UTILIZING POLYSOMNOGRAPHIC SLEEP MARKERS AS

PREDICTORS OF MOOD STATE AND RESPONSE TO

ANTIDEPRESSANT TREATMENT

by

PHILIP ABRAHAM SALEH

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Cell and Systems Biology

University of Toronto

© Copyright by Philip Abraham Saleh (2009)

ii

ABSTRACT

Philip Abraham Saleh Master of Science (2009)

Graduate Department of Cell and Systems Biology University of Toronto

Depression is commonly associated with abnormal sleep architecture. This thesis

undertook to assess sleep architecture as a biological correlate of self and observer-rated

depressive state, and consists of three studies. The first used a categorical approach to

examine the association of sleep architecture with subjective mood in a community

sample of 74 preoperative patients, and found no association between high depression

scores and hypothesized sleep markers. The second followed 16 patients with Major

Depression who were treated with the antidepressant mirtazapine in an 8 week

longitudinal study during which they underwent polysomnography on 6 occasions. It was

found that classes of sleep markers (REM latency or REM, arousal index, and slow wave

sleep) tend to predict response when analyzed concurrently. The third study was

methodological in nature, and found that commercially available software for automating

eye movement counts did not show strong correspondence with visually scored

polysomnographic data.

iii

ACKNOWLEDGMENTS There are a great number of people without whom the completion of this thesis would not

have been possible. First and foremost, thank you to my graduate supervisor Dr. Colin

Shapiro for trusting in me and allowing me to get my feet wet in scientific research. I

cannot stress enough how much his tireless dedication and mentorship has meant to me

and this project.

I also much appreciate the constructive and friendly advice that Drs. John Peever and

Robert Levitan have provided me over these last two years as my advisory committee

members.

Further, I would like to extend a sincere thank you to Dr. Jianhua Shen, who was kind

enough to give me access to his meticulously collected data .

Thank you to Dr. Aiala Barr and Dr. Maragatha Kuchibhatla for their enormous help with

my statistical analysis, to Dr. Carlyle Smith for allowing me time in his laboratory to use

the PRANA system and to Laura Ray for her patience, problem-solving ability and

guidance in training me to operate it.

Finally, I would like to thank my close friends Jonah Flynn and Veronica Champagne,

and my family for their unwavering love and support.

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TABLE OF CONTENTS Abstract ii Acknowledgments iii Table of contents iv List of abbreviations ix List of tables xii List of figures xv 1 Introduction and Literature Review 1 1.1 Statement of the problem 2 1.2 Major Depression 3 1.2.1 Diagnosis and classification 3 1.2.2 Specifiers and subclassification 5 1.2.3 Neurobiology 6 1.3 Sleep 10 1.3.1 Behavioural definition 10 1.3.2 Neurophysiological definition 10 1.3.3 Generation of sleep and wakefulness 13 1.4 Sleep as a diagnostic tool for depression 17 1.4.1 Background 17 1.4.2 Mechanistic links between sleep and MDD 19 1.4.3 Seasonality and sleep in MDD 26 1.4.4 Age and gender effects on sleep EEG in MDD 27 1.4.5 Discrimination between MDD patients and euthymic controls 28

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1.4.6 Recent advances 29 1.4.7 Antidepressants and sleep 30 1.5 Thesis rationale 33 1.5.1 Research questions 33 1.5.2 Study objectives 34 1.5.3 Hypotheses 34 2 Sleep markers of depression as predictors of subjective mood 36 2.1 Introduction 37 2.2 Methods 40 2.2.1 Chart review process 40 2.2.2 The STOP questionnaire 40 2.2.3 Questionnaires evaluated in this study 41 2.2.4 Sleep Markers Screen Design 43 2.2.5 Data Analysis 45 2.2.6 Ethics approval 45 2.3 Results 45 2.3.1 Initial analysis and demographic data 45 2.3.2 Sleep markers as predictors of CES-D scores 47 2.3.3 Sleep markers and the FSS, ESS and AIS 48 2.4 Discussion 48 2.4.1 Sleep markers were not associated with subjective low mood 48 2.4.2 Sleep markers of depression are associated with subjective

insomnia 51

2.4.3 Limitations 51

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2.5 References 52 3 Changes in polysomnographic sleep markers in response to

mirtazapine 56

3.1 Introduction 57 3.2 Methods 59 3.2.1 Study description 59 3.2.2 Original study methodology 60 3.2.3 PSG procedure 61 3.2.4 Chart review procedure 61 3.2.5 Data analysis 62 3.3 Results 63 3.3.1 Time course of changes in HDRS-17 and BDI-II 63 3.3.2 Age correlations 67 3.3.3 Sleep onset latency, sleep efficiency, arousal index and REM

percentage 68

3.3.4 REM latency 69 3.3.5 REM density 70 3.3.6 Slow wave sleep 72 3.3.7 SWS reversal 73 3.3.8 Sleep parameters as predictors of mirtazapine response 73 3.3.9 Relationship of early sleep parameters with depression response 75 3.3.10 Analysis using classes of antidepressants 83 3.4 Discussion 85 3.4.1 Time course of sleep changes is not consistent 86

vii

3.4.2 Sleep predictors of mood state 89 3.4.3 Classes of variables are also good predictors of depression scores 90 3.4.4 Self-rated versus clinician-administered measures of mood 91 3.4.5 Limitations 93 3.4.6 Conclusions: A possible model 94 3.5 References 97 4 Methodological considerations: automation of rapid eye

movement scoring 100

4.1 Introduction 101 4.2 Methods 102 4.2.1 Study population 102 4.2.2 Data preparation and analysis 103 4.3 Results 104 4.3.1 Statistical comparison of automated and visual measures 104 4.3.2 Comparison of rapid eye movement scoring following the

removal of outliers 105

4.4 Discussion 107 4.5 References 108 5 Conclusions 112 6 Appendices 116 A1 Center for Epidemiologic Studies Depression Scale 117 A2 Epworth Sleepiness Scale and Fatigue Severity Scale 118 A3 Athens Insomnia Scale 119 A4 17-item Hamilton Depression Rating Scale 120

viii

A5 Beck Depression Inventory II 122 A6 Percent change from baseline for HDRS-17 and absolute change

from baseline for BDI-II across time points 124

A7 Spearman age correlations for changes from baseline in sleep

parameters, HDRS-17 and BDI-II 125

A8 Descriptive statistics for absolute change from baseline for SOL,

SE, AI, REMP 126

A9 Descriptive statistics for SWS at Nights 2 and 9 in responders

and non-responders according to the HDRS-17 and BDI-II at Days 30 and 58

128

7 Compiled reference list 129

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LIST OF ABBREVIATIONS 5-HT/5-HT2/5-HT3 Serotonin/serotonin receptor AASM American Academy of Sleep Medicine ACTH Adrenocorticotropic hormone AHI Apnea Hypopnea Index AI Arousal Index AIS Athens Insomnia Scale BDI Beck Depression Inventory BDI-II Beck Depression Inventory II BDNF Brain-derived neurotrophic factor cAMP Cyclic adenosine monophosphate CES-D Center for Epidemiological Studies Depression Scale CREB cAMP response element binding protein CRH Corticotropin releasing hormone DLMO Dim light melatonin onset DSM-IV Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TR Diagnostic and Statistical Manual of Mental Disorders –

Text Revision DST Dexamethasone suppression test ECG Electrocardiogram EDS Excessive daytime sleepiness EEG Electroencephalography/electroencephalogram EMG Electromyography/electromyogram

x

EOG Electrooculography/electrooculogram ESS Epworth Sleepiness Scale FSS Fatigue Severity Scale GABA γ-aminobutyric acid HDRS-17 17-item Hamilton Rating Scale for Depression HPA axis Hypothalamic-pituitary-adrenal axis IL-1 Interleukin-1β IL-6 Interleukin-6 IQR Interquartile range LT Light treatment MAOI Monoamine oxidase inhibitor MDD Major Depressive Disorder mv/min Eye movements per minute N Population size NA Noradrenaline NAc Nucleus accumbens NREM Non rapid eye movement sleep PFC Prefrontal cortex PKA Protein kinase A PSG Polysomnography/polysomnogram REM Rapid eye movement sleep REMD REM density REML REM latency

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REMP REM percentage SAD Seasonal Affective Disorder SD Standard deviation SE Sleep efficiency S.E. Standard error SIGH-SAD Structured Interview Guide for the Hamilton Depression

Rating Scale, Seasonal Affective Disorders SNRI Selective norepinephrine reuptake inhibitors SOL Sleep onset latency SSRE Selective serotonin reuptake enhancer SSRI Selective serotonin reuptake inhibitor SWS Slow wave sleep SWS1 and SWS2 Time spent in slow wave sleep in the first and second sleep

cycles SWSREV Reversal of slow wave sleep TCA Tricyclic antidepressant TNF-α Tumour necrosis factor α VLPO Ventrolateral preoptic nucleus VTA Ventral tegmental area

xii

LIST OF TABLES Table Page 1.1 Rechtschaffen and Kales criteria for sleep stage scoring 12 1.2 Possible circadian etiologies for psychiatric disorders in different

affective populations 20

1.3 Polysomnographic disturbances associated with MDD 21 1.4 Degree of REM suppression of selected antidepressant medications 25 1.5 Sleep and circadian abnormalities possibly implicated in SAD 27 2.1 Polysomnographic disturbances associated with MDD 39 2.2 Population demographic and sleep apnea data, separated according to

CES-D subgroup 46

2.3 Association of total sleep markers of depression, and the 3 individual

subcategories with CES-D scores 47

2.4 Association of total sleep markers of depression with elevated CES-D

scores in patients with and without sleep apnea 48

2.5 Association of total sleep markers with the FSS, ESS and AIS 48 3.1 Summary statistics for HDRS-17 and BDI-II across time points 64 3.2 Number of responders and non-responders across the study according to

HDRS-17 and BDI-II criteria 67

3.3 Spearman rank correlations between age and changes from baseline 68 3.4 Descriptive statistics for absolute change in REML from baseline 70 3.5 Descriptive statistics for absolute change in REMD from baseline 71 3.6 Descriptive statistics for absolute change in percent SWS from baseline 73 3.7 Proportion of subjects with reversal of SWS 73 3.8 Mixed model analysis of individual sleep markers of depression as

predictors of HDRS-17 scores 74

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3.9 Mixed model analysis of individual sleep markers of depression as predictors of BDI-II scores

75

3.10 Spearman correlations of sleep changes from baseline at Nights 2 and 9

with percent changes from baseline of HDRS-17 at Days 30 and 58 76

3.11 Spearman correlations of sleep changes from baseline at Nights 2 and 9

absolute changes from baseline of BDI-II at Days 30 and 58 77

3.12 Descriptive statistics for REMD at Nights 2 and 9 in responders and non-

responders 78

3.13 Descriptive statistics for REMD at Nights 2 and 9 in responders and non-

responders according to the HDRS-17 and BDI-II at Day 58 79

3.14 Descriptive statistics for REML at Nights 2 and 9 in responders and non-

responders according to the HDRS-17 and BDI-II at Day 30 80

3.15 Descriptive statistics for REML at Nights 2 and 9 in responders and non-

responders according to the HDRS-17 and BDI-II at Day 58 81

3.16 Descriptive statistics for AI at Nights 2 and 9 in responders and non-

responders according to the HDRS-17 and BDI-II at Day 30 82

3.17 Descriptive statistics for AI at Nights 2 and 9 in responders and non-

responders according to the HDRS-17 and BDI-II at Day 58 83

3.18 Mixed model analysis of HDRS-17 using combinations of sleep

parameters 84

3.19 Mixed model analysis of BDI-II using combinations of sleep parameters 85 4.1 Descriptive statistics for visually and automatically scored REMD 105 4.2 Descriptive statistics for visually and automatically scored REMD,

excluding the 8 uppermost values obtained with automated scoring 106

A6.1 HDRS-17 124 A6.2 BDI-II 124 A7.1 125 A8.1 Sleep onset latency 126 A8.2 Sleep efficiency 126

xiv

A8.3 Arousal index 126 A8.4 REM percentage 127 A9.1 Day 30 128 A9.2 Day 58 128

xv

LIST OF FIGURES

Figure Page 1.1 Typical sleep EEG 13 1.2 Key neural projections from the VLPO 14 1.3 Graphic representation of the sleep switch 16 2.1 Chart selection process for this study 43 2.2 Distribution of self-rated CES-D scores within ‘low mood’ and ‘normal’

CES-D groups 46

3.1 HDRS-17 and BDI-II scores over the course of the study 64 3.2 Proportions of responders at each treatment day for HDRS-17, BDI-II 66 3.3 Changes from baseline in SOL, SE, AI, and REMP on treatment nights 69 3.4 Changes from baseline in REML on all 5 treatment nights 70 3.5 Changes from baseline in REMD on all 5 treatment nights 71 3.6 Changes from baseline in percent SWS on all 5 treatment nights 72 3.7 A possible conceptualization of SWS response to mirtazapine 96 4.1 Sample screen of automated Rapid Eye Movement Detection using the

PRANA software system 104

4.2 Association of visual and automated REMD scoring 105 4.3 Association of visual and automated REMD scoring, excluding the 8

uppermost values obtained with automated scoring 106

1

Chapter 1

Introduction and literature review

2

1.1 Statement of the problem

Although not perceived with the same degree of sympathy or gravity, depression imposes

a level of impairment of quality of life which is thought to be comparable, and indeed

sometimes more severe, than many other medical disorders (Spitzer et al., 1995). In the

general population, the lifetime prevalence of Major Depressive Disorder (MDD) as

assessed by the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) has

been estimated at between 7 and 17% (Robins & Regier, 1991; Kessler et al., 1994; Jonas

et al., 2003; Hasin et al., 2005; Weissman et al., 1996). Such a high prevalence highlights

the need to develop effective approaches to diagnosis and treatment. This thesis will

examine the degree of specificity of sleep abnormalities for depressive illness and

symptoms, and assess the potential of changes in sleep architecture in identifying affected

individuals and predicting treatment outcomes.

3

1.2 Major Depression

1.2.1 Diagnosis and classification

Though many methods exist to assess the presence or absence of depression, a clinical

diagnosis in North America is typically achieved using the Diagnostic and Statistical

Manual of Mental Disorders, the most recent edition of which outlines ten possible

classifications of mood disorders – Major Depressive Disorder, Dysthymic Disorder,

Depressive Disorder Not Otherwise Specified, Bipolar I Disorder, Bipolar II Disorder,

Cyclothymic Disorder, Bipolar Disorder not Otherwise Specified, Mood Disorder Due to

a General Medical Condition, Substance-Induced Mood Disorder, Mood Disorder Not

Otherwise Specified (American Psychiatric Association, 2000).

The DSM-IV-TR defines a Major Depressive Episode as the following:

A. Five (or more) of the following symptoms having been present during the

same 2-week period and represent a change from previous functioning, with at

least one of the symptoms being (1) depressed mood or (2) loss of interest or

pleasure.

(1) Depressed mood most of the day, nearly every day, as indicated by either

subjective report or observation made by others.

(2) Markedly diminished interest or pleasure in all, or almost all, activities most

of the day, nearly every day.

4

(3) A change of more than 5% body weight in a month, or decrease or increase in

appetite nearly every day.

(4) Insomnia or hypersomnia nearly every day.

(5) Psychomotor agitation or retardation nearly every day.

(6) Fatigue or loss of energy nearly every day.

(7) Feelings of worthlessness or excessive or inappropriate guilt nearly every day.

(8) Diminished ability to think or concentrate, or indecisiveness, nearly every

day.

(9) Recurrent thoughts of death, recurrent suicidal ideation without a specific

plan, or a suicide attempt or a specific plan for committing suicide.

B. The symptoms do not meet criteria for a Mixed Episode.

C. The symptoms cause clinically significant distress or impairment in social,

occupational, or other important areas of functioning.

D. The symptoms are not due to the direct physiological effects of a substance or

a general medical condition.

E. The symptoms are not better accounted for by Bereavement.

5

The diagnosis of MDD describes a clinical course which includes one or more Major

Depressive Episodes. However, a history of Manic, Mixed, or Hypomanic episodes

precludes diagnosis of MDD.

1.2.2 Specifiers and subclassification

Within the broad array of mood disorders outlined by the DSM-IV, the diagnosis of

MDD similarly encapsulates varied and heterogeneous subtypes. The clinical status and

features of an episode can be described using the following specifiers:

• Mild, Moderate, Severe without Psychotic Features, Severe With Psychotic

Features

• Chronic

• With Catatonic Features

• With Melancholic Features

• With Atypical Features

• With Postpartum Onset

Specifiers are employed to denote the pattern of episodes as follows:

• Longitudinal Course Specifiers (i.e. With or Without Full Inter-episode Recovery)

• With Seasonal Pattern

6

The wide variety of precipitants and symptom clusters make the task of describing the

biology of MDD in a general sense extremely challenging. However, the task is made

more reachable by understanding it in the context of its varied causes and subtypes. It

remains possible that further understanding the biological basis for depressive subtypes

will yield potential for more specific, albeit narrower, diagnosis. Characteristic sleep

patterns in depression will be discussed in this regard.

1.2.3 Neurobiology

Structural changes

The current state of research into the neurobiological basis for depression has been

recently reviewed (Krishnan & Nestler, 2008). Malfunction in several neural circuits of

the brain, particularly in the limbic regions, which are involved in regulating emotion,

reward and executive function, have been implicated in the pathogenesis of depression

(Sheline et al., 1996). Both grey matter volume reductions and glial cell loss have been

observed, most consistently in the hippocampus and prefrontal cortex (PFC). Alterations

in hippocampal volumes are consistent with the reductions in declarative memory and

recollection memory which have been linked to depression (Sheline, 2003). However,

these structural observations have not been uniform, and will require further investigation

to be teased apart from effects of co-morbid conditions and medication histories

(Harrison, 2002; Krishnan & Nestler, 2008)

7

Mechanistic hypotheses

Monoamines:

Abnormalities in monoamine signalling, particularly serotonin and noradrenaline (NA),

are the best known and characterized neurophysiological changes in MDD (Pittenger &

Duman, 2008; Heninger et al., 1996). This is consistent with the structural findings in the

PFC, which is an important site of monoamine projections (Sheline, 2003). The centrality

of monoamine neurotransmission in the pathogenesis of MDD is reflected in drug design,

with virtually all antidepressants designed to increase monoamine activity either by

inhibiting neuronal reuptake (for example, by the selective serotonin reuptake inhibitors –

SSRIs), preventing degradation (e.g. monoamine oxidase inhibitors – MAOIs), or

promoting synaptic release of serotonin and NA (e.g. mirtazapine, a tetracyclic

antidepressant) (Kennedy et al., 1998; Holm & Markham, 1999). In spite of nearly

immediate increases in monoamine transmission, however, improvement in mood

typically requires several weeks, suggesting a more complex mechanism of action.

Secondary changes at the level of gene expression may also therefore play a role.

Upregulation of the cAMP-PKA-CREB signalling cascade, which is thought to regulate

neuroplasticity and neurogenesis, has been demonstrated in both the PFC and

hippocampus following the administration of SSRIs, MAOIs and also selective

norepinephrine reuptake inhibitors (SNRIs) (Pittenger & Duman, 2008). Increased

neurogenesis in these areas may therefore tie together the cognitive and mood

improvements observed with antidepressant treatment.

8

Glucocorticoids:

Abnormalities in hypothalamic-pituitary-adrenal (HPA) axis function have also been

suggested. Hypercortisolism is found in 40-60% of patients with MDD, and increased

circulating levels of corticotropin releasing hormone (CRH) and adrenocorticotropic

hormone (ACTH) have also been observed (Parker et al., 2003). Hypersecretion of

glucocorticoids as well as exogenous administration in rodents have been associated with

decreased neurogenesis in the hippocampus (Gould et al., 1992). However,

glucocorticoid dysregulation may in fact only be associated with severe episodes of

depression or those which include symptoms of psychosis (Gould et al., 1992; Nestler et

al., 2002; Brouwer et al., 2005).

Cytokines:

Since elevated glucocorticoid activity in response to stress is typically

immunosuppressant, there have been studies examining whether the balance of cytokines,

which are involved in mediating immune response, is altered in depression. An

interesting observation in support of this notion is that approximately 30% of individuals

given recombinant interferon treatment develop symptoms of depression (Loftis &

Hauser, 2004). In addition, mice with deletions in genes coding for interleukin-6 (IL-6)

and tumour necrosis factor α (TNF-α) exhibit behavioural phenotypes similar to

antidepressant response (Chourbaji et al., 2006; Simen et al., 2006). However, these have

shown inconsistent results in both clinical and animal studies. Immune disruption may

not be essential to the development of depression, but may nevertheless exist in a subset

of affected individuals (Dunn et al., 2005).

9

Brain-Derived Neurotrophic Factor:

Recently, attention has been drawn to the high levels of expression of brain-derived

neurotrophic factor (BDNF) in limbic structures, including the hippocampus. In mice,

survival of newly born hippocampal neurons has been shown to require functional BDNF

signalling (Sairanen et al., 2005). The hypothesis that BDNF may be implicated in

depression is corroborated by observed reductions in BDNF-mediated signalling in

response to stress and, conversely, increased signalling with chronic administration of

antidepressants (Duman & Monteggia, 2006). In a rodent study, infusion of BDNF into

the hippocampus elicited antidepressant effects (Shirayama et al., 2002). However,

studies involving BDNF have not consistently supported these findings, and in other

regions of the brain such as the ventral tegmental area (VTA) and nucleus accumbens

(NAc), BDNF shows a pro-depressant effect, with selective knockout of the BDNF in

these producing antidepressant-like responses (Berton et al., 2006; Eisch et al., 2003).

Discovering the key factors involved in hippocampal neurogenesis and their precise, local

expression patterns could be instrumental in determining exactly what pattern of

neurological activity is implicated in the pathogenesis of depression.

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1.3 Sleep

1.3.1 Behavioural definition

The ubiquity of sleep in the animal kingdom, from fruit flies to nematodes to zebrafish,

points to its deep roots in our evolutionary past. In humans, sleep can be defined on both

a behavioural and electrophysiological basis (Allada & Siegel, 2008). As Allada and

Siegel outline, three criteria need to be present – quiescence (reduced motor activity),

elevated arousal thresholds (the stimulus required to elicit a response is greater) and

homeostatic regulation (an increase in drive to enter the state with increased time spent

outside of the state) – for a behavioural state to be characterized as sleep. In addition,

stereotypic posture and rapid state reversibility following relatively intense stimulation

have also been described (Shapiro & Hepburn, 1976).

1.3.2 Neurophysiological definition

Sleep can also be neurophysiologically characterized using electroencephalography

(EEG). Classically, human sleep has been broadly categorized into five stages, known

collectively as sleep macroarchitecture (Figure 1.1) (Rechtschaffen & Kales, 1968). Stage

1 sleep is sometimes considered to be a transitional state between waking state and

consolidated stage 2 sleep, and is indicated by the presence 2-7 Hz EEG activity. Stage 2

sleep, in which approximately half the night is typically spent, is characterized by the

presence of sleep spindles (0.5s to 1.5s high frequency bursts) and K-complexes (large,

high voltage waveforms). Stage 3 and 4 sleep are together known as slow wave sleep

(SWS) and are distinguished on the basis of whether there exist high voltage, low

frequency delta waves in 20-50% (Stage 3) or greater than 50% (Stage 4) in a given 30

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second epoch of sleep. Stages 1 to 4 are collectively called non-REM (NREM) sleep. The

original consensus based guidelines for scoring sleep stages are outlined in Table 1.1

(Rechtschaffen & Kales, 1968). Similar to stage 1 sleep, rapid eye movement (REM)

sleep consists of low voltage, mixed frequency EEG waves. However, REM sleep also

typically contains two distinguishing features – muscle atonia and rapid eye movements.

Muscle atonia, observed as a “chin drop” on an electromyogram (EMG) with electrodes

typically placed submentally, is the key feature which distinguishes REM from all other

stages of sleep. Eye movements, which are observed using electrooculography (EOG),

are not essential but are typical in REM sleep. The presence of eye movements on an

EOG trace and EMG muscle twitches are considered episodic or “phasic” REM events,

whereas “tonic” REM sleep refers to the state of cortical desynchronization and muscle

atonia present in REM sleep (Orem, 1980).

12

Table 1.1 – Rechtschaffen and Kales criteria for sleep stage scoring.

Sleep Stage Scoring Criteria

Waking >50% of the page (epoch) consists of alpha (8-13 Hz) activity or low voltage, mixed (2-7 Hz) frequency activity.

Stage 1 50% of the epoch consists of relatively low voltage mixed (2-7 Hz) activity, and <50% of the epoch contains alpha activity. Slow rolling eye movements lasting several seconds often seen in early stage 1.

Stage 2 Appearance of sleep spindles and/or K complexes and <20% of the epoch may contain high voltage (>75 µV, <2 Hz) activity. Sleep spindles and K complexes each must last >0.5 seconds.

Stage 3 20%-50% of the epoch consists of high voltage (>75 µV), low frequency (<2 Hz) activity.

Stage 4 >50% of the epoch consists of high voltage (>75 µV) <2 Hz delta activity.

REM Relatively low voltage mixed (2-7 Hz) frequency EEG with episodic rapid eye movements and absent or reduced chin EMG activity.

(Rechtschaffen & Kales, 1968) In 2007, the American Academy of Sleep Medicine (AASM) established new guidelines

for polysomnography (PSG) studies, in which Stage 3 and Stage 4 sleep were combined

into NREM Stage 3 sleep (Iber et al., 2007).

13

Figure 1.1 – Typical sleep EEG. Characteristic EEG patterns for waking and drowsy states as well as the individual stages of sleep are depicted. In REM sleep, typical EOG and EMG activity are also shown. Figure adapted from Hauri (1994).

1.3.3 Generation of sleep and wakefulness

Whether an individual is in a state of sleep or wakefulness is determined by the action of

both sleep and wake generating systems. The cell groups responsible for stimulating

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arousal of the cerebral cortex and the thalamus are located in the hypothalamus, basal

forebrain and brainstem (Saper et al., 2005). The activity of neural projections from these

areas is inhibited during sleep by neurons containing γ-aminobutyric acid (GABA),

which are located within the ventrolateral preoptic nucleus (VLPO) of the hypothalamus

(Figure 1.2).

Figure 1.2 – Key VLPO projections. During sleep, the VLPO inhibits arousal systems in the brainstem and hypothalamus. The neurotransmitters employed by these systems are also indicated. GABA, γ-aminobutyric acid; Gal, galanin; His, histamine; 5-HT, serotonin; NA, noradrenaline; ORX, orexin; Ach, acetylcholine; DA, dopamine. The above figure was taken from Saper et al. (2005).

15

GABAergic activity is characteristic of the sleep state, and is maximally present during

NREM sleep (Siegel, 2004). The mutually inhibitory action of sleep and arousal systems

is likely responsible for the presence of discrete, identifiable sleep and arousal states.

This so-called “flip-flop” switch is also regulated by orexinergic neurons in the lateral

hypothalamus, which activate monoaminergic neurons in the tuberomammillary nucleus

of the hypothalamus, and locus coeruleus and dorsal raphe nuclei in the brainstem. In the

sleep state, inhibitory activity from the VLPO suppresses both the orexinergic and

monoaminergic systems. In the awake state, inhibitory monoaminergic projections to the

VLPO predominate, and result in a disinhibition of wake-generating systems (Saper et

al., 2005). These interactions are depicted in Figure 1.3. REM sleep in particular is

thought to be controlled by cholinergic neurons in the pedunculopontine and laterodorsal

tegmental nuclei, which are also highly active during wakefulness (Shiromani et al.,

1992).

16

Figure 1.3 – (a) The wakefulness state. Monoaminergic neurons inhibit the VLPO, which downregulates their own inhibition by the VLPO and disinhibits orexinergic neurons, which further facilitate monoamine activity. (b) The sleep state. VLPO activity inhibits both monoaminergic and orexinergic systems, which decreases inhibition of the VLPO by monoaminergic neurons. The above figure was adapted from Saper et al. (2005).

17

1.4 Sleep as a diagnostic tool for depression

1.4.1 Background

The only established method of diagnosing depression at the present time is by way of

clinical psychiatric interview. Although generally effective, this method has nevertheless

demonstrated several limitations. It has been suggested that physician training in the

diagnosis and management of depression is often inadequate, resulting in suboptimal

levels of identification and treatment in the primary care setting (Davidson & Meltzer-

Brody, 1999), with the lowest detection rates in those with milder MDD (Coyne et al.,

1995). In addition, patients sometimes do not realize that they are depressed, and as a

result do not seek treatment. Various self-rating scales subjectively assess depressive

symptoms. Some examples are the Brief Carroll Depression Scale (Carroll et al., 1981b),

the Long and Short Beck Depression Inventories (Beck et al., 1961), the Center for

Epidemiological Studies Depressed Mood Scale (CES-D) (Radloff, 1977), and the Zung

Self-Rating Depression Scale (Zung, 1965). In general, these questionnaires tend to

perform with sensitivities and specificities in the 80% range (Carroll, 1998). However,

the reliability of self-rating scales can be influenced by the literacy and competency of

the subject, and tendency for some to either exaggerate or understate symptoms

(Hamilton & Shapiro, 1990). Bagby and colleagues questioned the effectiveness of the

Hamilton Depression Rating Scale, an instrument commonly used in psychiatric practice

to identify depressive symptoms and their severity, based on its poor content validity and

only adequate convergent validity (compared against various rating methods including

the structured clinical interview for the DSM-IV) and discriminant validity (Bagby et al.,

2004). Even structured psychiatric interviews using the DSM-IV have inherent

18

limitations, as the DSM-IV diagnostic protocol does not always identify MDD (Ruscio et

al., 2007). These results suggest that the lack of a consistent set of affective markers of

MDD preclude any particular rating scale from fully capturing the spectrum of MDD

symptomatologies.

The discovery of a set of objective biological markers for depression could therefore be

useful in improving the accuracy of diagnosis. Hypotheses of HPA axis dysregulation

and noradrenergic dysfunction in depression led to numerous studies of the

dexamethasone suppression test (DST) as a potential diagnostic tool for mood disorders

(Carroll et al., 1976; Carroll et al., 1981a; Kalin et al., 1981). Specifically,

dexamethasone non-suppression has been observed in 40-55% of patients with

melancholic depression, with the highest prevalence of 64% observed in those with

psychotic depression (Antonijevic, 2008). However, low overall diagnostic sensitivity

and specificity has cast doubt on the prospect of routine clinical use of the DST (Arana et

al., 1985). Moreover, further refinements of the DST have failed to improve upon it.

Though sensitivities closer to 90% for all MDD patients have been found using the Dex-

CRH test, which combines dexamethasone injection with a subsequent administration of

a bolus injection of corticotropin-releasing hormone (CRH), specificities associated with

this test have been found to be even lower than with the DST.

Sleep disturbance, however, has shown some greater promise diagnostically. In depressed

individuals, sleep disturbance is the most commonly observed physical complaint

(Hamilton & Shapiro, 1990), with approximately 80% complaining of insomnia and the

19

remaining 20% of hypersomnia (Kennedy et al., 1998). Rates of fatigue have been shown

in epidemiologic studies to be as high as 94% or greater in the MDD population and rates

of excessive daytime sleepiness (EDS) are also significantly elevated in depressed

individuals, with a prevalence of 17-30% as compared with approximately 5% in the

general population (Baker et al., 1971; Bixler et al., 2005; Posternak & Zimmerman,

2001; Tylee et al., 1999; Weissman et al., 1996). Furthermore, the prevalence of

depressive symptoms has been shown to be elevated in patients with insomnia (Hohagen

et al., 1993; Hamilton & Shapiro, 1990; Shapiro et al., 2004; Schramm et al., 1995) and

sleep disordered breathing (Beutler et al., 1981; Deldin et al., 2006; Reynolds, III et al.,

1984). A study in the general population showed that 40% of subjects with insomnia

presented with a mental illness within 6 months versus 16% of subjects without insomnia

(Ford & Kamerow, 1989). Taken together, these results suggest an increased incidence of

MDD in those with sleep disturbances. Consequently, it can be reasonably expected that

many individuals who are referred to sleep clinics will be clinically depressed.

1.4.2 Mechanistic links between sleep and MDD

Sleep and circadian abnormalities have long been hypothesized to have etiological

relevance in the development of affective illness, of which some hypotheses are

summarized in Table 1.2. It has been proposed that those with rapid cycling affective

disorders, such as manic depressive illness, have affective cycles which progress

according to phase relationships in two circadian subsystems, one of which has lost its

entrainment to 24-hour time cues or zeitgebers (Kripke et al., 1978). Overall circadian

phase advance has also been observed in MDD patients, and has been attributed to

20

abnormally fast rhythms of intrinsic circadian pacemakers (Wehr & Goodwin, 1981;

Kripke, 1983). In a group of individuals with manic depression, Wehr and colleagues

demonstrated a temporary antidepressant effect of advancing sleep/wake times,

suggesting that desynchronization of sleep with circadian phase may have an effect on

mood (Wehr et al., 1979).

Table 1.2 – Possible circadian etiologies for psychiatric disorders in different affective populations. Population studied Hypothesis Patients with rapid-cycling affective disorders, such as manic depression

Affective cycles progress according to phase relationships in two circadian subsystems, one of which has lost its entrainment to 24-hour time cues

Patients with MDD Circadian phase advance due to abnormally fast rhythms of intrinsic circadian pacemakers

Patients with manic depression Desynchronization of sleep/wake cycles with overall circadian phase

Patients with MDD In the context of the two-process model of sleep regulation, a deficiency in homeostatic Process S accumulation during the day results in decreased SWS at night and predisposes to depression

Reviewed in (Wehr et al., 1985)

Several sleep EEG variables also tend to be altered in depression (Table 1.3). Such

changes in sleep architecture have been examined in the context of two prominent

neurobiological hypotheses. The first, proposed by (McCarley, 1982), assumes that

cholinergic and aminergic (noradrenergic or serotonergic) activity are reciprocal, and that

a bias toward cholinergic activation results in the commonly observed decrease of REM

latency (REML) in MDD. This hypothesis is called into question by the fact that several

antidepressants, such as nefazodone and amineptine, are observed to promote REM sleep,

21

implying that REM disinhibition and MDD are not tightly coupled (Perri et al., 1987;

Sandor & Shapiro, 1994; Sharpley et al., 1992). Several studies have nevertheless shown

that cholinomimetics on average do cause a greater decrease of REML in MDD patients

than in controls (Berger et al., 1989; Riemann & Berger, 1992; Riemann et al., 1994;

Sandor & Shapiro, 1994). The relative REM suppressant action of several antidepressants

is summarized in Table 1.4.

Table 1.3 – PSG disturbances associated with MDD. Sleep Disturbance Category PSG Disturbance

NREM Sleep Decreased SWS Decreased SWS in NREM-1 vs. NREM-2

REM Sleep Reduced REML Increased REM during first half of the night Increased REMD Increased overall REMP

Sleep Continuity Increased SOL Increased wake time Early morning awakenings Sleep fragmentation Decreased SE

Adapted from (Kennedy et al., 1998) The second hypothesis, a two process model of sleep regulation, shares the reciprocity

assumption of the cholinergic activation model and relates to SWS (Borbely, 1982).

According to this hypothesis, the amount of slow wave activity over the course of the

night is determined by the accumulation of propensity for SWS (Process S) during

waking hours, which interacts with basal circadian drive for sleep (Process C). It is

thought that MDD patients are deficient in Process S. The antidepressant nature of the

22

response by MDD patients to sleep deprivation, coupled with a concomitant increase of

slow wave activity lend support to this model, suggesting that an increase in Process S

during sleep deprivation acts to overcome the deficiency, and thereby remediate

depressive symptoms (Borbely et al., 1981; Borbely & Wirz-Justice, 1982; Dijk et al.,

1987). Both hypotheses, though not entirely convincing at this stage, provide distinct and

testable physiological frameworks for examining the association of sleep and depression.

HPA axis, glucocorticoids and sleep Though the notion that hormones of the HPA axis impact sleep is not in doubt, the nature

of these interactions has been difficult to disentangle (Buckley & Schatzberg, 2005).

Studies have found that glucocorticoids cause decreases in REM sleep (Gillin et al.,

1972), and also increases in SWS and time spent awake (Born et al., 1989; Born et al.,

1991). Further research has suggested that dose and relative activation of glucocorticoid

and mineralocorticoid receptors determine the changes in sleep EEG which are observed.

For example, low doses of hydrocortisone decrease wakefulness and increase SWS,

whereas in high doses, the reverse effect is seen (Born et al., 1991; Vazquez-Palacios et

al., 2001).

However, a strong finding has been that increases in CRH tend to cause increases in EEG

frequency (Ehlers et al., 1986; Holsboer et al., 1988). Additionally, decreases in SWS and

increases in light sleep and awakenings have been observed (Holsboer et al., 1988). This

could implicate HPA hyperactivity in the pathogenesis of insomnia as well as depression.

In particular, CRH may have a crucial role to play, as it has been shown to activate both

23

downstream HPA hormones and the sympathetic (locus coeruleus/noradrenergic) nervous

system (Vgontzas et al., 1998). Moreover, in view of the association between obesity,

emotional stress and EDS irrespective of the presence or absence of sleep apnea, it has

been suggested that a subtype of obesity with these characteristics could be related to

HPA axis hyperactivity and also hypocytokinemia, which is associated with decreased

sleep efficiency (Vgontzas et al., 2008).

Interleukin-1β and TNFα

Factors which have been more recently implicated in the etiology of depression also

appear to regulate and modulate sleep. It is reasonable to suggest that cytokines could

have such a role given their functions related to sickness and immunity, a time when

sleep is notably abnormal.

A substantial body of evidence points to the involvement of interleukin-1β (IL-1) and

TNFα in the regulation of sleep-wake behaviour (Opp, 2005). Specifically, following the

administration of either IL-1 or TNFα, slow wave activity during periods of NREM sleep

has been shown to increase through direct action on the preoptic area of the

hypothalamus. When TNFα has been locally applied to the surface of the somatosensory

cortex in rats, such increases have also occurred and were localized to the cerebral

hemisphere to which the TNFα was applied (Yoshida et al., 2004).

In addition, IL-1 has been shown to interact with the serotonergic system, where

depletion of brain serotonin and antagonism of serotonin receptors were found to

24

interfere with cytokine-mediated increases of NREM sleep (Imeri et al., 1997; Imeri et

al., 1999). This interaction appears to be reciprocal, as IL-1 is also able to cause the

release of serotonin in the brain, including in the dorsal raphe nucleus, which contains IL-

1 receptors (Manfridi et al., 2003).

These data indicate that in addition to their role in depression, abnormalities in cytokine

signalling may also be involved in disrupting sleep.

Brain-derived neurotrophic factor (BDNF)

The literature regarding BDNF as a neurotrophic factor which could be involved in

modulating sleep is somewhat sparse. Nevertheless, some recent studies have shown that

neuronal plasticity and the regulation of slow wave activity are associated, and in one

study, it was found that increased exploratory behaviour in rats was associated with both

cortical induction of BDNF gene expression and increasing slow wave activity during

subsequent sleep (Huber et al., 2007). In a study by Faraguna and colleagues, it was

found that cortical unilateral microinjections of BDNF in waking rats resulted in

increased delta activity in their subsequent sleep period in the injected relative to the

contralateral brain hemisphere (Faraguna et al., 2008). Whether deficiencies in BDNF

expression contribute to the oft-observed decreased quantity of slow wave sleep in

depression however remains to be elucidated.

25

Table 1.4 – Degree of REM suppression of selected antidepressant medications.

Class of Antidepressant Name of Drug REM Effect REML Effect TCAs Amitriptyline ↓↓↓ ↑

Doxepin ↓↓ ?

Imipramine ↓↓ ?

Nortryptiline ↓↓ ↑ Desipramine ↓↓ ↑ Clomipramine ↓↓↓↓ /

Trimipramine ↔ ↑ Amineptine ↑ ↓ Nortriptyline ↓ ↔

MAOIs Phenelzine ↓↓↓↓ /

Tranylcypromine ↓↓↓↓ ↑ Moclobemide ? ↑ Brofaromine ↓? ↑ Paragyline ↓↓↓↓ /

SSRIs Fluoxetine ↓ ↑ Paroxetine ↓↓ ?

Sertraline ↔ ↔ Fluvoxamine ↓↓ ↑ Zimelidine ↓? ↑ Vilazodone ↓↓↓↓ /

Citalopram ↓ ↑ SNRIs Duloxetine ↓↓ ↑

Nefazodone ↑ ↔ Venlafaxine ↓? ↑

NRIs Atomoxetine ↓ ↔ Reboxetine ↓ ↑ Viloxazine ↓↓ ↑

Bupropion ↔ ↔ Other Trazodone ↓ ?

Mirtazapine ↓? ↑ Mianserin ↔ ↑ Melatonin ↑ ↔

REM effect = amount of REM sleep over the course of a night; REML effect = change in REML. Trends were compiled from (Bart et al., 2008; Brezinova et al., 1977; Buysse et al., 1996; Cajochen et al., 1997; de Paulis, 2007; Ferini-Strambi et al., 2004; Kluge et al., 2007; Mayers & Baldwin, 2005; Sandor & Shapiro, 1994; Shen et al., 2005; Van Bemmel et al., 1993; Winokur et al., 2001)

(↓) to (↓↓↓↓): Mild suppression to complete or near complete suppression; decrease in REML (↑): Increase in REM/REML (↔): No change (↓?): Suppression of REM sleep, degree unknown (?): Trend unknown (/): Not applicable due to complete or near complete suppression of REM sleep

26

1.4.3 Seasonality and sleep in MDD

Although predictable patterns of sleep change have been observed in non-seasonal MDD,

sleep in patients with seasonal affective disorder (SAD), another subtype of MDD

(Rosenthal et al., 1984), does not mimic that in non-seasonal depression. Proposed sleep

and circadian abnormalities which are possibly linked to the presence of SAD are

summarized in Table 1.5. In contrast to non-seasonal MDD, hypersomnia rather than

insomnia appears to be a more characteristic feature and is reported in the majority of

SAD cases (Anderson et al., 1994; Rosenthal et al., 1984). Compared with non-seasonal

MDD, studies of sleep EEG in SAD are less numerous and have been more equivocal.

Decreased SWS has been observed in one study (Anderson et al., 1994), but has not been

corroborated elsewhere (Brunner et al., 1996). Increased REM sleep in SAD patients was

also demonstrated (Palchikov et al., 1997), but did not vary with season, indicating trait-

like qualities of this marker. On a baseline PSG in a group of females with SAD prior to

treatment, decreased SE was observed, corroborating the notion that reported

hypersomnia in SAD may be associated with increased time in bed, but not necessarily

increased time asleep (Shapiro et al., 1994; Shen et al., 2005). In female patients with

SAD treated with nefazodone, sleep onset latency (SOL) and stage 1 sleep were seen to

decline over an 8-week period, whereas SE showed significant increases (Shen et al.,

2005). In an exploratory study of power spectra in 23 SAD patients and as many controls,

it was found that SAD patients had significantly greater mean power density than did

controls during NREM sleep in all frequency bands except sigma (Schwartz et al., 2001),

which may reflect increased hyperpolarization of thalamocortical neurons during NREM

sleep (Steriade et al., 1991). In sum, sleep changes in SAD do not parallel those seen in

27

non-seasonal MDD, and a consistent set of sleep markers has not been satisfactorily

shown to exist. SAD should therefore be considered separately when assessing

depression from both pathophysiological and diagnostic perspectives.

Table 1.5 – Sleep and circadian abnormalities possibly implicated in SAD. Hypothesis Associated Observations

Circadian phase delay/suboptimal phase angle Delayed melatonin, cortisol, and core body temperature relative to controls, which improves following LT. Deviation from a phase angle of 6 hours between the DLMO and mid-sleep explains 35% of the variance in SIGH-SAD scores.

Decreased circadian amplitude Decreased amplitude of core body temperature, though not improved following successful LT for mood. Abnormal regulation of facial skin temperatures across the NREM-REM sleep cycle.

Decreased photoperiods in fall and winter Increased SIGH-SAD scores with decreasing daily light exposure. Increased duration of melatonin secretion, which may cause increase sleep and help trigger a depressive episode. Observed deficiencies in vitamin D3 in late winter, which may modulate both the sleep-wake cycle and mood.

Taken from (Saleh et al., 2009)

1.4.4 Age and gender effects on sleep EEG in MDD

Several EEG parameters are also known to covary with age and gender. REML, for

instance, has shown significant negative age dependence, becoming progressively shorter

with increasing age, with a more pronounced trend in depressed patients (Gillin et al.,

28

1981; Knowles & MacLean, 1990). Quantities of slow wave activity have also been

demonstrated to differentiate younger patients from age matched controls more

effectively than older ones (Armitage et al., 2000b). Contrastingly, REM density

(REMD) does not appear to vary significantly with age (Gillin et al., 1981; Lauer et al.,

1991), and may therefore be a more consistent marker.

With respect to gender, depressed males have been shown to have less SWS and fewer

delta wave counts than depressed women (Reynolds, III et al., 1990; Armitage et al.,

2000b). One study showed a blunted slow wave response to a 3-hour sleep delay in

depressed men as compared to healthy males and both depressed and healthy females

(Armitage et al., 2005). In contrast, women and particularly adolescent girls with MDD

have demonstrated lower interhemispheric and intrahemispheric temporal coherence –

the degree of synchrony or coupling between all-night EEG rhythms – than men

(Armitage et al., 1999; Armitage et al., 2000a). This has more recently led to the

hypothesis that depression in women tends to be characterized by ultradian rhythm

abnormalities and chaotic EEG measures, whereas in men the problem is homeostatic in

nature and can be accounted for in the context of the two-process model (Armitage &

Hoffmann, 2001).

1.4.5 Multivariate discrimination of MDD patients and euthymic controls

In a discriminatory capacity, extensive study of PSG variables has failed to demonstrate

that any single sleep parameter is able to completely distinguish depressed patients from

healthy controls, with false positive rates as high as 20%, 40%, or greater (Benca et al.,

29

1992; Thase et al., 1997). Multivariate analyses, on the other hand, may prove more

useful in this regard. Gillin and colleagues were able to correctly classify 82% of a

sample of 18 primary depressed patients on the basis of total sleep time, total recording

period, SOL, early morning wake time, REM time and REM percentage (REMP) (Gillin

et al., 1979). In another study, a sensitivity of 61% and specificity of 93% was achieved

based only on REML and REMD (Feinberg et al., 1982). In addition, Feinberg et al. were

able to cross-validate a five variable linear discriminant function with a moderate degree

of success using a separate dataset (55% sensitivity and 82% specificity). Various other

multivariate analyses have been conducted with variable predictive power (Buysse &

Kupfer, 1990).

1.4.6 Recent advances

With inconsistent results, efforts in formulating biological diagnostic models for

depression have slowed significantly since the mid 1980s. However, newer candidate

EEG markers of MDD have since emerged. In 1986, Kupfer et al. demonstrated that

levels of SWS are characteristically low in only the first cycle of NREM sleep (Kupfer et

al., 1986; Kupfer et al., 1990). This introduced the concept of the delta sleep ratio, which

states that relative quantities of slow wave activity in the first and second cycles of

NREM sleep, and not global SWS, are important in distinguishing MDD patients from

healthy controls. This has not been clearly observed in other psychiatric disorders.

Schizophrenia, which shares many PSG characteristics with MDD such as decreased

REML, overall SWS and abnormal temporal distribution of NREM sleep, is

characterized by higher slow wave activity in the first sleep cycle than in MDD (Hiatt et

30

al., 1985; Ganguli et al., 1987; Buysse & Kupfer, 1990). In other words, schizophrenic

patients show a normal distribution of SWS.

Temporal coherence has also been studied in some detail. Both men and women have

displayed elevated right hemispheric fast-frequency EEG activity, reduced right

hemispheric beta-delta coherence, and decreased beta and theta interhemispheric

coherence (Armitage, 1995; Armitage et al., 1999; Armitage et al., 2000b; Armitage et

al., 2000a). From a clinical perspective, further study and standardization of analytic

techniques are required prior to the widespread inclusion of EEG coherence as a

diagnostic measure (Armitage, 1995). Recent results nevertheless point to promising

avenues for further research. The incorporation of new PSG variables in addition to

various subjective, self-reported results may yield a more robust predictive algorithm for

MDD and patient response to antidepressants.

1.4.7 Antidepressants and sleep

Antidepressants have been well demonstrated to affect sleep architecture. The impact of a

particular antidepressant on sleep is largely dependent on the neurotransmitter receptor

system on which it acts (Wilson & Argyropoulos, 2005). SSRIs such as fluoxetine and

citalopram, which increase serotonergic response by preventing the reuptake of serotonin,

have generally been shown to suppress overall REM sleep, increase REML and decrease

sleep continuity (Neckelmann et al., 1996; Saletu et al., 1991; Vasar et al., 1994), with

REM rebound observed upon withdrawal of treatment (Feige et al., 2002; Van Bemmel et

31

al., 1993). Increases in SWS (Levitan et al., 2000) and delta sleep ratio have also been

seen to accompany remediation of depressive symptoms in SSRIs (Feige et al., 2002).

Tricyclic antidepressants (TCAs) have generally affected sleep architecture similarly to

SSRIs, although this class of drugs has demonstrated more variable results. As seen with

SSRIs, the TCAs imipramine and clomipramine have been shown to suppress REM sleep

and increase REML (Klein et al., 1984; Suzuki et al., 2002). Trimipramine, another TCA,

contrastingly lacks a REM suppressive effect and is strongly sleep promoting, causing

decreased SOL, increased SE and longer sleep time (Dunleavy et al., 1972; Nicholson et

al., 1989; Ware et al., 1989).

Other antidepressants have also had characteristic effects on sleep. Mirtazapine, which

blocks α2-adrenoreceptors (Haddjeri et al., 1995), generally causes increased sleep time,

efficiency and continuity, increased REML, a decreased number of REM episodes,

increased duration of the first REM episode and increased SWS in the first but not the

second sleep cycle (Schittecatte et al., 2002; Shen et al., 2006; Winokur et al., 2003).

Nefazodone, a 5-HT2 receptor antagonist, has also demonstrated favourable effects on

sleep. Neither REM nor SWS is significantly affected in MDD patients treated with

nefazodone, but patients on this drug nevertheless have shown consistently decreased

SOL and increased SE and total sleep time (Hicks et al., 2002; Scharf et al., 1999; Shen

et al., 2005; Vogal et al., 1998). Such positive changes in sleep quality have also been

observed in patients with SAD, particularly in women (Shen & Shapiro, 2002; Shen et

al., 2005).

32

One study of EEG power spectra in patients with MDD treated with either paroxetine or

tianeptine interestingly showed that male responders to treatment, regardless of

medication, tended to show decreases in spectral power in the high sigma (14-16 Hz)

range (Murck et al., 2003). Male non-responders and females in general did not show this

pattern, highlighting the need to evaluate depression separately across genders. Also

noted in this study was that REMD after 1 week of treatment predicted antidepressant

response to paroxetine. However, the authors failed to measure sleep EEG parameters

prior to the start of treatment, which would be necessary in understanding the full course

of response to these medications.

Widespread changes in sleep architecture in affective patients taking antidepressants

suggest that sleep variables may couple with clinical course. The presence and degree of

alterations in sleep EEG in response to pharmacological treatment may therefore parallel

progression of mood and relate to clinical outcome.

33

1.5 Thesis rationale

Given their common association with MDD, the establishment of a particular set of sleep

architecture changes which can with high specificity identify affected individuals could

be useful in supplementing diagnosis, and furthermore in the triage of patient response to

antidepressants. Although the literature does contain some investigation in this regard,

there exists a notable absence of consideration of the commonly observed SWS

redistribution which has been shown to distinguish depressed and control groups.

Performance on fatigue and sleepiness questionnaires has also been neglected as a

potential complement to identifying individuals who are potentially affected. In light of

moderate success in aforementioned multivariate discriminant analyses of EEG

parameters in addition to advances in PSG and the potential of computer generated

measures over the last 25 years, it remains possible that there does exist a set of sleep

markers which can help reliably identify individuals with MDD or a subgroup thereof.

1.5.1 Research questions

1. Is it possible to identify changes in sleep architecture that are associated with

symptoms of depression based on subjective rating scales for depression, fatigue,

sleepiness and insomnia?

2. Is the presence of a broad range of sleep markers associated with the presence of

depressive symptoms?

3. Are changes in mood as a result of treatment with the antidepressant mirtazapine

associated with changes in sleep markers of depression?

34

1.5.2 Study objectives

1. To examine the association between subjective self-report questionnaires linked

to symptoms of MDD and changes in sleep architecture associated with

depression.

2. To discern whether the presence of a wide range of sleep markers (SWS

abnormalities, REM sleep abnormalities, and sleep continuity disturbances) is

linked to rating scale scores suggestive of depression.

3. To examine whether sleep architectural changes precede or parallel response to

mirtazapine treatment.

1.5.3 Hypotheses

1. Characteristic MDD sleep architecture exists in patients suspected of depression

and may also be associated with subjective self-reports on questionnaires

pertaining to common symptoms of depression.

2. A broad spectrum of sleep markers relates more closely to subjective mood scores

than any single marker taken on its own.

3. Changes in sleep markers of MDD are associated with antidepressant response.

35

The body of this thesis will focus on two studies which attempt to address these

objectives and hypotheses in a more focused manner, and will touch upon

methodological issues as well. Because the following chapters were written in the format

of studies which could be submitted for peer-reviewed publication, some repetition will

therefore exist.

36

Chapter 2

Sleep markers of depression as predictors of subjective mood

37

2.1 Introduction

As a disorder capable of causing impairment of Health Related Quality of Life to a

degree which has been likened to several other debilitating medical pathologies (Spitzer

et al., 1995), depression has a broad scoping impact on daytime functioning which is

often under-recognized and under-appreciated, particularly in the primary care setting

(Davidson & Meltzer-Brody, 1999). Major Depressive Disorder (MDD) is also quite

common in the population with epidemiological studies estimating the lifetime

prevalence at between 7 and 17% (Hasin et al., 2005; Jonas et al., 2003; Kessler et al.,

1994; Robins & Regier, 1991; Weissman et al., 1996).

Sleep disturbance is the most commonly observed physical complaint among

individuals with depression (Hamilton & Shapiro, 1990). As such, it is highly pertinent

to determine which specific aspects of sleep are associated with depression and in this

context, how they relate to the pathophysiology of the disorder. Rates of fatigue have

been shown in epidemiologic studies to be as high as 94% or greater in the MDD

population and rates of excessive daytime sleepiness (EDS) are also significantly

elevated in depressed individuals, with a prevalence of 17-30% as compared with

approximately 5% in the general population (Baker et al., 1971; Bixler et al., 2005;

Posternak & Zimmerman, 2001; Tylee et al., 1999; Weissman et al., 1996).

Changes in sleep architecture associated with MDD (Table 2.1) have been examined in

the context of two prominent neurobiological hypotheses. The first, proposed by

McCarley (1982), assumes that mechanisms controlling rapid eye movement (REM)

38

sleep and slow wave activity are reciprocal, and that a bias toward cholinergic

activation at the expense of aminergic (noradrenergic and serotonergic) activity results

in the commonly observed decrease of REM latency (REML) and increased quantity of

REM sleep in MDD. This hypothesis is called into question by the fact that several

antidepressants, such as nefazodone and amineptine, are observed to promote REM

sleep, implying that REM disinhibition and MDD are not tightly coupled (Perri et al.,

1987; Sandor & Shapiro, 1994; Sharpley et al., 1992). Several studies have nevertheless

shown that cholinomimetics on average do cause a greater decrease of REML in MDD

patients than in controls (Berger et al., 1989; Riemann & Berger, 1992; Riemann et al.,

1994; Sandor & Shapiro, 1994). The relationship between REM and slow wave sleep

(SWS) has also been examined in the context of the two-process model of sleep

regulation (Borbely, 1982). The antidepressant nature of the response by MDD patients

to sleep deprivation, coupled with a concomitant increase of slow wave activity lends

support to the notion that MDD patients may be deficient in Process S, suggesting that

an increase in Process S during sleep deprivation acts to overcome the deficiency, and

thereby remediate depressive symptoms (Borbely et al., 1981; Borbely & Wirz-Justice,

1982; Dijk et al., 1987).

39

Table 2.1 – Polysomnographic disturbances associated with Major Depressive Disorder. Sleep Disturbance Category Polysomnographic Disturbance

NREM Sleep Decreased SWS Decreased Delta Sleep in NREM-1 vs. NREM-2

REM Sleep Reduced REML Increased REM during first half of the night Increased REMD Increased overall REM

Sleep Continuity Increased SOL Increased wake time Early morning awakenings Sleep fragmentation Decreased SE

Adapted from (Kennedy et al., 1998)

In spite of these observed relationships, associations of sleep markers with the

subjective, self-perceived dimension of depression have not yet been studied. However,

several self-reported depression screening instruments such as the Center for

Epidemiological Studies Depression Scale (CES-D) and the Beck Depression Inventory

(BDI) have been shown to correlate with clinical diagnosis of MDD with sensitivities

and specificities in the 80% range (Carroll, 1998). Whether the presence of

abnormalities in multiple domains of sleep disturbance linked to depression (REM

sleep disturbance, SWS disturbance, sleep continuity disturbances) are more predictive

of depression than those with only a narrower range of sleep changes also has yet to be

concretely examined. The aim of this study was to examine the degree to which

disturbance in all three broad categories of sleep markers of depression is associated

with perceived low mood in a population referred to the clinic neither for sleep

complaints nor for depression.

40

2.2 Methods

2.2.1 Chart review process

This is an observational, retrospective study, in which charts were reviewed from a

prior prospective study of 2467 preoperative patients aged 18 and older who were

screened by the Department of Anesthesia at Toronto Western Hospital for possible

sleep apnea. The screen employed in that study was the STOP questionnaire. Of the

2467 screened patients, 211 agreed to undergo a single night of polysomnography

according to the standard protocol outlined by Rechtschaffen and Kales (1968),

irrespective of being identified as ‘high risk’ or ‘low risk’ according to the STOP

questionnaire. Of those 211, 100 agreed to undergo a psychiatric assessment using the

Structured Clinical Interview for the DSM-IV (SCID). These 100 patients were also

asked to complete a standard battery of questionnaires employed by the Sleep and

Alertness Clinic at Toronto Western Hospital dealing with various aspects of sleep,

mood, alertness and behaviour. Of interest for this study were the Center for

Epidemiologic Studies Depression Scale (CES-D), Fatigue Severity Scale (FSS),

Epworth Sleepiness Scale (ESS) and Athens Insomnia Scale (AIS).

2.2.2 The STOP questionnaire

The STOP questionnaire was developed by the Department of Anesthesia at Toronto

Western Hospital and assesses risk of sleep apnea using four questions: 1. Do you snore

loudly?; 2. Do you feel tired, fatigued and sleepy during the daytime?; 3. Has anyone

observed you stop breathing during your sleep?; 4. Do you have or are you being

treated for high blood pressure? Patients answering ‘Yes’ to two or more of these

41

questions are identified as ‘high risk’ for sleep apnea. The STOP questionnaire has

been validated with 74% sensitivity for those patients with an Apnea Hypopnea Index

(AHI) of greater than 15 (Chung et al., 2008).

2.2.3 Questionnaires evaluated in this study

The questionnaires which were examined in this study are described below (For the

complete questionnaires, see Appendix 1-3):

Center for Epidemiological Studies Depression Scale (CES-D) This is a self-administered 20-item scale used to measure depressive symptoms in the

general population. The questionnaire measures various components of depression,

such as depressive mood, guilt, psychomotor retardation, appetite, and sleep

disturbance. Patients rate how often they experienced symptoms in the past week from

(0); “some or little of the time” to (3); “most or all of the time”. Scores 16 and above

are suggestive of low mood (Hann et al., 1999).

Epworth Sleepiness Scale (ESS)

This is an 8-item questionnaire designed to measure daytime sleepiness. Patients are

asked to evaluate their chance of dozing off on a scale of (0); “no chance of dozing to

(3); “high chance of dozing” in various situations, such as watching television, driving

or being a passenger in a car and talking to someone. The total score is then calculated.

Scores of 10 or greater are indicative of excessive daytime sleepiness (Johns,1991).

42

Fatigue Severity Scale (FSS)

This is a 9-item questionnaire intended to indicate fatigue and its interference in day-to-

day activities. Respondents are asked to rate their level of agreement from (1); strong

disagreement to (7); strong agreement with 9 statements relating to level of fatigue,

ease of becoming fatigued, and how debilitating fatigue tends to be toward their social

life, work life, and physical activities. A mean item score is calculated. A score of 4 or

greater suggests increased fatigue. (Krupp et al., 1989).

Athens Insomnia Scale (AIS)

This is an 8-item, self-report questionnaire which assesses sleep complaints and

possible cases of insomnia (Soldatos et al., 2000). On a scale of increasing severity

from 0 (“no problem at all”) to 3 (“very serious problem”), patients are asked to rate

various aspects of their sleep including duration, continuity, daytime sleepiness and

overall quality of sleep. A cutoff of 10 for epidemiological studies has been proposed to

have optimal specificity and sensitivity (Soldatos et al., 2000; Soldatos et al., 2003)

Charts which contained both questionnaire and PSG data, numbering 74, were selected

for this study (See flowchart in Figure 2.1).

43

Figure 2.1. Chart selection process for this study.

2.2.4 Sleep markers screen design

The purpose of this study was to assess the association between sleep markers of

depression and subjective diagnosis of depression with the intent of examining whether

those who had subjectively low mood had a broad set of these markers. In attempting to

achieve this, a binary criterion for ‘present’ or ‘absent’ sleep markers of depression was

2467 patients

screened prior to

surgery using the STOP questionnaire

211 of these patients

agreed to undergo PSG studies

100 agreed to undergo

a detailed psychiatric

assessment including several questionnaires

74 charts with PSG

data and completed

CES-D questionnaires were included

44

designed, with presence of sleep markers assumed only if one feature in each of 3

subcategories of sleep markers – 1. SWS abnormalities; 2. REM sleep abnormalities; 3.

Sleep continuity disturbances – were scored as positive.

For Category 1, slow wave sleep abnormalities were operationalized as follows: A

patient with either a percentage of SWS 25% or more below normative values for the

patient’s age and gender (Williams et al., 1974) or with lower SWS in the first sleep

cycle than in the second was scored as positive for Category 1.

For Category 2, REM sleep abnormalities were operationalized as follows: A patient

with either a REML of shorter than 70 minutes or percentage of REM sleep throughout

the sleep period of 25% or more above normative values for the patient’s age and

gender (Williams et al., 1974) was scored as positive for Category 2.

For Category 3, sleep continuity disturbances were characterized as a sleep onset

latency (i.e. latency to Stage 2 sleep) of greater than 30 minutes, an arousal index (AI)

of greater than 20 arousals/hour or a sleep efficiency (SE) of less than 85% for those 60

and below and 80% and below for those above 60. The presence of any such an

abnormality would yield a positive score for Category 3.

45

2.2.5 Data analysis

Individual patient scores in each category were examined. Those patients who were

scored as positive for all three categories were considered positive for having ‘sleep

markers of depression’.

Fisher’s Exact Test was then used to examine relationships between categorical

variables.

2.2.6 Ethics approval

Ethics approval for chart review was obtained from the Research Ethics Board of the

Toronto University Health Network.

2.3 Results

2.3.1 Initial analysis and demographic data

Of the 74 charts used in this study, 3 FSS, 4 ESS and 2 AIS questionnaires were not

properly completed. If a chart contained one or more missing questionnaires, it was

nevertheless included in the study for those questionnaires in the chart which were

complete, provided that the CES-D was properly completed (which was the case for all

74 selected charts).

As shown in Table 2.2, proportions of genders, mean age and distribution of patients

with and without sleep apnea were similar across patients with elevated and normal

CES-D scores.

46

Table 2.2 – Demographic and sleep apnea data of the study population who completed both PSG and questionnaires, separated according to CES-D subgroup. CES-D >= 16 CES-D <16 Male, Female 9, 11 29, 25 Mean Age (SD) 56 (8.8) 58 (11.7) Sleep Apnea 9 27 No Sleep Apnea 11 27

Figure 2.2 graphically depicts the distribution of CES-D scores within the “low mood”

and “normal” CES-D groups. As shown in the figure, the group with low mood had

greater variability in CES-D scores.

0 1

0

10

20

30

40

CESD

Group Figure 2.2 – Distribution of self-rated CES-D scores within “low mood” (0) and “normal” (1) CES-D groups. Upper and lower extreme values represent the range of scores observed within each group. The vertical ranges encapsulated within each box represent the interquartile range. The hatch mark and horizontal line represent the mean and median, respectively.

47

2.3.2 Sleep markers as predictors of CES-D scores

As depicted in Table 2.3, no significant trend or association was observed between

CES-D score group and presence or absence of sleep markers, or among the 3

subcategories of markers. Of the 3 categories, only Category 2 (REM changes) had a

greater proportion of patients who scored positive for this sleep marker in the high

CES-D group, but this result did not approach significance. With respect to SWS, the

opposite trend was observed, though this was similarly nonsignificant. Results did not

change when the item on the CES-D pertaining to sleep (Item 11) was removed from

the analysis (data not shown).

Table 2.3 – Association of total sleep markers of depression, and the 3 individual subcategories with elevated (≥16) versus normal (<16) CES-D scores. Proportions of patients in each category are indicated in the left column, and the right column represents a p-value for the association. CES-D Total Markers (+) Category 1 (+) Category 2 (+) Category 3 (+) ≥16 1/20 6/20 6/20 18/20 <16 5/54

1.00 24/54

0.30 13/54

0.77 49/54

1.00

Table 2.4 indicates that there was no apparent difference with respect to the association

of sleep markers of depression with low mood among those with and without sleep

apnea.

48

Table 2.4 – Association of total sleep markers of depression with elevated (≥16) versus normal (<16) CES-D scores in patients with and without sleep apnea. In each category, proportions of patients who have sleep markers of depression are indicated in the left column, and the right column represents the p-value for the association. CES-D Markers Apnea Markers No Apnea ≥ 16 1/9 0/11 <16 3/27

1.00 2/27

1.00

2.3.3 Sleep markers and the FSS, ESS and AIS

Presence or absence of sleep markers was also not associated with ESS or FSS data

(Table 2.5). However, a significant association was observed with the AIS, with sleep

markers present in 18.5% versus 2.2% of patients with elevated and normal AIS scores,

respectively.

Table 2.5 – Association of total sleep markers with elevated FSS, ESS and AIS. Proportions of patients in each category are indicated in the left column, and the right column represents a p-value for the association. Markers FSS ESS AIS + 3/6 4/6 5/6 - 32/65

1.00 28/64

0.40 22/66

0.03

2.4 Discussion

2.4.1 Sleep markers were not associated with subjective low mood

The purpose of this study was to act as a first attempt at broadly characterizing the

sleep EEG signature of depression as consisting of the 3 distinctive categories of SWS,

REM sleep and sleep continuity (Kennedy et al., 1998). Though nonspecifically

associated with MDD on their own, it could nevertheless be the case that the presence

of abnormalities in all categories concurrently may be more specifically indicative of

49

depressive state. In addition, this particular sample represents an interesting population,

with none of the patients having been referred for sleep complaints or depression.

Instead they were selected to have PSG studies following a simple screen prior to

surgery, and not following a clinical referral. In light of the results presented, this study

does not appear to strengthen the case for using PSG in assessing the subjective

dimension of depression.

In spite of meta-analysis by Benca and colleagues (1992) which demonstrated that no

sleep marker alone is capable of predicting depressive state, moderate success of

multivariate sleep models of MDD would suggest that there could be some link

between a larger constellation of sleep variables and depression (Buysse & Kupfer,

1990). As a clinical tool, a subjective, self-rated scale such as the CES-D could be

useful in conjunction with PSG sleep markers in screening for possible MDD. In the

primary care setting, the CES-D has been validated as having a sensitivity of 79% and a

specificity of 73% in detecting depression (Carroll, 1998). The lack of observed

association indicates a lack of utility in this regard with respect to this sample, which

consisted of generally middle aged and elderly patients.

Of the 3 marker categories, only those scoring positively in Category 2 were seen to be

in greater proportion in the high CES-D group. Though nonsiginificant, it nevertheless

remains possible that in a larger sample, those who do have increased REM

abnormalities may reflect a sub-cluster of depressed patients for whom increased

cholinergic activity at the expense of aminergic activity in turn produces increases in

50

REM sleep and increases propensity for depression (McCarley, 1982). Decreased

serotonergic activity has also been described as a possible link between disturbed sleep

and increased suicidal behaviour (Singareddy & Balon, 2001). Both REM suppression

and increases in slow wave activity have been observed with the administration of

serotonin agonists in healthy subjects (Lawlor et al., 1991).

One reason for the apparent lack of association of sleep markers of depression with low

mood could be the high proportion of individuals with sleep apnea (0.49) who may

have scored highly on the CES-D due to symptoms secondary to their sleep disorder.

Some studies have suggested that patterns of EEG activity differ between primary and

secondary depressives (Feinberg & Carroll, 1984; Thase et al., 1984). Additionally,

sleep fragmentation and frequent arousals associated with sleep apnea may prevent a

more characteristic sleep profile from occurring. Interestingly, however, this study

found no higher an association of sleep markers with high CES-D among the 33

patients with and the 36 without sleep apnea.

Another possibility is that severity of depression among patients in this sample was not

enough to elicit the broad-based EEG changes associated with MDD. Several sleep

changes, including decreased delta sleep ratio (delta counts in the first relative to the

second sleep cycle) and overall slow wave activity, have been associated with more

severe depression and lower rates of remission (Kupfer et al., 1990; Jindal et al., 2003).

It would therefore be interesting to see whether this 3-category model has greater

explanatory power in a sample of more severely depressed patients.

51

2.4.2 Sleep markers of depression are associated with subjective insomnia

There was also a significant trend toward increased insomnia among patients with a

positive score for markers of depression. However, although subjective insomnia is the

most commonly observed sleep symptom of MDD (Kennedy et al., 1998), subjective

insomnia was not associated with high CES-D score in this sample (Data not shown).

2.4.3 Limitations

There are several limitations to this study. As was mentioned, there was a relatively

high prevalence of sleep apnea in this population (49%) which falls well above rates in

the general population (Racionero Casero et al., 1999; Zamarron et al., 1999) and as a

result disturbed mood may be secondary to this medical disorder, a distinction that was

not made in this study. Patients also only came for only one night of diagnostic study,

which may result in abnormal sleep due to poor habituation to surroundings referred to

as the “first night effect” (Agnew, Jr. et al., 1966). The retrospective nature of this

study also did not allow us to control for factors such as medication use, since a

complete list of medications was not available. Lastly, this was not a depressed sample,

and it therefore did not contain a full spectrum of depression severity. A large,

prospective, controlled study containing patients with varying degrees of depression

would be necessary to fully answer the questions raised here. Nevertheless, this study

represents a first attempt to classify and operationalize sleep markers of depression in a

way that could be potentially clinically useful in the sleep laboratory setting by

employing standard variables obtained in PSG studies and utilizing easily administered

questionnaires. In spite of this negative result, larger scale prospective studies and

52

further refinement of a diagnostic model for sleep markers would be useful to obtain a

conclusive impression of the associations between sleep and subjective depression.

2.5 References

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Baker, M., Dorzab, J., Winokur, G., & Cadoret, R. J. (1971). Depressive disease: classification and clinical characteristics. Compr.Psychiatry, 12, 354-365.

Benca, R. M., Obermeyer, W. H., Thisted, R. A., & Gillin, J. C. (1992). Sleep and psychiatric disorders. A meta-analysis. Arch.Gen.Psychiatry, 49, 651-668.

Berger, M., Riemann, D., Hochli, D., & Spiegel, R. (1989). The cholinergic rapid eye movement sleep induction test with RS-86. State or trait marker of depression? Arch.Gen.Psychiatry, 46, 421-428.

Bixler, E. O., Vgontzas, A. N., Lin, H. M., Calhoun, S. L., Vela-Bueno, A., & Kales, A. (2005). Excessive daytime sleepiness in a general population sample: the role of sleep apnea, age, obesity, diabetes, and depression. J.Clin.Endocrinol.Metab, 90, 4510-4515.

Borbely, A. A. (1982). A two process model of sleep regulation. Hum.Neurobiol., 1, 195-204.

Borbely, A. A., Baumann, F., Brandeis, D., Strauch, I., & Lehmann, D. (1981). Sleep deprivation: effect on sleep stages and EEG power density in man. Electroencephalogr.Clin.Neurophysiol., 51, 483-495.

Borbely, A. A. & Wirz-Justice, A. (1982). Sleep, sleep deprivation and depression. A hypothesis derived from a model of sleep regulation. Hum.Neurobiol., 1, 205-210.

Buysse, D. J. & Kupfer, D. J. (1990). Diagnostic and research applications of electroencephalographic sleep studies in depression. Conceptual and methodological issues. J.Nerv.Ment.Dis., 178, 405-414.

Carroll, B. J. (1998). Carroll Depression Scales Manual. North Tonowanda, NY: MultiHealth Systems.

Chung, F., Yegneswaran, B., Liao, P., Chung, S. A., Vairavanathan, S., Islam, S. et al. (2008). STOP questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology, 108, 812-821.

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Davidson, J. R. & Meltzer-Brody, S. E. (1999). The underrecognition and undertreatment of depression: what is the breadth and depth of the problem? J.Clin.Psychiatry, 60 Suppl 7, 4-9.

Dijk, D. J., Beersma, D. G., Daan, S., Bloem, G. M., & Van den Hoofdakker, R. H. (1987). Quantitative analysis of the effects of slow wave sleep deprivation during the first 3 h of sleep on subsequent EEG power density. Eur.Arch.Psychiatry Neurol.Sci., 236, 323-328.

Feinberg, M. & Carroll, B. J. (1984). Biological 'markers' for endogenous depression. Effect of age, severity of illness, weight loss, and polarity. Arch.Gen Psychiatry, 41, 1080-1085.

Hamilton, M. & Shapiro, C. M. (1990). Depression. In D.F.Peck & C. M. Shapiro (Eds.), Measuring Human Problems: A Practical Guide ( John Wiley and Sons Inc.

Hann, D., Winter, K., & Jacobsen, P. (1999). Measurement of depressive symptoms in cancer patients: evaluation of the Center for Epidemiological Studies Depression Scale (CES-D). J.Psychosom.Res., 46, 437-443.

Hasin, D. S., Goodwin, R. D., Stinson, F. S., & Grant, B. F. (2005). Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch.Gen.Psychiatry, 62, 1097-1106.

Jindal, R. D., Friedman, E. S., Berman, S. R., Fasiczka, A. L., Howland, R. H., & Thase, M. E. (2003). Effects of sertraline on sleep architecture in patients with depression. J Clin.Psychopharmacol, 23, 540-548.

Johns, M. W. (1991). A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep, 14, 540-545.

Jonas, B. S., Brody, D., Roper, M., & Narrow, W. E. (2003). Prevalence of mood disorders in a national sample of young American adults. Soc.Psychiatry Psychiatr.Epidemiol., 38, 618-624.

Kennedy, S., Parikh, S. V., & Shapiro, C. M. (1998). Defeating Depression. Thornhill, ON: Joli Joco Publications Inc.

Kessler, R. C., McGonagle, K. A., Zhao, S., Nelson, C. B., Hughes, M., Eshleman, S. et al. (1994). Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Survey. Arch.Gen.Psychiatry, 51, 8-19.

Krupp, L. B., LaRocca, N. G., Muir-Nash, J., & Steinberg, A. D. (1989). The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch.Neurol., 46, 1121-1123.

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Kupfer, D. J., Frank, E., McEachran, A. B., & Grochocinski, V. J. (1990). Delta sleep ratio. A biological correlate of early recurrence in unipolar affective disorder. Arch.Gen.Psychiatry, 47, 1100-1105.

Lawlor, B. A., Newhouse, P. A., Balkin, T. J., Molchan, S. E., Mellow, A. M., Murphy, D. L. et al. (1991). A preliminary study of the effects of nighttime administration of the serotonin agonist, m-CPP, on sleep architecture and behavior in healthy volunteers. Biol Psychiatry, 29, 281-286.

McCarley, R. W. (1982). REM sleep and depression: common neurobiological control mechanisms. Am.J.Psychiatry, 139, 565-570.

Perri, R. D., Mailland, F., & Bramanti, P. (1987). The effects of amineptine on the mood and nocturnal sleep of depressed patients. Prog.Neuropsychopharmacol.Biol.Psychiatry, 11, 65-70.

Posternak, M. A. & Zimmerman, M. (2001). Symptoms of atypical depression. Psychiatry Res., 104, 175-181.

Racionero Casero, M. A., Garcia, R. F., Pino Garcia, J. M., Prados, S. C., Diaz, L. S., & Villamor, L. J. (1999). [The sleep apnea syndrome as a health problem. An estimation of its prevalence and morbimortality]. An.Med.Interna, 16, 97-102.

Riemann, D. & Berger, M. (1992). Sleep, age, depression and the cholinergic REM induction test with RS 86. Prog.Neuropsychopharmacol.Biol.Psychiatry, 16, 311-316.

Riemann, D., Hohagen, F., Bahro, M., & Berger, M. (1994). Sleep in depression: the influence of age, gender and diagnostic subtype on baseline sleep and the cholinergic REM induction test with RS 86. Eur.Arch.Psychiatry Clin.Neurosci., 243, 279-290.

Robins, L. N. & Regier, D. A. (1991). An overview of psychiatric disorders in America. In Psychiatric Disorders in America: The Epidemiologic Catchment Area Study. ( New York: NY: Free Press.

Sandor, P. & Shapiro, C. M. (1994). Sleep patterns in depression and anxiety: theory and pharmacological effects. J.Psychosom.Res., 38 Suppl 1, 125-139.

Sharpley, A. L., Walsh, A. E., & Cowen, P. J. (1992). Nefazodone--a novel antidepressant--may increase REM sleep. Biol.Psychiatry, 31, 1070-1073.

Singareddy, R. K. & Balon, R. (2001). Sleep and suicide in psychiatric patients. Ann.Clin.Psychiatry, 13, 93-101.

Soldatos, C. R., Dikeos, D. G., & Paparrigopoulos, T. J. (2000). Athens Insomnia Scale: validation of an instrument based on ICD-10 criteria. J Psychosom.Res, 48, 555-560.

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Soldatos, C. R., Dikeos, D. G., & Paparrigopoulos, T. J. (2003). The diagnostic validity of the Athens Insomnia Scale. J Psychosom.Res, 55, 263-267.

Spitzer, R. L., Kroenke, K., Linzer, M., Hahn, S. R., Williams, J. B., deGruy, F. V., III et al. (1995). Health-related quality of life in primary care patients with mental disorders. Results from the PRIME-MD 1000 Study. JAMA, 274, 1511-1517.

Thase, M. E., Kupfer, D. J., & Spiker, D. G. (1984). Electroencephalographic sleep in secondary depression: a revisit. Biol Psychiatry, 19, 805-814.

Tylee, A., Gastpar, M., Lepine, J. P., & Mendlewicz, J. (1999). DEPRES II (Depression Research in European Society II): a patient survey of the symptoms, disability and current management of depression in the community. DEPRES Steering Committee. Int.Clin.Psychopharmacol., 14, 139-151.

Weissman, M. M., Bland, R. C., Canino, G. J., Faravelli, C., Greenwald, S., Hwu, H. G. et al. (1996). Cross-national epidemiology of major depression and bipolar disorder. JAMA, 276, 293-299.

Williams, R. L., Karacan, I., & Hursch, C. J. (1974). Electroencephalography (EEG) of Human Sleep: Clinical Applications. Toronto: John Wiley & Sons.

Zamarron, C., Gude, F., Otero, Y., Alvarez, J. M., Golpe, A., & Rodriguez, J. R. (1999). Prevalence of sleep disordered breathing and sleep apnea in 50- to 70-year-old individuals. A survey. Respiration, 66, 317-322.

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Chapter 3

Changes in polysomnographic sleep markers in response to mirtazapine

57

3.1 Introduction

Antidepressant effect is almost universally associated with changes in sleep architecture

(Mayers & Baldwin, 2005). Most frequently, these are observed to be suppression of

REM sleep and increases in REM latency (REML). Such changes implicate sleep or

modulators of sleep in the etiology of depression. Moreover, it has become increasingly

clear that REM suppression is neither the sole sleep variable nor a necessary consequence

of mood improvement with pharmacological treatment. Some antidepressants, such as

nefazodone for example, do not follow this trend (Shen et al., 2005). It is also true that

other changes in sleep architecture and pattern can be relevant. For example, increases in

delta sleep ratio have been observed with treatment with sertraline, an SSRI, compared to

placebo (Jindal et al., 2003). Delta sleep ratio following treatment is also likely predictive

of recurrence of a depressive episode (Kupfer et al., 1990).

Because there is typically a time lag between the start of a course of pharmacological

treatment and the onset of clinically significant mood improvement (Hochli et al., 1986),

there has been some interest in the discovery of biological correlates of treatment

response. For instance, a recent study found that nonresponse to cognitive behavioural

therapy or venlafaxine was related to increased glucose metabolism at the interface of the

pregenual and subgenual cingulate cortices based on positron emission tomography data

(Konarski et al., 2009).

Sleep markers also present potential correlates. The possibility that rapid changes in

state-dependent sleep markers early in treatment, or trait-like indicators of tendency

58

toward a positive response would allow for more rapidly tailoring treatment strategies to

individual patients. Some studies have examined whether polysomnographic (PSG) sleep

parameters at baseline or shortly following onset of treatment can predict treatment

outcomes. Studies of amitriptyline, a tricyclic antidepressant (TCA), have shown that in

some cases, decreases in REM sleep and increases in REML occurred long prior to the

appearance of clinical improvement. (Kupfer et al., 1976; Gillin et al., 1978; Kupfer et

al., 1981). Gillin and colleagues in a study of 6 patients also found that greater REM

suppression was associated with more pronounced improvement of symptoms (Gillin et

al., 1978). Two studies of clomipramine, another TCA, have produced contrasting results.

Hochli and colleagues again found that degree of REM suppression the first night after

beginning treatment was associated with clinical improvement (defined as a 30%

improvement on the Hamilton Depression Rating Scale) in 10 inpatients with endogenous

nonpsychotic depression (Hochli et al., 1986). In disagreement with this, another study

found that increasing delta power and redistribution of delta power to the earlier portion

of the night were the only changes early in treatment which predicted longer-term

response to clomipramine (Kupfer et al., 1989). More recently, a double-blind study of

MDD patients treated with either paroxetine (a SSRI) or tianeptine (a selective serotonin

reuptake enhancer – SSRE), it was found that lower REM density at week 1 of treatment

was predictive of response to the drug (Murck et al., 2003).

As with virtually all pharmacological treatments for MDD, mirtazapine has also been

demonstrated to have an effect on sleep. Mirtazapine is believed to act through

antagonism of α2-adrendergic autoreceptors and heteroreceptors and through the post-

59

synaptic blockade of 5-HT2 and 5-HT3 serotonin receptors (Holm & Markham, 1999).

This causes an increase in 5-HT1A-mediated serotonergic and noradrenergic

neurotransmission.

With respect to sleep, mirtazapine has been shown in both healthy patients and volunteers

to cause increases in REML and amount of SWS, and decreases in night-time awakenings

and number of REM episodes (Holm & Markham, 1999; Ruigt et al., 1990; Shen et al.,

2006).

This study was therefore conducted in order to determine whether changes in sleep

markers of depression (REM sleep, SWS and sleep continuity) are able to predict

treatment outcome in an 8-week longitudinal study, with the hypothesis that sleep

changes would predict mood improvement and could therefore have prognostic value.

3.2 Methods

3.2.1 Study description

This is a retrospective secondary analysis of a longitudinal, prospective study conducted

in 2003 and 2004 by the Department of Psychiatry at the University of Toronto. The

initial study protocol was approved by the Research Ethics Board of the University

Health Network, and approval for retrospective chart review was subsequently obtained

for the purposes of the current analysis. Patients were recruited via referrals from local

family practitioners and psychiatrists.

60

3.2.2 Original study methodology

In the initial study, included patients were all deemed to meet the DSM-IV diagnostic

criteria for MDD according to both their psychiatric history and their evaluation using the

MINI, which is a short structured diagnostic interview modeled after the DSM-IV criteria

for psychiatric disorders. Patients needed to be 18 years old and older, and all scored 17

or greater on the 17-item Hamilton Rating Scale for Depression (HDRS-17), 1 or greater

on the 3 sleep symptom items of the HDRS-17 (Appendix 4), and 6 or greater on the AIS

(Appendix 3). Subjects were cleared of any neurological problems or abnormal

laboratory findings prior to the study.

Exclusion criteria were as follows: any patients who took fluoxetine within 4 weeks,

MAOIs within 2 weeks, and any psychotropic medications or herbal treatments with

suggested antidepressant or sleep properties within 1 week; hypersensitivity to

mirtazapine; women who were pregnant or breast feeding; night shift workers; bipolar

disorder or psychotic features in the current episode of depression or any psychotic

disorder; history of alcoholism (greater than 14 drinks weekly) or drug abuse within the

previous year; and any clinically significant organic system diseases.

The study lasted a total of 9 weeks, including an initial screening visit, and mirtazapine

treatment lasted 58 days. After an initial screening (baseline) night of polysomnography,

patients returned for sleep studies following the start of the treatment protocol at 5

additional time points: Night 2, Night 9, Night 16, Night 30 and Night 58. At each time

point including the baseline night (except for the HDRS-17 on treatment Night 2),

61

subjective and observer rated depression scores were collected using the HDRS-17 and

Beck Depression Inventory II (BDI-II – see Appendix 5), respectively.

The treatment protocol consisted of taking one mirtazapine tablet (30 mg) orally each

night 30 minutes prior to lights out. At baseline, a placebo tablet was instead taken.

3.2.3 PSG procedure

Patients were studied using a standardized PSG montage (Rechtschaffen & Kales, 1968),

with lights out at 11 pm and lights on at 7 am. An EEG (Central – C3-A1, C4-A1; and

Occipital – O1-A1, O2-A2), submental electromygram (EMG), horizontal and vertical

electrooculogram (EOG), and electrocardiogram (ECG) were all conducted using

standard electrode positioning on the subjects’ head and body. Efforts were made to

reduce impedance levels for EEG to the suggested level of 10 kΩ. Abdominal and chest

leads, as well as nasal-oral thermistor were used to rule out sleep apnea, and anterior

tibialis EMG recordings were used to rule out periodic leg movements. Oxygen

saturation was measured using pulse oximetry.

3.2.4 Chart review procedure

Patient charts were retrieved and PSG parameters relevant to depression were retrieved.

These were REMP (percent REM sleep), REML , SWS (percent stage 3 and 4 sleep),

SWS1 and SWS2 (total time spent in stage 3 and 4 sleep in the first and second sleep

cycles, respectively), AI (arousal index), SOL (sleep onset latency), and SE (sleep

efficiency).

62

Available electronic records of sleep PSG recordings were also retrieved and viewed

using the Sleep View sleep analysis software for the calculation of REM density

(REMD). Eye movements in REM sleep were visually scored according to deflections in

the left eye channel. Rapid deflections in EOG channels which were 25 µV or greater in

amplitude were counted. In recordings where baseline EEG activity was of high

amplitude, the 25 µV criterion was less stringently applied, and eye movements were

compared to pre-sleep biocalibration data for left-right, up-down and blinking eye

movements. Whole night REMD was then calculated by multiplying by 2 the total

number of visually scored eye movements divided by the number of 30 second epochs

recorded, to yield a final value of REMD in units of eye movements per minute

(mv/min).

3.2.5 Data analysis

All statistical analysis was performed using SAS 9.0 statistical software. Spearman rank

correlations were used to calculate both age correlations and to determine correlations

between sleep changes and depression response from baseline. The Wilcoxon signed rank

test was used in order to determine whether changes in variables from baseline were

significant. The Wilcoxon rank sum test was used to test for differences in sleep variables

in responders and non-responders to mirtazapine. Finally, a linear mixed model analysis

was used to analyze how individual dynamics in sleep parameters are related with

dynamics in depression scores.

63

‘Responders’ on the HDRS-17 were classified as those patients at a given time point who

had a reduction in HDRS-17 score from baseline of 50% or greater. ‘Responders’ on the

BDI-II were defined according to ranges of severity outlined in the original publication of

the scale (Beck et al., 1996) – ‘Normal’ (0-13); ‘Mild’ (14-19); ‘Moderate’ (20-28);

‘Severe’ (29-63). All subjects who moved into a category milder than that at baseline

were classified as responders. Those in the ‘Normal’ category at baseline who remained

as such were also considered responders.

3.3 Results

3.3.1 Time course of changes in HDRS-17 and BDI-II

Summary statistics for both the HDRS-17 and BDI-II are shown in Table 3.1. Percent

change from baseline in HDRS-17 scores (See Table A6.1) was also significant at all 4

measured time points (Wilcoxon p<0.01 at all time points). At Day 58, all subjects who

remained enrolled in the study had seen decreased HDRS-17 scores. Though both scales

showed pronounced decreases over time, the HDRS-17 displayed a more rapid and

consistent rate of change, with median score decreasing consistently through the study.

Most of the change in BDI-II scores occurred between Day 30 and Day 58 (See Table

A6.2). However, median BDI-II scores also decreased substantially from Day 2 to Day 9.

Scores on the BDI-II showed greater variability than those on the HDRS-17. Time

progression of depression scores is illustrated in Figure 3.1.

64

Table 3.1 – Summary statistics for HDRS-17 and BDI-II across time points. Scale Day N Range Mean s SE Median IQR HDRS-17 Bn 16 (17, 33) 22.81 4.98 1.25 21.5 (18.5, 26.5) 9 15 (4, 28) 15.73 6.62 1.71 15 (12, 21) 16 13 (2, 27) 12.85 7.03 1.95 11 (10, 14) 30 15 (3, 26) 12.73 7.23 1.87 9 (7, 19) 58 11 (2, 19) 7.45 6.33 1.91 5 (3, 13) BDI-II Bn 16 (6, 55) 25.75 13.95 3.49 21 (17, 35) 2 16 (6, 53) 25.56 14.90 3.72 22 (16.5, 36.5) 9 16 (3, 54) 22.44 17.67 4.42 15.5 (9.5, 40.5) 16 16 (2, 56) 22.13 18.24 4.56 15.5 (8.5, 40.5) 30 15 (3, 52) 22.07 16.71 4.31 16 (7, 36) 58 14 (2, 47) 15.93 15.47 4.14 10 (5, 25)

0

5

10

15

20

25

30

35

Bn 2 9 16 30 58

Treatment Day

BD

I-II

/HD

RS

-17

Dep

ress

ion

Sco

re

HDRS-17

BDI-II

Figure 3.1 – HDRS-17 (lozenges, solid line) and BDI-II (squares, dashed line) scores over the course of the study. There were two dropouts in this study, both occurring after Day 30. One dropout had a

HDRS-17 score of 7, and the other had a score of 20 on Day 30. Both were included in

the analysis for the length of time that they were included in the study.

65

The trend across time points for the proportion of study subjects defined as responders to

mirtazapine treatment for both HDRS-17 and BDI-II is shown in Figure 3.2. As with

HDRS-17 scores, the proportion of responders also increased steadily across study time

points. However, the strongest increase in proportion of self-rated responders between

treatment Days 2 and 58 occurred between Day 30 and Day 58.

66

a)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

9 16 30 58

Treatment Day

Pro

p. (R

espo

nder

s)

b)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

2 9 16 30 58

Treatment Day

Pro

p. (

Res

po

nd

ers)

Figure 3.2. Proportions of responders at each treatment day for a) HDRS-17; and b) BDI-II. Proportions were calculated as the number of responders divided by the total number of subjects with HDRS-17 or BDI-II scores available at the particular time point.

The level of agreement between HDRS-17 and BDI-II responders was also examined

(Table 3.2). Of all observations over the course of the study where both HDRS-17 and

BDI-II scores were available for response status to be calculated, 64.8% agreement

between response criteria was observed (p<0.05, Fisher’s Exact Test). However, in 15

cases, patients identified as responders on the HDRS-17 were identified as non-

67

responders on the BDI-II, whereas in the reverse case, only identified responders on the

BDI-II were identified as non-responders on the HDRS-17.

Table 3.2 – Number of responders and non-responders according to HDRS-17 and BDI-II criteria from measurements taken throughout the study. Out of the 54 pairs of depression scores, the number of concordant pairs are indicated in third column. The p-value for the association was obtained using Fisher`s Exact Test. Values were only included if both BDI-II and HDRS-17 response values were present. HDRS-17 BDI-II % Agreement p N (Responders) 35 24 64.8 0.02 N (Non-responders) 19 30

3.3.2 Age correlations

According to a non-parametric correlational analysis, the response of depression as well

as 2 sleep variables showed trends toward association with age (Table 3.3 – See Table

A7.1 for age correlations of all variables) at some time points. Both percent change in

HDRS-17 scores from baseline and absolute change in BDI-II appeared to be negatively

related to age, with larger associations observed toward the end of the treatment period.

The relation with BDI-II at treatment Day 58 was observed as significant at the 0.05

alpha level (ρ=0.63, p<0.05). Moreover, SOL and REMD appeared to show some

positive relation to age, though only change in REMD at treatment Day 30 was

statistically significant (ρ=0.73, p<0.05). The magnitude of the association with REMD

also appeared to increase over the course of the study, though the significance of the

relationship disappeared at treatment Day 58.

68

Table 3.3 – Spearman rank correlations between age and changes from baseline at different time points. HDRS-17 values were calculated as percent changes from baseline, whereas all other variables were calculated as absolute changes. Day 2 9 16 30 58 HDRS-17 -- -0.08 -0.55 -0.31 -0.52 BDI-II -0.14 -0.29 -0.14 -0.35 -0.63* SOL 0.45 0.11 0.41 0.09 0.35 REMD -0.25 0.07 0.36 0.73* 0.55 * p<0.05

3.3.3 SOL, SE, AI and REMP Data describing the progression of SOL, SE, AI, and REMP are located in Appendix 8.

Changes in these four sleep variables across time are shown in Figure 3.3. SOL did not

show any consistent trend over the course of the study, and highly variable changes from

baseline produced no results approaching significance. SE did increase significantly at

Nights 2 and 9 (Wilcoxon p<0.05), but the significance disappeared at Night 16 and

onward (however, the trend remained positive). A trend toward increased AI was also

observed. This approached significance at Night 9 (Wilcoxon p=0.051), and was

significant at Night 16 (Wilcoxon p<0.01). Again, this trend was no longer significant at

Nights 30 and 58. Decreases from baseline in REMP fluctuated over the course of the

treatment protocol, and approached significance at Night 16 (Wilcoxon p=0.09) while

reaching significance at Night 58 (Wilcoxon p<0.01).

69

*

*

**

**

-6

-4

-2

0

2

4

6

8

2 9 16 30 58

Treatment Night

Ch

ang

e in

var

iab

le

SOL

SE

AI

REM

Figure 3.3 – Changes from baseline in SOL, SE, AI, and REM percent on all 5 treatment nights. Changes in REM and SE were recorded as percent values; SOL in minutes; and AI in events/hr. * p<0.05 ** p<0.01

3.3.4 REM latency

As previously reported, there was an overall trend toward increased REML from baseline

levels during over the course of this study (See Figure 3.4). This result from the

Wilcoxon signed rank test was significant at the 0.05 alpha level on Nights 2, 16 and 30,

and at the 0.001 alpha level on Night 58 (Table 3.4). There was also a positive trend on

Night 9 but this did not reach significance (Wilcoxon p=0.09). Though a rapid change

was clearly observed on Night 2 (Mean=62 min.), much of this increase was lost at Night

9. The increase in REML then occurred gradually, and reached similar levels as Night 2

only at Night 58 (Mean=65 min.). Table 3.4 characterizes the changes in REML with

respect to baseline values in further detail.

70

***

*

*

0

10

20

30

40

50

60

70

80

90

100

2 9 16 30 58

Treatment Day

Ch

ang

e in

RE

ML

(m

in.)

*

Figure 3.4 – Changes from baseline in REML on all 5 treatment days. Vertical error bars denote standard errors at each time point. * p<0.05 *** p<0.001 Table 3.4 – Descriptive statistics for absolute change in REML from baseline (SD – standard deviation; IQR – interquartile range). Wilcoxon sign rank score and associated p-value p(S) for significance from baseline are indicated. Day 2 9 16 30 58 N 16 16 16 16 14 Mean (SD) 61.88 (90.64) 24.56 (85.40) 26.94 (91.37) 35.81 (76.93) 65.11 (48.14) Median (IQR) 66.3 (74.8) 32.8 (80.75) 34 (74.0) 46.3 (92.0) 54.8 (71.5) Range (-208, +166.5) (-235.5, +118) (-256.5, +171) (-196, +134.5) (-3, +150) IQR (+38, +112.75) (+5.25, +86) (+5.25, +79.25) (-6.75, +85.25) (+28.50, +100) S (Wilcoxon) 48 33 40 42 51.5 p(S) 0.011 0.09 0.039 0.029 0.0002

3.3.5 REM density

REMD showed rapid increases following the onset of treatment, which were sustained

until Night 30 of the treatment protocol (See Figure 3.5, Table 3.5). Changes from

baseline were significant on Night 2, immediately following the start of treatment

(Wilcoxon p<0.05) and mean and median values through Night 9 (Wilcoxon p<0.01) and

71

Night 16 (Wilcoxon p<0.01). REMD at Night 30 was exactly on the border of statistical

significance (Wilcoxon p=0.05). However, REMD appeared to return to baseline levels

by Night 58, with none of the patients who remained in the study maintaining prior

increases.

**

***

-1

0

1

2

3

4

5

6

2 9 16 30 58

Treatment Day

Ch

ang

e in

RE

MD

(m

v/m

in.)

Figure 3.5 – Changes from baseline in REMD on all 5 treatment days. Vertical error bars denote standard errors at each time point. * p<0.05 **p<0.01 Table 3.5 – Descriptive statistics for absolute change in REMD from baseline (SD – standard deviation; IQR – interquartile range). Wilcoxon sign rank score and associated p-value p(S) for significance from baseline are indicated. Day 2 9 16 30 58 N 13 13 12 10 9 Mean (SD) 2.79 (3.77) 3.47 (2.97) 3.66 (4.23) 2.72 (3.79) 0.37 (1.55) Median (IQR) 3.1 (3.89) 3.2 (9.48) 3.3 (4.16) 3.0 (14.02) -0.3 (5.41) (Min, Max) (-3.06, +8.92) (-2.16, +7.32) (-2.72, +13.58) (-5.68, +8.34) (-1.77, +3.64) S (Wilcoxon) 32.5 40.5 32 19.5 4.5 p(S) 0.02 0.002 0.009 0.05 0.65

72

3.3.6 Slow wave sleep

Average SWS remained above baseline levels throughout the treatment period (See

Figure 3.6, Table 3.6). Changes with the start of treatment were rapid and pronounced,

and reached significance at Night 2 (Wilcoxon p<0.05). After a slight decrease from

Night 2 to Night 9, increases were seen above Night 2 levels on both Nights 16 and 30

(Wilcoxon p<0.01). However, significance was abolished at Night 58, with mean

increase in SWS falling below half that at Night 30 (mean increases were 8.4% and 4.1%,

respectively). There was therefore no discernible pattern in the strength of SWS increases

over time.

**

***

0

2

4

6

8

10

12

2 9 16 30 58

Treatment Day

Ch

ang

e in

SW

S (

%)

Figure 3.6. Changes from baseline in percent SWS on all 5 treatment days. Vertical error bars denote standard errors at each time point. * p<0.05 ** p<0.01

73

Table 3.6 – Descriptive statistics for absolute change in percent SWS from baseline (SD – standard deviation; IQR – interquartile range). Wilcoxon sign rank score and associated p-value p(S) for significance from baseline are indicated. Day 2 9 16 30 58 N 16 16 16 16 14 Mean (SD) 5.88 (9.62) 4.6 (8.93) 6.93 (9.18) 8.38 (9.76) 4.14 (10.93) Median (IQR) 4.6 (13.35) 5.6 (9.35) 9.0 (9.45) 8.6 (8.9) 3.5 (12.9) (Min, Max) (-6.2, +31.8) (-13, +23.1) (-7.6, +28.4) (-7.7, +30.6) (-19.9, +26) S (Wilcoxon) 41 33 52.5 57 23.5 p(S) 0.03 0.09 0.004 0.002 0.11

3.3.7 SWS reversal

There was no clear trend in the proportion of patients who had lower quantities of SWS

in the first than in the second sleep cycle (Table 3.7). However, a smaller proportion of

the patients had reversed SWS at Night 58 than at baseline.

Table 3.7 – Proportion of subjects with larger amounts of SWS in the first than in the second sleep cycle. Day Baseline 2 9 16 30 58 N 16 14 14 15 14 14 N (1) 10 6 8 10 8 7 Proportion (1) 0.63 0.43 0.57 0.67 0.57 0.5

3.3.8 Sleep parameters as predictors of mirtazapine response According to the linear mixed model analysis (Tables 3.8 and 3.9), AI, REML, SWS

were significant positive predictors of individual variability in HDRS-17 (p<0.05) and

REM was a significant negative predictor (p<0.05) whereas REMD approached

significance as a positive predictor (p=0.08). First, second and third-order time effects

were also observed, with time (t) and time3 (t3) registering as significant negative

predictors of HDRS-17 scores in virtually all models (except for t3 in the model of

74

SWSREV, which approached significance). In contrast, the square of time (t2) was

observed to positively predict HDRS-17 scores in the linear mixed model of each sleep

variable (p<0.05).

Table 3.8 – Mixed model analysis of individual sleep markers of depression as predictors of HDRS-17 scores. Parameter effects (β) and first to third-order time effects [β(t) β(t2), and β(t3)], respectively are tabulated (S.E. – standard error). Significant and near-significant sleep parameters are indicated in bold type.

SOL SE AI REML REMP REMD SWS SWS REV

β -0.02 -0.03 0.20 0.03 -0.24 0.41 0.13 -0.66 S.E. 0.04 0.07 0.08 0.01 0.11 0.23 0.06 1.39 p 0.66 0.70 0.02 0.02 0.03 0.08 0.03 0.64 β(t) -1.20 -1.50 -1.20 -1.20 -1.25 -1.43 -1.26 -1.13

S.E. 0.30 0.42 0.29 0.28 0.28 0.36 0.29 0.31 p 0.0001 0.001 <0.0001 <0.0001 <0.0001 0.0002 <0.0001 0.001

β(t2) 0.04 0.05 0.04 0.04 0.04 0.05 0.04 0.03 S.E. 0.01 0.02 0.01 0.01 0.01 0.02 0.01 0.02 p 0.01 0.01 0.01 0.004 0.003 0.01 0.004 0.03

β(t3) -0.0004 -0.001 -0.0004 -0.0004 -0.0005 -0.0005 -0.0004 -0.0004 S.E. 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002

p 0.01 0.01 0.03 0.01 0.01 0.02 0.01 0.07 Linear mixed model analysis of sleep parameters as individual predictors of BDI-II

scores in general did not appear to as strongly predictive as with the HDRS-17.

Compared to the HDRS-17, significance was lost for REML and AI (though at p=0.06,

AI nevertheless approached significance). In addition, REMD no longer approached

significance as a predictor (p=0.21). However, the direction of trends did not change

between the two scales. In contrast with the HDRS-17, the mixed model did not indicate

that time was a significant predictor of BDI-II.

75

Table 3.9 – Mixed model analysis of individual sleep markers of depression as predictors of BDI-II scores. Parameter effects (β) and first to third-order time effects [β(t) β(t2), and β(t3)], respectively are tabulated (S.E. – standard error). Significant and near-significant sleep parameters are indicated in bold type. SOL SE AI REML REMP REMD SWS SWS

REV β 0.13 0.01 0.37 0.03 -0.57 0.74 0.37 -1.31 S.E. 0.10 0.16 0.19 0.03 0.27 0.59 0.15 3.30 p 0.20 0.96 0.06 0.29 0.04 0.21 0.02 0.69 β(t) -0.53 -0.60 -0.48 -0.54 -0.63 -0.67 -0.70 -0.43

S.E. 0.72 1.02 0.72 0.72 0.70 0.89 0.70 0.75 p 0.47 0.56 0.51 0.45 0.37 0.45 0.32 0.57

β(t2) 0.02 0.02 0.01 0.02 0.03 0.02 0.03 0.01 S.E. 0.03 0.05 0.03 0.03 0.03 0.04 0.03 0.04 p 0.55 0.61 0.82 0.54 045 0.66 0.46 0.76

β(t3) -0.0002 -0.0002 -0.0001 -0.0002 -0.0003 -0.0002 -0.0003 -0.0001 S.E. 0.0004 0.001 0.0004 0.0004 0.0004 0.001 0.0004 0.0005 p 0.53 0.59 0.91 0.52 0.43 0.73 0.48 0.77

3.3.9 Relationship of early sleep parameters with depression response Next, the level of association between sleep marker changes from baseline at Nights 2

and 9 with HDRS-17 and BDI-II scores at Days 30 and 58 was evaluated. Spearman rank

correlation was found to be significant only for SOL at Night 58 (Table 3.10).

Nevertheless, nonsignificant trends did appear elsewhere. Change from baseline in SOL

at Nights 2 and 9 consistently showed a negative correlation with HDRS-17 scores at

both Days 30 and 58, though Night 9 SOL was a better predictor of HDRS-17 at Day 30,

whereas Night 2 SOL negatively predicted HDRS-17 at Day 58. Very slight negative

trends (p>0.05) were observed for both REML and REMP, whereas a slight positive

trend was observed for REMD, though these were not near significance.

76

Table 3.10 – Spearman correlations of changes from baseline at Nights 2 and 9 for sleep variables with percent changes from baseline of the HDRS-17. Day HDRS-17 Day 30 HDRS-17 Day 58

SOL 2 -0.15 -0.77** 9 -0.39 -0.13 AI 2 -0.23 -0.07 9 -0.01 0.25 SE 2 0.36 0.38 9 -0.16 -0.23 REML 2 -0.15 -0.45 9 -0.06 -0.14 REMP 2 -0.18 -0.18 9 -0.28 -0.02 REMD 2 0.03 0.22 9 0.16 0.40 SWS 2 0.24 0.22 9 -0.13 -0.39 **p<0.01 As with the HDRS-17, SOL tended to show negative association with BDI-II scores

(Table 3.11), though none of these neared significance at the 0.05 alpha level.

Nonsignificant negative associations with REMP were similarly observed. However, the

slightly positive trend with REML was not as clear with REML, and oppositely to the

HDRS-17, REMD showed a slight (nonsignificant) negative association with the BDI-II.

77

Table 3.11 – Spearman correlations of changes from baseline at Nights 2 and 9 for sleep variables with absolute changes from baseline of BDI at Days 30 and 58. No correlations were found to be statistically significant. Day BDI-II Day 30 BDI-II Day 58

SOL 2 -0.21 -0.36 9 -0.24 -0.42 AI 2 -0.10 0.01 9 -0.16 -0.08 SE 2 -0.28 -0.36 9 0.00 -0.25 REML 2 0.02 -0.33 9 0.19 0.18 REMP 2 -0.16 -0.30 9 -0.40 -0.44 REMD 2 -0.15 0.08 9 -0.36 -0.33 SWS 2 -0.20 0.03 9 -0.07 -0.19

Changes from baseline in sleep parameters at Nights 2 and 9 were also compared in

responders and non-responders at Days 30 and 58 for sleep parameters which showed

statistically significant changes from baseline to Night 2. Tables 3.12-3.17 depict

descriptive statistics for REML, REMD, and AI in responder and non-responder groups.

SWS did not produce any consistent trends and was omitted from this section (See Tables

A9.1 and A9.2 for results). For REMD (Tables 3.12 and 3.13), no clear distinction using

the non-parametric Wilcoxon rank sum test was observed between groups at Night 30 for

either the HDRS-17 or the BDI-II. However, a nonsignificant trend toward smaller

increases in REMD was observed in the 7 responders compared to the 2 non-responders

for the HDRS-17 on Night 58. However, this trend did not differentiate BDI-II response

groups.

78

Table 3.12 – Descriptive statistics for REMD at Nights 2 and 9 in responders and non-responders according to the HDRS-17 and BDI-II at Day 30. The discrepancy between responders and non-responders was assessed using the Wilcoxon rank sum test, for which the associated p-values are indicated. REMD Responders Non-

Responders p

HDRS-17 N 6 6 0.48

Change at Night 2 Mean (SD) 1.3 (3.2) 3.3 (3.9)

Median (IQR) 2.1 (5.2) 3.4 (2.8) (Min, Max) (-3.1, 5.1) (-2.9, 8.9) N 6 6 0.82

Change at Night 9 Mean (SD) 2.9 (3.6) 3.7 (2.6)

Median (IQR) 2.4 (6.2) 3.4 (4.7) (Min, Max) (-2.2, 7.0) (0.7, 7.3) BDI-II N 5 7 1.00 Mean (SD) 2.2 (3.1) 2.5 (4.1)

Change at Night 2

Median (IQR) 3.1 (1.2) 2.3 (6.8) (Min, Max) (-3.1, 5.1) (-2.9, 8.9) N 5 7 1.00

Change at Night 9 Mean (SD) 3.1 (2.4) 3.4 (3.6)

Median (IQR) 2.9 (1.5) 3.8 (6.2) (Min, Max) (0.8, 7.0) (-2.2, 7.3)

79

Table 3.13 – Descriptive statistics for REMD at Nights 2 and 9 in responders and non-responders according to the HDRS-17 and BDI-II at Day 58. The discrepancy between responders and non-responders was assessed using the Wilcoxon rank sum test, for which the associated p-values are indicated. REMD Responders Non-

Responders p

HDRS-17 N 7 2 0.11

Change at Night 2 Mean (SD) 1.5 (2.9) 6.7 (3.2)

Median (IQR) 2.3 (5.2) 6.7 (4.5) (Min, Max) (-3.1, 5.1) (4.5, 8.9) N 7 2 0.22

Change at Night 9 Mean (SD) 2.9 (3.3) 5.6 (2.5)

Median (IQR) 2.9 (6.2) 5.6 (3.5) (Min, Max) (-2.2, 7.0) (3.8, 7.3) BDI-II N 9 3 1.00 Mean (SD) 2.5 (3.4) 2.8 (5.9)

Change at Night 2

Median (IQR) 3.1 (3.3) 2.3 (11.9) (Min, Max) (-3.1, 8.1) (-2.9, 8.9) N 9 3 0.86

Change at Night 9 Mean (SD) 3.3 (3.0) 3.1 (3.7)

Median (IQR) 3.2 (4.0) 1.3 (6.6) (Min, Max) (-2.2, 7.0) (0.7, 7.3)

With respect to REML (Tables 3.14 and 3.15), a general trend toward greater increases

from baseline in REML in responders compared to non-responders at Day 30 was

observed. Though this result did not approach statistical significance, increases at Night 2

were greater in both the BDI-II and HDRS-17 responder groups, and increases at Night 9

were greater in HDRS-17 responders but were virtually equal among responders and non-

responders according to the BDI-II.

80

Table 3.14 – Descriptive statistics for REML at Nights 2 and 9 in responders and non-responders according to the HDRS-17 and BDI-II at Day 30. The discrepancy between responders and non-responders was assessed using the Wilcoxon rank sum test, for which the associated p-values are indicated. REML Responders Non-

Responders p

HDRS-17 N 8 7 0.20

Change at Night 2 Mean (SD) 98.9 (56.5) 19.3 (113.4)

Median (IQR) 99.5 (104.5) 63 (153) (Min, Max) (23, 166.5) (-208, 113) N 8 7 0.46

Change at Night 9 Mean (SD) 38.9 (41.3) -5.2 (116.1)

Median (IQR) 38.3 (35.3) 21 (143) (Min, Max) (-45.5, 91) (-235.5, 113.5) BDI-II N 8 7 0.35 Mean (SD) 87.6 (74.3) 32.2 (110.4)

Change at Night 2

Median (IQR) 95.5 (110) 63 (78.5) (Min, Max) (-48.5, 166.5) (-208, 113) N 8 7 0.54

Change at Night 9 Mean (SD) 20.1 (45.6) 16.4 (119.4)

Median (IQR) 31 (58.3) 32 (103.5) (Min, Max) (-50, 81) (-235.5, 113.5)

81

Table 3.15 – Descriptive statistics for REML at Nights 2 and 9 in responders and non-responders according to the HDRS-17 and BDI-II at Day 58. The discrepancy between responders and non-responders was assessed using the Wilcoxon rank sum test, for which the associated p-values are indicated. REML Responders Non-

Responders p

HDRS-17 N 8 3 0.95

Change at Night 2 Mean (SD) 72.1 (69.4) 78.8 (22.4)

Median (IQR) 74.8 (98) 69 (41.5) (Min, Max) (-48.5, 165.5) (63, 104.5) N 8 3 0.78

Change at Night 9 Mean (SD) 26.6 (51.4) 55.5 (50.5)

Median (IQR) 32.3 (70.5) 32 (92.5) (Min, Max) (-50, 91) (21, 113.5) BDI-II N 11 3 0.90 Mean (SD) 74.0 (59.0) 72.6 (36.5)

Change at Night 2

Median (IQR) 69 (78.5) 63 (71) (Min, Max) (-48.5, 165.5) (42, 113) N 11 3 0.46

Change at Night 9 Mean (SD) 34.9 (51.2) 65.3 (66.5)

Median (IQR) 32 (60) 93 (124) (Min, Max) (-50, 118) (-10.5, 113.5)

AI (Tables 3.16 and 3.17) showed paradoxical results in the HDRS-17 and BDI-II

groups. For the HDRS-17, AI tended to be decreased in responders relative to non-

responders, though the reverse was observed when AI at Night 2 was observed according

to HDRS-17 response at Day 30. The trend of lower AI in responders approached

significance when AI at Night 9 was used to predict HDRS-17 response at Day 58. BDI-

II results were highly variable, and no significant trends were observed. However, AI in

this case was consistently greater in the responder group than in the non-responder group.

82

Table 3.16 – Descriptive statistics for AI at Nights 2 and 9 in responders and non-responders according to the HDRS-17 and BDI-II at Day 30. The discrepancy between responders and non-responders was assessed using the Wilcoxon rank sum test, for which the associated p-values are indicated. AI Responders Non-

Responders p

HDRS-17 N 7 7 0.56

Change at Night 2 Mean (SD) 4.01 (8.49) 1.19 (6.64)

Median (IQR) 2.9 (14.0) 1.5 (8.6) (Min, Max) (-5.5, 18.8) (-5.7, 13.6) N 8 7 0.61

Change at Night 9 Mean (SD) 1.74 (4.94) 4.03 (6.34)

Median (IQR) 2.3 (7.5) 2.1 (2.41) (Min, Max) (-6.6, 6.9) (-5.3, 13.9) BDI-II N 7 7 0.30 Mean (SD) 5.3 (9.47) -0.1 (3.75)

Change at Night 2

Median (IQR) 4.9 (18.4) 1.50 (6.0) (Min, Max) (-5.5, 18.8) (-5.7, 3.8) N 8 7 0.34

Change at Night 9 Mean (SD) 4.04 (5.90) 1.4 (5.25)

Median (IQR) 4.3 (5.6) 1.9 (7.4) (Min, Max) (-6.6, 13.9) (-5.3, 10.7)

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Table 3.17 – Descriptive statistics for AI at Nights 2 and 9 in responders and non-responders according to the HDRS-17 and BDI-II at Day 58. The discrepancy between responders and non-responders was assessed using the Wilcoxon rank sum test, for which the associated p-values are indicated. AI Responders Non-

Responders p

HDRS-17 N 7 3 0.42

Change at Night 2 Mean (SD) 0.64 (5.94) 4.5 (8.41)

Median (IQR) -0.8 (9.7) 2.9 (16.6) (Min, Max) (-5.5, 10.9) (-3.0, 13.6) N 8 3 0.08

Change at Night 9 Mean (SD) 1.06 (4.49) 8.83 (6.21)

Median (IQR) 1.3 (5.8) 10.7 (12.0) (Min, Max) (-6.6, 6.9) (1.9, 13.9) BDI-II N 10 3 0.49 Mean (SD) 1.9 (6.56) -0.43 (4.61)

Change at Night 2

Median (IQR) 1.1 (8.0) 1.5 (8.6) (Min, Max) (-5.5, 13.6) (-5.7, 2.9) N 11 3 0.34

Change at Night 9 Mean (SD) 3.57 (5.96) 0 (4.65)

Median (IQR) 3.5 (5.9) 1.9 (8.7) (Min, Max) (-6.6, 13.9) (-5.3, 3.4)

3.3.10 Analysis using classes of antidepressants Based on the finding of the linear mixed model that REMP, REML, AI, and SWS were

individually significant predictors of HDRS-17 scores and REMD was a near-significant

predictor, three separate groupings of these variables were tested, again using a linear

mixed model analysis, to determine whether they are predictive of depression scores (See

Tables 3.18 and 3.19). Each model included one parameter pertaining to REM sleep, one

parameter pertaining to sleep continuity, and one parameter pertaining to SWS (Model 1

– REMP, AI and SWS percent; Model 2 – REML, AI and SWS; and Model 3 – REMD,

AI and SWS). In Model 1 and Model 2, 2 out of 3 sleep parameters were significant

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predictors of HDRS-17 scores and 1 parameter approached significance (Table 3.18). In

the case of Model 1, AI and SWS were significant, whereas in Model 2, REML and SWS

were significant. First, second and third-degree time effects were also observed in the

HDRS-17 model. The BDI-II models (Table 3.19) were not as successfully predictive,

though SWS was significant in all three models. In Model 1, REMP also approached

significance, whereas in Model 3, AI approached significance. In addition, no time

effects were observed in the BDI-II model.

Table 3.18 – Mixed model analysis of HDRS-17 using 3 different combinations of sleep parameters, Model 1 – REMP, AI, SWS; Model 2 – REML, AI, SWS, Model 3 – REMD, AI, SWS. Parameter effects (β) including first to third-order time effects are tabulated. Sleep parameters of interest are indicated in bold type. Model 1 Model 2 Model 3 REMP β -0.18 - - S.E. 0.10 - - p 0.09 - - REML β - 0.02 - S.E. - 0.01 - p - 0.02 - REMD β - - 0.25 S.E. - - 0.23 p - - 0.28 AI β 0.17 0.14 0.13 S.E. 0.08 0.06 0.10 p 0.03 0.08 0.19 SWS β 0.13 0.14 0.24 S.E. 0.06 0.06 0.06 p 0.03 0.02 0.0003 t β -1.29 -1.24 -1.42 S.E. 0.27 0.27 0.32 p <0.0001 <0.0001 <0.0001 t2 β 0.04 0.04 0.04 S.E. 0.01 0.01 0.02 p 0.004 0.01 0.01 t3

β -0.0004 -0.0004 -0.0004 S.E. 0.01 0.0002 0.0002 p 0.02 0.02 0.04

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Table 3.19 – Mixed model analysis of BDI-II using 3 different combinations of sleep parameters, Model 1 – REMP, AI, SWS; Model 2 – REML, AI, SWS, Model 3 – REMD, AI, SWS. Parameter effects (β) including first to third-order time effects are tabulated. Sleep parameters of interested are indicated in bold type. Model 1 Model 2 Model 3 REMP β -0.44 - - S.E. 0.25 - - p 0.09 - - REML β - 0.02 - S.E. - 0.03 - p - 0.33 - REMD β - - 0.18 S.E. - - 0.54 p - - 0.74 AI β 0.30 0.31 0.47 S.E. 0.19 0.19 0.22 p 0.11 0.12 0.04 SWS β 0.38 0.39 0.72 S.E. 0.15 0.15 0.16 p 0.01 0.01 <0.0001 t β -0.74 -0.67 -0.82 S.E. 0.68 0.69 0.76 p 0.28 0.34 0.28 t2 β 0.02 0.01 0.01 S.E. 0.03 0.03 0.04 p 0.61 0.70 0.75 t3

β -0.0001 -0.0001 -0.00001 S.E. 0.0004 0.0004 0.0004 p 0.73 0.84 0.98 3.4 Discussion

Though there have been reports regarding sleep quality and sleep architecture with

mirtazapine (Aslan et al., 2002; Mayers & Baldwin, 2005; Winokur et al., 2003), there

has not yet been a study examining which sleep markers or classes thereof are specific

predictors of mirtazapine response. This study has the advantage over previous

examinations of sleep response to treatment in two ways. First, the technique of linear

mixed model analysis has made the analysis of individual-level effects possible. As

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opposed to aggregate values, this more individualized measure allows for a more acute

understanding of which sleep markers are truly predictive. As a consequence of this, the

second improvement that this study provides is its analysis of classes of sleep parameters,

which, as discussed below, appear to be strongly predictive of response. In spite of its

small sample size, this study nevertheless has the benefit multiple repeated measures of

both sleep architecture and self-rated and clinician-administered mood assessment, thus

allowing for the assessment of trends which could be suggestive of the need for further

study.

According to prior studies, it appears to be the case that antidepressant response is

generally slow to act, and one report indicated patients who showed minimal response by

week 5 of treatment could nevertheless be classified as responders as early as week 6

(Quitkin et al., 1996). This is a cause for uncertainty with respect to when a particular

treatment option should be considered ineffective. Therefore, one component of the study

addressed the question of whether there exist sleep parameters which can be more rapidly

predictive, potentially increasing efficiencies with respect to treatment selection.

3.4.1 Time course of sleep changes is not consistent

Sleep architecture in patients administered mirtazapine showed marked and strikingly

rapid changes. By Night 2, SE, REML, REMD, SWS, all showed significant changes;

while AI and REMP showed nonsignificant trends which then became significant at later

time points.

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However, although sleep in this study population was in general rapidly altered, the time

course of changes across the study was not consistent across parameters. For parameters

which did appear to be altered with treatment, three general patterns of change were

observed, with REMD, SE, and AI; REML and SWS; and REMP comprising the three

clusters of variables which had similar variation with time. SWS reversal did not show

any particular trend, and may not be a robust marker without looking more deeply into

the microarchitecture of delta activity during sleep, which has been done in the past

(Kupfer et al., 1990). Another possibility is that rather than a marker of MDD disease

state, such a distribution of SWS is in fact a trait indicator of susceptibility to depression.

As reported previously, mirtazapine has been shown to increase SE in both healthy

(Aslan et al., 2002) and depressed patients (Winokur et al., 2003). This study was no

exception to this trend. However, increases in SE were not retained throughout the course

of treatment, and essentially returned to baseline levels by Night 58. This was similarly

the case with REMD, which again showed a rapid increase followed by a decrease to

baseline levels between Night 16 and Night 58. AI did not as closely mirror this pattern,

with initial increases taking longer to increase to the level of statistical significance

(Night 16). However, these increases dampened from Night 16 to Night 58, and AI

similarly neared baseline levels by the end of the study. These changes in AI may be the

result of increased serotonergic neurotransmission, which is known to cause increases in

sleep fragmentation (Wilson & Argyropoulos, 2005), possibly due to stimulation of post-

synaptic 5-HT2 receptors, for which agonists are known to interfere with sleep (Lawlor et

al., 1991). Secondarily, increasing AI may be related to the weight gain reported in this

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study population (Shen et al., 2006), which may have predisposed patients to arousals due

to airway obstructions during sleep, as with obstructive sleep apnea.

The second class of changes occurred with REML and SWS. In both cases, a rapid initial

increase was observed, followed by a reversal of this trend from Night 2 to Night 9, and

then more gradual increases following Night 9. However, this trend was considerably

clearer with REML as compared with SWS. Also in contrast with REML, there was a

decrease in SWS from Night 30 to Night 58, which is more consistent with changes in

SE, REMD and AI.

Finally, REMP had a distinctive pattern of changes. Trends toward decreasing REMP

were only present at Nights 2, 16 and 58, and these trends reached significance only at

Night 58. REMP thus showed a more unstable profile than the other parameters which

were observed to change with treatment.

It is difficult to determine conclusively the cause of the non-linear profiles observed in

this study. However, antidepressants are known to have distinguishable short and long-

term effects. In the shorter term, monoamine signalling tends to be rapidly increased

(Krishnan & Nestler, 2008). There is mounting evidence that these changes then produce

secondary neuroplastic changes at the level of gene transcription and translation in the

brain, which are slower to occur (Pittenger & Duman, 2008). It is therefore plausible that

rapid sleep changes are the product of rapid increases in serotonergic and noradrenergic

neurotransmission. In the case of REML, SWS percent and REMP, the dampening of the

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trend observed at Night 2 could be related to homeostatic sleep mechanisms in response

to the initial effect. Later changes could then reflect the secondary increases in

neuroplasticity, which cause more gradual changes in sleep architecture. Finally, many

variables (SE, AI, REMD, SWS) showed a final regression to the baseline mean at Night

58, perhaps indicating a role for longer-acting homeostatic mechanisms as well.

3.4.2 Sleep predictors of mood state

According to the performed mixed models, individual variability in AI, REML, REMP

and SWS percent (all statistically significant) and REMD (approaching significance)

were found to be predictors of individual variability in HDRS-17 scores. As expected,

increasing sleep fragmentation as measured using AI, and increasing REMD were both

predictive of increasing depression scores. All other predictors, however, showed the

reverse trend. It would be expected that REM suppression, which is common to most

antidepressants and is usually reflected in increasing REML and decreasing REMP,

would be associated with decreasing and not increasing HDRS-17 scores. Instead, the

opposite was observed. In addition, increasing SWS percent would normally be taken as

an indicator of improving mood. Again, the opposite scenario was seen. These results

highlight the need to take into account that global trends may not always reflect

individual-level effects.

Though results were not entirely consistent and a larger sample size would be required to

confirm observed trends, some relationships between early sleep changes and later

response were observed with the HDRS-17. It is reported here that change in REMD

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from baseline to Night 2 appears to some degree to predict response at 8 weeks. Subjects

seemed to better tolerate smaller increases in REMD, which is consistent with its status

as a marker of depression (Riemann, 2007; Feinberg et al., 1982). Results for REML

were highly variable, but in general, greater increases at Night 2 were related to response

at Night 30 on the HDRS-17. This nevertheless did not approach significance, and the

fact that this trend only appears to have existed at Night 30 suggests that REML changes

may not predict sustained response.

The best prediction of HDRS-17 response according to changes in AI from baseline

occurred at Night 9, which was near significance in being lower in responders than non-

responders at Day 58. However, for all variables above, a larger study would be better

able to elucidate the more robust and consistent markers.

The data regarding early sleep predictors of mood also reinforce the notion that

electrophysiological activity during sleep is more tightly coupled with observer-rated,

rather than self-rated mood, and is further discussed below.

3.4.3 Classes of variables are also good predictors of depression scores

Perhaps the most striking finding of this study has been that grouping one REM variable,

one SWS variable and one sleep continuity variable had a considerable amount of success

in predicting individual variability in depression scores. This comes in support of the

initial hypothesis in this thesis that REM, SWS and sleep continuity when analyzed

together may provide precision in characterizing response to treatment. Previous

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multivariate analyses of depression [See reviews (Armitage, 2007; Buysse & Kupfer,

1990)] have not taken into account which classes of variables are involved in response.

However, the observed trend that changes in disparate aspects of sleep architecture

appear to be strong predictors of mood progression suggests that such changes may be an

electrophysiological signature of depressive state. If this is indeed the case, such changes

should be observed in response to other antidepressants as well, and could provide a

novel approach to predicting or assessing treatment success. Similarly to the observation

that residual insomnia is a significant predictor of risk of MDD relapse following

treatment (Ohayon & Roth, 2003), it is possible that groupings of sleep architectural

markers could carry with them information about present and future course of response to

drug therapy, and the above results suggest that this area requires further investigation.

3.4.4 Self-rated versus clinician-administered measures of mood

Though both the HDRS-17 and the BDI-II indicate some level of mood improvement, it

is clear that both cannot be taken as equivalent measures. While the HDRS-17 showed

steady improvement over time, results on the BDI-II were more equivocal between Days

2 and 30, with most of the improvement occurring at two points – immediately after

starting treatment, and between Days 30 and 58. These results on the BDI-II are line with

the notion that, though neurotransmission is almost immediately altered in response to

treatment, there is a time lag required for secondary effects at the level of gene expression

to manifest themselves. The clinician-administered HDRS-17 also supports this notion in

that perceived improvement occurred over the course of the study, and not only shortly

following the onset of treatment.

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Response on the two depression scales used here were found to be associated to some

degree, though response on the BDI-II appeared to be a more conservative measure, with

79% of discordant pairs occurring when only the HDRS-17 (i.e. the clinician) scored the

patient as a responder. It has been suggested that, similarly to other self-rated depression

scales, symptoms on the BDI-II tend to be underreported on average (Hunt et al., 2003).

It is therefore possible that underestimation of baseline depression scores on the BDI-II

impaired the ability of the scale to perceive response, irrespective of whether patients

were in fact responding to treatment.

Three possible explanations could account for the observed discrepancies. The first is that

clinicians are better attuned to the precise signs and systems indicative of MDD, whereas

patient bias and patient interpretation of the question could yield different results

difficulties in the proper transfer of information between patient and clinician could result

in misinterpretation of mood state; past reports of only moderate correlations between the

BDI-II and HDRS-17 (Beck et al., 1996) suggest that perhaps they measure truly

different constructs, and are both valuable and distinct measures of mood state and

response to treatment. It has been suggested that changes in the operational definition of

depression has changed since the publication of the Hamilton Depression Rating Scale in

1960, and that scale items are only partly related to MDD as it is presently defined in the

DSM-IV (Bagby et al., 2004).

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3.4.5 Limitations

There are several limitations to this study, the largest of which is evidently its sample

size. With only 16 patients, it would be difficult to make firm statements about trends that

were observed, particularly given the number of comparisons performed. Nevertheless, a

correction to the significance criterion due to multiple comparisons was not made, as this

study would likely best serve as an impetus for further research. It was instead thought

best to report any and all trends observed, with the caveat that the number of comparisons

may produce some spurious results. In addition, the small sample size may not provide

sufficient power to confirm significant trends, and a larger size would be required to

confirm, for example, that REMD is indeed a significant predictor of HDRS-17 scores. A

strength of this study, however, is its longitudinal nature and the fact that a large number

of PSG studies were performed on each patient, which provides a glimpse into sleep

fluctuations at a level of detail that is rarely possible. When performing a mixed model

analysis, it is also important to note that variables are assumed to follow a normal

distribution, which may not have been the case. Lack of placebo control also hinders

generalizability of this study, as all effects studied here were assumed to be drug effects.

Additionally, all except two study subjects were women, and therefore gender effects

could not be taken account. Lastly, two analytical issues exist. First, REMD was visually

scored, which lends itself to scorer bias and potential inconsistencies (discussed in the

ensuing chapter). Second, a quantitative EEG analysis was not performed, which would

provide a higher-resolution, quantitative picture of sleep EEG dynamics.

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3.4.6 Conclusions: A possible model

It has been previously reported that REMD may vary in inverse proportion to sleep need,

and therefore increasing REMD may reflect sleep satiety (Aserinsky, 1973). Support for

this hypothesis comes from the fact that REMD tends to increase over the course of a

nocturnal sleep period. It has also been found that REMD is decreased following nights

of sleep deprivation (Lucidi et al., 1996). In the current study, all patients who were

included in the mirtazapine protocol needed to have scored 6 or higher on the Athens

Insomnia Scale (AIS), suggesting some level of sleep deficit. It is therefore possible the

increase in REMD observed here may be a reflection of homeostatic mechanisms induced

by mirtazapine which are acting to alleviate sleep need. In both responders and non-

responders, REMD appears to have been increased. However, increases in responders

were on average slightly smaller than those in non-responders. In general, REMD also

returned to near baseline levels by Night 58, which could indicate decreased homeostatic

pressure following a normalization of sleep debt. The reason that those who did not

respond as effectively to mirtazapine had greater REMD could be as a result of a failure

of homeostatic mechanisms to fully compensate for increased need.

Such a situation could be reconciled with SWS percent as a positive predictor HDRS-17

in the context of the 2-process model of sleep regulation (Borbely, 1982; Borbely &

Wirz-Justice, 1982). Though mirtazapine appears to facilitate the SWS, less pronounced

HDRS-17 response in certain subjects could be related to a failure of SWS to stabilize in

an ideal range for those individuals. Increases in SWS during mirtazapine treatment

beyond such an ideal range may therefore be detrimental rather than beneficial to mood

95

state, and the homeostatic system remains unstable. In this sense, increases in SWS could

be associated with a failure of the body to adequately resolve a perceived ‘sleep need’

due to increases above and beyond a target level, rather than falling below it (see Figure

3.7 for a conceptualization of such a situation). Deficiencies in the dynamics of

accumulation and dissipation of slow wave activity have been previously reported in

MDD, and are consistent with the above hypothesis (Armitage et al., 2000).

Lastly, if SWS reciprocally inhibits the drive for REM sleep (McCarley, 1982), it would

also be expected that REM suppression and increased REML are associated with

increasing HDRS-17 scores, which was indeed observed in this study. This hypothesis is

speculative and theoretical in nature, and more thorough and likely quantitative analysis

(either EEG or at a more localised level) would likely be required to address its

plausibility. However, it is able to unify the observed sleep trends in relation to the course

of response to this decidedly effective antidepressant.

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a)

b)

c)

Figure 3.7 – A possible conceptualization of SWS response to mirtazapine. The figure depicts process S of the 2-process model across the diurnal cycle, with the horizontal line representing the ideal quantity of SWS. a) Pre-treatment. SWS is generally below the ideal level. b) Optimal response to mirtazapine. Deficiency in SWS is corrected, reaching an ideal state. c) Suboptimal response to mirtazapine. SWS increases beyond ideal level, resulting in an unstable system. This figure was modified from (Achermann, 2004).

97

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Armitage, R., Hoffmann, R., Fitch, T., Trivedi, M., & Rush, A. J. (2000). Temporal characteristics of delta activity during NREM sleep in depressed outpatients and healthy adults: group and sex effects. Sleep, 23, 607-617.

Aserinsky, E. (1973). Relationship of rapid eye movement density to the prior accumulation of sleep and wakefulness. Psychophysiology, 10, 545-558.

Aslan, S., Isik, E., & Cosar, B. (2002). The effects of mirtazapine on sleep: a placebo controlled, double-blind study in young healthy volunteers. Sleep, 25, 677-679.

Bagby, R. M., Ryder, A. G., Schuller, D. R., & Marshall, M. B. (2004). The Hamilton Depression Rating Scale: has the gold standard become a lead weight? Am.J.Psychiatry, 161, 2163-2177.

Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation.

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Borbely, A. A. & Wirz-Justice, A. (1982). Sleep, sleep deprivation and depression. A hypothesis derived from a model of sleep regulation. Hum.Neurobiol., 1, 205-210.

Buysse, D. J. & Kupfer, D. J. (1990). Diagnostic and research applications of electroencephalographic sleep studies in depression. Conceptual and methodological issues. J.Nerv.Ment.Dis., 178, 405-414.

Feinberg, M., Gillin, J. C., Carroll, B. J., Greden, J. F., & Zis, A. P. (1982). EEG studies of sleep in the diagnosis of depression. Biol.Psychiatry, 17, 305-316.

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Chapter 4

Methodological Considerations: automation of rapid eye movement scoring

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4.1 Introduction

Though it is generally ignored in the clinical setting, the microarchitecture of REM sleep

has been of considerable research interest for over thirty years. Specifically, REM density

(REMD), usually calculated as a number of eye movements per unit of time, has been

studied in relation to a wide array of research questions such as personality disorders

(Battaglia et al., 1999; Cohen, 1975), parasomnias (Bokey, 1993; Schenck et al., 1987),

sleep disordered breathing (Carrasco et al., 2006; Verma et al., 2001), learning and

memory (Peters et al., 2007; Smith et al., 2004), eating disorders (Lauer et al., 1990;

Levy et al., 1987), drug use and alcohol dependence (Kowatch et al., 1992; Gann et al.,

2001), narcolepsy (Vankova et al., 2001), post-traumatic stress disorder (Dow et al.,

1996), various psychopathologies and patient response to psychoactive drugs and

psychotherapy (Armitage, 2007; Murck et al., 2003).

Unfortunately, more widespread use of REMD in research and clinical settings has been

hindered by three key factors: the laborious nature of visually scoring eye movements

(the current gold standard); inadequate generalizability of results due to a lack of

standardization of what exactly constitutes a rapid eye movement; and scorer bias

(Agarwal et al., 2005).

Because scoring of eye movements is both tedious and time-consuming, the idea has

existed for some time that an automated rapid eye movement scoring algorithm could be

created whereby polysomnographic sleep records could be scanned for putative eye

movements, which could then be easily converted into a REMD value (Degler, Jr. et al.,

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1975; Ktonas & Smith, 1978; Smith et al., 1971). However, few studies have actually

employed such an algorithm (Ktonas & Smith, 1978; Hatzilabrou et al., 1994; Doman et

al., 1995; Varri et al., 1995; Takahashi & Atsumi, 1997; Agarwal et al., 2005). Two of

these (Ktonas & Smith, 1978; Doman et al., 1995) studies found high (>90%) agreement

between visually counted and automated eye movement counts. However, only one study

as far as one can discern uses software which is commercially available (Agarwal et al.,

2005). In that study, the REM scoring algorithm of the Stellate acquisition system was

used to detect individual eye movements based on negative instantaneous product of left

and right EOG channels, correlation coefficient of the two EOG channels, and rapid eye

movement scoring rules which were derived to provide optimal sensitivity and specificity

for detecting eye movements. After training the system on five subjects, the criteria were

tested on 5 different subjects, which produced a sensitivity and specificity of 67.2% and

77.5%, respectively.

This analysis will use all-night REMD values in order to characterize the performance of

another commercially available biosignal processing system for which automated rapid

eye movement analysis is achieved using a considerably simpler algorithm.

4.2 Methods

4.2.1 Study population

For a description of the study population, see Chapter 2. Forty-two of the PSG records

from 11 patients in this group were selected for the calculation of automated REMD

values.

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4.2.2 Data preparation and analysis

Automated rapid eye movement detection was accomplished using the PhiTools

PRANA® polygraphic recording analysis software system located in the sleep research

lab at Trent University. Data were first converted from their original format (compatible

with the Embla® Rembrandt sleep EEG system) to European Display Format using the

SleepAid conversion tool and then stored on optical discs, which were then transported to

Trent University.

Files were imported into the PRANA analyzer and were processed using the Rapid Eye

Movement Detection plug-in (Figure 4.1). Left and right electrooculogram (EOG)

channels were collapsed into a single bipolar EOG channel for the purposes of the

analysis. Regions of the bipolar EOG recording which contained chin electromyogram

(EMG) artifact in the 18-60 Hz range (with a background-dependent threshold) were

filtered by the software. The 0.3-10 Hz range in the bipolar EOG channel was also

filtered. Horizontal and vertical eye movements were detected using an amplitude

criterion of 25 µV and a slope criterion of 10 µV/ms. Training regarding the use of the

PRANA software was provided by an experienced technician, and a step-by-step

methodology for importing the recordings into PRANA and proceeding with eye

movement detection was carefully presented. A statistical comparison between all-night

REMD values obtained visually and those obtained using PRANA was then conducted.

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Figure 4.1. Sample screen of automated Rapid Eye Movement Detection using the PRANA software system. Eye movements were detected according to the bipolar LOC-ROC channel and are indicated by vertical lines. Monopolar EOG channels and chin EMG are also displayed. The hypnogram for this study is found in the upper left corner. 4.3 Results

4.3.1 Statistical comparison of automated and visual measures

The descriptive statistics for the pooled sample of visually and automatically scored

values of REMD are shown in Table 4.1. Relative to visually scored eye movements, the

computer algorithm appeared to estimate larger numbers of eye movements. In addition,

there was no clear correlational relationship between the two modes of measurement, and

the Spearman rank correlation did not reach statistical significance (ρ=0.25, p=0.11). A

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linear regression analysis (See Figure 4.2) again confirmed the lack of strong association

between automated and visually scored eye movement counts.

Table 4.1 – Descriptive statistics for visually scored and automatically scored REMD. Units are in µV/ms. Spearman correlation between these two variables was found to be nonsignificant (ρ=0.25). REMD (Visually scored) REMD (Automated scoring) N 42 42 Mean (SD) 7.5 (2.5) 16.8 (12.8) Median 7.6 14.5 (Min, Max) (3.5, 13.4) (2.5, 47.9)

0

10

20

30

40

50

60

0 2 4 6 8 10 12 14 16

REMD (Visually scored)

RE

MD

(A

uto

mat

ed s

cori

ng

)

Figure 4.2. Association of visual (predictor variable) and automated (response variable) REMD scoring. All values are in units of (mv/min). The r2 value for the fit is 0.03, (F=1.39, p=0.24).

4.3.2 Statistical comparison following the removal of 8 outliers Inspection of the data in Figure 4.2 suggested that in 8 cases, the PRANA algorithm was

severely overestimating eye movement counts. For comparison, the data was modeled

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with the exclusion of these 8 values. Descriptive statistics for this analysis are displayed

in Table 4.2. Modest correlations were observed following the removal of these points,

but these again only approached significance (ρ=0.33, p=0.06). The least-squares

regression (Figure 4.3, however, did depict a significant relationship (r=0.43, F=7.06,

p=0.01).

0

5

10

15

20

25

30

0 2 4 6 8 10 12 14 16

REMD (Visually scored)

RE

MD

(A

uto

mat

ed s

cori

ng

)

Figure 4.3. Association of visual (predictor variable) and automated (response variable) REMD scoring, excluding the 8 uppermost values obtained with automated scoring. All values are in units of (mv/min). The r2 value for the fit is 0.18, (F=7.06, p=0.01). Table 4.2 – Descriptive statistics for visually scored and automatically scored REMD, excluding the 8 uppermost values obtained with automated scoring. Units are in µV/ms. Spearman correlation between these two variables was found to be nonsignificant (ρ=0.33). REMD (Visually scored) REMD (Automated scoring) N 34 34 Mean (SD) 7.5 (2.6) 11.6 (6.8) Median 7.5 10.3 (Min, Max) (3.5, 13.4) (2.5, 24.1)

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4.4 Discussion

REMD has been implicated in robust findings in several research areas. Increased REMD

is associated with improved cognition in patients with dementia (Bahro et al., 1993; Ihl,

2003) and is also associated with improved procedural memory consolidation and IQ

(Smith et al., 2004; Peters et al., 2007). More relevant to the subject of this thesis,

increased REMD is also associated with psychopathologies such as PTSD (Dow et al.,

1996; Pillar et al., 2000), and with the presence of and response to treatment for MDD

(Berger & Riemann, 1993; Wichniak et al., 2000; Murck et al., 2003). The development

of a sensitive and specific algorithm for automatically calculating REMD which is

accurate and standardized has the potential to greatly increase the inclusion of this

parameter in the electrophysiological characterization of sleep in various study

populations.

However, this study did not find a sufficient agreement between visual and automated

scores to support the exclusive use of automation in analyzing REM sleep. Nevertheless,

several factors could explain the discrepancy. Poor signal quality could substantially

complicate the task of automated scoring due to incomplete filtering of increased noise

and the inclusion of spurious deflections as candidate eye movements. Further, what

appears to be a single deflection from the perspective of a human scorer may appear as

multiple deflections from the perspective of a software algorithm. A possible solution to

this problem would be to place a lower limit on the time interval between successive eye

movement bursts. Third, in eliminating chin EMG artifacts from the bipolar EOG

channel, EOG information indicating true eye movements may have been lost. These

108

issues must be addressed as a necessary if not sufficient requirement for achieving high

reliability.

There are limitations to this study. In general terms, the sample size was relatively small.

However, sample sizes in validation studies of automated algorithms for counting eye

movements have typically included between 10 and 25 subjects, which does not differ

greatly from the sample size reported here (Doman et al., 1995; Takahashi & Atsumi,

1997; Agarwal et al., 2005). Signal quality in many of the study recordings contained a

high degree of noise, which may account for the pronounced overestimation of REMD

values by the automated algorithm. In addition, the threshold criterion for registering an

eye movement could also be further optimized to reflect a greater sensitivity and

specificity.

In conclusion, visual and automated REMD scores were not significantly correlated in

this study, and further refinement of either the scoring algorithm or the threshold eye

movement criteria are required before the current gold standard of visual scoring can be

supplanted by reliable automated eye movement detection..

4.5 References

Agarwal, R., Takeuchi, T., Laroche, S., & Gotman, J. (2005). Detection of rapid-eye movements in sleep studies. IEEE Trans.Biomed.Eng, 52, 1390-1396.

Armitage, R. (2007). Sleep and circadian rhythms in mood disorders. Acta Psychiatr.Scand.Suppl, 104-115.

109

Bahro, M., Riemann, D., Stadtmuller, G., Berger, M., & Gattaz, W. F. (1993). REM sleep parameters in the discrimination of probable Alzheimer's disease from old-age depression. Biol.Psychiatry, 34, 482-486.

Battaglia, M., Ferini, S. L., Bertella, S., Bajo, S., & Bellodi, L. (1999). First-cycle REM density in never-depressed subjects with borderline personality disorder. Biol.Psychiatry, 45, 1056-1058.

Berger, M. & Riemann, D. (1993). Symposium: Normal and abnormal REM sleep regulation: REM sleep in depression-an overview. J Sleep Res., 2, 211-223.

Bokey, K. (1993). Conversion disorder revisited: severe parasomnia discovered. Aust.N.Z.J Psychiatry, 27, 694-698.

Carrasco, E., Santamaria, J., Iranzo, A., Pintor, L., De, P. J., Solanas, A. et al. (2006). Changes in dreaming induced by CPAP in severe obstructive sleep apnea syndrome patients. J Sleep Res., 15, 430-436.

Cohen, D. B. (1975). Eye movements during REM sleep: the influence of personality and presleep conditions. J Pers.Soc.Psychol, 32, 1090-1093.

Degler, H. E., Jr., Smith, J. R., & Black, F. O. (1975). Automatic detection and resolution of synchronous rapid eye movements. Comput.Biomed.Res., 8, 393-404.

Doman, J., Detka, C., Hoffman, T., Kesicki, D., Monahan, J. P., Buysse, D. J. et al. (1995). Automating the sleep laboratory: implementation and validation of digital recording and analysis. Int.J Biomed.Comput., 38, 277-290.

Dow, B. M., Kelsoe, J. R., Jr., & Gillin, J. C. (1996). Sleep and dreams in Vietnam PTSD and depression. Biol.Psychiatry, 39, 42-50.

Gann, H., Feige, B., Hohagen, F., van, C. D., Geiss, D., & Dieter, R. (2001). Sleep and the cholinergic rapid eye movement sleep induction test in patients with primary alcohol dependence. Biol.Psychiatry, 50, 383-390.

Hatzilabrou, G. M., Greenberg, N., Sclabassi, R. J., Carroll, T., Guthrie, R. D., & Scher, M. S. (1994). A comparison of conventional and matched filtering techniques for rapid eye movement detection of the newborn. IEEE Trans.Biomed.Eng, 41, 990-995.

Ihl, R. (2003). The impact of drugs against dementia on cognition in aging and mild cognitive impairment. Pharmacopsychiatry, 36 Suppl 1, S38-S43.

Kowatch, R. A., Schnoll, S. S., Knisely, J. S., Green, D., & Elswick, R. K. (1992). Electroencephalographic sleep and mood during cocaine withdrawal. J Addict.Dis., 11, 21-45.

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Ktonas, P. Y. & Smith, J. R. (1978). Automatic REM detection: modifications on an existing system and preliminary normative data. Int.J Biomed.Comput., 9, 445-464.

Lauer, C. J., Krieg, J. C., Riemann, D., Zulley, J., & Berger, M. (1990). A polysomnographic study in young psychiatric inpatients: major depression, anorexia nervosa, bulimia nervosa. J Affect.Disord., 18, 235-245.

Levy, A. B., Dixon, K. N., & Schmidt, H. (1987). REM and delta sleep in anorexia nervosa and bulimia. Psychiatry Res., 20, 189-197.

Murck, H., Nickel, T., Kunzel, H., Antonijevic, I. A., Schill, J., Zobel, A. et al. (2003). State markers of depression in sleep EEG: dependency on drug and gender in patients treated with tianeptine or paroxetine. Neuropsychopharmacology, 28, 348-358.

Peters, K. R., Smith, V., & Smith, C. T. (2007). Changes in sleep architecture following motor learning depend on initial skill level. J Cogn Neurosci., 19, 817-829.

Pillar, G., Malhotra, A., & Lavie, P. (2000). Post-traumatic stress disorder and sleep-what a nightmare! Sleep Med.Rev., 4, 183-200.

Schenck, C. H., Bundlie, S. R., Patterson, A. L., & Mahowald, M. W. (1987). Rapid eye movement sleep behavior disorder. A treatable parasomnia affecting older adults. JAMA, 257, 1786-1789.

Smith, C. T., Nixon, M. R., & Nader, R. S. (2004). Posttraining increases in REM sleep intensity implicate REM sleep in memory processing and provide a biological marker of learning potential. Learn.Mem., 11, 714-719.

Smith, J. R., Cronin, M. J., & Karacan, I. (1971). A multichannel hybrid system for rapid eye movement detection (REM detection). Comput.Biomed.Res., 4, 275-290.

Takahashi, K. & Atsumi, Y. (1997). Precise measurement of individual rapid eye movements in REM sleep of humans. Sleep, 20, 743-752.

Vankova, J., Nevsimalova, S., Sonka, K., Spackova, N., & Svejdova-Blazejova, K. (2001). Increased REM density in narcolepsy-cataplexy and the polysymptomatic form of idiopathic hypersomnia. Sleep, 24, 707-711.

Varri, A., Kemp, B., Rosa, A. C., Nielsen, K. D., Gade, J., Penzel, T. et al. (1995). Multi-centre comparison of five eye movement detection algorithms. J Sleep Res., 4, 119-130.

Verma, A., Radtke, R. A., VanLandingham, K. E., King, J. H., & Husain, A. M. (2001). Slow wave sleep rebound and REM rebound following the first night of treatment with CPAP for sleep apnea: correlation with subjective improvement in sleep quality. Sleep Med., 2, 215-223.

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Wichniak, A., Riemann, D., Kiemen, A., Voderholzer, U., & Jernajczyk, W. (2000). Comparison between eye movement latency and REM sleep parameters in major depression. Eur.Arch.Psychiatry Clin.Neurosci., 250, 48-52.

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Chapter 5

Conclusions

113

Poor sleep is a central complaint in MDD, and as such should always be at the very least

considered when evaluating both illness state and recovery. The overall question posed in

this thesis is – how reliable are qualitative and quantitative methods of measuring sleep in

predicting mood state, and moreover, assessing changes in this state in response to

treatment? Although those in the community sample (Chapter 2) who rated themselves as

depressed did not show distinctive markers of depression, they nevertheless did complain

of poor sleep. Therefore, at the community level, it may be the case that a broader, self-

rated questionnaires or clinical interviews are most effective at characterizing depression

both in general and as it relates to sleep. However, it remains possible that following a

formal diagnosis of MDD, constellations of known sleep markers do exist. The second

portion of this thesis took this matter into account, and dealt only with those individuals

formally diagnosed as having MDD, with markedly different observations. Sleep markers

were at times seen to relate quite closely to treatment response, and in some cases,

changes were very rapid, occurring only two days following the start of treatment. If

these data are borne out by future studies, they could signal a true value in sleep

evaluation as a prognostic screen for responsiveness to mirtazapine.

In efforts to develop clinically relevant models, those relating to pharmacological

intervention rather than trying to identify MDD state have the advantage of having

relatively uniform pharmacological effects. A compound (in this case mirtazapine) is

administered which directly modulates neurotransmission at specific sites, and may

therefore be expected even in a heterogeneous population to exhibit some consistency in

the way those changes interact with sleep architecture. A problem exists when trying to

114

identify state markers prior to intervention in an initially heterogeneous population,

because such heterogeneity can be expected at the pathophysiological level as well. In

such a case, the task is to group this population into biologically meaningful classes. As

the pathophysiology of depression becomes better and better characterized, it becomes

increasingly clear that MDD is a definition which encompasses a number of different

etiologies. Whether patients with depression, be it melancholic, bipolar, with psychotic

features, seasonal, or another variant can be robustly identified based on their sleep

remains to be seen.

Perhaps the most important contribution made by this thesis is the suggestion that a shift

in the research paradigm in sleep and depression from indiscriminately considering sleep

architectural parameters to considering them in the context of breadth of changes,

particularly when considering response to antidepressant medications. Specificity and

prognostic utility may well prove to be increased if sleep architectural changes with

treatment are considered within the paradigm of separate classes (REM sleep, slow wave

sleep and sleep fragmentation) rather than focusing on any individual category.

The final topic of this thesis is methodological, pertaining to the potential utility of

automation eye movement analysis. In this study, automated scoring of REM density did

not appear to be capable of matching visual scoring in terms of accuracy. However,

methods of analyzing sleep – including more sophisticated algorithms for standardizing

and identifying eye movements and also the budding field of quantitative EEG analysis –

are becoming increasingly powerful. This will likely provide new opportunities to

115

characterize neuropsychiatric state and identify both those individuals who require

intervention and those likely to respond to it.

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Chapter 6

Appendices

117

Appendix 1 Center for Epidemiologic Studies Depression Scale (CES-D).

118

Appendix 2 Epworth Sleepiness Scale (ESS) and Fatigue Severity Scale (FSS).

119

Appendix 3 Athens Insomnia Scale (AIS).

120

Appendix 4 Hamilton Depression Rating Scale. Items 1-17 were used in this study.

121

122

Appendix 5 Beck Depression Inventory II (BDI-II).

123

124

Appendix 6 Percent change from baseline for HDRS-17 and absolute change from baseline for BDI-II across time points (SD – standard deviation; IQR – interquartile range interval; s(µ) – standard error of mean).

Table A6.1 – HDRS-17. Day 9 16 30 58 N 15 13 15 11 Mean -31 -46 -45 -70 Median -30 -42 -53 -76 SD 28 21 31 21 s(µ) 7 6 8 6 (Min, Max) (-76, +39) (-88, -14) (-83, +44) (-91, -34) IQR (-45, -23) (-62, -37) (-67, -37) (-88, -46) p( t < T) 0.0006 <0.0001 <0.0001 <0.0001 S (Wilcoxon) -50 -45.5 -54 -33 p(S) 0.003 0.0002 0.0009 0.001 Table A6.2 – BDI-II. Day 2 9 16 30 58 N 16 16 16 15 14 Mean -0.19 -3.31 -3.63 -4.8 -9.14 Median -0.5 -5 -3.5 -3 -10.5 SD 5.26 7.36 8.55 6.14 7.72 s(µ) 1.31 1.84 2.14 1.59 2.06 (Min, Max) (-10, +11) (-15, +10) (-20, +10) (-16, +7) (-24, +9) IQR (-2, +3) (-8.5, +1.5) (-10.5, +3.5) (-9, -1) (-13, -4) p( t < T) 0.89 0.09 0.11 0.009 0.007 S (Wilcoxon) -1 -30.5 -30.5 -46 -46.5 p(S) 0.96 0.12 0.12 0.006 0.002

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Appendix 7 Spearman age correlations for changes from baseline in sleep parameters, HDRS-17 and BDI-II. HDRS-17 changes were calculated as percent changes from baseline, whereas all other variables were calculated as absolute changes.

Table A7.1 Day 2 9 16 30 58 HDRS-17 -- -0.08 -0.55 -0.31 -0.52 BDI -0.14 -0.29 -0.14 -0.35 -0.63* SOL 0.45 0.11 0.41 0.09 0.35 REMD -0.25 0.07 0.36 0.73* 0.55 SE 0.21 0.26 0.04 0.05 0.08 AI -0.05 0.10 0.05 -0.03 -0.29 REML 0.41 -0.04 0.004 0.17 0.24 REMP 0.15 0.44 0.23 0.24 0.15 SWS -0.37 -0.29 -0.31 -0.14 0.27 * p<0.05

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Appendix 8 Descriptive statistics for absolute change from baseline for SOL, SE, AI, REMP (SD – standard deviation; IQR – interquartile range interval; s(µ) – standard error of mean). Wilcoxon sign rank score and associated p-value p(S) for significance from baseline are indicated.

Table A8.1 – Sleep onset latency. Day 2 9 16 30 58 N 16 16 16 16 14 Mean 3.28 -1.19 2.47 -1.09 -0.107 Median -1.75 -1.0 -1.0 -2.0 -0.25 SD 19.99 11.71 18.78 13.05 16.83 s(µ) 5.00 2.93 4.69 3.26 4.50 (Min, Max) (-29, +51) (-24.5, +19) (-25.5, +57) (-25, +30.5) (-28.5, 40.5) IQR (-6.5, +8.75) (-8.25, +8.25) (-7.5, +13) (-8.25, +7.75) (-6, +6) p(t < T) 0.52 0.69 0.61 0.74 0.98 S (Wilcoxon) -5.5 -6.5 1 -8.5 -2.5 p(S) 0.79 0.73 0.97 0.61 0.89 Table A8.2 – Sleep efficiency. Day 2 9 16 30 58 N 16 16 16 16 14 Mean 6.6 3.74 2.9 4.16 0.24 Median 7.4 3.5 5.5 5.9 3.8 SD 9.13 6.76 10.28 10.00 11.96 s(µ) 2.28 1.69 2.57 2.50 3.20 (Min, Max) (-10.4, +19.9) (-7.4, +18) (-22.9, +21.5) (-11.8, +24) (-18.6, +16.7) IQR (-0.95, +14.25) (0, +6.9) (-2.05, +7.55) (-4.10, +8.35) (-14.2, +7.8) p( t < T) 0.01 0.04 0.28 0.12 0.94 S (Wilcoxon) 43 40.5 26 26.5 5.5 p(S) 0.025 0.035 0.19 0.18 0.76 Table A8.3 – Arousal index. Day 2 9 16 30 58 N 15 16 16 14 14 Mean 2.69 3.02 4.73 2.99 1.45 Median 2.9 2.8 5.2 2.0 1.5 SD 7.20 5.45 5.41 8.05 7.06 s(µ) 1.86 1.36 1.35 2.15 1.89 (Min, Max) (-5.7, +18.8) (-6.6, +13.9) (-4.0, +19.2) (-8.6, +16.5) (-11.1, +19.6) IQR (-3.1, +4.9) (+1.05, +6.55) (+0.75, +7.25) (-3.6, +8.3) (-3.2, +5.1) p( t < T) 0.17 0.043 0.003 0.19 0.46 S (Wilcoxon) 16 38 57 17.5 13.5 p(S) 0.38 0.051 0.002 0.30 0.42

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Table A8.4 – REM percentage. Day 2 9 16 30 58 N 16 16 16 16 14 Mean -2.64 -0.71 -2.7 -1.06 -4.14 Median -4.0 -1.1 -1.9 -2.3 -4.6 SD 6.64 4.65 5.39 6.06 4.35 s(µ) 1.66 1.16 1.35 1.52 1.16 (Min, Max) (-12.6, +10.1) (-8.4, +8.5) (-15.1, +5.4) (-9.3, +7.8) (-9.4, +3.6) IQR (-7.27, +2.65) (-4.5, +2.15) (-4.9, +0.15) (-5.65, +4.45) (-8.0, -1.9) p( t < T) 0.13 0.55 0.06 0.49 0.003 S (Wilcoxon) -30 -12 -33.5 -13 -42.5 p(S) 0.13 0.56 0.09 0.53 0.005

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Appendix 9 Descriptive statistics for SWS at Nights 2 and 9 in responders and non-responders according to the HDRS-17 and BDI-II at Days 30 and 58. The discrepancy between responders and non-responders was assessed using the Wilcoxon rank sum test, for which the associated p-values are indicated.

Table A9.1 – Day 30. SWS Responders Non-Responders p HDRS-17 N 8 7 0.46

Change at Night 2 Mean (SD) 3.5 (6.4) 8.5 (12.9)

Median (IQR) 1.8 (9.9) 5.9 (18.6) (Min, Max) (-5.1, 13.1) (-6.2, 31.8) N 7 7 0.61

Change at Night 9 Mean (SD) 3.8 (11.8) 3.8 (11.8)

Median (IQR) 5.6 (17.3) 5.6 (17.3) (Min, Max) (-13, 23.1) (-13, 23.1) BDI-II N 8 7 0.78 Mean (SD) 4.4 (8.4) 7.5 (12.0)

Change at Night 2

Median (IQR) 3.4 (15.4) 3.2 (14.4) (Min, Max) (-6.2, 15.1) (-3.5, 31.8) N 8 7 0.87

Change at Night 9 Mean (SD) 3.5 (8.0) 5.8 (11.0)

Median (IQR) 4.9 (11.9) 5.6 (10.9) (Min, Max) (-10.3, 11.7) (-13.0, 23.1) Table A9.2 – Day 58. SWS Responders Non-Responders p HDRS-17 N 8 3 0.28

Change at Night 2 Mean (SD) 2.7 (7.3) 8.3 (10.3)

Median (IQR) 1.3 (12.2) 13.3 (18.6) (Min, Max) (-6.2, 13.1) (-3.5, 15.1) N 8 3 0.92

Change at Night 9 Mean (SD) 3.5 (7.9) 1.4 (12.8)

Median (IQR) 4.9 (11.9) 5.6 (24.5) (Min, Max) (-10.3, 11.7) (-13, 11.5) BDI-II N 11 3 0.46 Mean (SD) 3.6 (7.5) 7.5 (5.2)

Change at Night 2

Median (IQR) 3 (15.5) 5.9 (10.1) (Min, Max) (-6.2, 15.1) (3.2, 13.3) N 11 3 0.88

Change at Night 9 Mean (SD) 2.8 (8.8) 3.5 (3.6)

Median (IQR) 5 (16) 5.6 (6.3) (Min, Max) (-13, 11.7) (-0.7, 5.6)

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