness compared to room air breathing, confining Our

hypothesis. The results from this study also showed that sleep-related hypoxia

disrupts REM sleep more profoundly than NREM sleep and I have hypothesized

that the disruptions observed in REM sleep are secondary to those of NREM

sleep (chapter 3).

The application of sleep-related hypoxia also accompanied an observable

decrease in body temperature. The influence of sleep-related hypoxia on REM

sleep seemed to be related to a concomitant decrease in core body temperature

in response to hypoxia, suggesting that the hypoxia-related sleep disturbance and

body temperature may be interrelated (chapter 3).

The changes observed in sleep-wake structure as well as in body

temperature rnay be related to the levels of arginine vasopressin (AVP) released

in response to hypoxia. It has been shown that application of hypoxia results in

increased plasma levels of AVP in sheep, dogç and cats (Alexander et al., 1972;

Rurak, 1978; Forsling and Ullmann. 1974; Walker, 1986). as well as a

corresponding increase in the AVP levels in the cerebrospinal fluid of both sheep

and dogs (Stark et al.. 1984, 1985; Wang et a., 1984). In addition, intravenous

injection of AVP has been shown to reduce REM sleep in a dose-dependent

manner in humans (Born et al., 1992). Of interest is the circadian rhythm

associated with the release of AVP from the SCN. Tominaga et al. (1992) have

shown that the AVP content in the SCN of adult rats peaks at the beginning of the

light phase (i.e., resting phase), and progressively decreases such that its trough

occurs at the end of the light phase. These observations were also reported in 12-

day old rat pups (Isobe et al., 1995), suggesting an eariy onset of circadian

modulation of AVP concentrations. As described previously (chapter 3). REM

sleep expression is low at the beginning of the light cycle, and is highest towards

the end of the light cycle in rats. Based on these observations, it may be argued

that the presence of high levels of AVP at the beginning of the sleep cycle.

suppreçses REM sleep and its subsequent decline towards the end of the sleep

cycle. allows for REM sleep expression to increase, hence. AVP may have a role

in REM sleep regulation. As a result, I speculate that the increase in AVP levels

in response to hypoxia may in part explain the observed reductions in REM sleep

expression in our study during both intermittent and continuous application of

h ypoxia.

AVP also plays a thermoregulatory action in the central newous system

and its antipyretic (i.e., fever reducing) actions have been extensively examined in

both rats and humans (Pittman et al., 1998; Naylor et al., 1986a; Veale et al.,

1984; Naylor et al., l986b; Oluyomi and Hart, 1992; Steiner et al., 1998). Since

central administration of AVP has been shown to cause hypothermia. it is

therefore plausible that hypoxia-induced AVP release mediates the decrease in

body temperature observed in Our study.

AVP is also an antidiuretic hormone involved in renal water conservation

leading to an increase in intravascular volume and thus is implicated in

hypertension. Hypertension is a wmmon feature amongst the clinical population

of OSA. Therefore, a hypoxia-induced increase in AVP levels during OSA may be

a mediator of increased blood pressure.

In our study. following the complete removal of sleep-related hypoxia, there

were significant compensatory increases in REM sleep (REM rebound) and

decreases in wakefulness compared to room air breathing, confirming our

hypothesis. Since adenosine iç believed to be a sleep-promoting substance,

which accumulates during prolonged wakefulness. it may be linked to the increase

in total percentage of sleep observed during the rebound phase. It is therefore

possible that the build-up of endogenous adenosine during the stimulus phase of

the study led to the increase in sleep need, as such rnediating the homeostatic

control of sleep expression observed in the recovery phase (Benington and Heller,

1995). Accordingly, the compensatory changes observed in both wakefulness and

REM sleep as a result of sleep-related hypoxia rnay have been due to sleep

reduction caused by hypoxia and not hypoxia per se.

4.2 Technical Limitations

The present study is associated with at least four limitations. This study

focused on the independent effects of sleep-related hypoxia as related to

obstructive sleep apnea. Breathing a hypoxic gas mixture as in this study leads to

hyperventilation and respiratory alkalosis. However, in this study we did not

compensate for the hypoxia-induced hypocapnia. The concem would be that the

hypocapnia could have decreased the cerebral blood flow (Payen et al., 2000)

thereby affecting sleep. However, previous studies have shown that an attempt to

alleviate the hyperventilation-induced (1 0% inspired 02) hypocapnia by addition of

4 Oh COz to the inspired gas does not prevent the sleep-wake disturbance

associated with hypoxia (Ryan et al., 1983). Nevertheless, there remains a

possibility that such an effect may have had some contributions to the sleep

disturbance I obse~ed. As well, obstructive sleep apnea is associated with

hypoxia and hypercapnia, rather than hypoxia alone. As such it is possible that

the combination of these stimuli may exert a more severe effect on sleep-wake

regulation than 1 observed with hypoxia alone. We did not rneasure artenal O,

levels but relied on previous studies to compare Our study to these measurements

(Lewis et al., 1973). We chose not to make arterial blood gas measurements to

avoid potential confounding effects of tether restraint on sleep-wake organization.

Finally, in order to separate the independent effects of hypoxia on sleep neuronal

mechanisms from arousal systems, a non-chemical stimulus (e.g. noise) could

have been used to produce arousals from sleep. Cornparison of the noise-induced

slee p dis ru ption wit h h ypoxia-induced sleep dis ru ption would provide a more clear

interpretation of the specific role of hypoxia on the observed sleep disturbances.

The observations made in this study support the notion that transient

hypoxia applied exclusively in sleep, as occurs in cornmon sleep-related breathing

disorders (e.g., OSA), results in significant disturbances in sleep-wake

mechanisms. Hypoxia mediates AVP release. which results in a decline in body

temperature and reductions of REM sleep expression. I speculate that the

profound effects of hypoxia on REM sleep mechanisms may underlie the adverse

consequences of sleep-related respiratory disorders that produce intermittent

hypoxia. Such effects rnay contribute to the excessive day-time sleepiness and

impaired memory and work performance in the OSA patients who regularly

expet-ience such hypoxic episodes.

4.3 Future Directions

The system that we have developed can be used in a variety of different

studies to elucidate the state-specific application of various stimuli on different

physiological mechanisms. For instance, it can be used to apply sleep-related

stimuli associated with obstructive sleep apnea such as hypoxia and hypercapnia,

to investiga te rnechanisms associated with sleep-wa ke dis ru ptions and

cardiovascular complications.

In this study we have shown the profound effects of sleep-related hypoxia

on REM sleep regulation. REM sleep is thought to be involved in processes such

as learning and memory consolidation (Nadel et al., 2000). prevention of this state

using our system (e.g., via noise-induced REM disruption) may provide us with a

tool to study the specific effects on memory and learning, which can be

compromised in the clinical population of OSA.

In this thesis I have speculated that the obsewed attenuation in REM sleep

as well as the decrease in body temperature in response to sleep-related hypoxia

rnay be mediated by the systemic and central release of AVP in response to

hyopxia (Alexander et al., 1972; Walker et al., 1986). lntravenous injection of

AVP has been shown to reduce REM sleep (Born et al., 1992) and its central

administration has been shown to cause hypothemia (Pittman et al., 1998; Naylor

et al., 1986). As well, AVP is an antidiuretic hormone and as such is implicated in

mediating hypertension (Ganong. 1995). As a result I have speculated that a

hypoxia-induced increase in AVP levels in response to OSA-related hypoxia may

be a mediator of the OSA-induced increase in blood pressure (Brooks et al..

1997). The system that I have developed may be utilised in future studies to

induce OSA-induced. sleep-dependent hypoxia and the concomitants changes in

systemic and central AVP levels may be detemined to explore its implication in

OSA-induced hypertension.

Also, insights into the neuronal sleep-wake mechanisms affected by OSA-

related stimuli can be gained. For instance. adenosine is released into the

extracellular cerebral compartrnents in response to both hypoxia and prolonged

wakefulness (White and Hoehn, 1991). Its role in the prevention of sleep in

hypoxia and promotion of sleep after prolonged wakefulness can be explored by

microdialysis of its antagonist under such state-specific circumstances. Finally,

since the synthesis of several state-specific neurotransmitters are Oz-dependent,

neurotransmittet meta bolism may be reduced in hypoxia (Gi bson and Duffy . 1981). Specifically, hypoxia has been shown to cause impaired synthesis of

acetylcholine, involved in the maintenance of an activated cortex. both during

wakefulness and REM sleep. Augmentation and 1 or inhibition of this

neurotransmitter in hypoxia. may provide evidence for its role in promotion of

wakefulness and inhibition of REM sleep in hypoxia.

REFERENCES

Agnew HW, Webb WB, Williams RL. The effects of stage four sleep deprivation. Electrbenceph Clin Neurohysiol. 17: 68-70, 1964.

Agnew HW Jr, Webb WB, Williams RL. Cornparison of stage four and 1-REM sleep deprivation. Perceptuai and Motor Skills. 24:851-858. 1 967.

Ahlsen G, Lo FS. Pojection of brain stem neurons to the perigeniculate nucleus and the lateral genuculate nucleus in the cat. Brain Res. 234: 454-458, 1982.

Airaksinen MS, Panula P. The histarninergic systern in the cyinea' pig central nervous system:An immunocytochemical mapping study using an antiserum against histamine. J. Comp. Neurol. 237:163-186, 1988.

Akerstedt T, Gillberg M. Sleep duration and the power spectral density of the EEG. Electroencephalogr Clin Neurophysioi. 64: 1 1 9-1 22. 1 986.

Alexander DP, Forsling ML, Martin MJ, Nixon DA, Ratcliffe JG, Redstone O, Tunbridge D. The effect of materna1 hypoxia on fetal pituitory hormone release in the sheep. Biol. Neonate. 21 : 21 9-228, 1972.

Arch JRS, Newsholme EA. The control of metabolism and the hormonal role of adenosine. Essays in Biochem. 14: 82-1 23. 1978.

Asanuma C. Neurophysiological actions of 5-Hydroxy-tryptamine in the vertebrate nervous system. Prog. Neurobiol. 35: 451 -468. 1989.

Asanuma C, Porter LL. Light and electron microscopic evidence for a GABAergic projection from the caudal basal forebrain to the thalamic reticular nucleus in rats. J. Comp. Neurol. 302: 1 59-1 72, 1990.

Ashcroft SJH, Ashcroft FM. Properties and functions of ATP-sensitive K- channels. Cell. Signalling. 2: 197-21 4, 1990.

Bakehe M, Miramand JL, Chambille B. Gaultier C, and Escourrou P. Cardiovascular changes during acute episodic repetitive hypoxic and hypercapnic breathing in rats. Eur. Respir. J. 8: 1675-1 680, 1 995.

Baker TL, McGinty DJ. Sleep-waking patterns in hypoxic kittens. Dev Psychobiol. 1 2(6): 56 1 -575, 1 979.

Beersma DGM, Dijk DJ, Blok CGH, Everhardus 1. REM sleep deprivation during 5 hours leads to an immediate REM sleep rebound and to suppression of non- REM sleep intensity. Electroencephalogr. Clin. Neurophysiol. 76: 1 14-1 22, 1 990.

Belousov AB, Godfraind J-M, Krnjevic K. Interna1 ca2+ stores involved in anoxic responses of rat hippocampal neurons. J. Physiol (Land). 486: 547-556. 1995.

Ben-Ari Y. Hippocampal potassium ATP channels and anoxia: presynaptic, postsynaptic or both. Trend Neurosci. 1 3: 409-4 10, 1990.

Benington JH, Trachsel L, Edgar DM, Heller HC, Dement WC. REM-sleep expression increases progressively dunng recovery sleep in rats with lesions of the suprachiasmatic nuclei. Sleep Res. 20:21, 199 1 .

Benington JH, Woudenberg MC, Heller HC. REM-sleep propensity accumulates during 2-hr REM-sleep deprivation in the rest period in rats. Neurosci Lett. 180: 76-80, 1994.

Benington JH, Heller HC. Restoration of brain energy metabolism as the function of sleep. Prog. Neurobiol. 45: 347-360, 1995.

Bennett LS, Stradling JR, Davies RJ. A behavioural test to assess daytime sleepiness in obstructive sleep apnea. J. Sleep Res. 6(2): 142-5, 1 997. Benoit O, Foret J, Bouard G, Merle B, Landau J, Marc ME. Habitual sleep length and patterns of recovery sleep after 24 hour and 36 hour sleep deprivation. Electroencephalogr. C 36 )physiol. 50:477-485. 1980.

Berger IJ, Oswald 1. Effects of sleep deprivation on behaviour subsequent sleep and dreaming. J Ment Sci. 1 O8:457-465, 1962.

Berger H. Ueber das electroenkephalogramm des menschen. IX. Arch. Psychiat. Nervenkr. 102: 538-557, 1934.

Bergmann BM, Winter JB, Rosenberg RS, Rechtschaffen A. NREM sleep with low-voltage EEG in the rat. Sleep. 10: 1-1 1, 1987.

Blake H, Gerard RW. Brain potentials during sleep. Am. J. Phsiol. 11 9: 693-703. 1937.

Borak J, Cieslicki JK, Koziej M, Matuszewski A, Zielinski J. Effects of CPAP treatment on psycholog ical status in patients with severe obstructive slee p a pnea . J. Sleep Res. 5(2): 123-7, 1996.

Borbely AA. A two process model of sleep regulation. Human Neurobiol. 1 :195- 204,1982.

Borbely AA, Achermann P: Concepts and models of sleep regulation , an overview. J Sleep Res. 1 :63-79, 1992.

Borbely AA, Baumann F, Brandeis D, Strauch 1, Lehmann D. Sleep- deprivation: effect on sleep stages and EEG power density in man. Electroencephalogr. Clin. Neurophysiol. 51 :483-493, 1981.

Borbely AA, Neuhaus H-U. Sleepdeprivation: Effects on sleep and EEG in the rat. J Comp Physiol. 133: 71 -87, 1979.

Borbely AA, WirzJustice A. Sleep deprivation and depression. A hypothesis derived from a model of sleep regulation. Hum. Neurobiol. 1 :205-2IO. 1982.

Born J, Kellener Cl Uthgenannt Dl Kern W, Fehm HL. Vasopressin regulates human sleep by reducing rapid-eye-movement sleep. Am J Physioi. 262(3.l): €295-300, 1 992.

Bowes G. Arousal responses to chernical stimuli during sleep. J. Dev. Physiol. 6(3): 207-1 3, 1984.

Brooks O, Horner RL, Kimoff RJ, Kozar LF, Render-Teixeira CL, Phillipson EA. Effect of obstructive sleep apnea versus sleep fragmentation on acute responses to airway occlusion in the dog. Am. J. Respir-CrkCare Med. 155:1609- 1111617,1997.

Brooks D, Horner RL, Kozar LF, Render-Teixeira CL, Phillipson EA. Obstructive sleep apnea as a cause of systemic hypertension: Evidence from a canine model. J. C h . Invest. 99: 106-1 09, 1997.

Brunner DP, Dijk DJ, Tobler 1, Borbely AA. Effect of partial sleep deprivation on sleep stages and EEG power spectra: evidence for non-REM and REM sleep homeostasis. Electroencephalogr. Clin. Neurophysiol. 75:492-499, 1990.

Buguet AG, Roussel BH, Watson WJ, Radomski MS. Cold-induced diminution of paradoxical sleep in man. Elecfroencephalogr. Clin. Neurophysiol. 46(1): 29-32, 1979.

Carman GJ, Mealey L, Thompson ST, Thompson MA. Patterns in the distribution of REM sleep in normal human sleep. Sleep. 7(4): 347-55, 1984.

Carskadon MA, Dement WC. Sleepiness and sleep state on a 90-min schedule. Psychophysiology. 14;127-133, 1977.

Clark DJ, Fewell JE. Body-core temperature decreases during hypoxic hypoxia in Long-Evans and Brattleboro rats. Can. J. Physiol. Phannacol. 72: 1528-1 531, 1994.

Cohen RA, Albers HE. Disruption of human circadian and cognitive regulation following a discrete hypothalamic lesion: a case study. Neurology. 41 (5): 726-9. 1991.

Cowan Al, Martin RL. lonic basis of membrane potential changes induced by anoxia in rat dorsal vagal motoneurones. J. Physiol (Lond). 455: 89-1 09, 1992.

Crunelli V, haby M, Jassik-Gerschenfeld D, Leresche N, Pirchio M. CI' and K'- dependent inhibitory postsynaptic potentials evoked by interneurons of the rat lateral giniculate nucleus. J. Physiol. Lond. 399: 1 53-1 76, 1988.

Czeisler CA, Weitxrnan E, Moore-Ede MC, Zimmerman JC, Knauer RS. Human Sleep: its duration and organisation depend on its circadian phase. Science. 21 0: 1264-1 267, l98Oa.

Czeisler CA, Zimmerman JC, Ronda JM, Moore-Ede MC, Weitxman ED. Timing of REM sleep is coupled to the circadian rhythm of body temperature in man. Sleep. 2:329-346, 198Ob.

Dantz B, Edgar DM, Dement WC. Circadian rhythms in narcolepsy: studies on a 90 minute day. Electroenceph. Clin. Neurophys. 9024-35, 1994.

Davis H, Wallace W. Factors affecting changes in the human electroencephalogram by standardized hyperventilation. Arch. Neuroi. Psychia t. 47:606-625, 4942.

De Carli F, Nobili L, Gelcich P, Ferrillo F. A method for the automatic detection of arousals during sleep. Sleep. 22: 561-572, 1999.

De Lima AD, Singer W. The brainstem projection to the lateral geniculate nucleus in the cat: Identification of cholinergie and monoarninergic elements. J. Comp. Neurol. 259: 92-1 19, 1 987.

Dement W. The effect of dream deprivation. Science. 131 :liO5-l707, 1960.

Dement WC. Studies on the function of rapid eye movement (paradoxical) sleep in human subjects. In: Jouvet M (ed) Aspects anatomo-fonctionnes de la physiologie du sommeil. Editions du Centre National de la Recherche Scientifique, Paris. pp 571-61 1, 1965.

Dement W, Greenberg S, Klein R. The effects of stage four sleep deprivation and delayed recovery. J Psychiat Res. 4: 141 -52, 1966. Dement W, Kleitman N. Cyclic variations of EEG during sleep and their relation to eye movements, body motility, and dreaming. Eiectroencephalogr. Clin. Neurophysiol. 9:673-690, 1 957.

Dijk DJ, Beersma DGM, Daan S. Quantitative analysis of the effects of slwo- wave sleep deprivation during the first 3 h of sleep on subsequent EEG power density. Eur Arch Psychiatry Neurol Sci 236:323-328. 1987.

Dijk DJ, B ~ n n e r DP, Borbely AA. EEG power density during recovery sleep in the moming . Electroencephalogr. Clin. Neurophysio/.78:203-214, 1 99 1 .

Dijk DJ, Czeisler CA. Paradoxical timing of the circadian rhythm of sleep propensity serves to consolidate sleep and wakefulness in humans. Neursci Lett. 166: 63-68, 1994.

Dijk DJ, Cziesler CA. Contribution of circadian pacemaker and the sleep homeostat to sleep propensity, sleep structure, electroencephalographic slow waves, and sleep spindle activity in humans. J Neurosci Lett. 166:63-68, 1995.

Dijk DJ, Daan S. Sleep EEG spectral analysis in a diurnal rodent: Eutamias sibiricus. J. Comp. Physiol. 165205-21 5, 1989.

Dubinski JM, Rothman SM. Intracellular calcium concentrations during "chernical hypoxia" and excitotoxic neuronal injury. J Neurosci. 1 1 : 2545-2551, 1991.

Edgar DM, Dement WC, Fuller CA. Effect of SCN lesions on sleep in squirrel mon keys: evidence for op ponant processes in sleep-wake regulation. J Neurosci. 13: 1065-1079, 1993.

Ellman SJ, Spielman AJ, Lipschultz-Brach L. REM deprivation update. In: SJ Ellrnan and JS Antrobus (Eds.), The mind in sleep. Psychology and Psychophysiology, John Wiley, New York. pp 369-367, 1991.

Ellman SJ, Spielman AJ, Luck D, Steiner SS, Halperin R. REM deprivation: a review. In: Arkin AM. Antrobus JS, Ellrnan SJ (Eds.). The mind in sleep. Lawrence Erlbaum, Hillsdale, New Jersey. pp. 41 9-68, 1978.

Endo T, Schwierin B, Borbely AA, Tobler 1. Selective and total sleep deorivation: effect on the sleep EEG in the rat. Psychiatry Res. 66:97-110, 1997.

Feinberg 1. Changes in sleep cycle pattern with age. J Psychiatr Res. 10:283- 306,1974.

Feinberg 1, Fein G, Floyd TC. EEG patterns during and following extended sleep in you ng adu lts. Electroencephalogr. Clin. Neurophysiol. 50:467-476, 1 980.

Feinberg 1, Floyd TC, March JD. Effects of sleep loss on delta (0.3-3 Hz) EEG and eye movement density: New observations and hypotheses. Electroencephalogr. Clin. Neurophysiol. 67:217-221, 1987.

Findley, L. J, M. J. Fabrizio, H. Knight, B. B. Norcross, A. J. LaForte, and P. M. Suratt. Driving simulator performance in patients with sleep apnea. Am. Rev. Respir. Dis. 140: 529-530, 1989.

Findley, L. J., M. P. Levinson, and R. J. Bonnie. Driving performance and automobile accidents in patients wRh sleep apnea. Clin. Chest Med. 13: 427-435. 1992.

Fletcher, E. C., and G. Bao. The rat as a mode1 of chronic recurrent episodic hypoxia and effect upon systemic blood pressure. Sleep 19: S210-212. 1996.

Fletcher, E. C., J. Lesske, W. Qian, C. C. Miller III, and T. Unger. Repetitive. episodic hypoxia causes diumal elevation of blood pressure in rats. Hypertension 19: 555-561. 1992.

Fonling ML, Ullmann E. Release of vasopressin during hypoxia. J. Physiol (Lond). 241 : 35P-36p (Abstr), 1974.

Franken P, Dijk DJ, Tobler 1, Borbely AA. Sleep deprivation in rats: Effects on EEG power spectral vigilance states. and cortical temperature. Am. J. Physiol. 261: R198-208, 1991 b.

Frappe1 PB, Daniels CB. Temperature effects on ventilation and metabolism in the lizard, Ctenophorus nuchalis. Respir. Physiol. 86(2): 257-70. 1991.

Friedman L, Bergmann BM, Rechtschaffen A. Effects of sleep deprivation on sleepiness. sieep intensity, and subsequent sleep in the rat. Sleep. 1: 369-391. 1979.

Fujimura N, Tanaka E, Yamamoto S, Shigemori M, Higashi H. Contribution of ATP-sensitive potassium channels to hypoxic hyperpolarizatin in rat hippocampal CA1 neurons in vitro. J. Neurophysioi. 77: 378-385, 1997.

Fujiwara N, Higashi H, Shimoji K, Yoshimura M. Effects of hypoxia on rat hippocampal neurons in vitro. J. Physiol. (Lond). 384: 131 -1 51. 1987.

Gardolfo G, Glin L, Lacoste G, Rodi M, Gottesmann C. Automated sleep-wake scoring in the rat on microcornputer apple II. int J Biomed Comput. 23: 83-95. 1988.

George CF, Nickerson PW, Hanley PJ, Millar TW, Kryger MH. Sleep apnea patients have more automobile accidents. Lancet. 2: 447, 1987. Gibbs EL, Lennox WG, Nims LF. Regulation of cerebral carbon dioxide. Arch. Aieuroi. Psychiat. 47:879-889, 1942.

Gibson GE, Duffy TE. lmpaired synthesis of acetylcholine by mild hypoxic hypoxia or nitrous oxide. J Neurochem. 36(1): 28-33. 198 1.

Gillberg Ml Akerstedt T. The dynamics of the first sleep cycle. Sleep. 14: 147- 154, 1991.

Gleeson K, Zwillich CW, White DP. The influence of increasing ventilatory effort on arousal from sleep. Am. Rev. Respir. Dis. 142: 295-300, 1990.

Goeller CJ, sinton CM. A microcomputer-based sleep stage analyser. Cornput Methods Prog Biomed. 29: 31 -36. 1989.

Grant DA, Davidson TL, Fewell JE. EEG analysis- Sleep scoring in man and rnammals. Automated scoring of sleep in the neonatal lamb. Sleep. 18(6): 439- 445,1995.

Greenberg, H. E., A. Sica, Dm Batson, and S. M. Scharf. Chronic intermittent hypoxia increases sympathetic responsivenesç to hypoxia and hypercapnia. J. Appl. Physiol. 86: 298-305, 1999.

Greene RW, Haas HL. The electrophysiology of adenosine in the mammalian cen ral nervous system. Prog. Neurobiol. 36: 329-34 1 , 1 99 1 .

Guyenet PG, Koshiya N, Huangfu D, Verberne AJ, Riley TA. Central respiratory control of A5 and A6 pontine noradrenergic neurons. Am. J. Physiol. 264(6.2): R I 03544, 1993.

Haddad GG,. Donelly DF. 0 2 deprivation induces a major depolarisation in brain stem neurons in the adult but not in the neanatal rat. J. Physiol (Lond). 429: 41 1 - 428,1990.

Haddad GG, Jiang C. 0 2 deprivation in the central nervous system: on mechanisms and injury. Prog Neurobiol. 40: 277-31 8, 1993.

Hale B, Megirian Dl Pollard MJ. Sleep-waking pattern and body temperature in h ypoxia at selected ambient temperatures. J Appl Physiol. 57(l5): 1 564-1 568, 1984.

Hallanger AE, Wainer BH. Ultrastnictural of Chat-immunoreactive synaptic terminals in the thalamic reticular nucleus of the rat. J. Comp. Neurol. 278: 486- 497,1988.

Hamer J, Wiedemann K, Berlet H, Weinhardt F, Hoyer S. Cerebral glucose and energy metabolism, cerebral oxygen consumption and blood flow in arterial hypoxaemia. Acta Neurochirurgica. 44: 151 -1 60, 1978.

Hanson AJ. Effect of anoxia on ion distribution in the rat brain. Pharmacol Rev. 65: 101-148, 1985.

Hendenon-Smart DJ, Read DJ. Ventilatory responses to hypoxaemia during sleep in the newbom. J. Dev. Physiol. l(3): 195-208, 1979.

Hirsch JC, Burnod Y. A synaptically evoked late hyperpolarization in the rat dorsal lateral geniculate nucleus in vitro. Neuroscience. 23: 457-468, 1987.

Hochachka PW, Lutz PL, Sick T, Rosenthal M, van der Thillart G. Surviving hypoxia. Boca Raton. FL: CRC, 1993.

Home JA. Why we sleep: The function of sleep in humans and other mammals. New York. Oxford University Press, 1988.

Horne JA. Dimensions to sleepiness. In: Monk TH (ed): Sleep, sleepiness and performance. New York, John Wiley and Sons. Pp 169-1 96,1991.

Horne JA. Human slow wave sleep and the cerebral cortex. J. Sleep Res. 1:122- 124, 1992.

Horner RL, Sanford LD, Annis D, Pack Al, Morrison AR. Serotonin at the laterodorsal tegmental nucleus suppresses rapid-eye-movement sleep in freely behaving rats. J Neurosci. 1 ; 17 (19): 7541-52, 1997.

Horner, R. L., D. Brooks, L. F. Kozar, E. Leung, H. Hamrahi, C. L. Render- Teixeira, H. Makino, R. J. Kimoff, and E. A. Phillipson. Sleep architecture in a canine mode1 of obstructive sleep apnea. Sleep 21 : 847-858, 1998.

Hughes HC, Mullikin WH. Brainstem afferentsto the iateral geniculate nucleus of the cat. Expl. Brain Res. 54: 253-258, 1984.

lsobe Y, Nakajima K, Nishino H. Arg-vasopressin content in the suprachiasmatic nucleus of rat pups: circadian rhythm and its development. Brain Res Dev Brain Res. 85(1): 58-63, 1995.

Issa, F. G. and C. E. Sullivan. The immediate effects of nasal continuous airway pressure treatment on sleep pattern in patients with obstructive sleep apnea syndrome. Electmenceph. Clin. Neurophysiol. 63: 10-1 7 , 1986.

Jarsky, T., and R. Stephenson. Scheduled respiratory stimuli entrain hamster activity rhythms. Physiologist 42 (5): A-7, 1 999.

Jones B. Basic mechanisms of sleep-wake states. In: Principles and practice of sleep medicine. WB Saunders Company. 145-162. 1994.

Johns TG, Piper DC, James GWL, Birtley RDN, Fischer M. Automated analysis of sleep in the rat. Electroenceph. Clin. Neurophysiol. 43 (1 ): 103-1 05. 1977.

Johnson LC, MacLeod WL. Sleep and awake behaviour during gradua1 sleep reduction. Perceptual and Motor Skills. 36: 87-97, 1973.

Jores HS, Oswald 1. Two cases of healthy insomina. Electroenceph Clin Neurophysiol. 24: 378-380, 1968.

Kales A, Tan T-L, Kollar EJ, Naitoh P, Preston TA, Malmstrom EJ. Psychosom Med. 32:189-200, 197 0.

Kaplin Al, Snyder SH, Linden DJ. Reduced nicotinamide adenine dinucleotide- selective stimulation of inositol 1,4,5-triphosphate receptors mediates hypoxic meobilisation of calcium. 3 Neurosci. 16: 2001 -201 1 , 1 996.

Karacan 1, Williams RL, Finely WW, Hursch CJ. The effects of naps on noctumal sleep: Influence on the need for stage4 REM and stage 4 sleep. Biol Psychiatr. 2:391-399, 1970.

Kass IS, Lipton P. Protection of hippocampal slices from young rats against anoxic transmission damage is due to better maintenance of ATP. J. Physiol. (Lond). 41 3: 1-1 1, 1986.

Katchman AN, Hershkowitr N. Adenosine antagonists prevent hypoxia-induced depression of excitatory but not in hibitory synaptic currents. Neurosci. Lett. 1 59: 123-126, 1993.

Kimoff, R. J., D. Brooks, R. L. Horner, L. F. Kozar, C. L. Render-Teixeira, V. Champagne, P. Mayer, and E. A. Phillipson. Ventilatory and arousal responses to hypoxia and hypercapnia in a canine model of obstructive sleep apnea. Am. J. Respir. CM. Care Med. 1 56: 886-894, 1 997.

Kohn N, Litchfield Dl Branchey M, Brebbia DR. An automated hybrid analyser of sleep stages in the rat. Electroenceph. Clin. Neurophysiol.37: 51 8-520. 1974.

Kraaier V, Van Huffelen AC, Wieneke GH. Quantitative EEG changes due to hypobaric hypoxia in normal subjects. Electroenceph. Clin. Neurophys. 69:303- 312, 1988.

Kraiczi, H., J. Magga, X. Y. Sun, H. Ruskoaho, X. Zhao, and J. Hedner. Hypoxic pressor response, cardiac size, and natriuretic peptides are modified by long-temi intermittent hypoxia. J. Appl. Physiol. 87: 2025- 2031,1999.

Krnjevic K. Membrane curent activation and inactivation during hypoxia in hippocampal neurons. In: Surviving hypoxia (Hochachka PW, Lutz PL. Sick T. Rosenthal M. van der Thillart G. eds), pp 365-387, Boca Raton, FI: CRC, 1989.

Kromer LF, Moore RY. A study of the organisation of the locus coeruleus projections to the lateral geniculate nuclei in the albino rat. Neuroscience. 5: 255- 271, 1980.

Lamphere, J., T. Roehn, R. Wittig, F. Zorick, W. A. Conway, and T. Roth. Recovery of alertness after CPAP in apnea. Chest 96: 1 364-1 367.1 989.

Lancel M, Kerkhof GA. Effects of repeated sleep deprivation in the dark- or light- period on sleep in rats. Physiol. Behav. 45: 289-297, 1989.

Lavie P. Ultrashort sleep-wake cycle: timing of REM sleep. Evidence for sleep- dependent and sleep-independent components of the REM cycle. Sleep. 70:62- 68, 1987.

Laszy J, Sarkadi A. Hypoxia-induced sleep disturbance in rats. Sleep. 1 3(3): 205-21 7, 1990.

Leblond J, Krnjevic K. Hypoxic changes in hippocampal neurones. J Neurophysiol. 62: 1-1 2. 1989.

Leger L, Sakai K, Salvert D, Touret M, Jouvet M. Delineation of dorsal lateral geniculate afferents from the cat brainstem as visualised by the horse radish peroxidase technique. Brain Res. 93: 490-496, 1975.

Leonard CS, Llinas R. Serotonergic and Cholinergie inhibition of mesopontine cholinergic neurons controlling REM sleep: an in vitro electrophysiological study. Neuroscience. 59(2): 309-30, 1994.

Levey Al, Hallanger AE, Wainer BH. Choline acetyltransferase immunoreactivity in the rat thalamus. J. Comp. Neurol. 257: 31 7-332. 1987.

Lewis LD, Ponten U, Siesjo BK. Arterial acid-base changes in unanaesthtized rats in acute hypoxia. Respir Physiol. 19: 31 2-321, 1973.

Lucidi F, Devoto A, Violani C, Mastracci P, Bertini M. Effects of different sleep duraiton on delta sleep in recover sleep. Psychophysiol. 34: 227-233, 1997.

Maron L, Rechtschaffen A, Wolpert EA. Sleep cycle during napping. Arch Gen Psychiafry. 1 1 : 503-507.1964.

Marrone O, Bonsignore MR, lnsalaco G, Bonsignore Go What is the evidence that obstructive sleep apnea is an important illness. Monaldi Arch Chest Dis. 53(6): 630-9, 1998.

Martin LJ, Brambrink A, Koehler RC, Traystman RJ. Primary sensory and forebrain motor systems in the new-bom brain are preferentially damaged by hypoixa-ischemia. J Comp Neurol. 377: 262-285, 1997.

Materi LM, Rasmusson DD, Semba K. Inhibition of synaptically evoked cortical acetylcholine release by adenosine: an in vivo microdialysis study in the rat. Neuroscience. 97(2): 21 9-26, 2000.

McCormick DA. Cellular mechanisms of cholinergie control of neocortical and thalamic neuronal excitability. In: Brain Cholinergic Systems. pp: 236-264. Eds M. Steriade and D Bisold. Oxford University Press: Oxford, IWO.

McCormick DA. Neurotransmitter actions in the thalamus and cerebral cortex and their role in neuromodulatin of thalamocortical activity. Prog Neurobiol. 39: 337-388, 1992.

Megirian, D., A. T. Ryan, and J. H. Sherrey. An electrophysiological analysis of sleep and respiration of rats breathing different gas mixtures: diaphragmatic muscle function. Electroenceph. Clin. Neurophysiol. 50: 303-31 3. 1980.

Mendelson WB, Vaughn WJ, Walsh MJ, Wyatt RJ. A signal analysis approach to rat sleep scoring instrumentation. Waking Sleep. 4: 1-8, 1980.

Meyer JS, Gotoh F, Tazaki Y, Hamaquchi K, lshikawa S, Nouilhat F, Symon L. Regional cerebral blood flow and metabolisrn in vivo. Arch Neurol. 7: 560-581. 1962.

Milusheva €A, Doda Ml Baranyi A, Vizi ES. Effect of h poxia and glucose Y+ deprivation in ATP level, adenylate energy charge and [Ca *],-dependent and independent relese of [3~]-dopamine in rat striatal slices. Neurochem. /nt. 28: 501 -507, 1996.

Mistlberger RE, Bergman BM, Waldenar W, Rechtschaffen A. Recovery sleep following sleep deprivation in intact and suprachiasmatic nuclei-lesioned rats. Sleep. 6:217-233, 1983.

Modem B, Mitchell G, Dement W. Selective REM sleep deprivation and compensation phenornena in the rat. Brain Res. 5:339-349, 1967. Monti JM, Monti D. Role of dorsal raphe nucleus serotonin 5-HTIA receptor in the regulation of REM sleep. Life Sci 14; 66(21): 1999-2012, 2000.

Moore RY. Circadian rhythms: basic neurobiology and clinical applications. Annu Rev Med. 48: 253-66, 1997.

Morrow TJ, Casey KL. A micorprocessor device for the real-time detection of synchronised alpha spindle activity in the EEG. Brain Res Bull. 16: 439-442. 1986.

Mortola J P, Renonico R. Meta bolic and ventilatory rates in new born kittens du ring acute hypoxia. Respir. Physiol. 73(1): 55-67, 1988.

Mortola, JP, Seifert EL. Hypoxic depression of circadian rhythms in adult rats. J. Appl. Physiol. 88: 365368, 2000.

Moses JM, Johnson LC, Naitoh P, Lubin A. Sleep stage deprivation and total sleep loss: effects on sleep be havior. Psychophysioi. 1 2: 1 4 1 -1 46, 1 975.

Nadel L, Samsonovich A, Ryan L, Moscovitch M. Multiple trace theory of human memory: computational. neuroimaging , and neuropsycholog ical resu lts. Hippocampus. 1 O(4): 352-68,2000.

Nakazawa Y, Kotorii M, Ohishima Ml Kotorii T, Hasuzawa H. Changes in sleep pattern after sleep deprivation. Folia Psychiatr Neurol Jpn. 32:85-93, 1978.

Naylor AM, Ruwe WD, Veale WL. Thermoregulatory actions of centrally- administered vasopressin in the rat. Neurophamaco/ogy. 25(7): 187-794. 1986.

Neckelmann D, Olsen OE, Fagerland S, Ursin R. The reliability and funciional validity of visual and semi-automatic SleepMlake scoring in the Moll-Wistar rat. Sleep. 1 7(2): 1 20-3 1 . 1 994.

Neuhause HU, Borbely AA. Sleep telemetry in the rat. II Automatic identification and recording of vigilance states. Electroenceph. C/h . Neurophysiol. 44: 1 1 5- 1 1 9, 1978,

Nieber K, Sevcik J, Illes P. Hypoxic changes in rat locus coeruleus neurons in vitro. J. Physiol. (Lond). 486: 33-46, 1995.

Nieber K, Eschke O, Brand A. Brain hypoxi: Effects of ATP and adenosine. Prog. Brain Res. 120: 287-297. 1999.

Nolan PC, Waldrop TG. Ventrolateral medullary neurons show age-dependent de polarisation to hypoxia in-vitro. Dev Brain Res. 9 1 : 1 1 1 -1 20. 1 996.

Oluyomi AO, Hart SL. Antinociceptive and thennoregulatory actions of vasopressin are sensitive to a V I -receptor antagonist. Neuropeptides. 23(3): 1 37- 42, 1992.

Panula P, Airksinen MS, Pirvola U, Korilainen E. A Histamine-containing neuronal system in human brain. Neuroscience. 34: 127-1 32, 1990.

Panula P, Pirvola U, Auvinen S, Airksinen MS. Histamine-immunoreactive nerve fibers in the rat brain. Neuroscience. 28: 585-610, 1989.

Pappenheimer, JR. Sleep and respiration of rats during hypoxia. J. Physiol. (Lond.) 266: 191 -207, 1977.

Pappenheirner, JR, Koski G, Fencl V. Extraction of sleep-promoting factor S from cerebrospinal Ruid and from brains of sleep-deprived animais. J Neurophysiol. 38: 1299-1 31 1, 1975.

Paschen W, Djuricic B. Comparison on in vitro ischemia-induced disturbances in energy metabolism and protein synthesis in the hippocarnpus of rats and gerbils. J. Neurochem. 65: 1692-1 697, 1995.

Patrick GTW, Gilbert JA. On the effects of loss of sleep. Psycho1 Rev. 3: 469- 483.1896.

Payen JF, Briot E, Tropres 1, Julien-Ooibec C, Montigon O, Decorps M. Reg ional cerebral blood volume response to hypocapnia using suscepti bility contrast MRI. NMR Biomed. 13(7):384-91, 2000.

Permeggiani PL. Interaction between sleep and thermoregulation: an aspect of the control of behavioral states. Sleep. lO(5): 426-35, 1987.

Phillipson EA. Sleep apnea- A major public health problern. The New England Journal of Medicine. 328: 1271 -3, 1993.

Phillipson EA, Sullivan CE, Read DJC, Murphy E, Kozar LF. Ventilatory and waking responses hypoxia in sleeping dogs. J. Appl. Physiol. 44: 51 2-520, 1978.

Phillis JW,Edstrom JP, Kostopoulos GK, Kirkpatrick JR. Effects of adenosine and adenine nucleotides on synaptic transmission in the cerebral cortex. Can. J. Physiol. Phannacol. 57: 1289-1 31 2, 1989.

Pissarek M, Garcia de Amba S, Schafer M, Sieler O, Nieber K, llles P. Changes by short-terni hypoxia in the membrane properties of pyramidal cells and the levels of purine and pyrimidine nucleotides in slices of rat neocortex; effects of agonists and antagonists of ATP-dependent potassium channels. Naunyn- Schmiedeberg's Arch. Pharmacol. 358: 430-439, 1998.

Pittman QJ, Chen X, Mouihate A, Hirasawa M, Martin S. Arginine Vasopressin. fever and temperature regulation. Prog. Brain Res. 1 19: 383-92. 1 998.

Pollard MJ, Megirian D, Sherrey JH. Differential effects of hypoxia on sleep of wam- and cold- acclimated rats. J Appl Physiol. 63(6): 21 89-21 94, 1987.

Rechtschaffen A, Bergmann BM, Gililand MA, Bauer K. Effects of Method, Duration, and sleep stage on rebounds from sleep deprivation in the rat. Sleep. 22 (1): 11-31, 1999.

Rechtschaffen, A. and Siegel, J.M. Sleep and Dreaming. In: Principles of Neuroscience. Fourth Edition, Edited by E. R. Kandel, J.H. Schwartz and T.M. Jessel, 936-947. McGraw-Hill, New York, 2000.

Roncagliolo M, Vivaldi EA. Time course of rat sleep variables assessed by a micorcomputer-generated data base. Brain Res Bull. 27: 573-580, 1991.

Ruigt GSF, Van Proosdij JN, Van Delft AML. A large scale, high resolution, automated system for rat sleep staging. 1. Methodology and technical aspects. Electroenceph. Clin. Neurophysiol. 73: 52-63. 1989.

Rurak DW. Plasma vasopressin levels during hypoxaemia and the cardiovascular effects of exogenous vasopressin in foetal and adult sheep. J. Physiol. (Lond). 277: 341-57, 1978.

Ryan AT, Megirian D. Sleep-wake patterns of intact and carotid sinus nerve sectioned rats du ring hypoxia. Sleep. 5(l): 1 -1 0, 1 982.

Ryan AT, Ward DA, Megirian D. Sleep-waking patterns of intact and carotid sinus nerve-transected rats during hypoxic-CO2 breathing. Exp Neurol. 80(2):337-48,1983.

Sampson H. Deprivation of dreaming sleep by two methods. Arch Gen Psychiatry. 1 3: 79-86, 1965.

Sauter C, Asenbaum S, Popovic R, Bauer Hy Lamm C, Klosch G, Zeitlhofer J. Excessive daytime sleepiness in patients suffering from different levels of obstructive sleep apnoea syndrome. J Sleep Res. Sep;9(3):293-30, 2000.

Shiromani Pj, Malik M, Winston S, McCarley RW. Time course of Fos-like immunoreactivity associated with cholinergically induced REM sleep. J Neurosci. 15(5.1): 3500-8, 1995.

Smith Y, Pare Dy Deschenes M, Parent A. Steriade M. Cholinergic and non- cholinergie projections from the upper brainstem cor@ to the visual thalamus in the cat. Expl Brain Res. 70: 1 66-1 80, 1988.

Sofroniew MV, Priestley JV, Consolarione A, Eckenstein F, Cuello AC. Cholinergie projections from the upper brainstem and pons to the thalamus in the rat, identified by combined retrograde tracing and choline acetyltransferase immunhistochernistry. Brain Res. 329: 21 3-223, 1985.

Stark RI, Daniel SS, Husain MK, Zubrow AB, James LS. Effects of hypoxia on vaspressin concentrations in cerebrospinal fluid and plasma in sheep. Neuroendocnnology. 38: 453-460, 1984.

Stark RI, Daniel SS, Husain MK, Zubrow AB, James LS. Cerebrospinal Ruid and plasma vasopressin in the fetal lamb: basal concentration and the effect of hypoxia. Endocrinology. 1 1 6 : 65-72. 1 985.

Steriade M, Curro Dossi R, Nunez A. Network modulation of a slow intrinsic oscillation of cat thalamocortical neurons implicated in sleep delta waves: cortically induced synchronization and brainstem cholinergic suppression. J. Neurosci. 10: 2541-2559, 1991.

Steriade M, Deschenes M. The thalamus as a neuronal oscillator. Brain Res. 320(1): 1-63, 1984.

Steriade M, McCorrnick, Sejnowski TJ. Thalamocortical oscillations in the sleeping and aroused brain. Science. 262: 679-685, 1993.

Steriade M, Pare Dl Parent A, Smith Y. Projections of cholinergic and non- cholinergic neurons of the brainstem core to relay and associational thalamic nuclei in the cat and macaque monkey. Neuroscience. 25; 47-67, 1988.

Steiner AA, Carnio EC, Antunes-Rodrigues J, Branco LG. Role of nitric oxide in sytemic vasopressin-induced hypothenia. Am. J. Physiol. 275(4.2): R937-41. 1998.

Sullivan, Ce E., and R. R. Gruntstein. Continuous positive aitway pressure in sleepdisordered breathing. In: Phciples and Practice of Sleep Medicine. Eds. Kryger, M. H., T. Roth, and W. C. Dement. Philadelphia. PA: WB Saunders, 1994. pp. 694-705.

Thompson AM. Biphasic responses of thalamic neurons to GABA in isolated rat brain slices-l 1. Neuroscience. 25: 503-51 2, 1 988.

Tobler I, Borbely AA. Sleep EEG in the rat as a function of prionnrakening. Electroencephalogr. Clin. Neurophysiol. 64:74-76, 1 986.

Tobler 1, Borbely AA, Groos G. The effect of sleep deprivation on sleep in rats with suprachiasmatic lesions. Neurosci Lett. 42: 49-54. 1983.

Tobler 1, Franken P, Scherschlicht R. Sleep and EEG spectra in the rabbit under baseline conditions and following sleep deprivation. Physiol Behav. 48: 121-129, 1990.

Tobler 1, Jaggi K. Sleep and EEG spectra in the Synan hamster (Mesocricetus auratus) under baseline conditions and following sleep deprivation. J. Comp. Physiol. 1 6 1 :449-459, 1 987.

Tominaga K, Shinohara K, Otorî Y, Fukuhara C, lnouye ST. Circadian rhythm of vasopressin content in the suprachiasmatic nucleus of the rat. Neuroreport. 3(9): 809-81 2, 1992.

Trachsel L, Tobler 1, Borbely AA. Electroencephalogram analysis of non-rapid eye movement sleep in rats. Am. J. Physiol. 255 (regulatory lntegrative Comp. Physiol. ) 24: R27-R37, 1988.

Tromba C, Salvaggio A, Racagni G, Volterra A. Hypoglycemia-activated K' channels in hippocampal neurons. Neurosci. Lett. 143: 185-1 89, 1992.

Trussell LO, Jackson MB. Dependence of an adenosine-activated potassium current on a GTP-binding protein in mammalian central neurons. J. Neurosci. 7: 3306-331 6, 1 987.

Van der Worp HB, Kraaier V, Wieneke GH, Van Huffelen AC. Quantitative EEG dunng progressive hypocarbia and hypoxia. Hyperventilation-induced EEG changes reconsidered . Electroencephalogr. Clin. Neurophysiol. 79: 335-34 1 . 1991.

Van Gelder RN, Edgar DM, Dement WC. Real-time automated sleep scoring: validation of a microcornputer-based system for mice. Am Sleep Dis Ass. 14(1): 48-55, 1991.

Van Luijtelaar ELJM, Coenen AML. An EEG averaging technique for automated sleep-wake stage identification in the rat. Physiol Behav. 33: 837-841, 1984.

Veale WL, Cooper KE, Ruwe WD. Vasopressin: its role in antipyresis and febrile conbulsion. Brain Res Bull. 12(2): 161 -1 65, 1984.

Wada H, lnagaki N, Yarnatodani A, Watanabe T. Is the histaminergic neuron system a regulatory center for whole-brain activity? Trends Neuroscience. 14: 415-420, 1991.

Watts AE, Hicks GA, Henderson G. Putative pre- and postsynaptic ATP- sensitive potassium channels in the rat substantia nigra in vitro. J. Neurosci. 15: 1365-3074, 1995.

Webb WB. Sleep behaviour as a biorhythm. 1n:Coquhoun WP (ed) Biological rhythm and human performance. Academic Press, London. pp 149-1 77,1971.

Webb WB, Agnew HW Jr. Stage 4 sleep: influence of time course variables. Science. 1 74: 1 3544 356, 1971.

Webb WB, Agnew HW Jr. The effects of chronic limitations of sleep length. Psychophysiology. 1 1 : 265-274,1974.

Webb WB, Friel J. Characteristics of "naturaï' long and short sleepers: A preliminary repot. Psycholog Reports. 27: 63-66, 1970.

Weitzman ED, Czeisler CA, Zirnmerman JC, Ronda JM. Timing of REM and stages 3 + 4 sleep during temporal isolation in man. Sleep. 2:391-407. 1980.

White TD, Hoehn K. Release of adenosine and ATP from newous tissue. In: Adenosine in the nervous system. Ed. TW. Stone. Academic Press. London. pp. 173-1 95, 1991.

Williams HL, Hammack JT, Daly RL, Dement WC, Lubin A. Responses to auditory stimulation, sleep loss and the EEG stages of sleep. Electroencephalogr. Clin. Neurophysiol. 16:269-279, 1964a.

Williams RL, Agnew HW Jr, Webb WB. Sleep patterns in young adults: An EEG study. Electroencephalogr. Clin. Neurophysiol.l7:376-381, 1964b.

Winson J. A simple sleep stage detector for the rat. Electroenceph. Clin. Neurophysiol. 41 : 179-1 82, 1976.

Witting W, Van der Werf D, Mirrniran M. An on-line automated sleep-wake classification system for laboratory animais. J Neurosci Methods. 66: 109-1 12. 1996.

Woolfe NJ, Butcher LL. Cholinergic systems in the rat brain. III. Projections from the pontomesencephalic tegmentum to the thlamus, tectum, basal ganglia. and basal forebrain. Bain Res. Bu//. 16: 603-637, 1986.

Wu& SW, Edgar DM. Circadian and homeostatic control of rapid eye movement (REM) sleep: Promotion of REM tendency by the suprachiasmatic nucleus. J of Neurosci 20(11): 4300-431 O, 2000.

Zulley J. Distribution of REM sleep in entrained 24 hour and free-ninning sleep- wake cycles. Sleep. 2: 377-389, 1980.

Zulley J, Schulz H. Sleep and body temperature in free-running sleep-wake cycles. 1n:Popoviciu L, Asgian B, Badiu G (eds) Sleep. Fourth European Congress of Sleep Research. Karger, basel. Pp 341 -342, 1980.

Zulley J, Wever R, Aschoff J. The dependence of onset and duration of sleep on the circadian rhythm of rectal temperature. Pfiugers Arch. 391 :3l4-318, 1981.

Schema of the circuit diagram of solenoid valve to apply hypoxia during sleep

. 0 2 = 0

O, Analyser I 100 % O2 Control 1

21 % O2 Control J mEpI 1 WAKE

1 (0,O) will open valve 1

m 10 ?/O 0, Control

( (d,1) will open valve 1

NOR Gates

1 AND Gates