CIRCADIAN RHYTHMS IN LUNG VENTILATION

IN WAKEFULNESS AND SLEEP

Kiong Sen Liao

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

Graduate Department of Physiology, University of Toronto

O Copyright by Kiong Sen Liao (2001)

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To my grandmother, parents and brothers

Abstract

Kiong Sen Liao. Department oj* Physiologr.. Universip of Toronto.

Sleep exerts important modulating intluences on breathing such that a decline in

lung ventilation in sleep predisposes individuals to respiratory impairment. Recent data

from awake humans also suggest that the circadian system intluences breathing. This

circadian intluence may be potentially important because a decline in ventilation due to

circadian effects may exacerbate the decline due to sleep. although this has not been

tested. Therefore. by measuring sleep-wake States (using electroencephalography and

electromyography). lung ventilation (using barometric plethysmo~gaphy) and metabolic

CO? production across 24 hour periods in six freely behaving rats. the present study tested

and confirmed the hypotheses that: (1) there are circadian rhythms in lung ventilation in

wakefulness. non rapid eye movement (NREM) sleep and REM sleep (al1 p<0.001). and (2)

the magnitude of the change in ventilation From wakefulness to NREM sleep is the same

across time of day (p=0.58).

III

Acknowledeements

"Give thanks to the Lord for He is good." (Psalm 1 36: 1 )

1 am grateful to God for His many blessings that He has given me throughout my

life and over these two years in this graduate expenence. He has been faithful and

provided for my needs at every step of the way. He has blessed me with a whole host of

people who have guided me when 1 was lost and perplexed. rncouraged me when I was

discouraged. picked me up when 1 fell. endured with me during tough times. strengthened

me when 1 was weak. challenged me when 1 became complacrnt and corrected and been

patient with me when I erred. The list could go on. but can be summarised in one

sentence ... the good Lord has provided for me in every conceivable way. He has made

the biggest difference in my life as 1 have my walked with Him.

1 would like to thank rny father. Pao Chun Liao and my mother. Chiao Yun Kuo

without whose love. sacrifices. hard work. encouragement and suppon. I would not have

had the many countless opportunities such as being able to attend university or do my

graduate studies. They have modelled canng, giving and dedication. 1 am grateful to my

brothers. Choy Sen and Liung Sen for al1 their love and support. 1 am also mindful of the

immense contribution that my late grandmother made in innumerable ways during my

upbringing. l am also thankful to Yuanni for al1 the help she has given me dunng this

projec t.

Over the past two years. I have worked under the joint supervision of Dr. Richard

Homer and Dr. Richard Stephenson. Both of thesr "chiefs" are very dedicated and

talented scientists. 1 am very grateful for al1 their guidance. advice. kindness. patience

and suppon in providing a very ennching Master's expenence. Through their course and

in the lab. both of then taught me to think critically and to ask questions in addition to a

lot of usefùl knowledge in research. sleep. breathing and chronobiology. 1 will remember

Dr. Horner for his generosity. efficiency. drive and attention to detail. 1 would like to

dedicate Figure 11 to him. 1 will remember Dr. Stephenson for his humility. gentlenrss.

wittiness and ability to put things in perspective.

I am grateful to Dr. James Duffin and the members of the Respiratory Research

group. Their constructive criticism and feedback has been invaluable. 1 would like to

thank Dr. Dufin for his graduate courses in Respiratory Physioloa and Electronics. both

of which were tremendous leaming opportunities. 1 would also like to thank him along

with Dr. Martin Ralph for their time and effort in serving on my M.Sc. supervisory

committee. 1 am gratehl to Dr. Scott Thomas and Dr. Dina Brooks for serving on my

M .Sc. examining committee.

1 want to thank my fhends and fellow graduate students in the lab. Hedieh

Hamrahi and Sandeep Sood. 1 have valued their encouragement. support. Company and

humour deeply. I would like to thank Beverly Chan for helping me with the data analysis

and surpries involved in my project. 1 also thank Hattie Liu and Xia Liu for their

encouragement.

1 b i l l not forget the many ways that my friends and teachers at Hebron School

and Chan and the staff at Shinkows Chinese Restaurant have blessed me richly. 1 am

thankfùl for al1 the prayen and support from my many aunts and uncles and friends at

Fint Alliance Church and St. John Ambulance Scarborough 432. Thcir warmth.

generosity and friendship meant a lot to me during the hard times.

The present study would not have been possible without hnding frorn the

Medical Rcsearc h Council of Canada. the Nat ional Science and Engineering Council of

Canada and the Ontario Thoracic Society. 1 am grateful for the Ontario Graduate

Scholanhip in Science and Technology which helped me finance this degree.

Table of Contents

Contents

Chapter 1: Introduction Ci rcadian Rhyt hms Sleep-wake States Replation of Respiration Effect of Sleep-wake State on Breathing Circadian Modulation of B reathing Hypot heses Rational for Hypotheses

Chapter 2: .Methods Expenmental Protocol Animal Preparation Expenmental Apparatus Data Analysis

Chapter 3: Results Section 1: Testing Hypothesis 1 and 2 Section 2: Additional Circadian Rhythms Analyses Section 3: Additional Analyses on Effects of Sleep-wake State Section 4: Additional Day-night Cornparisons

Chapter 4: Discussion Technical considerations C ircadian related changes in lung ventilation Potential rnechanisms rnediating the circadian rhythms in lung ventilation The circadian rhythm in ventilation nomalized for COz production in

NREM sleep Sleep-wake related changes in breathing. rnetabolism and body temperature Future research opportunities Conclusions

References

Page

List of Abbreviations Used

SCN

EEG EMG EOG NREM REM Wake

8 1

n 0 a Pi

P z %Bz/%6i

Tb

Mb

BTPS

PB Pb~20 PCH~O

SwvP STPD

Circadian Rhythrns Suprac hiasmatic nucleus

Sleep Electroencep halogram Elec tromyogram Electrooculogram Non-rapid eye movement sleep Rapid eye movement sleep Wakefiilness

EEG freattencies Delta 1 (0.5-2Hz) Delta 2 (24Hz) Theta (4-7.5 Hz) Alpha (7.5- 13 5 H z ) Beta 1 (1 3.5-20Hz) Beta 2 (20-30Hz) Ratio of high (p?) to low (61)

E EG frequencies

Other Core body temperature

(degrees Celsius. OC) Body mass

Conditions Body temperature pressure

(-37°C. 760mrnHg) Barometric pressure Alveoli water vapour pressure Water vapour pressure in

animal chamber Saturated water vapour pressure Standard temperature pressure

(273 Kelvin. 760 rnrnHg)

CV

CO? DRG f

H+ mmHg ml mumin PaCOl

phCo2 PCOl

Pa02

PO2

0 2

t r t~

TOT Y .A

Y COz

Y,

i' ,/i' COz

Y ,Y CO?

VRG VT

ANOVA

Respiration Coefficient of variation

(percent. %) Carbon dioxide Dorsal respiratory group Respiratory frequency (breaths

per minute Hydrogen ion Millimetres of mercury Millilitres Millilitres per minute Artenal partial pressure of

carbon dioxide Alveolar partial pressure Partial pressure of carbon

dioxide Anenal partial pressure of

oxygen (mmHg) Partial pressure of oxygen

(mmHg) Oxygen Inspiratory duration (seconds) Expiratory duration (seconds) Total breat h durat ion (seconds) Alveolar ventilation (ml/ min) Rate of production of CO?

(BTPS, mumin) Lung ventilation (inspired)

(ml /min) Lung ventilation normalized

for COz production Alveolar ventilation

normalized for ventilation Ventral respiratory group Tidal volume (ml. BTPS)

Statistics Analysis of Variance

List of Figures

Figure Number and Figure Contents

Introduction

1 - 1 : Graphical representation of a sine wave. 1 - 1-2: Schematic of the respiratory control system. 1 - 1 -2: Oxygen saturation and the ventilatory response to O?.

1-4: Metabolic hyperbolae at two different metabolic rates. 1 - 1-5: Metabolic hyperbola and the ventilatory response to COz. -

1-6: The chernoreflex control of breathing. 1 - 1-7: Mechanism of decrease in ventilation and increase in PaC02 on going -

from wakefulness to NREM sleep.

1-8: Pictorial representation of the hypotheses tested in the experiment. 1 - 1-9: Mechanisms of sleep-wake and time of day changes in lung ventilation in -

the rat.

1 - 10: Hypnogram of the rat sleep-wake cycle. -

Methods

2-1 : X-ray of EEG and EMG electrodes and the telemetry unit implanted in the - rat.

2-2: Schematic of experimental apparatus. 1 - 2-3 : Graphs of inspired Fractional CO2 concentration vs. time of day. 1 - 2-4: Correlations of inspired CO: (%) vs. deviations from mean lung -

ventilation.

Page

Figure Xumber and Figure Contents (continued)

Results

3-1: Raw EEG. EMG and breathing traces. -

3-2: Graphs of %Bz/%6i, EEG and EMG amplitude in each sleep-wake state - across time of day.

3-3: Graphs showing circadian rhythms in iung ventilation in wakefulness. - NREM and REM sleep.

3 4 : Comparisons of lung ventilation at two tirnes of day. -

3-5: Plots of metabolic COz production. ventilation normalized for CO, - production and body temperature across time of day.

3-6: Graph of phase relationships between lung ventilation. metabolic rate. - ventilation normalized for CO? production. body temperature and respiratory frequency.

3-7: Variability of breathing across time of day. -

3-8: Sleep-wake state etTects on breathing. -

3-9: Sleep-wake state effects on metabolism. ventilation normalized for CO2 - production and body temperature.

3-10: Additional day-night cornparisons in breathing. metabolism and sleep - related variables.

Discussion [llustration of conclusions.

Page

Chapter 1: Introduction

The regulation of respiration is highly complex and lung ventilation may be

modified by many factors such as sleep-wake states and metabolic rate. However, very

few studies have examined whether the circadian timing system plays a role in

modulating ventilation or how sleep-wake states, metabolism and lung ventilation

interact with circadian time. This topic is the focus of this thesis. This research may be

potentially important in revealing the role of the circadian timing system in the seventy

of common respiratory disorders such as hypoventilation syndromes. Speci fically, a

noctumal decline in ventilation may add to and exacerbate, the decline in lung ventilation

due to sleep and produce severe respiratory impairment in susceptible individuals.

With this in mind, the purpose of this study was two fold. First, to determine

whether circadian rhythms in lung ventilation are present in each sleep-wake state. The

second aim of the study was to determine whether the change in magnitude of lung

ventilation from wakefulness to sleep is the same across the day. Pnor to discussing the

methodology and answers to these questions, however, it is necessary to provide a basic

overview of circadian rhythms, sleep-wake states and the regulation of breathing to act as

a hmework with which the present study can be undentood. By no means, however, is

this intended to be a comprehensive review of each of these vast and complicated fields.

Rather, the discussion will be limited to phenornena most relevant to the present study.

CIRCADIAN RHYTHMS

Cyclical variations in behavioural and physiological variables are an inherent

property of living organisms (Aschoff, 198 1). Perhaps the most widely studied of these

rhythms across a large range of species, are those that are approximately 24 hours in

duration. i.e., circadian rhythrns. Circadian rhythms can be observed at many levels of

organization ranging from gene transcription to the physiological and behavioural levels

(Moore, 1999). In this study, the circadian rhythms of the sleep-wake cycle. body

temperature, metabolism and lung ventilation are of interest. Hence, greater emphasis

will be paid to these circadian rhythms rather than the multitude of others that have been

studied. However, in this section, these fonner three will be mentioned whilst over

viewing sorne of the basic concepts in circadian rhythms. The founh mentioned rhythm,

lung ventilation, is linked to the other three, but since it is the focus of this thesis, it will

receive greater attention towards the end of this introductory section.

Circadian rhythms, by definition, are cyclical and are conveniently represented

graphically as a sinusoidal waveform. There is an associated set of terminology relevant

in their discussion - period, mesor, minima and maximum value, amplitude and

acrophase. These terms are illustrated in the Figure 1-1.

The phase of a rhythm refers to the time of an instantaneous point in the cycle

relative to some other point. Phase relationships are often important in determining

possible causal relationships between two oscillating variables. If for example, two

oscillating variables have the same phase they could be dependent on each other. The

period is the time interval between recurrences of a defined phase of a rhythm. Circadian

rhythms have a penod of approximately 24 hours. Hence, the origin of the term circadian

(fiom circa meaning about, and, dies meaning day or about 24 hours). If the period is

less than 24 hours, it is termed an ultradion rhythm. A rhythm that is greater than 24

hours is tenned an infradian rhythm. When an organisrn is entrained or synchronized to

an exogenous (extemal) 12- hour light 12-hour dark cycle. the period of a measured

physiological or behavioural rhythm will also be 2 1 houn. However. if placed in constant

light or dark. the period of a circadian rhythm will begin to free run with a period that is

close to, but not exactly. 24 hours (Pittendrigh. 1960).

The mesor is the anthmetic mean of al1 instantaneous values of an oscillatory

variable within one cycle. The minima and maximum are as their names suggest (see

Figure 1 - I ). They are usually represented as the trough and the peak of a waveform of the

dependent variable plotted against time. The amplitude of a rhythm is the magnitude of

the difference between the rnesor and the peak or the trough value. The acrophase of a

rhythm refers to the time value corresponding to the peak of a sine funcrion.

Fipure 1-1: Craphical representation of a sine wave and the associated terminology.

I Period ,-4

Minimum (Troughi

A cornmon method of assessing whether a physiological or behavioural variable

possesses a circadian rhythm is cosinor analysis (Nelson, Tong, Lee, & Halberg, 1979).

In this method, in which a cosine or sine wave is fitted to the raw data by least squares,

the amplitude, mesor and the acrophase can easily be detennined. However, information

on the actual shape of the waveform of the vanable plotted across time is assumed to be

sine or cosine.

The circadian rhythms in body temperature are one of the most widely measured

of physiological rhythms in mammals and birds. The rhythm in body temperature is a

fairly robust rhythm that is present in many species, and under many expenmental

conditions. Consequently, it is ofien used as a reliable marker of the circadian phase of an

organism (Refinetti & Menaker, 1991). The amplitude of the body ternperature rhythms

in most homeotherms are fairly low, given that there is only a deviation of a one or two

degrees from a mesor value (in the range of 37-39°C). Under entrained conditions, body

temperature maximum occurs dunng the active portion and the minimum occurs during

the rest portion of the rest-activity cycle. Thus, for noctumal animals such as rats, which

are more active dunng the night, the maximum occurs during the dark phase of the light-

dark cycle. For these organisms, the body temperature minimum will occur during the

light phase when they are mainly resting (Refinetti & Menaker, 1991). For diurnal

organisms such as humans, the reverse is true - body temperature is higher dunnp the day

and low the body temperature minimum occurs a few houn pnor to waking (Czeisler,

Weitzman, Moore-Ede, Zimmeman, & Knauer, 1980).

Two fundamental propenies of circadian rhythms are their generation by

endogenous pacemakers and entrainment by environmental stimuli (Moore, 1997). One

possible explanation for the presence of a circadian rhythm is that it represents a response

to an exogenous cycle such as the extemal light-dark cycle, which also, has a period of 24

houn. However, this is not the case, given that circadian rhythms such as the circadian

sleep-wake cycle persist in studies where humans, for example, have been placed in

environments devoid of any time cues (e.g., Dijk & Czeisler, 1995). Under these constant

conditions, circadian rhythms become free mnning with their intrinsic period that is close

to, but not exactly 24 hours. Together, these data suggest that circadian rhythms are

endogenously generated. In order for the endogenously generated rhythm to be

synchronized to an exogenous cycle, the pacemakers mnning these rhythms must be reset

or entrained by exogenous stimuli known as zeitgebers (meaning time giver) (Moore,

1997). Thus, in the presence of a 12-hour light: 12- hour dark, light-dark cycle, as was the

case in Our experiment, al1 circadian rhythms would be expected to have a penod of 24

hours.

Zeitgebers (clock resetting stimuli) may be broadly classified into two categories

- photic (light based) stimuli or non-photic (non light) based stimuli. As judged by its

ability to cause large phase shifis in circadian rhythms, light is the most potent zeitgeber

(Pittendrigh, 1960). Examples of non-photic stimuli include social interaction

(Mrosovsky, 1988) and exercise (Edgar, Martin. & Dement, 199 1).

It is widely accepted that circadian rhythms are generated endogenously within

cells, tissues and organs (Moore, 1999). Pacemaker clocks, of which there are several, are

capable of synchronizing these inherent rhythms with different penods. in mammals. the

suprachiasmatic nucleus (SCN) located in the anterior hypothalamus has been established

as the pnmary pacemaker or time keeping mechanism (Ralph, Foster, Davis, & Menaker,

1990). When lesioned in rats for example. it results in the loss of many circadian rhythrns

such as locomotor and drinking behaviour (Stephan and Zucker. 1972). adrenal

corticosterone (Moore and Eichler. 1972). sleep-wake cycles (Eastman. Mistleberger. &

Rechtshaffen. 1983: Ibuka & Kawamura. 1975). metabolic CO2 production (Nagai.

Nishio. & Nakagawa. 1985) and body temperature (Eastman et al.. 1983). Common to

al1 these experiments. is the disruption of the temporal organization of these

physiological and behavioural variables when the SCN is lesioned. thus illustrating the

role of the SCN as a timekeeper. What is not changed. however. is the quantity of each

variable. For example. when SCN lesions destroy the circadian sleep-wake rhythm in

rats. the total arnount of time spent awake and asleep and the proportion of NREM and

REM sleep remains (Eastman et al.. 1983).

Perhaps the most convincing evidence for the importance of the SCN as the

primary time keeping mechanism in mammals are Iiom SCN transplant experiments

çonducted by Ralph et al.. (1990). When fetal SCN were transpianted into arrhythrniç

SCN lesioned animals. circadian rhythms were restored. Moreover. the restored rhyihms

had a period charactenstic of the SCN donor. These experiments drmonstrated that in

mammals. the SCN is the site of the circadian pacemaker. and that the intrinsic period of

a çircadian rhythm is penetically determined.

In addition to studies related to the SCN. the neural pathways by which external

eues such as light and social interaction are able to influence the SCN and its output to

various physiological systems have been investigated. There are several afferent inputs

into the SCN which mediate entrainment (Moore. 1997) such as the retinohypothalmic

tract. the interpniculate leaflet and the ventral lateral geniculate nucleus. Photic input

from the visual system is sent directly via the retinohypothalmic tract to the SCN

(Johnston, Moore. & Morin. 1988). Photic information is also sent indirectly via the

intergeniculate leaflet (Morin. Blanchard. & Moore. 1992) and the LGN (Card & Moore.

1982). The lateral pniculate nucleus. which has been shown to be important in mediating

non-photic entrainment. is also thought to be involved in integrating photiç and non

photic entraining information to the SCN (Moore. 1997).

The SCN. which shows a circadian rhythm in neuronal firing (Inouye &

Kawamura. 1979). is able to çontrol circadian rhythms such as sleep-wake and drinking

rhythms by efferent projections to regions involved in the control of these functions.

Although the number of projections from the SCN is relatively few. there are many areas

that reçeive seçondary projections from the SCN. The primary projections from the SCN

include the subparaventricular zone and the paraventriçular nuclei of the hypothalamus.

with lesser ones to the basal forebrain and the midline thalamus (Watts. 199 1 ). Primary

projections to the hypothalamus result in circadian information being relayed to the

anterior pituitary. the hypothalamiç. and the brainstem reticular formation regions

associated with autonomie regulation. the control of merabolism. body temperature and a

temporal organization of sleep-wake cycles (Moore. 1997)

It has often been proposed thai by usine an interna! time keeping mechanism. an

organism is better adapted to its environment. not only in space. but also in time (Moore-

Ede. 1986). This would enable orpanisms to predict and anticipate various occurrences in

the extemal environment such as availability of food. and thus. be able to synchronize

both behavioural and physiolopical changes to these events.

SLEEP-WAKE STATES

Like circadian rhythm, sleep-wake states are an integral component of

mammalian behaviour. Sleep is an essential behavioural phenomenon known to exist in

al1 marnmalian species studied so far (Zepelin, 1994). indeed, the consequences of sleep

deprivation can be debilitating and even cause death if extended for a sufticient duration

of tirne (Rechtschaffen. Bergmann, & Winter, 1983). Yet, currently the function of sleep

is both unknown and highly controversial (Rechtschaffen, 1998), but there is general

agreement that it is necessary, restorative, and beneficial.

Sleep is not a hornogeneous phenomenon, but rather, consists of two

fundamentally distinct neurophysiological states - rapid-eye-movement (REM) sleep and

non-REM (NREM) sleep (Aserinsky & Kleitman, 1953; Dement & Kleitman, 1957). One

of the most obvious charactenstics of sleep is that it possesses a circadian rhythm. which

is controlled by the SCN (Ibuka & ffiwamura, 1975). The quantity and duration of sleep

is species (Zepelin, 1994) and age dependent (Bliwise, 1994). However, in general,

within these bouts of sleep, M E M sleep and REM sleep Vary cyclically with an ultradian

rhythm whose duration and timing is also species and age dependent. For example, in

adult humans, who are primarily diumal, sleep is generally consolidated to an average

eight hour period during the night, or dark phase. of the light-dark cycle. Within this

period sleep consists of cycles of NREM sleep and REM sleep Iasting approximately 90

minutes. This is in contrast to smaller animals such as noctumally active rats, which

sleep in both light and dark phases. but have the majority of their sleep (approximately

65%) occumng during the day or light phase. Their sleep-wake cycles are also of a

shorter duration than humans (Trachsel, Tobler, & Borbely, 1 986).

The mec hanisms regulating sleep timing, duration and propensity are largely

unknown. However, sleep regdation is thought to depend on the interaction of circadian

and homeostatic mechanisms (Borbely & Achermann, 1999; Dijk & Czeisler, 1995). The

basis of the concept that sleep is homeostatically regulated comes frorn sleep deprivation

experiments where it has been observed that sleep depnvation is usually followed by

sleep compensation or rebound sleep. This rebound sleep that follows. rareiy

compensates for the lost sleep time, but it may be more intense as indicated by a larger

proportion of the "deeper" stages of sleep (Tobler, Borbely, & Groos, 1983; Borbely &

Achermann, 1 999). However, the timing and duration of these bouts of rebound sleep are

dependent on the circadian phase of the body temperature rhythm indicating that sleep is

also regulated by a circadian component (Edgar, Dement, & Fuller, 1993; Borbely &

Achermann, 1999). Although it is known that there is a circadian component to sleep

timing, the mechanism, or mechanisms. by which circadian oscillators modulate sleep

timing and structure remain largely unknown.

Identification and organization of sleep-wake states

At any given time of the day, mammals are in one of three sleep-wake states -

wakefulness, NREM and REM sleep. NREM sleep is often called quiet sleep or

synchronized sleep, whereas REM sleep is oflen referred to as active. desynchronized,

paradoxical or dreaming sleep. Each of these sleep-wake states, which have an

underlying anatomical, neurophysiological and physiological basis, can be precisely

identified by a combination of behavioural and electrophysiological characteristics

(Carskadon & Dement, 1994; Rechtschaffen & Kales, 1968).

Behaviourally, wakefulness. is characterized by eyes that are open and a mental state

that is alert and responsive to the environrnent. Likewise, sleep can be characterized

behaviourally by four cnteria. Firstly, there is very little movement during sleep.

Secondly, there is a stereotypic posture associated with sleep, e.g., humans tend to sleep

lying down. Thirdly, there is a reduced response to stimulation and an increased threshold

for arousal. Fourthly, it is reversible. That is. a sleeping organism can easily be

awakened, but the same cannot be said of death, anaesthesia or coma.

Earlier studies of sleep-wakehlness states were primanly defined by behavioural

criteria. Over the last few decades, electrophysiological recordings in the form of the

electroencep halogram (E EG), electromyogram (EMG) and the electrooculograrn (EOG)

have become the standard means of distinguishing sleep-wake states (Phillipson &

Bowes, 1986). The advantage of these physiological measures which correlate well with

the behavioural measures, are that they are more accurate at measuring central nervous

systern state than behavioural observation. For example, a person lying down with eyes

shut may appear asleep, but in fact, may be awake. Furthemore, and more importantly,

these measures are able to discem NREM fiorn REM sleep, as well as subdivisions

within these two sleep-wake states.

nie EEG is based on recording the summated wavefom activity at the surface of

the skull which is thought to reflect activity of large numben of underlying cortical

neurons. The EMG measures the surnmated potentials of muscle activity using electrodes

placed on the skin or surface of muscles (usually From the neck). For the EOG, the

potentials generated from eye rnovements are measured. in many cases, just the EEG,

and, the EMG or the EOG are suficient to make accurate judgments of the sleep-wake

state. Using the EEG alone, it is difficult to distinguish wakefulness and REM sleep

primanly because they are both states of central nervous system activation. This is

reflected by the desynchronized EEG pattern (consisting of medium to high fiequencies

and low amplitude EEG waveforms) in both wakefulness and REM sleep. To distinguish

these two sleep-wake states, the EMG is necessary to assess postural muscle tone, which.

is characteristically absent during REM sleep, but is present during wakefulness (see

Figure 3-1 in RESULTS).

REM sleep. however, is not a homogenous sleep-wake state but is classified as either

tonic or phasic REM sleep. Whilst the EEG is desynchronized in both tonic and phasic

REM sleep. the presence of rapid eye movements and twitches of othenvise atonic

muscles distinguishes p hasic REM sleep (Cankadon & Dement, 1994; Rechtschaffen &

Kales, 1 968).

Human NREM sleep has ofien been divided into four stages - stage 1, stage 2, stage

2 and stage 4. As one progresses from stages 1 and 2 (often referred to as "light" sleep) to

stages 3 and 4 (often referred to as "deep" sleep), there is a decrease in the fiequency and

an increase in the amplitude of the EEG waveforms. Due to the low fiequencies of the

EEG in the deeper stages of NREM sleep, stages 3 and 4 are commonly referred to as

slow wave sleep (Cankadon & Dement, 1994; Rechtschaffen & Kales, 1968).

The fiequencies of the EEG are commonly divided into bandwidths called alpha,

beta, theta and delta. During wakefulness, the EEG generally possesses waves in the

alpha and beta bandwidths. Alpha activity consists of regular medium fiequency waves

of 8-1 2 Hz, and occurs during rest, when a subject is not panicularly aroused or engaged

in strenuous mental activity. Alpha rhythm is more prevalent when the eyes are closed.

Beta activity, consisting of regular and mostly low amplitude waves of 13-30 Hz, is

present when subjects are alert and attentive to their environment. Beta activity also

occurs in REM sleep. Theta activity (4-7 Hz) occurs when the subject is drowsy and is in

transition from wakefulness to NREM sleep. As NREM sleep deepens, there is a

progressive increase in delta activity, which consists of low frequency (0.5-4 Hz) and

high amplitude EEG waveforms (Rechtschaffen & Kales, 1968).

The physiological basis of sleep-wake states has been the subject of much research.

Numerous techniques, such as lesioning, single ce11 recordings and stimulation by

chemical and electrical means, have been employed to elucidate the neural mechanisms

of sleep. Several different loci in the hypothalamus and the brainstem have been

implicated, but no single region of the brain has been defined as the "sleep centre".

[nstead, the genesis of sleep-wake states appears to be the product of interaction of

clusters of nerve cells at various sites in the brain, including the reticular formation. the

serotonergic neurons of the dorsal raphe and the nor-adrenergic neurones of the locus

coeruleus (Jones, 1994).

The reticular formation, a diffise system of nerve ce11 bodies and fibres in the brain

stem, running from the medulla to the thalamus, plays a major role in the generation of

wakefulness. Stimulation of this area, for example, leads to arousal in an otherwise

sedated animal (Morruzi & Magoun, 1949). Within this area are neurones of the lateral

donal tegmental nuclei and pedunculo-pontine nuclei (Stenade, Datta, Pare, Oakson, &

Cun-Dossi, 1990). Also involved in wakefulness and EEG desynchronisation are areas

in the basal forebrain. The newotransrnitters involved in wakefulness, and their sites of

release (in brackets), include acetylcholine (basal forebrain and pons), histamine

(tuberomammillary nucleus), norepinephnne (locus coeruleus) and serotonin (dorsal

raphe) (Shiromani, Scarnmell, Shenn, & Saper. 1999). Tonic activity from these regions

is relayed via ascending pathways to the cerebral cortex to produce cortical activation

that occurs during wakefulness (Szymusiak, 1995).

While wakefulness is the result of the excitation of the reticular activating system,

NREM sleep is charactenzed by inhibition of this region by the inhibitory

neurotransmitter, G A B k from the ventrolateral preaptic area (Shiromani et al., 1999).

There are also regions in brain that have been implicated for triggenng NREM sleep such

as the preoptic anterior hypothalamus and basal forebrain pre-optic areas (Shi romani et

al., 1999). Lesioning these areas results in long lasting insomnia (Nauta. 1 946), electncal

stimulation leads to a NREM sleep like pattern on the EEG (Sterman & Clemente. 1962),

and recordings of cells in this area show cells which change their firing rate in NREM

sleep (Szymusiak & McGinty, 1986).

Cholinergic and cholinoreceptive pontine reticular formation neurons are thought to

play an important role in regulating REM sleep. Lesions of the dorsolateral pontine

tegmentum (including the lateral dorsal tegrnental nuclei and pedunculo-pontine nuclei)

can disrupt and eliminate REM sleep (Webster & Jones. 1988). The muscle atonia that is

characteristic of REM sleep is thought to occur by postsynaptic inhibition and

disfacilitation (i.e., a reduction in the tonic discharge of tonically active presynaptic

excitatory neurons) of spinal motoneurons via brain stem mechanisms (Chase & Morales,

1 994).

In addition to understanding the generation of sleep-wake states. the profound impact

of sleep-wake states on many aspects of regulatory physiology have also been studied

especially when the sleep-wake state have clinical implications. However, addressing the

many changes that occur for example, in the cardiovascular system or autonornic nervous

system with sleep, is beyond the scope of this thesis (see Coote, 1982; Horner, 2000 for

reviews). The impact of sleep-wake state on the respiratory system will be discussed, as

it is important in understanding the impact of sleep-wake States on lung ventilation. Pnor

to doing so, it will be necessary to overview important concepts in the regulation of

breathing.

REGULAT~ON OF RESPIRATION

One of the most fundamental functions of breathing is to facilitate gas exchange

across the lungs, and in doing so, supplying oxygen (O2) and removing carbon dioxide

(CO2) to and h m the tissues. Oz is an essential substrate and COz is the byproduct of

cellular aembic catabolism, the process by which many organisms consente vital

chemical energy in the form of adenosine triphosphate (ATP). When the lungs are

ventilated, the diffision of O2 and COr along their partial pressure gradients across the

alveoli (the gas exchange portions of the lungs) is facilitated. Using the respiratory

control system to regulate lung ventilation, the body can maintain the blood gas and

hydrogen ion concentrations in arterial blood within a narrow range. Breathing can be

altered by many factors such as emotion, speech, exercise etc. However. the present

discussion will be limited to factors goveming the control of resting ventilation, as this is

the focus of this thesis. In this regard. in this section, an oveMew of the respiratory

system will be given, and mechanisms such as the central and petipheral chemoreflexes

and the effect of wakefulness involved in determining resting lung ventilation will be

discussed.

The respiratow control system

Essentially, breathing is controlled by two anatomically separate. but Functionally

integrated elements referred to as the metabolic (or automatic) control system and the

behavioural (or voluntary control system) (see Figure 1-2) (Berger. Mitchell. &

Severinghaus. 1977: Phillipson. 1978). The metabolic control system onpinates in

brainstem (pons and the medulla) structures and is primarily concemed with blood gas

Fipure 1-2: Schematic of the respiratory control system and its cornponents (modified from P hillipson, 1978)

Controllers

Cerebral Cortex

(Behavioural control)

Retfcular Activatlnn - System Brain Stem

v Wakefulness +f (~etabol ic controi 1.e. 1-. I

Stimulus) Wood Ga. hom.ostasis~+ 1

Respiratory I

Motonmurons I 7

Oiaphragm &

Intercostal Muscle

Upper Almays : 8 tungs

I

Upper Airway and Lung Receptors

Propnoreccpton

Penpheral and Central Chemorecepton 1

homeostasis. The behavioural control systern anses in cortical and forebrain stmctures

and is involved in activities such as speech, which utilize the respiratory system for non-

respiratory functions (Phillipson, 1978).

The metabolic control system can be conceptualized as a feedback control system

that is composed of a central controller, effectors and sensors (see Figure 1-2). Each

component of the metabolic control system has been studied separately using a vanety of

neurophysiological and neurohistological techniques. In addition, the integrated

responses of this metabolic control system have been studied using a variety of stimuli

such as hypercapnia (high COr) and hypoxia (low Oz) in animals and humans in much

detail (see Cunningham, Robbins, & Wolff (1986) for review). The present study,

involving the measurement of lung ventilation in each sleep-wake state under normal

room air conditions, represents a study of the integrated response of the respiratory

control system across time of day.

The central controller, located in the brainstem, is the site of the respiratory

rhythm generator needed for automatic breathing (Berger et al., 1977; Feldman & Smith,

1995). Evidence for this cornes From intact brainstem and spinal cord preparations, which

can generate respiratory motor nerve output in the absence of rostral or sensory afferents.

in neonatal and fetal rats, respiratory related patterns continue following removal of the

brainstem (and spinal cord) to an in vitro chamber, and, an isolated section of a particular

slice of the medulla will even generate respiratory related rhythms (Smith, Ellenberger,

Ballanyi, Richter. & Feldman, 1991). These experiments support the existence of a

pacemaker mode1 of respiratory rhythm generation and the region hypothesized to be

involved is the pre-Botzinger region (Smith, Ellenberger, Ballanyi, Richter, & Feldman,

199 1; Feldman & Smith, 1995).

Located in the dorsal respiratory group (DRG) and ventral respiratory group

(VRG) of the medulla oblongata are respiratory neurons whic h display sync hronized

activity wit h both inspiration and expiration. DRG neurons are mainly inspiratory, whilst

neurons in VRG, contain both inspiratory (located more rostrally) and expiratory (located

more caudally) neurons. initially, the DRG and the VRG were thought to contain the

respiratory rhythm generator, but it is now believed that respiratory rhythms are

generated in the rostral ventrolateral medulla. (Duffin, Emre, & Lipski. 1995). Evidence

for this cornes fiom the ceasing of respiratory rhythmogenesis following local cooling of

this region for example (Budzinska, Euler, Kao, Pantaleo, & Yamamoto. 1985).

Aithough the locus of respiratory rhythms has been detemined, the mechanisms by

which it is generated remains to be elucidated. Currently, there is evidence for pacemaker

models in neonatal and fetal rats as alluded to earlier. However in adults, the absence of

cells with pacemaker like properties make network rnodels of rhythm generation appear

to be more likely (Duffin et al., 1995).

The rhythmic impulses fiorn the respiratory rhythm generator are relayed to spinal

and cranial motoneurons, which in tum, innervate pnmary and secondary respiratory

muscles involved in generating airflow and maintaining an effective air passage fcr this

airflow. The pnmary respiratory muscles, such as the diaphragm and the intercostals,

when contracted during inspiration, create a negative pressure by increasing the volume

of the c hest cavity, thereby generating airtlow into the lungs. When these muscles relax

on expiration, air is expelled from the lungs. The electrical impulses leading to

contraction of the diaphragm and the intercostal muscles, originate From motoneurons

located in the ventral horn of the spinal cord and, are relayed to these muscles via the

phrenic and the extemal intercostal nerves, respectively. The secondary respiratory

muscles such as the laryngeal, pharyngeal and hypoglossal (skeletal) muscles modulate

airway resistance and are innervated by the cranial motoneurons (Feldman & Smith.

1995).

Respiratory motoneurons receive both phasic and tonic afferent inputs that affect

their excitability and output (Orem, 1994). Phasic input is received from the respiratory

rhythm generator neurons in the brain stem. Tonic inputs, on the other hand, are of many

different forms and types. Examples of tonic afferents include inputs from the sensors of

the respiratory control system such as lung stretch recepton, central and penpheral

chemoreceptors and non-specific activity of the reticular formation (Phillipson & Bowes.

1986). The lung stretch receptors are located in the muscular portions of the walls of the

Iower airways (bronchi and bronchioles) throughout the lungs, and are innervated by the

vagus nerve (Guyton & Hall, 1996). When the lungs become inflated, they increase their

rate of firing, and consequently, terminate further inspiration, prolong expiration and

decrease respiratory rate, in what has been termed the Hering-Breuer inflation reflex. h

this way, excess inflation of the lungs is prevented (Berger et al., 1977). The afferents

fiom the central and peripheral chemorecepton, which are vital in acid base and blood

gas homeostasis, and the reticular formation, will be discussed in greater depth below.

When these afferents are abolished, one by one, in intact unanaesthetized dogs, there

is a step wise reduction in respiratory Frequency and lung ventilation corresponding to the

progressive suppression of the major respiratory drives related to wakefùlness, vagal,

penpheral and central chemoreceptor stimuli (Sullivan, Kozar, Murphy, & Phillipson,

1978). Moreover, this result also suggests that individual afferent inputs may be summed

to determine the overall level of tonic drive needed for ventilation.

Chemical control of breathing

The central and peripheral chemoreceptors, which constitute an essential part of

the chemical regulatory mechanisms of the respiratory control system, adjust ventilation

in such a way that alveolar and artenal pariial pressure of CO2 (PC02) is tightly

regulated. These sensors form one of the most important afferent inputs involved in the

regulation of breathing under normal conditions. indeed in models of the regulation of

breathing in the resting state, breathing is often assumed to be determined to a large

extent by chemical stimuli and wakefulness (Duffin, 1990). Therefore, understanding the

fûnction and behaviour of central and peripheral chemorecepton, and the role of

wakefulness, is usefd in providing insights into factors and mechanisms which may

possibly govern circadian rhythms in lung ventilation in any given sleep-wake state.

Stimuli involved in control of breathiog

Both the central and penpheral chemoreceptors are sensitive to H+ and CO2, but

only the peripheral chemorecepton are sensitive to Oz (Gorualel Almaraz, Obeso, &

Rigual, 1992). Of these three chemical stimuli, CO2 and / or H+ have been shown to be

more important in controlling lung ventilation under normoxic (normal room air)

conditions in which the fiactional concentrations of Oz and COz in ambient air are 2 1.9%

(16OmmHg) and 0.03%, respectively (Ganong, 1999). The role of O2 in maintaining

normal ventilation requirements under normoxic conditions is relatively minor. hdeed,

the partial pressure of O: (PO?) may Vary quite considenbly without çhanping the total

O2 saturation of arterial blood (see

Figure 1-3 panel A). Hemoglobin.

the oxygen carrying protein in the

blood is fully saturated at normal

arterial PO? (PaO:) of 100 mmHg

(Nattie. 1999). It is also almost

fully saturated with even

considerably lower levels of

ventilation. The presence of a

sigmoid relationship between PaO?

and hemoglobin Oz saturation

means that levels of ventilation

which result in PaOl between 70

mmHg and 100mmHg have little

Figure 1-3: Graphs of the ( A ) oxygen saturation and PO2 and (B) the ventilatory response to O2 at a constant PCOz(see text for details) (modifieci from Yattie, 1999).

effect on the total amount of 0: carried in artenal blood (see Figure 1-31. At normal

levels of ventilation. the baseline firing of carotid and aortic bodies constitute a tonic

drive to the respiratory control system. Removal of this tonic drive for example by

denervation of these structures. results in Iesser resting ventilation (Olson. Vidruk. &

Dempsey. 1988). This tonic drive differs from the changes in afferent input from the

carotid body to the brain that results from minute to minute changes in PaCO? or PaO?.

The ventilatory sensitivity to PaO?. defined as the change in ventilation for a unit change

in Pa02, is low at levels just above or below the normal value of 100mmHg. Therefore.

the control of alveolar ventilation just above or below the normal value is unlikely to be

determined by the Pa02.

in contrast to the ventilatory responses of PaOz, the sensitivity of the ventilatory

response to PC02 is much higher, with only an increase of a few rnrnHg in PC02 fiom the

normal value being sufficient to stimulate ventilation significantly (see Figure 1-5). Thus.

the stimulus that provides input usetùl in the maintenance of normal ventilatory

requirements is CO2 (Nattie, 1 999).

Funher evidence for the importance of COr in normal ventilation comes fiom

experiments on awake sheep (Phillipson, Duffin, & Cooper, 1981). Using a carbon

dioxide membrane lung to add and remove COr to and ftom the lungs of awake sheep,

the level of metabolic COz production was linked to the level of lung ventilation in a

linear manner. When the rate of removal of COz equaled the rate of metabolic CO2

production by the animal, PaC02 remained normal. but ventilation ceased. Thus, these

investigators were able to show that one of the most critical afferent inputs required for

the generation of respiratory rhythms are stimuli related to metabolic CO2 production.

The linear relations hip between lung ventilation and metabolic rate expressed as

either, the rate of O2 consumption (Y 02) or the rate of COz production ( Y CO2), has also

been seen in many examples that increase rnetabolic rate, such as, exercise (Wasserman.

Whipp, & Casaburi, 1986), cold exposure or hypoxia (Saiki, Matsuoka, & Monola,

1994). It has been observed that ventilation is more closely related to Y COz than Y Oz

(Phillipson, Duffin, & Cooper. 198 1 ; Wasserman, Whipp, & Casaburi, 1986). However,

the exact nature of this link between lung ventilation and CO2 production is unclear

(Mortola and Gautier, 1995).

Given that the stimulus that is involved in the maintenance of normal ventilatory

requirements is likely to be CO2 (Nattie, 1999), attention can now be focused on factors

that govern the concentrations of this gas (quantified as partial pressure of CO?, PC02) in

arterial blood.

Determination of resting ventilation and PaC02

The normal artenal PCOr (PaCO?) in mammals of approximately 40 mmHg is

linked to acid base balance and the maintenance of a normal extracellular pH of 7.4

(Nattie, 1999). PaCOr values represent a balance between the metabolic production of

COr by the body tissues, and the amount of ventilation of the alveolar space. For

example, a decrease in alveolar ventilation ( Y a) at a constant rate of COz production

increases anerial and alveolar PC02. This increase in PCOz is detected by the

chemoreceptors leading to the stimulation of ventilation, which eliminates COz and

decreases PaC02 to its equilibrium value. From this illustration, two observations on the

relationship between PaCOz and ventilation can be made. Fintly, it is evident that

ventilation alters PaC02 at a given metabolic rate (also see Figure 1-4). Second,

ventilation is itself affected by PaC02 levels (also see Figure 1-5). The first relationship

is ofien referred to as the ventilation equation, ventilation hyperbola, or the metabolic

hyperbola. The second is called the ventilatory response to COz or the chemoreflex, and,

is the feedback component in the control of ventilation. Together, these relationships are

usefbl in conceptualking the control of ventilation as consisting of a control loop

consisting of two pans - the fonvard loop and the feedback loop (Cunningham et al.,

1986; Dufin, 1990).

Integrating the forward and feedback components involved in the chernical

control of breathing is valuable in deterrnining the PaC02 and ventilation under difTerent

conditions. in steady state, the resting PaCOz level (which will equal alveolar PC02 since

equilibration has occurred) can be calculated. Since the metabolic hyperbola and the

ventilatory response to COz share the same axes. they can be superimposed on each other

after reversing the axes of the metabolic hyperbola (see Figure 1-5). The intersection of

these graphs represents an equilibnum between the rate of production of COz and the rate

at which the lungs elirninate the CO2. This equilibrium point, which gives an indication

of the prevailing ventilation or PaCO? levels, may be altered either by shifts in the

metabolic hyperbola (caused by changes in metabolic rate). or. by changing the

charactenstics of the chemoreflex cuwe. A decrease in metabolic rate and altered

chemoreflexes dunng sleep, for example. will reduce ventilation and increase the PaC02

(see Figure 1-7). Therefore, undentanding the metabolic hyperbola and the

c hernore flexes are important in understanding and predict ing resting lung ventilation

under various conditions.

The metabolic hyperbola

The forward loop, s h o m by the metabolic hyperbola, refers to the effects that

changing ventilation has on PaCOr at a given metabolic rate. It expresses al1 possible

pain of the independent variable, alveolar ventilation (Y A) and the dependent variable.

PACOz (see Figure 14). PaCOz is proportional to the ratio of the rate of production of

CO? from metabolism (Y CO2) and the rate at which it is eliminated from the lung via

ventilation of the alveoli. It can be expressed as an equation:

PaCOz = k Y COz

Y A

where PaCOz is the partial pressure of CO? in artenal blood. Y COz is the rate of

production of COz. Y .+ is alveolar ventilation and k is a constant relating the solubility of

COz gas in blood.

Higher metabolic COz production during exercise. or during the active portion of

the rest-activity cycle. for example. has the etTect of shifting the curve to the right (Le..

for a given ventilation. PaCO? is higher). Fipure 1-4 showing rnetabolic hyperbolae for two different rates of COr production in

Converçely* a drap in metaboli' 'Oz hurnans (derived from White et al. 1985).

production dunng sleep or during the rest

phase of the rest-activity cycle. causes a

leftward shift in the metabolic hyperbola

(i.e.. for a given level of ventilation PaCOz

is lower). Since the metabolic rates oscillate

across a 24-hour penod (e.g.. in Aschoff and

Pohl. 1970). we could expect a whole

continuum of metabolic hyperbolae to Alveolar Ventilation (Umin)

represent these changes in metabolism across times of day.

The Chemoreflexes

As mentioned earlier. C02/H+ at the chemoreceptors represents a major tonic

afferent input to the respiratory rhythm generator and is consequently able to influence

respiratory output considerably. This tonic input from the central and penpheral

chemoreceptors to drive ventilation on

stimulation by COdH+ is called the

chemoreflexes (which may be

graphically illustrated by a graph of

the effects of PaCO- on ventilation -

see Figure 1-5). Also known as the

ventilatory response to CO?, the

chemoreflexes have been studied by

measunng lung ventilation to varying

PaCO? (which itself is changed by

altenng the inhaled CO2

concentrations) under steady state

conditions. or. by using techniques

Fipure 1-5 showing the ventilatory response to CO2, the metabolic hyperbola and the equiiibrium point of these two relationships to give resting alveolar ventilation and PaCOI in the awake adult human (derived from data in White (1 985) and P hitliason (1 978)).

L,

'Ë . , Ventilatory 3 ' , % Response to CO2 Y /

hyperbola

such as the rebreathing method (e-g.. Read ( 1967) and moditied rebreathing technique

Mohan & Duffin (1997)). Using these techniques. the ventilatory response to CO? has

shom to be approximately linear above a certain threshold (see Figure 1-6 panel D). The

dope of this line represents the sensitivity of the response of the chemoreceptors to C02.

The intersection of this line with the PaCO2 axis (at which point the corresponding level

of ventilation is zero) has cornrnonly been extrapolated to represent the threshold or the

PCO? value at which PaCO? no longer drives breathing. However. since there is a basal

level of ventilation independent of PaCOz in normal awake individuals. the actual value

of the threshold is the PaCOt at which ventilation increases above basal ventilation (see

Figure 1-6 panel D). This ventilatory threshold is represented by the inflection point of

the ventilatory response to CO?. To get the ventilatory response to CO1 for PaC02 values

below the ventilatory threshold, the body stores of CO2 need to be reduced by voluntary

hyperventilation (e.g in Duffin & McAvoy, 1988) or mechanical ventilation. The

resulting chemoreflex curve in humans has the shape of a hockey stick or dogleg (see

Figure 1-6). However, obtaining the COr response From resting ventilation yelds a COr

response curve similar to the one depicted in Figure 1-5.

The chemosensitivity can be altered by many conditions such as hypoxia (Mohan

& Duffin, 1997), time of day (Raschke & MIller, 1989; Spengler, Czeisler, & Shea.

2000) and sleep-wake state (Douglas, White, & Weil et al., 1982). Of direct relevance to

this study, are the impacts of sleep-wake state and time of day on sensitivity. There is a

reduction in chemosensitivity in NREM sleep and REM sleep compared to wakefulness

(Douglas et al., 1982). in addition to time of day changes in chemosensitivity. there are

also reports of circadian rhythms in threshold (Stephenson, Mohan, Dufin, & Janky,

2000). These changes in the chemoreflex, whether by altering the threshold or

c hemosensitivity, can have a marked influence on ventilation.

Ventilation fiorn any set of chemoreceptor stimuli, including those present under

resting conditions, can be predicted by adding together the contributions to ventilation of

the central and perip heral chemoreflexes, in addit ion to the c hemoreceptor independent

effect stimulatory effect of wakefulness (Duffin, 1990; Duffin et al, 2000) (see Figure 1-

6). The ventilatory responses to central and peripheral chemoreceptors simulation are

both linear, but the central chemoreflex response is more sensitive to COz / H+ than the

penpheral chemoreflex in normoxia or hyperoxia (Dufin, 1990) (see Figure 1-6 panels A

and B). However, the sensitivity of the penpheral chemoreceptors to CO2, is modulated

by the Pa02, such that sensitivity is increased at low Pa02 (Dufin, 1990) (see Figure 1-6

panel B). The nature of this interaction between low O2 and H+ is multiplicative rather

than additive (Cunningham et al., 1986; Duffin, 1990).

The central chernoreflex is mediated by the central chemoreceptors, which are

located in the brainstem and are perfused by artenal blood from the ventral surface by the

basilar artery (Nattie. 1999). Central chemoreceptors were traditionally thought to be

located on, or near to. the surface of the ventrolateral medulla (Loeschcke, 1982;

Mitchell, Loeschcke, Massion, & Severinghaus, 1963), but experiments that have

employed techniques such as focal acidification (using the carbonic anhydrase inhibitor

acetazolamide) suggest that they are present in many locations in the brainstem (e.g.,

Coates, Li, & Nattie, 1993). These areas include the locus coeruleus and the medullary

raphe (Bernard, Li. & Nattie, 1996). which are involved in arousal state regulation.

Central chemoreceptors respond to changes in hydrogen ion concentrations in their

imrnediate environment. However, this hydrogen ion concentration is more closely

related to PaCOz than arterial hydrogen ion concentration (Duffin, 1990).

The mechanism by which PaC02, rather than artenal hydrogen ion concentration,

is able to influence the hydrogen ion concentration in the vicinity of the central

chemoreceptors, involves the relatively rapid diffusion of CO2 corn arterial blood across

the blood brain banier, and, the carbonic anhydrase catalyzed reaction of CO2 with water

to form hydrogen and bicarbonate ions. in contrat to CO2, H+ in arterial blood does not

cross the blood brain bamier easily because it is a polar ion. The relationship of H+ and

CO2 in the environment of the central chemoreceptors cm be expressed using the linear

form of the Henderson Hasselbach equation: [H+] = 24 PC02/ [HC03'] (where [H+] is

the hydrogen ion concentration (in nanomoleçllitre) and [HC03'] is the bicarbonate ion

concentration (in millimoles/litre) which at normal resting ventilation is 24

millimoles/litre). It must be noted, however, that the PC02 at the central c hemorecepton

is not the same as that in arterial blood, even when fully equilibrated. It is in fact higher.

at values close to mixed venous cerebral blood because the low blood flow rates in brain

tissue cause a lag between changes in PaCOz and PCOz at the central chemoreceptors

(Dufin, 2 990).

Unlike the central chemorecepton, which respond to hydrogen ion concentration

only, the peripheral chemoreceptors, the mediators of the penp heral c hemoreflexes,

respond to both hydrogen ion concentration and Pa02. Hence, they are the primary 0-

sensors in the respiratory control system and serve a protective function against low PO2

in arterial blood (Gonzalez, Almaraz, Obeso, & Rigual, 1992). The peripheral

chemorecepton consist of the carotid and aortic bodies and are located at the bifurcation

of the common carotid arteries. The carotid bodies send afferent inputs to the brain via

the carotid sinus branch of the glossopharyngeal nerve, and fibres from the aortic bodies

ascend via the vagus nerve (Ganong, 1990). In marnmals, it is estimated that the carotid

bodies are responsible for approxirnately 90% of the response to hypoxia (Gonzalez,

Almamz, Obeso, & Rigual, 1992).

The penpheral chemoreceptors are estimated to also be responsible for 2040% of

the response to arterial hypercapnia and pH, with the remaining 50-80% of the response

to hypercapnia being mediated by the central chemoreceptors (Gonzalez, Aimaraz,

Obeso, & Rigual, 1992). This result suggests that the central chemoreceptors may be

relatively more important in seîting the level of ventilation in response to changes in

PaC02. Comrnon methods used to elucidate the relative importance of the peripheral and

the central c hemoreceptors in the vent ilatory responses to increased or decreased PaC02,

include removal of the peripheral contribution either by surgical means (Le., carotid body

denervation (Olson et al., 1988) or by the administration of hyperoxic gas mixtures (e.g

Heeringa, Berkenbosch, De Goede. & Olievier, 1979). n ie role of the penpheral

chemorecepton in contributing to the ventilatory response to CO2 under normoxic

conditions has also been estimated using these techniques. but the relative importance of

these chemorecepton in this role may Vary with species (Tenney & Boggs, 1986).

Wakefulness stimulus

in addition to the central and penpheral contributions to breathing, there is also a

neural drive to respiratory neurons that is varied between States of alertness, but is

independent of chemoreceptive stimuli. The site of origin of this neural drive is the

midbrain reticular formation, a region implicated in the generation of wakefulness. When

this region is stimulated, like wakefulness, it also increases rate and depth of breathing

(Orem, 1 994).

The influence of sleep-wake state on breathing was first demonstrated by Fink

( 196 1), who subsequently temed the stimulatory effect of wakehilness on breathing

independent of CO2, the "'wakefulness stimulus". When normal subjects hyperventilate to

lower their PaC02 below threshold leveis, they continue to breath if they are awake.

However, if asleep, drowsy or anest hetized, breathing completely ceases (apnea) after

hyperventilation (Fink, 196 1 ; Datta, Shea, Homer, G u , 199 1). Under these conditions,

the tonic stimulatory effect of wakefulness on respiratory output is no longer present and

breathing is almost entirely dependent on metabolic stimuli CO2. However. if PaC02 is

below threshold levels after hyperventilation. there is an absence of stimuli to drive

breathing. and thus. breathing ceases.

Fipure 1-6: The chernoreflex control of breathing consists of the central (panel A) and two peripheral (panels B and C) chemoreflexes. When added together with the basal ventilation due to wakefulness they can be used to predict the ventilation under a vanety of conditions. One erample is shown in panel D which shows the prediction of ventilation at different PO1 levels, including those a t normoxia (POZ=100mmHg), in awake adult humans (modified from Duffrn, 1990 and Mahamed, 2000).

C e n t r a l x Petioheral Chemoreflexes

Ventilation Ventilation Ventilation Litresfmin Litreslmin Litreslmin

A =O- B 40 - 40 '

30 ; 30 '

20 - 2o 1 10 10 -

O , I i 0 I 1 50 30 40 50 60 30 40 60 50 100 150

PC02mmHg PCOzmmHg P02mmHg

CHEMOREFLEX CONTROL OF BUEATHING

Basa1 lcvd vcntilrtioo ~uocirted with

wakrfulnas stimulus

Ventilatory reuponsrs COZunder variow P02mmHg

"1 40

Normal Equilibrium

\Ictabolic Hyperbol.

10 at R a i

A v

O 1 - I I 1

Point

Effect of Slee~-wake State on Breathing

Changes in ventilation fiom wakefulness to sleep have been documented across

many species including humans (White. Weil. & Zwillich, 1985). dogs (Phillipson.

Murphy, & Kozar. 1976) and rats (Pappenheirner, 1977). There is an overall decline in

alveolar ventilation and breathing becomes more regular on going from quiet waking to

NREM sleep. In general. these

reductions in alveolar ventilation are

either mediated by a decrease in tidal

volume in humans or a decrease in

respiratory frequency in animais

(Krieger. 1989). These changes in

ventilation are accompanied by a 10-

30% reduction in metabolic rate as

indicated by a decrease in O? uptake

and CO? production (Brebbia &

Aitushuler. 1965: White et al.. 1985).

The ventilatoiy responses to COz are

also altered as a function of sleep-wake

state. in NREM sleep. there is a

decreased sensitivity to CO? but no

change in threshold of the COz

Figure 1-7 shows the mechanisrns involved in the decrease in ventilation on going from wakefulness to NREIM sleep. The intersection of the metabolic hyperbola (line a) and the ventilatory response to CO2 during wakefulness yields the equilibrium point 1. O n falling into NREM sleep, metabolic rate declines, shifting the metabolic hyperbola Ieft (line b). The loss of the "wakefulness stimulus" during NREM sleep rnakes the ventilatory response to COz tess sensitive (shown by line d). The net result is a new equilibrium point during NREM sleep (point 2 ) a t which ventilation is lower and P a C 0 2 is higher compared to wakefulness (derived from Phillipson. 197R\

response (Douglas et al.. 1982). Together these changes result in an incrrase of 2-7 rnrnHg

in PaCOz (see Figure 1-7 for mechanism) (Phillipson. 1978).

Ventilation that occurs during REM sleep is variable. Some studies in human infants

and adults have found increases, whereas others in cats have found decreases. During REM

sleep, breathing is irregular and there are large variations in tidal volume and respiratory

frequency (Phillipson et al., 1976; Remmers. Bartlett, & Putman, 1976). Periods of

hyperventilation, regular breathing as well as apneas of varying length have been noted in

REM sleep. Consequently, there are large fluctuations of instantaneous measurements of

ventilation on a breath-to-breath and minute-to-minute basis. This large vanability in

breathing indicates that steady state ventilation may not exist in REM sleep (Phillipson &

Bowes, 1986).

The mechanisms involved in the decrease in lung ventilation during NREM sleep

are related to the withdrawal of the stimulatory effects of the ''wakefulness stimulus" on

breathing. Wakefulness exens a tonic stimulating effect on the rnedullary respiratory

neurons. Falling asleep is associated with the withdrawal of this tonic drive to the

respiratory motor neurons (reviewed in Orem, 1994; Phillipson & Bowes, 1986), which

ultimately affects respiratory motor output. Respiratory related neurons in the ventral

medulla, for example, have been observed to show either a decrease in rate of discharge

or cessation of firing dunng sleep compared with that dunng wakefulness (Orem,

Montplaisir, & Dement, 1974). The l o s of wakefulness has rnany different efkcts on the

muscles of the respiratory system which. in addition to decreases in breathing rates and

alveolar ventilation, are manifested as decreases in peak inspimtotory aimow rate and

increases in upper airway resistance (Orem Netick, & Dement, 1977), for example. It has

been proposed that the degree to which a respiratory muscle will be affected in sleep

depends on the ratio of respiratory and non-respiratory inputs to the motoneurons

innervating the respiratory related muscle (Orem & Dick, 1983). Thus, the activity of the

diaphragrn, which receives more respiratory input than non-respiratoiy input, will be less

affected by sleep than muscles of the upper ainvay such as the genioglossus (tongue

muscle), whic h receives a greater degree of non-respiratory input (Orem, 1994).

The reductions in activity of these important respiratory muscles dunng sleep are

of clinical relevance. For example, in a pathological condition known as obstructive sleep

apnea. sleep related relaxation of pharyngeal muscle activity leads to upper ainvay

narrowing. snoring and upper airway obstruction, which, disrupt sleep, produce anerial

oxygen desaturation and significant hemodynamic changes (Homer, 2000). in another

condition known as congenitaf hypoventilation syndrome, there is a cessation of breathing

during sleep because of the absence of any ventilatory responses to CO2 which are vital in

driving ventilation during sleep in the absence of a wakefulness drive (Shea 1996). Lefi

untreated these conditions are potentially debilitating and life threatening.

Unlike NREM sleep, the mechanisms by which ventilation changes in REM sleep

are cornplex and cannot be attributed to a single event such as withdrawal of the

wakefulness stimuli (Phiilipson & Bowes. 1986). Like wake fulness, there are many

different factors and physiological mechanisms which control ventilation in REM sleep.

Regulation of breathing in REM sleep. unhke in NREM. is largely independent of the

metabolic control system. Therefore, spontaneous changes and gas exchange are probably

not attributable to the metabolic control w e m (Phillipson & Bowes, 1986).

Circadian Modulation of Breathing

Unlike the impact of sleep-wake state on breathing, knowledge on the modulation

of the breathing via the circadian system is limited. Studies that have focused on the

changes in breathing across time of day have been relatively few in number, both in ternis

of describing the changes that occur across the day, and, in the mechanisms mediating

these changes. The reasons for the spane literature in this field probably stems from the

fact that this is a relatively new subject in the field of the control of breathing. Ln

addition, t here are also difficulties of measunng breathing across long penods of time.

whilst controlling for the potential masking effects of sleep-wake state. However.

evidence is accumulating that suggests that the circadian system is involved in the

modulation of breathing, either directly through the respiratory control system. or

indirectly through circadian rhythms in other variables such as metabolic CO2 production

or sleep-wake state, which as previously discussed, are known to influence breathing.

Several studies in awake humans have investigated changes in the respiratory

control system across time of day. Raschke & Moller (1989) found that

chemoresponsiveness to CO2 possessed a circadian rhythm, with the trough of the rhythm

(Le., minimum chemosensitivity, maximum threshold) occumng at 5 a.m. Stephenson et

al. (2000) also found that the chernoreflex threshold (maximum at 6 a.m.) possessed a 24-

hour rhythm, but they found no rhythm in chemosensitivity. The reasons for these

differences are unclear, but it is possible that they may have been caused by di fferences

in protocol. For example, in Raschke & M6ller (1 989)'s study, subjects were allowed to

sleep dunng the study, but were woken up to make respiratory measurernents. In contrast,

in Stephenson et al. (2000)'s study, subjects were constantly awake throughout the length

of the forty hour experimental period.

Times of day differences in chemoreflexes have also been tested in rats and

ducks. The ventilatory responses of awake rats exposed to 3.5% COz were higher during

the night (active) phase compared to the day (rest) phase (Peever & Stephenson. 1997).

suggesting that c hernosensitivity varies across time of day. Similarly. the vent ilatory

response of diving ducks to progressive asphyxia (progressive hypercapnia and hypoxia)

has been found to be lower at night, a time of day when these anirnals remain submerged

longer (Woodin & Stephenson, 1 998).

Like the studies on the circadian influences on the respiratory control system, few

studies have measured tirne of day changes in lung ventilation. Two studies. one in awake

humans, and the other in rats, have show that there are 24 hour rhythms in ventilation.

Spengler, Oliver, Czeisler, & Shea (1997) have shown that a circadian rhythm in

ventilation is present in hurnans kept awake for 40 hour periods under constant conditions

(using the constant routine protocol). This result suggests that there may be circadian

influences on breathing independent of sleep-wake state. Siefen et al. (2000) also showed

that that there was a circadian rhythm in lung ventilation in freely behaving rats.

However, in this study, the sleep-wake States of the rats were not considered as the data

were pooled into 20-minute bins regardless of sleep-wake state. Knowing that rats are

asleep for greater periods of time during the day and are predominantly awake dunng the

night, it is not clear fiom this study, if the circadian rhythm in lung ventilation is

independent of the circadian related changes in sleep-wake state.

Although Peever and Stephenson (1997) considered the effect of wakefulness in

their analysis of time of day differences in breathing in seven rats, they found no

statistically significant differences between lung ventilation dunng the day and lung

ventilation at night. This result suggests that lung ventilation in awake rats does not

possess a circadian rhythm. However. there was a trend towards higher lung ventilation at

night, suggesting that day night differences in Iung ventilation could have been found had

the vanability in the data been less with the use of a larger sample size. It is possible that

this lack of difference may have been a result of lung ventilation being relatively higher

than the expected value dunng the light phase. and. relatively lower than the expected

value dunng the dark phase, or a combination of both. These hypothetical scenarios may

be possibly explained by two expenmental shortcomings. Fintly, arousing the animal to

make measurernents dunng the light phase (when the animal is normally asleep) would

likely cause lung ventilation to be higher during this phase. Secondly. since no EEG or

EMG recordings were used to discriminate sleep-wake States. it is possible that somr of

the measurements were inadvertently made in drowsy, rather than fûlly awake rats. If

such were the case dunng the dark phase, for instance, this would depress lune

ventilation during this phase.

Although the studies discussed above suggest that there are circadian rhyt hms in

lung ventilation independent of sleep-wake state, they are by no means conclusive. They

have either neglected the effect of sleep-wake state on lung ventilation or measured lung

ventilation across 24 hours in wakefulness, but not in sleep. To date, we are not aware of

any studies that have determined if there are circadian rhythrns in lung ventilation in

wakefulness g& NREM sleep or REM sleep. With a deficiency of studies in this area, it

remains to be known if circadian rhythms in lung ventilation are independent of sleep-

wake state, or if the effect of sleep-wake states on lung ventilation can be added to the

circadian effect of lung ventilation.

Hv~otheses

The aim of the present study was to determine the effect of time of day on lung

ventilation taking into account the effect of sleep-wake state. Two hypotheses (see

Figure 1-8) were proposed and tested:

1. Lung ventilation has a circadian rhythm in wakefülness. NREM and REM sleep.

2. The magnitude of change in lung ventilation from wakehlness to NREM sleep is

the sarne across the day (in Figure 1-8: 1-2 = 3-4).

Fipure 1-8 shows a pictorial representation of the two hypotfieses tested in this experiment.

Lig ht Phase Dark Phase

~y pot-hesis -m.*i(

12 24 Time of Day (hours))

Rationale for the hv~otheses

There are many physiological and behavioural variables that oscillate with a 24-

hour rhythm such as sleep-wake state (Eastman et al., 1983), metabolic rate (Aschoff &

Pohl, 1970; Nagai, Nishio, & Nakagawa, 1985; Spengler et al.. 2000) and body

temperature (Eastman et al.. 1983). These specific variables are also known to influence

breathing (Mortola & Gautier, 1995). Therefore. provided the mechanisms mediating the

relationships between these variables and breathing are similar across t ime of day, lung

ventilation will also oscillate in phase with these variables to give circadian rhythm in

lung ventilation.

in this study, by rneasunng and comparing lung ventilation in a given sleep-wake

state across time of day, the masking effects of sleep-wake state on breathing can be

minimized. in this way, any time of day changes that occur in lung ventilation can be

attnbuted to factors other than sleep-wake state. Of these factors, circadian oscillations in

metabolic COz production and the chemoreflexes are likely to play a key role in

mediating circadian rhythms in lung ventilation in each sleep-wake state. As discussed

previously, there are circadian rhythms in CO2 production (e.g., Aschoff & Pohl, 1970).

time of day changes in the chemoreflexes (Peever & Stephenson, 1997; Raschke &

Mdler, 1989; Spengler et al., 2000; Stephenson et al., 2000) in addition to a close

relationship between CO2 production and ventilation (Phillipson et al.. 1981).

Furthemore, there is evidence that ventilation matches COz production at two times of

day in awake adult rats (Peever and Stephenson. 1997) and in rat pups (Saiki & Mortola,

1995). This suggests that PaCOz is similar at these two times or varies relatively little

across time of &y in a given sleep-wake state in comparison to sleep-wake changes in

PaCQ (Spengler et al., 2000). When combined al1 together, these observations (as

illustrated in Figure 1-9 below), suggest that circadian rhythms in lung ventilation are

mediated indirectly by the mechanisms underlymg the chernical control of breathing.

There may also be more direct mechanisms which involve the possibility of

neural projections fiom the SCN, the pnnciple mammalian pacemaker. to parts of the

respiratory control system such as the respiratory rhythms generator or the central

chemorecepton in the brain stem. This is a plausible hypothesis given that there are

secondary projections to the brain stem from the hypothalamus (Moore. 1 997). However.

currently. although SCN efferents have been widely mapped. their functions and the

mechanisms by which they affect other target systems are poorly understood (Piggins &

Rusak, 1999).

Figure 1-9: Hypothetical mechanisms involved in mediating sleep-wake state and time of day changes in lung ventilation in the rat (based on data from Peever and Stephenson, 1997: PhilIipson and Bowes; Lai et al. 1979) (See text below for explanation).

A Sleeii-Wake State Effects /durina the dayl

B Tirne of Dav Effects lin NREM sleepl

Fieure 1-9: Circadian variations in lung ventilation in wakefulness. NREM and REM sleep can be predicted based upon the principles underlying the chernoreflex control of breathing. PaCOz is the balance of the metabolic COz production rate and the rate at which COz is eliminated by alveolar ventilation. A PaCO: value of 30 mmHg is representative of mammals during waketùlness. On falling slrep. tonic inputs from the reticular activating system to the respiratory motoneurons are withdrawn result ing in a decline in lung ventilation. This decline in lung ventilation exceeds the decline in metabolic rate from wakefulness to sleep. Consequently. there is an increase in PaC02 during NREM sleep.

Panel A illustrates the effects of sleep-wake on lung ventilation. metabolic rate and PaCO. dunng the trouah of the metabolic rate rhythm in the a. The equilibrium PaCO. and alveolar ventilation in wakefulness (point 1) and NREM sleep (point 2) are the intersection points of the metabolic hyperbolae (at the lowest COz production rates in a 24 hour penod) (line a and line b) and the ventilatory response to COz (line c and line d) in each of these sleep-wake States. Metabolic hyperbolae show the changes in PaCO: that result from spontaneous changes in alveolar ventilation at a given metabolic rate whilst the ventilatory responses show the changes in alveolar ventilation that result from induced changes in PaC02.

Panel B shows the time of day effects on lung ventilation. metabolic rate and PaC02 in the a. For clarity. only the metabolic hyperbolae and the CO2 responses at times corresponding to the trough (during the day) and the peak of the circadian CO?

production (during the night) in NREM sleep are illustrated. However, the concepts for wakefùlness are similar. Line b and line e represent the expected metabolic hyperbolae at the 24-hour minimum and maximum metabolic rates in NREM, respectively. Line d and line f show the expected corresponding ventilatory response to COz at these times. Since metabolic rates oscillate with a circadian rhythm, a continuum of metabolic hyperbolae between line b and line e would be expected. Point 2 and point 4 represent the equilibrium values of PaC02 and alveolar ventilation at the peak and trough of the CO2 production rhythm in NREM. In order to maintain PaC02 constant in a given sleep-wake state (at an estimated PaC02 of 45 mmHg in NREM sleep), alveolar ventilation would also be expected to oscillate in phase with the circadian rhythm in CO2 production. The predicted values of alveolar ventilation across time of day would be expected to lie on a vertical line between point 2 and point 4. Assuming ventilation of dead space in the lungs is kept constant, lung ventilation would be expected to be proportional to a given level of alveolar ventilation. Point 2 and point 4 in this figure would be expected to correspond to point 2 and point 4 in Figure 1-8.

Having discussed the theoretical basis of the present experiment, attention will

now be tumed towards testing the hypotheses. The primary objective of the experiment

was to measure breathing, metabolism and body temperature in all the three sleep-wake

states (wakefûlness, NREM and REM sleep) across a 24-hour penod in freely behaving

rats. in order to generate sufficient data in al1 three states across a 24-hour period, rats

were studied because of their naturally occumng polyphasic sleep-wake cycles (see

Figure 1-10) (unlike adult humans whose sleep is consolidated to a period of

approximately 8 hours during the night). That is, even though they are classified as being

noctumal, their sleep is not confined solely to the day but also occurs during the night

when they are predominantly awake and more active.

Lung ventilation and metabolism were measured using non-invasive closed

system whole body plethysmography. Sleep-wake state was measured electrographically

using surgically implanted cortical EEG and neck EMG electrodes attached to a telemetry

unit. Body temperature was used as a measure of the circadian phase of the rats. The

generated data when plotted as a hinction of tirne of day was then tested for the presence

or absence of circadian rhythms.

Fipure 1-10 illustrates the polyphasic nature of rat sleep on a hypnogram of typical rat sleep- wake behaviour across a 24-hour period. Hypograrns show the occurrence and duration of a particular sleep-wake state in a span of time.

(Source: Trachsel et al., 1988)

Tirne after light onset (hours)

O 12 24

REM 1

Chapter 2: Methods Experimental arotocol

Expenments were performed on six male Sprague Dawley rats (mean * SEM

body mass: 3591 19%). Surgery was performed and the rats were allowed to recover on a

12 hour light - 12 hour dark cycle. Durinp this recovery phase. the rats were allowed to

habituate to the experimental apparatus for shon (4 hour) periods of time. About 24

hours before the start of the experiment. parameters of an on-line computerized sleep

detection system were set to aid the expenmenter in making decisions of the sleep-wake

state across the 24-hour day (see :kfeasrtrernent of' sleep-wake stares atzd bodv

temperature). At least 10 hours before breathing and metabolism measurements were

made. the rats were placed in the animal chamber (see Figure 2-2) which contained corn

based rnaterial mixed with bedding from the home cage to reduce the novelty of the

situation. Food and water were freely availablr. Recordings lastrd 24 hours foollowing the

10-hour farniliarization interval. Each rat was studied twice with at least 24 hours

between experiments.

Animal preparation

The rats were implanted with a three-channel radio transmitter (TL 10 M-3 F-50

EET - Data Sciences International) connected to EEG and EMG elcctrodes (see Figure 2-

1) as follows:

hzesthesia und oreparution: Surgery was performed under gencral anesthesia and using

sterile conditions. Anesthesia was induced by an intra peritoneal injection of ketamine

35

(8 5mgkg): xylazine ( 1 5 mgkg). As the sugery progressed. the inhalation anesthetic.

halothane (typically between 0.1 - 2.0%) was

Prior to rnaking any incisions. the rats

buprenorphine (0.0 1 -O.O5mg/kg) as an anal

administered in O? enriched air as required.

were also intra-peritoneally administered

gesic. atropine ( I mgkg) to prevent upper

airway secretions ftom blocking the aimay and 3ml of 0.9% sterile saline for hydration.

Subsequently. the head and abdominal areas were shaved and stenlized with 70% alcohol

and betadine. The telernetry unit was soaked in 0.9% glutaraldehyde for 12 hours

followed by at least one hour sterile saline for sterilization.

Irn~lantation of' ihe telemetv mit: After the pedal withdrawal reflex was no longer

present. a 3-cm incision was made in the skin on top of the skull. The rat was then placrd

in the supine position. an midline incision was made in the skin overlying the abdomen

and another incision was made along the linea alba to expose the peritoneal cavity. The

transmitter was loosely sutured to the rectus abdominus muscle using 3-0 non-absorbable

silk. The leads from the telemetry unit were pushed through a puncture made in the

abdominal muscle and subcutaneously tunneied to the incision in the head region. The

abdominal incisions of the muscle layer and the epidrrmai layer were then sutured using

3-0 vicryl absorbable suture.

Imolantation ot'EhfG and EEG electrodes: After the rat was placed in the prone position.

its head was stabilized using a Kopf stereotaxic frame. A 3-cm midline incision was

made in the muscle layer on the top of the skull. The surface of the skull was cleared of

connective tissue and then swabbed with hydrogen peroxide to establish a clean surface

on which dental acrylic could set.

The incision in the skin on top of the skull was extended caudally, the underlyng

layer of the dorsal neck muscle was exposed and the two EMG electrodes were sutured

into the splenius muscle on either side of the midline using 3-0 non-absorbable silk. Each

EMG electrode was anchored to the splenius muscles with a caudally placed suture and

covered with a silastic sheath to prevent irritation or injury to the muscle. This sheath was

held in place by another suture to the splenius muscles. Next, three holes were drilled into

the skull to house 0-80 x 118" stainless steel screws. Two were used for EEG electrodes

and one served as the ground (reference) electrode. The first EEG electrode was located 2

mm anterior and to the lefi of bregma. and the second. was placed 2 mm posterior and to

the right of the first EEG electrode. The ground rlectmde was situated 2 mm anterior and

to the right of bregma. The tips of the EEG and ground leads From the telemetry unit were

placed in their respective holes and screwed in with 0-80 x li8" stainless steel screws.

Fipure 2-1: X-ray of a rat implanted with EEG and EMG electrodes, temperature sensor and biotelemetry unit barometric plethgsmograph.

Dental acrylic was poured over the screws to firmly anchor the electrodes to the skull and

to provide electrical insulation. The incision on the head was closed with 3-0 absorbable

vicryl and the rats were allowed to recover. During recovery, the incisions were checked

for signs of infection and the general state of health of the animal was monitored.

Experimental Apparatus

Barometric plethysmography cornbined with biotelemetry was used to measure

breathing. metabolisrn and sleep in freely behaving rats (see Figure 2-1 and Figure 2-2).

Figure 2-2: Schematic of the experimental apparatus used to measure lung ventilation, metabolism. body temperature and sleepwake states.

1 1 1 1 Acquisition of Breathing

1mYI and Metabolism signals

CO2 and 0 2

Air In

Chartes' Law Pressure is proportional to Temperature (when volume is constant).

23' C - During inhahtion, . . air is warmed and

humidified $\ F in lungs * L' LA pressure in

?-$L animal chamber i (constant volume)

rises. -$ Reverse occurs in N Y exhalation.

EEG, EMG, Body Temperature Signai Receiver and Processor 1

Measurement o f /un p ventilation

In whole body plethysmography. the rat is placed in a constant volume container

attached to a very sensitive differential pressure transducer capable of detecting small

changes in pressure associated with breathing (Drorbaugh & Fenn. 1955: hlortola &

Frappell. 1998). When air is inhaled. it is warmed and humidified in the lunp. Since this

heating of the gas occurs in a container of constant volume. by Charles law (pressure a

temperature). there is an increase in pressure in the chamber. When air is exhaled from

the lungs the opposite occurs. That is. the warmer. more humid air of the lungs is cooled

and condenses. leading to a decrease in pressure. These pressure changes when

transduced into an electrical voltage form the basis of the calculation of tidal volume (VT)

by the following equation (Drorbaugh & Fenn. 1955):

VT ~BTPSI = Pm * Vca~ * Ga

Pal

whrre. Pm is the voltage change caused by respiratory related pressure changes in the

chamber. Pd is the voltage change in the pressure transducer caused by injection of a

known volume of gas (Val) and Ga is a dimensionless constant that represents the ratio of

the tidal volume at alveolar conditions (VT.\) to the small increase in volume that occurs

when tidal gas expands from its volume at chamber conditions (VTC) . i.r.. Ga = VTA

It was calculated using the equation:

Ga = T b (PB'PcH~o)

Tb( PB -PCH?O)- TJ'B-P~HIO)

where. Tb and Tc are the alveolar and chamber temperatures (in Kelvins). respectively. PB

is the barometnc pressure. and PbHZo and PCH~O are the water vapour pressures of the gas

in the alveoli and the chamber, respectively. PCWO was calculated as follows:

Pc~ ro = S WVP, xRH

1 O0

where, SWVPc is the saturated water vapour pressure at the chamber temperature. Tc. and

RH is the relative humidity (%) in the chamber.

Respiratory tiequency (0 in breaths min" was calculated as:

f = 6 0

TOT

where. t ~ o ~ is the total breath duration calculated by summing inspiratory time (ti) and

expiratory time (tE). That is. TOT = t~ + (E.

Lung ventilation ( ? 1, ml min". BTPS) was calculated by:

i',=t-IV*

The use of barometric plrthysmograp hy to measure lung ventilation non-

invasively in small animals and infants has been widespread (Monola and Frappell.

1998). Good correlations between baromctric plethysmography and other rstablished

techniques to measure breathing such as pneumotachography. have lent validity to the

measurement of breathing using this technique (e.g. Drorbaugh and Fenn. 1955: Stahel

and Nichol. 1988; Siefert et al. 2000).

The barometnc plethysmograph (see Figure 2-2) used in our experiment consisted

of a 9 litre animal chamber and an identical reference chamber placed in a water bath.

The purpose of the reference chamber and water bath. respectively. was to shield the

apparatus from potentially disruptive changes in ambient pressure and temperature.

Pressure tluctuations associated with the rat breathing in the animal chamber were

measured using a differential pressure transducer (Validyne Engineering: Mode1 DP-45-

14) attached to outlets fiom the animal and reference chambers (see Figure 3-1 for

representative traces of breathing during wakefulness, NREM and REM sleep). The

relative humidity and temperature of the animal chamber was measured by a

thermohygrometer (Cole Parmer: Model 37950- 10. resolutions of 0.1 OC and 0.1 % for the

t hermometer and hygrometer, respect ively).

The analog signals from the differential pressure transducer and the

thermohygrometer were fed into and recorded on an eight-channel data acquisition

system ( ADinstmments: Mac Lab 8/s) driven by a Macintosh computer (Performa

5200CD). The same data acquisition system was used to process and record the signals

from the CO? and O? analyzers and the body temperature sensor described below (sec

Measuremenr of metabolic rate and Me~srirentenr of' sleep-wake srare and bo&

temperarure below).

To make measurements of breathing. three steps were followed. First. the chan

record (Grass Lnstmments: Model 78D) was switched on at a speed of 5 mmis to get a

paper record of the sleep-wake state. Second. the animal chamber was completely sealed

using a solenoid valve (Cole Panner: Model 01367-97. 118" x P4". 12 VDC) that

simultaneously closed both the inlet and outlcts. Third. the rate of data sampiing on the

data acquisition system was increased to 100 samples/sec from 4 samples/second to

prevent any distortion of the respiratory signal by alaising duc to inadequate sarnpling

frequencies. When the concentration of CO2 concentration in the animal chamber had

built up to approximately 0.5% (approximately 2-5 minutes since chamber closure

depending on sleep-wake state of the animal). the solenoid values were opcned to resume

airtlow to the animal chamber. When the animal chamber was open. the sampling rate

was reduced and the chart record switchrd off. Over the course of a 24-hour period. the

chamber was closed to make the required measurements between 50 to 80 times.

Fipure 2-3 shows the mean concentration of COr (O/') in ambient air in each of the analyzed portions of data in each sIeep-wake state.

A

0.8 WAKE - - NREM -

REM - - - - - - -- - --

O - - . - . . . 0.4 - - .-. - - -. r- -

I -. - -. .

m 2. g2< L=$$g i g s C .. 5. , -. - - Cr.

0.2 - - - rr

Time since light onset (hours)

However. not al1 these data could be used due to reasons such as activity leading to

movement arti fact and insufficient durat ion in a given sleep-wake state. Throughout the

expenment. the COz concentration in the ambient air in the chamber was maintained

below 0.65%. However. the mean CO2 concentrations of the analyzed data were

considerably Iower and random across the 24-bour experiment as show in Figure 2-3

below.

The 24-hour mean k standard error of the mean (SEM) of inhaled CO2 dunng

measurements were 0.3 1 f 0.0 1% in wakefulness. 0.29 k 0.01% in NREM sleep and 0.28

f 0.01% in REM sleep. These ambient COz concentrations during each sleep-wake state

were not significantly different (p>0.05) fiom each other and the variance in ventilation

explained by inspired CO, concentration were negligible (see Figure 2 4 below: i = 0.08

in wakefulness. ? = 0.03 in NREM sleep and ? = 0.00 in REM sleep). Therefore. there

was no ventilatory response to CO2 at the low CO? concentrations present in the animal

chamber. Furthemore, the random ambient CO? concentrations across the day prompted

us to assume that the effects of CO2 concentrations on lung ventilation across the day

were negligible.

During the times when ventilation was not recorded, air was constantly flushed

through the animal chamber at a flow rate of approximately 3 litres per minute to supply

k s h air and prevent build up of CO? in the chamber.

Fipure 2-4: Graph of the deviation from the 24hour mean lung ventilation plotted versus inhaled CO2 concentration ( O h ) in wakefulness. NREM and REM sleep. The R' between these two variables in each sleep-wake state is also shown.

- WAKE

REM

r = 0.08 (Wake) r * = 0.03 (NREM)

r = 0.00 (REM)

lnspired CO2 (%)

Measurement o f metabolic rate

Metabolic rate. as indexed by the rate of CO, production (mumin). was estimated

by measuring the rate of accumulation of CO? in the animal chamber during the brief

intervals when the inlet and outlets of the animal chamber were closed. Air in the animal

chamber was continuously sampled for analysis of fiactional CO? and O? concentration

(%) and retumed to the animal chamber in a closed circuit. The circuit consisted of a gas

dner (active ingredient anhydrous calcium sulphate). a roller pump (Masterflex: Model

US Drive with US Easy load II pump head). pressure dampers and high resistance (26G

needles) to reduce pressure fluctuations associated with the roller pump. an

electrochemically based O? analyzer (Amtek: Model S-3Nl) and an inka red based CO2

analyzer (Amtek: Model 3D-3A) al1 connected in senes. The roller purnp circulated air

around the circuit at a tlow rate of approximately 50 mljmin. Before the start and

approximately 12 hours into the rxpenment. the COI and 0: analyzers were calibrated

with 2 1.45% 0: and 5.09% CO? gas. By injecting a 60ml bolus of pure CO: gas into the

empty chamber. the lag tirne for a change in the CO2 concentration in the charnber to be

detected by the CO? analyzer was approximately I O seconds.

The formula used for calculating metabolic rate (Y CO2. ml. min". STPD) was:

Y CO2 = d[COr]. Vc - dt

where. d[C02] is the rate of change of CO2 concentration and V, is chamber volume. - dt

The change in CO, concentration per minute was denved from the dope of a linear

regession line fitted through fractional concentration of COz plotted over a time penod

ranging From 1-2 minutes. The volume of the expenmental chamber was determined by

injecting a known volume of pure CO? gas into the chamber (without an animal present).

recording the changes in fractional concentration of COz (%) and substituting into the

following equat ion:

Volume of chamber = Volume of pure CO2 injected into c hamber

Change in fractional CO2 concentration

In the presence of the rat. body density was assumed to be 1 .O3 @ml (Stephenson. 1993)

and v,' was calculated as:

v,' = V, - Mb - 1 .O3

where Mb is the mass of the rat (g) and v,' is the volume of gas in the animal chamber

with the rat present inside it.

Although the ambient concentration of O? was measured. an estimate of the

metabolic rate using the rate of uptake of 0: was not calculated because of the low

resolution and highly noisy. inadequately filtered signal. Throughout the experiment. the

ambient inhalrd Oz concentration was maintained at normoxic levels (between 20.0% and

Measurernertt of'S1ee~-wake stares arid B o d ~ ternRerarrrre

Sleep-wake states were scored visually using the EEG and neck EMG signais

recorded on chan. The signals from the cortical EEG electrodes and neck EMG

electrodes were transduced by the three-channel telemetry unit (Data Sciences: Mode1

TL I OM3-FSO-EET) and emitted as radio waves. A thermal sensor (resolution O. 1 O C . 90%

(mean f SEM) response time of 129 t 9 seconds) inside the telemetry unit measured core

body temperature (Tb). Althouph the thermal sensor allowed circadian changes in body

temperature to be measured. the response times were too slow to permit shorter-term

changes in body temperature associated with changes in sleep-wake state to be

determined. The radio signals from the telemetry unit were picked up by a receiver (Data

Sciences: Model RPC- 1 ) under the plethysmopph. and then fed into a digital to analog

converter (Data Sciences: Model UA-10 Universal Adapter) which also amplified the

analog signal 1000 times. The analog signals were Funher amplified six and a half times

using a buffer amplifier (CWE Inc: Model DC-936 Buffer) (for a total of 6250 times) and

filtered (EEG signal: O. 1 - 100Hz. EMG signal: 3-1OOHz) before being fed into a chan

recorder to give a paprr record (speed: j r nds ) of the EEG and EMG. The analog signals.

afier conversion to digital signals (at a sampling rate of 300 Hz). were also sent to a

separate computer system (IBM Compatible 386. 16 MHz) that auiomatically determined

sleep-wake state by analyzing the fiequencies of the EEG and the amplitudes of the EMG

signals (Hamrahi. Chan. & Horner. in press).

The analysis of the EEG. in 6-second rpochs. used the interval histogram rnethod

(Kuwahara et al.. 1988) to determine the percent of the signal in each of the following six

bandwidths: &(OS-2Hz). &(24Hz). O(4-7.5Hz). a(7.5- l3.5Hz). P i ( 1 XWOHz) and

p2(20-3OHz). In this method which is software dnven. the amplitude of the EEG signal

was divided into 32 equally spaced horizontal slice lines. A period (reciprocal to

frequency) was rneasured as the time interval between two points at which the same slice

line crossed consecutive positive-going slopes of the EEG signal (Kuwahara et al.. 1988).

A histogram was then constnicted for these intervals. and from this histogram. the

percent distribution of the frequencies was calculated.

Additionally. the %P2/%8, and EEG and EMG amplitudes were determined. From

these signals. the computer made a judgment of the sleep-wake state using an algorithm

based on the ratio of %Pz/%& and EMG amplitudes (Homer et al., 1998; Hamrahi et al..

in press). in this algorithm, wakefulness and REM sleep was differentiated from NREM

sleep by a higher %P2/%& in wakefulness and REM sleep compared to NREM sleep.

That is. ftequencies of the EEG are higher in wakefulness and REM sleep compared to

NREM sleep (also see Figure 3-2 in RESULTS). Having similar %PL/%& magnitudes.

wakefulness and REM sleep were differentiated by the absence of muscle tone and low

EMG values in REM sieep. This algorithm has previously been validated in rats by

Hamrahi. et al.. (in press).

Prior to the stan of the experiment. the tidelity of transmission of the EEG and

EMG signals were tested by inputting a 0.5V amplitude. lOHz sine wave generated from

a signal generator into the telemetry unit. and. recording and verifiing the output on the

sleep computer and chan record. The resulting output on the sleep computer indicated

that 99.8% of the signal was in the a(7.5-l3.5Hz) range as expected. After implantation

of the transmitter in the rat. the %P2/%tii and €MG thresholds were then set by

repeatedly observing these parameters in al1 three sleep-wake States Wr approximately 5

hours. During the experiment. the computer judgments were used only as an aid to

determining the sleep-wake stata of the rats across the day. In this respect. since

rxperiments were 21 hours long and drowsiness on the pan of the experimenter was

inevitable. the computer assisted sleep scoring proved invaluable. ARer completion of the

experiment. the chan records of the EEG and EMG were used as definitive in assessing

sleep-wake state before breathing and metabolism measures were made.

Data Analysis

Selection of ~or t ions of data suitable for anaiysis and calculations performed

Thiny-second periods of established wakefulness. NREM and REM sleep were

selected From the chan records to give a sampie of at least 30 breaths in each of these

sleep-wake states. These selected episodes occurred at least 20 seconds afier wakefulness

or sleep onset. and. at least 20 seconds afler the chamber was closed. These critena were

set to ensure that the rats were in established sleep-wake states and to avoid measuring

breaths that were associated with a minor arousal that may have occurred due to changes

in the noise level in the chambrr. When the rats were first placed in the chamber. it was

observed From the EEG that the rats were startled by noise of the solenoid valve used to

close the chamber. However. aRer the rats were regularly errposed and habituated to these

noises during the 10-hour familiarization period. arousal from the operation of the

expenmental apparatus was no longer an issue at the start OC and duing the experiment.

For eac h of the selected episodes. the corresponding breat hing. metabolic rate.

body ternprrature and EEG and EMG related data was determined. For each breath in the

selected episodes. lung ventilation (i' ,). respiratory fiequency (0. tidal volume (VT).

inspiratory time (ti). expiratory time (tE), total breath duration (tTor) were calculated.

Pressure signals deemed to be the result of movement artifact. sighs or sniffs were

omitted from the analysis. In each sleep-wake state for each intervention. the mean value

of these parameters from the selected breaths were then calculated and used in

subsequent analyses. In addition. the coefticient of variation (CV) for tidal volume.

frequency and lung ventilation was computed to give an estimate of the variability of

breathing. The equation used to calculate the coefficient of variation for each selected

episode was:

Coefficient of Variation = Standard deviation x 100

Also for each selected episode, the mean % of the EEG frequency in the 6, and pz

bandwidths. the %Br to %&ratio (%P2/%6i) and the mean EEG and EMG amplitudes

were computed.

Analvsis of Circadian Rhvthms in Breathin~. Metabolism and Bodv Temperature

In each sleep-wake state. the presencc or absence of circadian rhythms in lung

ventilation. respiratory frequency. tidal volume (and their associated coefficients of

variation), CO2 production. ventilation normalized for COz production and body

temperature was detemined by tïtting a sinusoidal curve through the normalized data set

for each variable.

Pre~ararion of' data for model fitting: So that the data from al1 animals could be

combined. prior to fitting a sine wave model. the inter-animal variability in the mean

value of eac h parameter was removed throuph normaiizat ion. This process of nonnalizing

the data for each rat in day 1 and day 2 occuned in two steps. First. a 24-hour mran

(rnranrhu) was calculated. Second. each individual data point was normalized and

expressed as a drviation from the rneanldb using the equation: Normalized value =

Value - rneanirm. These normalized data were then replotted as a hnction of time

before fitting a sine wave model to the data.

The normalized values for both days I and 2 of the enperiment were combined

afler no differences in mean lung ventilation were found between day 1 and day 2 of the

expenment (F[1. 231 < 1, p=0.98 using a 2 way Repeated Measures ANOVA, with sleep-

wake state (Wakefulness. NREM sleep. REM sleep) and day (Day 1, Day 2) as factors.

Model: The general equation of the fitted model was:

y = yu + asin(2iidb + c)

where. y is the physiological variable. y0 is the mesor (24 hour fitted mean value level.

equal to O for time adjusted deviations). a is the amplitude. x is the variable time, b is the

period set at 24 hours. and c is the acrophase - Il12 radians. The assumption that the

period of the sinusoidal function was 24 houn was based on the fact that the rats were

rntnined to a 12: 12 light-dark cycle.

A computer program (Jandel Scienti fic: Sigmaplot 4) was used to f i t the model by

regression analysis of variance. In this process. an algorithm that minimized the

deviations between the fitted model and the actual data (by the Least Squares method)

was used. In addition to fitting a sine wave model to the deviations of the data. Sigmaplot

4 also calculated the probabilities that the fitted mode1 possessed a circadian rhythm. A p-

value of <O.Os was accepted as statistically significant.

.-lssessirw differences in acro~lzases

The phase relationships between the acrophases of the circadian rhythms in lung

ventilation. CO? production. ventilation normalized for CO2 production. body

temperature and respiratory frequency in each sleep-wake state were assessed using t-

tests from acrophase values and their standard erron generated fiom the titted model. The

equation (Ott. 1993) used was:

t = meanl - mean?

d (SEM,? + SEM??)

degrees of freedom = ni +n? -2

where mean and SEM (standard error of the mean) are in hours and nl and n? are the

number of samples used in the calculation of mean, and mean? respectively.

Determininp the mamitude of change in lunp ventilation from wakefulness to

'IREM across time of day

To test hypothesis 2. the mean change in lung ventilation from wakefulness to

NREM slerp from two 5-hour time periods in the day (light phase) and night (dark phase)

were compared using a 2 way Repeated Measures ANOVA with sleep-wake state

(wakefulness. NREM sleep) and time of day (day. night) as factors. Thesr windows of

data corresponded approximately to the peak and trough of the temperature rhythm and

began 2 hours afier lights on and lights off. respectively.

Additional analvses performed to determine possible causes of time of dav chanees

in lunp ventilation

In addition to companng the magnitude of change in lung ventilation from

wakefulness to NREM sleep during the day and night. other variables such as CO2

production. ventilation nonnalized for CO: production. body temperature. the ratio of

high to low EEG fiequencies (%Pz/%&) and EEG and EMG amplitudes were compared

ar two times of day in wakefulness and NREM sleep only. REM sleep was not included

in this analysis because of a lack of availability of data in this sleep-wake state in some

rais at some times of the day. These means were then compared using a 2 way Repeated

Measures ANOVA with the factors being sleep-wake state (wakefulness. NREM sleep)

and time of day (day. night). If significant differences were found using the ANOVA.

Tukey's post hoc test was used to identib the source of these differences.

Chapter 3: Results

General observations on behaviour and slee~mwake ~atterns

As the experiment was conducted. the rats did not appear to be disturbed by the

experimental apparatus. Although no forma1 quantitative analysis was conducted on their

sleep-wake cycles. the distribution of measurernents made in eac h sleep-wake state were

retlective of the ultradian rhythms in rats sleep-wake cycles (see Figure 3-2). In this

regard. there was an uneven distribution of each sleep-wake state across a 24-hour period.

The rats generally slept more during the light phase (day) and generally were more awake

and active during the dark phase (night). When asleep. REM sleep was more predominant

towards the end of the light phase. but was not very frequent during the dark phase. These

behaviour patterns across each 24-hour experirnental day in addition to the circadian

body temperature rhythms (see Figure 3-5 row D) are characteristic of freely behaving

rats entrained to a IZ hour light-dark cycle.

The computer based EEG and EMG analysis was consistent with EEG and EMG

traces used in visual scoring methods (see Figure 3-1). Three examples from the

combined data can be used to illusrrate these consistencies. Firstly as shown in Figure 3-2

panel A. the mean of the ratio of high to low EEG frequencies (%P2/%61) was higher in

wakefulness and REM sleep compared with NREM sleep (F[1.23) = 9.0. p=0.03).

Secondly. the EEG amplitudes as illustrated in Figure 3-2 panel D. were greater in

NREM sleep t han in wakrfùlness and REM sleep (F( 1.23) = 60.0. pc0.00 1 ). Thirdly. the

neck EMG amplitudes were highest in wakefulness followed by NREM sleep. and thrn.

REM sleep (F[1.23] = 132.1. p<0.00 1) (see Figure 3-2 panel E). Although there are some

higher EMG values in NREM sleep. these were due to the effects of posture on muscle

tone.

Shortly after the lights came on. the rats exhibited short bouts of "deep" sleep that

showed up on the EEG as low frequency. high amplitude waves. As tirne progressed.

visual observation of the EEG showed that the amplitude was relatively similar. but the

percentage of lowest frequency bandwidth. delta 1 (%& ) decreased exponentiall y

throughout the light phase from its maximum. shortly at the beginning of the day (see

Figure 3-2 panel C).

On average. alter combining the two 24 hour periods of experiments for each rat.

the mean I standard error of mean nvmber of measurements per rai were 32.1 I 3.0 in

wakefulness. 15.0 f 1.7 in NREM sleep and 15.0 + 1.6 in REM sleep. Aççompanying

each of these measurements was a paper record of the EEG. EMG and breathinp traces.

Representative traces retlecting the breathinp and the EEG and EMG in wakelulness.

NREM and REM sleep are shown below in Figure 3-1.

Figure 3-1: Raw EEG. EMG and breathing traces in wakefdness. NREM and REhI slee~.

Fieure 3-2: Ratio of high (%p2) to low (%61) EEG frequencies and the EEG and EMG amplitudes in each of the analyzed portions of data in wakefulness, 'IRE31 and REM steep (n=6).

NREM REM -

Time since light onset (hours)

in this chapter, the results of the analyses conducted on the data in this experiment

are presented in four sections. Section 1 presents data to answer the questions posed by

the hypotheses stated in the Introduciion. Section 2 provides the results of additional

circadian analyses performed on variables such as CO? production. ventilation

norrnalized for COr production. body temperature and variability in breathing. Using

combined data across the 24-hour experimental durations. Section 3 provides data to

study the effects of sleep-wake state on breathing and metabolisrn. Finally in Section 4.

using data from the peak and trough of the body temperature rhythm. additional day-night

comparisons of certain EEG and EMG data and metabolisrn were conducted to make

funher inferences on possible mechanisrns involved in mediating the circadian rhythms

in lung ventilation in each sleep-wakr state.

Section 1 :TESTING HYPOTHESIS 1 and 2

a) Hypothesis 1: Circadian rhythrns in lung ventilation in wakefulness, NRELM and

RE 34 sleep

Figure 3-3 row A shows the circadian rhythms in lung ventilation that were found

in wakefulness (F[3.192] = 1 1.3. p<0.0001). NREM sleep (F[3,269] = 8.9, p<0.0001) and

REM sleep (F13.891 = 9.7. p<0.0001). The prak of these circadian rhythms occurred

during the dark phase as indicated by the mean acrophase (for al1 three sleep-wake states)

of 17:29 hours (1 7 hours 29 minutes) after lights on. The amplitudes of these rhythms

were 7.7%. 6.8% and 10.1 % of their corresponding 24-hour mean values in wakefulness.

NREM and REM sleep. respectively.

The 24 hour rhythms in lung ventilation in waketùlness and NREM sleep were

due to circadian rhythms in respiratory frequency (wake: F[3.192] = 8.6. p<0.000 1.

NREM: F[3.269] = 2 1.3. p<0.000 1). rather than tidal volume (see Figure 3-3 rows B and

C). In contrast. there was no significant circadian rhythm in respiratory frequency during

REM sleep (F[3.89] = 1.8. p=0.16). The acrophases of the 21 hour rhythms in respiratory

frequency in wakefulness and NREM sleep were 18:14 t 1:11 and 17:37 f 0:14 hours

aRer light onset. respectively. and the amplitudes were 7.2% and 7.5% of the mesor

values in wakefulness and NREM sleep, respectively. Tidal volume did not possess a

circadian rhythm in al1 three sleep-wake states (wake: F[3. 1921 < 1. p4.O. NREM:

F[3,269] < 1. p=O.jO and REM: F[3.89] < 1. p=0.51) (see Figure 3-3 row C).

Fipure 3-3: Graphs showhg the circadian rhythms in lung ventilation (v 1) in wakefulness. NREII sleep and RESI sleep (row A) obtained by fitting a sine wave through plots of lung ventilation across 24-hour periods. Row B and C show the components of these 24-hour rhythms in lung ventilation - tidal volume (VT) and respiratory frequency (f) (n=6). The presence of a circadian rhythm is marked with an asterisk

NREM - *

REM -

Time since light onset (hours)

b) Hypothesis 2: The magnitude of change in lung ventilation from wakefulness to

YREM sleep at two times of day

The data that were used to test hypothesis 2 consisted of two five-hour windows.

beginning 2 hours afler lights on and off (frum both experimental days). to give an

estimate of the lung ventilation in wakefulness and NREM sleep dunng the light phase

(referred to as "day") and dark phase (referred to as "night"). REM sleep was not used in

this day night cornpanson because of the lack of REM sleep data for one rat dunng the

dark phase.

A 24.2% (91 ml min") decline in ventilation occurred from wakefulness to

NREM sleep during the day and a similar decrease of 23.6?6 ( 10 1 ml min") was observed

during the night phase (F[ 1.23 j < 1. p=0.58) (see Figure 3-4 below).

Fipure 34 : Lung ventilation during two times of day in wakefulness and NREM sleep and the magnitude of change in lung ventilation from wakefulness to NREM sleep during the day and night (n=6). * indicates a significant tirne of day effect (p<O. 05) and t indicates a significant sleepwake state effect (p<O.OS)

01 5 a

200 Day Night Time of Day

Time of Day

D ~ Y Night

Section 2: AûDlTlONAL CiRCADlAN RHYTHMS ANALYSES

a) Body temperature. COt production and ventilation normalized for CO2

production

in addition to circadian rhythms being obsewed in lung ventilation in

wakefulness. NREM and REM sleep. circadian rhythms in these three sleep-wake States

were also found in CO2 production and body temperature. Ventilation nomalized for

CO? production had a significant 21-hour rhythm in NREM sleep. but not wakefulness or

in REM sleep (see Figure 3-5. Table 1 and below for funher details).

CO: production had a circadian rhythm in wakefulness (F[3.192] = 14.2.

p<0.000 1). NREM sleep (F[3.268] = 41.9. pc0.0001) and REM sleep (F[3.84] = 7.2.

p=0.0003) (see Figure 3-5 row B). The amplitudes of these rhythms were 9.0%. 9.6?/0 and

10.8% of thrir 21-hour mesor values in wakefulness. NREM sleep and REM sleep

respectively. The mean acrophase of these rhythms was 1858 hours after Iight onset (see

Figure 3-6).

Whrn ventilation was normalized foi CO2 production. a circadian rhythm was

detected only during NREM sleep (F[3.268] = 5.7. p=0.0008). but not during

wakehlness (F[3.192] c 1. p= 0.53 or REM slerp (F[3.84] < 1. p = 1.00) (see Figure 3-5

row C). The acrophase of this rhythm was 9 3 2 hous aller light onset (see Figure 3-6)

and the amplitude was 2.5 (5.9% of the mesor). There were no intluential data points in

this data set (al1 data points had Cook's D test values of < 1 ). When the acrophases of the

ventilation and CO? production rhythm in NREM sleep were compared. there were no

signi ficant di fferences (p>0.05). indicating that these two rhythms in NREM sleep were

in phase.

Al1 rats possessed a circadian body temperature rhythm in wakehlness (F[3,192]

= 68.4. pc0.000 l), NREM sleep (F[3.269] = 58.5. p<0.000 1) and REM sleep (F[3,89] =

8.9, p<0.0001) (see Figure 3-5 row D). The acrophase of these rhythms in al1 three sleep-

wake states occurred approxirnately 17 houn alter light onset (see Figure 3-6). There

were signi ficant di fferences between the amplitudes of t hese temperature rhythms. with

the highest amplitude being in wakefulness (0.6'C) and the lowest in REM sleep (0.3OC)

(see Table 3- 1). However. as mentioned before. there were no differences between the

24-hour means of the temperature rhythm in wakeîùlness. NREM and REM sleep. This

was likely due to a combination of the slowly changing nature of the body temperature

signal in comparison to the fairly rapid changes in sleep-wake state and the relatively

slow response time of the temperature sensor. Thus. the acrophase values obtained are

more likely to reflect the overail acrophase of the body temperature rhythm rather than

the acrophase of this variable in each sleep-wake state.

b) Phase relationships between variables

Figure 3-6 shows the acrophases of lung ventilation. COi production. ventilation

normalized for CO? production. body temperature and respiratory frequency in each

sleep-wake state. There were no differences in the acrophases of circadian rhythms of

lung ventilation. COI production. body temperature and respiratory tiequency in each

sleep-wake state (al1 pO.05) That is, these rhythms were al1 in phase. There were also no

differences in phase for each of the above variables across sleep-wake statcs (al1 p>0.10

using t-test). In pneral. the peak of the lung ventilation. CO: production and body

temperature peaked around the middle of the dark phase in al1 three states (in the range of

16:44 - 19:36 hours after lights on).

One variable that did show a significant phase difference between the rest of the

variables was ventilation normalized for COr production (Y [/Y COz) in NREM sleep

(p<0.05). The acrophase of this circadian rhythm occurred approximately 8 hours in

advance of the acrophases of the rhythms in lung ventilation. CO? production. body

temperature and respiratory frequency.

Figure 3-5: Graphs showing lung ventilation (row A), CO2 production (row B), ventilation normalized for COz production (row C ) and body temperature (row D) as a function of timc of day in wakefulness, XREM and REM sleep (n=6). The presence of a circadian rhythm is s h o m using an asterisk (*).

WAKE 500 -

NREM REM -

Time since light onset (hours)

Fipure 3-6: Phase relationships between the acrophases of the circadian rhythm in lung ventilation, COz production, ventilation normalized for COZ production, body temperature and respiratory frequency ( n 4 ) . Each point indicates the mean acrophase and the error bars represent standard error bars. (* indicates that there was a statistically significant ( ~ 4 . 0 5 ) difference between ventilation normalized for CO2 production in NREM sIeep and the other variables listed).

Lung Ventilation 1 (mllmin) 1

1

: WAKE I NREM .$ REM

CO2 production I l

(rnllmin) O

Ventilation nonnalised for '

CO2 production I l

Body I

Temperature (OC) 1 1

I

Respiratory Frequency

(breathslmin) j

O 6 12 18 24

Time since light onset (hours)

c) Variability in lung ventilation, respiratory frequency and tidal volume

Though there were circadian rhythms in lung ventilation in wake fulness. NREM

and REM sleep. no circadian rhythms in the coefficients of variation (CV) for lung

ventilation were found in al1 three sleep-wake States (wake: F13.1921 < 1. p=0.41.

NREM: F[3.269] = 2.3. p=0.08. REM: F[3.86] < 1. p=0.78) (see Figure 3-7 row A).

Coefficients of variation for respiratory frequency were arrhythmic in wake (F[3.192] <

1. p=0.98) and REM (F[3.86] 4. p=0.5). However. for NREM sleep. the CV for

respiratory frequency was rhythmic (F[3.269] = 6.9. p=0.0002) (see Fipre 3-7 row B).

No circadian rhythms in CV for tidal volume were observed (wake: F[3.192] c 1. p=0.37.

NREM: Fi3.2691 < 1. p= l .O and REM (F[3.86]4. p=0.84) (see Figure 3-7 row C).

Firure 3-7: Breath to breath variability in lung ventilation (t CV). respiratory frequency (f CV) and tidal volume (VT CV) in wakefulness, NREM and REM sleep across a 24-hour period (n=6). The presence of a circadian rhythm is indicated with an asterisi(*).

Time since lig ht onset (hours)

Section 3: ADDITIONAL ANALYSES on EFFECTS of SLEEP-WAKE STATE

In the analyses conducted in this section. the 24-hour means of each parameter in

wakefulness. NREM and REM sleep were compared using a One Way Repeated

Measures ANOVA.

a) Lung ventilation, respiratory frequency, tidal volume and their assaciated

coefficients of variation

Sleep-wake state (wakefulness, NREM and REM sleep) had significant effrcts on

the 24-hour means in lung ventilation (F[Z. 171 = 40.2) (see Figure 3-8 panel A). The

differences in lung ventilation across sleep-wake states depended on both respiratory

Frequency (F[Z. 171 = 19.0. p<O.OO 1 ) (see Figure 3-8 panel) and tidal volume (F[ 2.171 =

49.7. p < 0.00 1 ) (see Figure 3-8 panel C).

The drop in ventilation from wakefulness to NREM slcep was due to a 15.1%

decline in tidal volume (p<0.05) and a 14.1% decrease in frequency of breathing

(p<0.05). Though tidal volume was 7.1% lower in REM sleep than NREM slrep. thrre

was no statistically significant difference in ventilation beiween REM sleep and NREM

sleep (p>O.Oj). This was probably due to a 10.2% increase in frequency in REM sleep.

which although non-significant was enough to counterbalance the decrease in tidal

volume.

The vanability in ventilation. respiratory frequency and tidal volume in eac h

sleep-wake statc are shown in Figure 3-8 panels E. F and G and the values are also listed

in Table 1. Sleep-wake states had a significant effect on the 21 hour mean coefficient of

variation (CV) for ventilation (F[2.17] = 18.5, p<O.OO 1 ). respiratory frequency (F[Z. 171 =

40.0, p<0.001) and tidal volume (F[2.17] = 47.2. p<0.00 1). CV for ventilation was lower

in NREM sleep (mean: 15.5 f 0.24%) than wakefulness (mean: 23.1 + 0.4 1%) and REM

sleep (mean: 24.3 t 0.24%) (p<0.05).

The higher variability in ventilation during wakefulness and REM sleep were also

reflected in the respiratory frequency and tidal volume. CV for respiratory Frequency was

9.6 f 0.17% in NREM sleep. 26.0 f 0.38% in wakefiiness and 3 1.6 f 0.53% in REM

sleep. Similarly for tidal volume. CV was 15.8 f 0.20% in NREM sleep. 23.7 +- 0.20% in

wakefulness and 26.6 + 0.25% in REM sleep.

Fipure 3-8 illustrates the sleep-wake state related changes in lung ventilation. respiratory frequency and tidal volume and their associated coefficients of variation (n=6).

WAKE NREM REM

WAKE NREM REM

WAKE NREM REM

WAKE NREM REM

WAKE NREM REM *

p<O.OS

WAKE NREM REM

b) CO1 production, ventilation norrnalized for CO2 production and body

temperature

Sleep-wake state (wakefulness. NREM and REM sleep) had significant effects on

the 24-hour means in ventilation (F[Z. 171 = 40.2). CO? production (F[2.17] = 8 1.2.

p<O.OO 1) and ventilation normalized for CO2 production (F[2. 171 = 4.5. p = 0.04) (see

Figure C 3-9 panels A. B. C). However. contrary to expectations. no sleep-wake state

related differences were be found betwern the mean 24 hour body temperatures (F[?. 171

< 1. p = 0.54) likely because of the slow response time of the body temperature sensors

inside the telemetry unit (see Figure 3-10 panel D).

Using data combined across the 24-hour periods. CO: production was 27.096

lower in NREM sleep and 35.0% lower in REM sleep compared with waketùlness.

However. there was no difference in CO? production between NREM sleep and REM

sleep (pO.05). The drcline in rnetabolic rate from wakefulness to NREM sleep and REM

sleep was matched by a 24.2% drop in ventilation from wakefulness to NREM sleep

(p<0.05) and a 26.6% decline in ventilation from wakefulness to REM sleep (p<0.05).

When the 24-hour mean ventilation was normalized for CO2 production. there

were no differences between wakefulness and NREM sleep (p>0.05). However.

ventilation normalized for CO: production was 19.0% and 12.8% higher in REM slrrp

compared to wake fulness and NREM sleep (both p<O.O5). respectively.

Figure 3-9 showing effects of sleepwake state on (A) Iung ventilation, (B) COl production, (C) ventilation normalized for CO2 production and (D) body temperature (n=6).

WAKE NREM REM 15

WAKE NREM REM 60 *

WAKE NREM REM

WAKE NREM REM * p<o.os

Table 1: 24 hour means. acrophases and amplitudes of ventilation (Y ,). CO1 production (Y CO,). ventilation normalized for COt production ( Y 11 Y CO,), body temperature (W. tidal volume (V,) and respiratory frequency (f). Coefficients of variation for ventilation (< , CV). tidal volume (V, ( 3 3 and respiratory frequency (f (3") are also shown (n=6).

Variable Parameter REM" Directionofchange

24-hour mem

5. CO, (mumin) Acmphue I hours:mi~~. )

Amplitude

A n = 6 but no values in REM sleep were recorded in one day in one rat.

10.0 + 0.8 18:36 I 1 : 16

0.9 t 0.1 (9.0';)

U' = NREM c REM

W = REM < NREM

I I 1

t Sigificantly different (pc0.05) using Tukey's multiple cornparison test.

Who ur mean co2 .\cmphase (hours:min. I

.bplitude

24-ho w man T b (OC) Xcrophase ( hours:&. )

.-litude

* Sipifrcantly different brtsed on pair bise cornparisons (Wake vs. NREM. Wake vs. REM. SREM vs. REM) using means and standard enors generated by fitting sine wave models through data.

7.3 2 0.6 1 R:47 2 Or34

0.7 10.1 (9.6'0)

37.4 + O. 1 1723 t 050 0.4 + 0.03

37.4 t O. 1 17:34 .t 0:39 0.6 f 0.04

The trends retlected in the 2 way Rh4 ANOVA and Tukey's post hoc test are shown in the last colurnn. Acrophases are expressed as hours and minutes afler light onset. The absence of amplitude and acrophase values indicates that no circadian rhythm was present for that variable. When percentages (%) are expressed for amplitudes. they refer to the % of the 24-hour mesor value.

7

39.9 f 1.6

6.5 k 0.6 19% + 1 : 19

0.7 2 0.2 ( 10.800)

37.3 i 0.1 : i l : 0.3 1 0.06

W > NREM = EREXtt W = NREM = REM W = W 3 I = REhl

42.1 k 2.4 9:32 I 2: 17

2.5 5 0.6 (5.3'0)

k* = ';REM = REM U ' = N R E M = R E ? J U' ' NREM REM*

47.5 2 4.5

Section 4: ADDITIONAL DAY-NIGHT COMPARISONS

Whilst the main purpose of the previous sections were to test for the presence of

circadian rhythms and the effects of sleep wake States on lung ventilation. the primary

aim of this section is to perform day-night cornparisons in wakefulness and NREM sleep

on the other variables such as CO? production and EEG and EMG related variables that

were measured along with lung ventilation. Like the data involved in testing hypothesis

2. the data for this section also consistrd of two five-hour windows. each beginning 2

hours afler lights on and off.

a) Day-night cornparison of CO2 production

In general. these results confinned the results from the circadian rhythrns analyses

performed on the data across II hours in wakefulness and NREM sleep. In both

wakefulness and NREM sleep. lung ventilation. body temperature. CO2 production and

were higher in the night (dark-active) phase than the day (light-rest) phase. (F[L 231 =

27.1. p=0.003: F[ 1. 231 = 26.1. p=0.004: (F[ 1. 231 = 25.5. p=0.004. respectively) (see

Figure 3- 10 panels A.B and D). In wakefulness. CO? production and ventilation increased

by 16.1% and 13.6% from day to night. respectively. These increases in CO: production

and ventilation from the day to night were similar in NREM sleep (17.5% for CO2

production and 14.3% for ventilation).

Corresponding to the 24.2% (91 ml min-') and 23.6% (101 ml min-') aecline in

lung ventilation that occurred from wakefulness to NREM sleep during the day and the

night respectively (see Section 1 of RESULTS). there were also similar changes in CO?

production. CO? production dropped by 30.5% (3.0 ml min-') on going from wakefulness

to NREM sleep during the day and a comparable decrease of 29.7% (3.4 ml min") during

the night (F[ 1.231 4 1. p=0.42) (see Figure 3- 10 panel B).

b) Day-night cornparison of %P2/%61, EEG and EMG amplitudes

Although no sipificant main effects of time of day were found in %P1/%61

(F[ 1.231 = 1.8. p=0.24), there was an interaction between time of day and sleep-wake

state in this variable (F[1.23) = 7.5. p=0.04) (see Figure 3-10 panel E). This interaction

was due to the different effects of time of day on %B2i%6i depending on whether the rat

was awake or in NREM sleep. More specifically. % P ? I % ~ ~ was lowcr during the day

compared to the night in NREM sleep (p<0.05). but was not different dunng day than the

night in wakefûlness (p>0.05).

In both wakrtiilness and NREM sleep. EEG amplitude (F[1.23] = 20.8. pc0.006)

(see Figure 3-10 panel E) and EMG amplitude (F[1.23] = 7.7. p=0.04) (see Figure 3-1 0

panel G) were hipher during the day than the night. There were also interactions between

time of day and sleep-wake state for EEG amplitude (F[1.23] = 2 1.9. p=0.005) (see

Figure 3-10 panel F). The EEG amplitude was 72.8% larger in NREM sleep than

waketùlness dunng the light phase. and 89.1% larger in NREM sleep than wakehlnrss in

the dark phase.

F i a r c 3-10 illustrates day-night cornparisoos in (A) lung ventiIation, (B) CC& production, (C) ventilation normalized for COI production, (D) body temperature, (E) ratio of high to low EEG frequencies, (F) EEG amplitudes and (G) ELMG amplitudes (n=6). Statistically significant (p4.05) day-night differences independen t of sleep-wake states effects (main effect of sleep-wake sta te are indicuted by an asterisk (k) and sleep-wake state effects independent of time of day (main effects of sleepwake state) are flagged using a cmss (t). The pound jrnbol (W) shows s t a t i s t i c a ~ ~ significant interactions (p4.05) between sleep-wake state and time of day.

Day Night

Time of day -W NREM + WAKE

D& ~ i g h t

Time of day

Chapter 4: Discussion

The present study confirmed both hypotheses that there are circadian rhythms in

lung ventilation in wakefulness. NREM and REM sleep, and. that the magnitude of the

change in lung ventilation from wakefulness to NREM sleep is the same across the day.

Circadian rhythms in lung ventilation have previously been studied in expenments that

have either not considered sleep-wake state (Seifert. Knowles. & Mortola. 2000) or only

looked at the waking state (Spengler, Oliver. Czeisler. & Shea. 1997). However. to Our

knowledge this is the first study that has found circadian rhythms in lung ventilation in

NREM and REM sleep. in addition to wakefulness. Furthemore. these sleep-wake States

were measured using electrographic techniques which are used routinely in sleep studies.

Of note. is the observation that the circadian rhythms in lung ventilation in each

sleep-wake state did not differ in amplitude or phase. but only varied in their mean 24-

hour level. Consequently. the change in magnitude in lung ventilation from wakefulness

to NREM (and REM) sleep was similar across time of day. This fact was confirmrd by

comparing the decrease in lung ventilation from wakefulness to NREM sleep at two

times of the day that corresponded to approximateiy the peak and trough of the body

temperature rhythm.

Taken together. these results imply that both time of day and sleep-wake state

may both modulate lung ventilation such that circadian and sleep-wake state effects on

lung ventilation may be additive. This result is of clinical relevance because if a decline

in lung ventilation due to circadian factors supenmposes a decrease in lung ventilation

due to NREM sleep, a daily minimum in lung ventilation during sleep would be

produced. It is during this daily minimum that respiratory symptoins of patients with an

underlying respiratory disorder characterized by impaired gas exchange rnay be

intensified. This time may pose a period of increased vulnerability to hypoventilation.

Technical considerations

The present experiment could be faulted for several reasons. One relates to the

measurement of breathing in this study. There are some disadvantages of measunng

breathing indirectly using the closed system method of barometnc plethysmography. It is

possible that closing the chamber and allowing CO, to build up to estimate the level of

CO? impacted lung ventilation. However, the correlation of the fractional concentration

of inspired CO2 venus ventilation (see Figure 2 3 ) indicated that there was no ventilatory

response to COz at the concentrations present in the experiment. Funhermore. the fact

that the mran ambient % CO2 concentrations were systematic across time of day during

measurements in al1 three sleep-wake states. aliows us to attribute the time of day

changes in lung ventilation to Factors other than inspired CO, concentration. The

advantage of the closed system method was that it ailowed fairly rapid estimatrs of

metabolic rate to be made during slrep-wake states that are short in duration in the rat.

The open tlow method of barometric plethysrnography (e.g.. Jacky. 1978) is unlikely to

stimulate breathing. but is incapable of measuring the rapid changes in metabolic rate that

occur with sleep-wake state.

Using the closed system method of barometric plethysmography. there is also a

potential concem of dismpting the sleep-wake cycles of the rat and causing phase shifis

because of the constant changes in environmental noise as the chamber was opened and

closed. However. the rats seemed to habituate to these patterns and their body

temperature rhythms and sleep-wake cycles suggests that they behaved like entrained and

undisturbed rats. For example. body temperature peaked during the active phase and was

at its minimum during the light phase. Also, the sleep-wake behaviour and EEG

characteristics across time of day were consistent with other studies of rats entrained to a

12 hour light dark cycle (Harnrahi et al.. in press; Trachsel et al.. 1988)

An additional concem relates to discerning sleep-wake states. Although the

judgment of sleep-wake state using electrographic means such as the EEG and EMG. is

less subjective than using behavioural criteria, it is possible to be somewhat subjective.

given that it is scored by the experimenter. This is most likely in making judgments of

wakefulness. NREM and REM sleep dunng transitions between these sleep-wake states.

For exarnplr. at the onset of NREM sleep. hiph frequency components of the EEG and

the EMG are still present. making it difficult to determine when NREM slrep actually

begins. In the present experiment. attempts were made io bypass these problems

associated with sleep-wake state transitions by choosing sections to measure breathing

and metabolism in which the sleep-wake state was established Tor a period ofat least 20

seconds. This duration was arbitrarily chosen to prevent the selection of sleep-wake state

transitions. but. whether or not the animal was in a physiological steady state

characteristic of each sleep-wake state cannot be determined. but assumrd. These

sections were then subjected to analyses of the EEG frequencies and amplitude and EMG

amplitudes and compared. Statistical tests veri fied the di fferences in %P2/%6i and E EG

and EMG amplitudes between sleep-wake states and were sirnilar to a previous study in

rats where a similar approach was used (Hamrahi et al.. in press).

Circadian related changes in lung ventilation

The presence of circadian rhythms in lung ventilation in wakefulness are

consistent with other expenments that have measured lung ventilation and'or assessed the

characteristics of the respiratory control system across time of day. In awake. but not

sleeping humans. circadian rhythms in lung ventilation (Spengler et al.. 1997).

chemosensitivity (Raschke & Moller. 1989; Spengler et al. 2000) and CO? response

threshold (Stephenson et al.. 2000) have been shown. Circadian rhythrns in lung

ventilation have also been demonstrated in Freely behaving rats where sleep-wake state

was not assessed (Seifert et al.. 2000). However unlike in humans. circadian rhythms in

the ventilatory response to CO2 have not been shown in rats. but there are suggestions

that it too may exist given that day night differences in the response to CO2 have been

found in awake adult rats (Peever and Stephenson. 1997).

These studies in rats are of most interest when it comes to comparing similar

studies using the same species. Unlike Peever and Stephenson (1997) who found no

statistically significant differences in ventilation in awake rats breathing normal room air

at IO a m . and 10 p.m.. we found that lung ventilation was significantly higher during the

night than the day. Possible reasons for this discrepancy rnay lie in the large variability

in Peever and Stephenson's (1997) lung ventilation data and differences in methodolo~.

Given that there was a trend towards higher lung ventilation at night than day in their

study. it is probable that this result in their study would have also been of statistical

significance had they had a larger sample size to reduce variability. in support of this. the

overall day-night difference in lung ventilation during wakefulness (mean * SEM) in this

study (5 1 -t 27 mVmin) was similar to Peever and Stephenson's (1997) study (46 * 30

mumin). However. due to the lower variability in Our data. we were able to find

statistically significant day-night differences in lung ventilation in contrast to Peever and

Stephenson (1 997).

The determination of wakefulness using behavioural criteria rather than using

EEG and EMG rneasures may have also contributed to a lack of difference in lung

ventilation between day and night in Peever and Stephenson's (1997) study. In their

study. it is possible that the rats could have been judged to be awake when actually

drowsy or asleep. If such was the case dunng the night. for example. overall lung

ventilation would be lower. Also. artificially arousing the rats so that measurements could

be made in wakefulness during the day (when the rats asleep more) could elevate lung

ventilation. Both of these scenarios would lead to day night differences in lung

ventilation being less likeiy to be found. By seleciing periods of established wakehlness.

NREM and REM sleep using electrographic criteria. thrse potential problems were

circumvented in Our study.

in contrast to Peever and Stephenson ( 1997). but consistent with Our study. Siefert

et al. (2000) also found day night differences in lung ventilation in air breathing rats

whilst showing a circadian rhythm in lung ventilation. They found that this circadian

rhythm in lung ventilation was coincident with the rest-activity rhythm and body

temperature. However. since sleep-wake States of the animals were not considered and

given that these rats are nocturnal animals which are predominantiy awake during the

night and asleep during the day. it is plausible that the changes that the day night

oscillations in lung ventilation were a result of time of day changes in sleep-wake state.

That is. rffects of sleep-wake state on lung ventilation may have masked the circadian

rhythm in lung ventilation. Usine the EEG and EMG to venQ sleep-wake states and

separate out potential masking effects of sleep-wake state on lung ventilation. the present

study demonstrates that the circadian rhythm in lung ventilation is independent of sleep-

wake state.

One possible manifestation of the masking effect of sleep-wake state in Siefert et

al.3 (2000) study can be found by comparing the different mechanisms by which lung

ventilation was altered as a function of sleep-wake state and time of day. In Siefert et al

(2000) the circadian rhythm in lung ventilation was attributed to time of day changes in

both tidal volume and respiratory frequency. In contrast. in our study. the iime of day

changes in lung ventilation in wakefulness and NREM sleep were due to changes in

respiratory fiequency only. However. sleep-wake state changes in lung ventilation were

due to both tidal volume and frequency. Therefore. the time of day changes in lung

ventilation due to tidal volume in Siefert et al's (2000) study can probably be attributed to

the masking effects of sleep-wake state.

Potential mechanisms mediating the circadian rhythm in lung ventilation

Of interest in this discussion are the potential mechanisms mediating the circadian

rhythms in lung ventilation in wakefùlness. NREM and REM sleep. in this regard. the

phase relationships between lung ventilation. metabolic COI production and body

temperature in each sleep-wake state are illuminating. The fact that al1 of these variables

have similar acrophases in each and across sleep-wake states at approximately 17 hours

afler light onset ( 5 hours after lights offset) suggests that they are inter-related.

There is evidence that the circadian rhythms in lung ventilation in each sleep-

wake state are in part caused indirectly by the circadian changes in CO? production and

body temperature. For example. when ventilation was nonnalized for CO? production the

circadian rhythm in lune ventilation was no longer present in wakehlness and REM

sleep. The constant ventilation to CO2 production ratio (i' &'CO2) across the day in

wakefulness and REM sleep implies that the circadian changes in lung ventilation

matches the circadian changes in metabolic CO: production in these two sleep-wake

states. This is consistent with other studies in which day-night comparisons of this

variable have been made in newbom (Saiki & Mortola, 1995) and adult rats (Peever &

Stephenson. 1997). It is also consistent with studies that have demonstrated the cntical

role of CO2 in determining lung ventilation (Phillipson et al.. 198 1 ) and with models of

respiratory control that emphasize the chemoreflex control of breathing (e.g.. Duffin.

1990) (also see Introduction). Since PaCO? is detemined by a balance of metabolic

production of COz and ventilation. the constant Y ,/Y COI ratio across the day in

wakefulness and REM slerp implies that PaCOz is also constant across the day in thsse

sleep-wake states.

Hints that thennoregulatory mechanisms may also be responsible for the circadian

changes in lung ventilation in each sleep-wake state comes from the observation that the

time of day changes in lung ventilation are mediated by changes in respiratory frequency

rather than tidal volume. It is interesting to note that the acrophase of respiratory

frequency. when fitted with the sine wave model. was exactly the same as that for body

temperature. The increase in respiratory frequency. in panting for example. is a method of

facilitating heat loss in species such as dogs and cats (Tenney & Boggs. 1986). Panting

causes a large increase in ventilation of the respiratory dead space. and therefore,

increases evaporative heat loss of the respiratory tract (Mortola & Gautier. 1995).

Although rats are not known to use panting as a method of heat loss (Gordon. 1990).

Boden, Hams. & Parkes (2000) have demonstrated that there is a respiratory drive in

addition to the increase in CO2 production at raised body temperatures in the rat. It is

possible that this additional drive. waxes and wanes to alter respiratory frequency in

phase with the circadian rhythm in heat production (also measured by COz production)

(Gordon. 1993) and body temperature.

To complicate matters Further. there are also the effects of arousal levels to

consider within each sleep-wake state which may have a direct or indirect impact on

vent ilation. We have arbitrarily divided sleep-wake state into the commonly accepted

stages of wakefulness. NREM and REM sleep. However. even within each of these sleep-

wake States there is a lack of unifonnity. Take for example. a cornpanson of the ratio of

the high to iow frequencies (%Pd%6i) of the EEG in NREM sleep using a five-hour

sample of data from the day and from the night (see Figure 3-10 panel E). The lower

%P$%& in NREM sleep during the day implies that the NREM sleep was "deeper"

during the day than during the night. In addition. there were time of day di fferences in the

EEG amplitude when considering both wakefulness and NREM sleep. It is known that in

humans. where NREM sleep is scored in more stages. that the stage of NREM slerp

influences the dope of CO2 response line (Douglas et al.. 1982). a key deteminant of

resting ventilation.

Though metabolic COz production. body temperature and arousal level in each

sleep-wake state may be involved in the circadian rhythm in lune ventilation. the present

study is unable to make any hrther conclusions on which of these variables may be more

quantatively or qualitatively important. or how they are related to each other given the

complicated nature of the interactions between body temperature, metabolic rate and lung

ventilation and sleep-wake state.

The circadian rhythm in ventilation normalized for CO2 production in YRE-M sleep

So far. the focus of the discussion on possible mechanisms mediating the

circadian changes in lung ventilation has been on indirect mechanisms such as CO?

production. body temperature and possible differences in arousal level within each slerp-

wake state. There is also the possibility that more direct mechanisms such as connections

between the primary mammalian circadian pacemaker. the SCN. and the components of

the respiratory system may exist. This study. which involves the integrated responses of

lung ventilation in each sleep-wake state across time of day. does not address this

question or which specific part of the respiratory control system may be involved.

However. there are suggestions that in addition to indirect mechanisms. there are also

more direct rnechanisms which may be involved. In this regard. the rather surptising

circadian rhythm in ventilation normalized for CO2 production (i' ,/i' COz) during NREM

sleep. but not in wakelùlness or REM sleep. is insighttùl.

The presence of a circadian rhythm in i' CO2 during NREM sleep implies

that across a 23-hour period. lung ventilation does not change proportionally to metabolic

COz production. and consequently. this suggests that PaCO: too may not be constant

across time of day in NREM sleep. The reason for the presence of a circadian rhythm in

i' CO? in NREM sleep. but a lack of one in wakehlness and REM sleep. is unclear.

When the acrophases of the ventilation and metabolism rhythm were tested. they

showed no differences. indicating that these two variables were in phase. Perhaps, NREM

sleep is the most stable sleep-wake state to observe such a rhythm given that the data for

the Y ,/Y COI in wakefulness and REM sleep is noisier. Before proceeding, it is

imporiant to insert a note of caution with regard to this result. Given that a large nurnber

of statistical tests were performed on the data. this data may represent a statistical arti fact.

More specifically. this result may possibly be a Type I statistical error. That is. falsely

rejecting the nul1 hypothesis that there is no 24-hour rhythm in Y ~li ' COz when it is in

fact tme.

Assuming that the circadian rhythm in d< CO? in NREM sleep is real. in

mechanistic terms. it suggests that across a 24-hour period in NREM sleep. the

chernoretlexes are influenced directly by the circadian timing system such that PaCO?

would also oscillate. This is not an unreasonable hypothesis given that direct modulation

of the respiratory control system by the circadian timing system has been suggested in

expenments in humans (Rascke and Moller. 1997: Stephenson et al. 2000; Spengler et al.

2000) and in ducks (Woodin and Stephenson. 1997). Intriguing and interesting to

speculate on. is the question as to why such a hypothetical mechanisrn might only be

observed in NREM sleep and not in wakehilness or REM sleep.

A possible functional role of the circadian rhythm in Y dY CO? in NREM sleep is

in REM sleep replation. Examination of the circadian rhythm shows that l i t COz in

NREM sleep nses throughout the light phase when the rats are ncrmally asleep to peak

approxirnately nine and a half houn before slowly beginning to decrease again. Likewlse

from persona1 observation in our study. the frequency and duration of REM sleep during

the rest (light) phase increased progressively from its minimum at the start of rest phase

(also see Figure 1-1 0 and Trachsel. Tobler. & Borbelly, 1988). Since PaCO? and H+ are

invenely related to Y $Y CO?, this pattern suggests that PaC02 and H+ are at their

minimum at the acrophase of the Y Ili' CO2 rhythm in NREM sleep. At this time. the

higher Y CO? value implies that ventilation is relatively higher for a given lrvel of

CO? production.

Dev & Loeschcke (1979) have proposed that hydrogen ions alter respiratory

discharge by inhibiting the metabolism of acetylcholine at the synapse. Like many other

enzymes. the activity of acetylcholinesterase. the enzyme which breaks down

acetylcholine. is pH sensitive. Cholinergie agents stimulate breathing when applied to the

ventral medullary surface. as do antagonists of acetylcholine esterase (e.g physostigmine)

(Chemiack. 1993). Dev & Loeschcke (1979) found that atropine. a muscarinic receptor

antagonist blocked the response to CO:. This has been confirmrd by Nattie. Mills. & Ou

(1988) who used more specific antagonists to show that the M2 muscannic binding site

was the pertinent receptor. Knowing that cholinergie and cholinoreceptive mechanisms

play a key role in the neurobioloa of REM sleep generation (Lydic & Baghodoyan.

1994). that acetylcholine. the neurotransmitter involved in these systems is modulatrd by

pH. and. that REM sleep always follows NREM sleep. it is possible that the Y ,/Y CO2

rhythm in NREM sleep represents a feed forward mechanism by which the respiratory

system regulates REM sleep by changing hydrogen ion concentration.

These ideas are consistent with two concepts. Firstly. it is compatible with the

proposa1 that REM slerp is hinctionally related to NREM sleep rather than waking (e.g.

Benington & Heller. 1994). Secondly. it is also consistent with the role of humoral inputs

affecting multiple aspects of neuronal circuits including their synaptic connectivity.

responsiveness to stimuli and neuronal composition in addition to the intrinsic properties

of neurons within the affected circuits (Knieger & Fang. 1999). Given that REM is a

distinct neurophysiological state (Phillipson & Bowes. 1986). H+ may be one such

humoral input modulating the neuronal circuitry involved in REM sleep. However. the

hypothetical role of circadian control of breathing in NREM sleep to Vary H+ across tirne

of day (which rnay possibly modulate neuronal circuitry involved in REM sleep

generation across time of day) rernains to be tested.

Sleep-wake related changes in breathing, metaboiism and body temperature

In general. our tindings of the effect of sleep-wake statç on lung ventilation are

consistent with other studies in humans (White et al.. 1985). dogs (Phillipson et al., 1976)

and rats (Pappenheimer. 1977) that have found lung ventilation to decline from

wakehlness to sleep. In rats. Pappenheimer (1977) found a 10-20% decline in minute

ventilation on going from wakefulness to NREM sleep. In cornparison. we found a mean

decline of 24.2% in lung ventilation from wakefulness to NREM sleep using data

generated over a 24-hour period. Though there was a decline in ventilation from

wakefulness to NREM sleep. we found no di fference in lung ventilation between NREM

sleep and REM sleep. There were also profound state related changes in the variability of

breathing as indicated by coefficients of variation in tidal volume. frequency and

ventilation consistent with what has been reported in the literature (Phillipson & Bowes.

1986). As expected. breathing during NREM sleep was regular and less variable than in

wakefulness and NREM sleep.

There were no changes in the coefficient of variation in lung ventilation and tidal

volume in al1 three sleep-wake states across time of day. However. there was a circadian

rhythm in the coefficient of variation in respiratory frequency during NREM sleep. but

not during wakefulness or REM sleep. The reasons for these novel observations are

unclear. but they may reflect time of day changes in the activity of the respiratory rhythm

generator.

The changes in ventilation that occurred from wakefulness to NREM sleep were

due to a decreased tidal volume and respiratory frequency. The decrease in respiratory

frequency in NREM is consistent with Megirian. Ryan. & Sherry's (1980) and

Pappenhiemer's (1977) study in rats. However. the decrease in tidal volume was different

to Pappenheimer (1977) who found either an increase or no change in tidal volume

dunng NREM sleep compared to wakefulness. The values on lung ventilation. tidal

volume and respiratory frequency in the rat literature are quite variable possibly due to

the large differences in environmental conditions. experirnental techniques. protocols and

strains that were used. indeed. even with similar experimental protocols Strohl et al..

(1997) found differences in respiratory and metabolic variables amongst strains of rat.

Our values for tidal volume in rats breathing room air agree with the values in the

literature ( c g . Holloway & Heath. 1984: Mortola. 199 1: Strohl et al.. 1997: Aaron &

Powell. 1993). The respiratory rate. however. was generally higher in our study in al1

sleep-wake states. Consequently. our values for lung ventilation were also generally

higher. Perhaps the primary reason that our vaiues are higher is because Our results

include data collected during the night when the metabolic rate. and hence. the lung

ventilation is higher. The majority of the experiments in the rat are likely to be collected

during the day. at a time when the rats are mostly resting and asleep. and therefore.

metabolic rate and respiration are likely to be lower. The absence of a correlation

between inspired COz level and lung ventilation (see Figure 2 4 ) indicates that this was

not due to the CO? level in the chamber stimulating breathing.

It must be noted that metabolic rate values in the awake rat are unlikely to be the

basal metabolic rate. which. is commonly and more easily measured in humans. Basal

metabolic rate. a reflection of the minimal metabolic expenditure required for the

maintenance of homeothermy. is defined as the metabolic rate of an individual that is

resting in a thermoneutral state. but not sleeping, 14 to 18 hours after eating (Gordon.

1993). Our values are unlikely to represent basal metabolic rate becausr it was very

difficult to achieve a state in which the rats are at absolute rest. but not sleeping. When

the rats were awake. they continually engaged in some form of motor activity such as

grooming. feeding. exploring etc. Bramante (1 958). for example. found that in a 5-hour

period of measuring activity and oxygen consumption simultaneously. the rat exhibited

no activity only 4.9% of the tirne. We measured breathing and metabolism during periods

in which there were no gross body movements (which cause large fluctuations in the

pressure traces of the plethysmogaph as well as markedly elevating metabolic rates) such

as when the rat was exploring. but undoubtedly engaged in some form of microactivities.

Thus. this needs to be considered when interpreting Our breathing and metabolic rate

values during wakefulness. in contrast to wakefulness. breathing and metabolism in

NREM and REM sleep are less afkcted by such concems.

in companng sleep-wake state related differences in metabolic COz production. as

expected we found that CO2 production was higher in wakehlness than both NREM and

REM sleep. but did not find any difference between NREM and REM sleep. This trend is

consistent with other studies in humans (White et al.. 1985), but another study in rats

found that in this species. metabolic rate was türther reduced in REM sleep (Schmidek.

Zachariassen. & Hammel. 1983). The reason for this discrepancy is probably duc to the

smaller sample sire and the fewer measurements of REM sleep metabolism in our study

that may have contributed to the trend towards lower COz production in REM sleep.

which was not statistically significant.

Of interest in discussing metabolism and ventilation is how these two variables

are related to each to other (as indexed by the ventilation normalized for metabolic CO2

production. lit CO.) to regulate PaCOz in cach of the sleep-wake States. PaCO? has

been reponed and calculated to increase dunng NREM sleep compared to wakehlness

(Pappenheimer. 1977: Phillipson & Bowes. 1986). This has been explainrd by a greatrr

decrease in ventilation relative to the decrease in metabolic rate that occurs with slrep.

which results in a lower CO. ratio. In contrast. but consistent with White et al.'s

(1985) study in humans. we observed no differences in ,/Y CO. between wakefulness

and NREM sleep. However. chanps in lung ventilation (i' 1) have a different effect on

PaCO? depending on whether the changes are mediated by changes in tidal volume or

respiratory frequency. Assuming that dead space volume is 0.75 ml (tiom an rstimatrd

value of 0.7 1 ml / 100g. Pappenheimer. 1977). and. that it is constant across sleep-wake

state. the alveolar ventilation (i' is equal to respiratory fiequency x (tidal volume -

dead space volume)) in wakefulness and NREM sleep can be recalculated from Table 1

to be -3 19ml /min. and -2 19 mumin respectively. Thus. the recalculated i- . , < CO:

values are 3 1.9 wakefulness and 30.0 in NREM sleep. Comparing i' CO2 instead of

i' d Y CO?. the lower Y CO2 value in NREM sleep compared to the equivalent value

in wakefulness. does indeed suggest that PaCOz is higher dunng NREM sleep than in

wakefulness.

in contrast to a lack of difference in Y [/Y CO? between wakefulness and NREM

sleep. i' [/Y COz was higher in REM sleep compared to wakefulness and NREM sleep.

This was due to a decline in metabolism. but not lung ventilation on p i n g fiom NREM

to REM. This irnplies that lung ventilation was higher relative to the metabolic rate

during this state. To date. we are not aware of any studies that have looked at the impact

of REM sleep on Y ,/Y CO2. so cornparison of this variable in rats is not possible.

However. the higher l i t COz is consistent with the finding that ventilation in REM

sleep is oAen independent of metabolic control and is intluenced by the behavioural

inputs (Orern. 1994: Phillipson & Bowes. 1986).

Whereas. lung ventilation and metabolism were dependent on sleep-wake state.

our data did not reveal any effect of sleep-wake state on body temperature. This is in

contrast to body temperature decreases on falling aslerp (Alfoldi. Rubicsek. Csemai. &

Obal. 1990: Li. Randall. & Nattie. 1999). This finding was likely due to the short

duration of sleep-wake cycles in the rat and the slow response time of the temperature

sensor. Although, it is likely that rats change their thermoregulatory set point uith sleep-

wake state (Glotzbach & Heller. 1976). core body temperature is unlikely to change very

quickly because of the thermal inertia present in the intemal ogans of the body. Since the

temperature sensor. which had a 90% response time of approximately 2-3 minutes. was

implanted in the peritoneal cavity. it is unlikely that the temperature changed rapidly

enough to be detected across sleep-wake states that oflen changed quicker than this 2-3

minute period.

Future researc h opportunities

There are many questions raised by this experiment that should provide the

stimulus for further research. Firstly, it is not known from the present experiment whet her

PaCO? is constant across time of day in a given sleep-wake state. There are suggestions

that PaCOz may be constant across the day in wakefulness and REM sleep. but whether

this is also true in NREM sleep needs to be verified given that there is a circadian rhythm

in Y ,/Y CO2 in this sleep-wake state. If so, experiments need to address its possible

functional significance including the proposai that it may be involved in REM sleep

regulation.

Secondly. questions need to be directed at the mec hanisms behind the circadian

rhythm in lung ventilation in rach sleep-wake state. The results of this experiment

suggest that the circadian rhythms in lung ventilation may in pan be an indirect product

of metabolism. body temperature or arousal level within a panicular sleep-wake state.

One possible approach to answenng this question would be to allow the rats to fiee run in

constant conditions. By examining the periods and phase relationships of rhythms in lung

ventilation. metabolism and body temperature. it may be possible to see which of these

variables is more closely coupled to lung ventilation.

One possible mechanism that is likely to be involved along with the circadian

rhythm in COz production. are time of day changes in the chernoreflexes which we

speculated on. However. it is yet to be determined if there are time of day dependent

changes in the respiratory control system in each sleep-wake state manifested as changes

in the thresholds or chemosensitivity or both. The circadian rhythm in NREM sleep

suggests that the circadian timing system may be directly involved in the modulation of

lung ventilation in this sleep-wake state. Given that there is a vast amount of literature on

the nervous system of the rat. ir may be feasible to perhaps study the neural basis of the

observations in this expçrirnent. Of potential benefit in this regard. would be studies in

which lung ventilation. body temperature. metabolism and sleep-wake state were

measured across the day in SCN lesioned rats.

Conclusions

Overall. the present experiment demonstrates that there are circadian rhythrns in

lune ventilation in wakefulness. NREM sleep and REM sleep. Furthemore, we have also

shown that the magnitude of the change in lung ventilation From wakefulness to NREM

sleep is the same across time of day. Togeth;;. these data show that the lung ventilation

may not only be modulated by sleep-wake state. but also by circadian factors. This is of

potential clinical sipificance for patients who already hypoventilate due to an underlying

respiratory abnonnality. For such individuals a time of day decline in lung ventilation

supenmposed on a decline in lung ventilation due to sleep. may exacerbate respiratory

symptoms.

Hvpothesis l

ë- 3

2 0 0 ~ O 12 24 Tirne tlnce llght onset (houn)

Time of Day

Day Night

These circadian rhythms in lung ventilation in each sleep-wake state are likely to

be mediated panly by time of day changes in metabolic COz production. body

temperature and arousal state within each sleep-wake state. When lung ventilation was

normalized for COz production (i' [li' CO?) there was no circadian rhythm in wakefulness

or REM sleep. However, v I/Y COz did exhibit a circadian rhythm in NREM sleep

suggesting that direct modulation of the respiratory control system may be involved in

this sleep-wake state.

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