Is Brain Function Impaired in Moderate Sleep Apnoea: Before and After Six Months of APAP.

by Russ J. Buckley

A thesis submitted in partial fulfilment of the requirements for the

Degree of Doctor of Philosophy (Medicine)

in the University of Otago, Christchurch, New Zealand

July 2021

Abstract

The physiological impact of severe levels of obstructive sleep apnoea and the benefits of treatment are well documented. Yet more of the population suffer sleep apnoea of moderate severity. However, we still have substantial gaps in our understanding on the physiological impact and treatment of people with sleep apnoea of moderate severity. Our study addressed whether moderate obstructive sleep apnoea (OSA) had an impact on cerebral blood flow, cognition, or microsleeps. It also explored the effect of 6–7 months of auto-titrating positive airway pressure (APAP) therapy on physiological measurements and quality-of-life in persons with moderate OSA.

Twenty-four patients aged 32–79 years (median age 59, 10 female), who had previously presented to the Christchurch Hospital Sleep Unit and subsequently diagnosed with sleep apnoea of moderate severity consented to our study. Following a Level II polysomnography to confirm their suitability to participate, thirteen of these participants, aged 43–79 years (median age 59, 4 female), were placed on APAP therapy, with instructions to use it for at least 4 hr per night for 70% or more of the nights. The remaining 11 participants aged 32–79 years (median age 62, 6 female), were placed in an untreated cohort. Placement in the treated cohort was dependent on order of presentation with those first recruited offered a place in the treated cohort. A third group of seven healthy controls aged 42–73 years (median age 62, 4 female) with an apnoea, hypopnea index (AHI) < 5 were recruited from a database of normal volunteers. The absence of sleep apnoea’s was confirmed by polysomnography in all seven participants. All three groups underwent an MRI ASL scan of cerebral perfusion, completed cognitive tests covering five cognitive domains – attention, executive functioning, memory, speed of processing, and visuospatial – and undertook a 30-minute task in which they were required to continuously track a constantly moving target for the purpose of detecting microsleeps. This behavioural data, along with video recordings of eye movement and closure, were visually rated to identify microsleeps. Finally, the Epworth sleepiness scale (ESS), Pittsburgh sleep quality index (PSQI), and Short-form 36 Health survey questionnaire (SF-36) were completed. All measurements were repeated after the intervention period of 6–7 months during which time the treated cohort received APAP therapy.

Global cerebral perfusion did not differ at baseline between participants with sleep apnoea of moderate severity and healthy controls (mean 49.26 ml/100g/min vs 48.73 respectively, p = .899, d = 0.06). Furthermore, global cerebral perfusion rates at follow-up for the treated cohort, were not significantly different to baseline (mean 51.64 vs 48.56, p = .150, d =0.42). Although a comparison of regional perfusion, as shown in the multi-sliced images, between the treated cohort, the untreated cohort, and the control group show a positive effect of change in the treated cohort over the other two, particularly in the right cerebral cortex/cingulate gyrus region and the right angular/supramarginal gyri, no statistically significant distinct regions of improved cerebral perfusion were identified.

i The normalized and standardized composite cognitive score also did not differ at baseline between moderate OSA and healthy controls (median 0.26 vs 0.73, p = .166, d = -0.62). Repeat testing of cognition at the completion of 6–7 months of APAP therapy found no evidence of a difference between baseline and follow-up on total cognition (mean = 0.22 vs 0.36, p = .560, d = -0.17. However, despite memory showing a significant improvement (p = .001) there was a clear test-retest effect in all three groups.

Microsleep was also tested at baseline. Participants with moderate OSA (0–126 microsleeps, median 0) and healthy controls (0–2 microsleeps, median 0) displayed a similar propensity to microsleeps (p = .253, d = 2.1). No difference in microsleeps were observed between participants compliant with APAP treatment compared to their baseline result (median 0, range 0–5, vs. median 0, range 0–13, p = .344). Several markers of day-time sleepiness and quality of live improved in our treatment cohort: Epworth Sleepiness Scale (median 8 down from 11, p = .003, d =1.96), PSQI (mean 15.69 up from 14.23, p = .046, d = 0.94) and Minnesota Short Form 36 (mean 76.31 up from 66.09, p = .035, d = 0.93).

This research was unable to demonstrate any change in cerebral perfusion, cognition, or microsleeps following 6-7 months of APAP treatment. However, improvements in daytime sleepiness, sleep quality and quality of life are consistent with the observation that most participants who used APAP in this study wished to continue its use past its conclusion. This indicates a clinical benefit in treating moderate OSA not otherwise supported.

ii Acknowledgements

A work of this nature would be difficult, if not impossible to achieve were it not for the effort of others. The assistance given and friendship offered to me by my supervisors, Professor Richard Jones, Professor Lutz Beckert, Dr. Carrie Innes and Dr. Paul Tudor Kelly, all deserve special mention. Also, I want to acknowledge the contribution of Dr. Michael Hlavac who was an enthusiastic member of the research team. Although I have been challenged at times by their considerable intellect and knowledge, their constant encouragement and patience, unrelenting enthusiasm, considerable support, and sound practical advice has provided me with a sound platform from which I could complete this study.

Special recognition of the considerable contribution made by those patient souls who volunteered for this study needs acknowledgement. Without people who are prepared to put their hand up when asked to participate in these studies, meaningful research would be limited. I asked a lot from those who participated. They had to undergo overnight sleep studies, tolerate the MRI scanner, be subjected to two and a half hours of cognitive and microsleep testing, not once but twice. In addition, those participants assigned to the treated cohort had to undertake six-months of APAP treatment which involved wearing a mask while they sleep. That they did that was an extraordinary display of tolerance, which on behalf of the sciences I applaud.

Also, I want to thank the Canterbury District Health Board Sleep Health Services team, who in addition to their normal workload, undertook the Sleep Studies and Pacific Radiology staff members who undertook the MRI ASL scans, often in the weekends. I would also like to recognize their friendship and encouragement both of which were much appreciated as was their interest in what this study was doing.

I am also appreciative of the friendship, help, and feedback extended to me by my colleagues at the New Zealand Brain Research Institute. Dr Tracy Melzer deserves special mention for his assistance with ASL processing.

It is also appropriate that I thank the University of Otago for its institutional support and for recognising the merit of this study and providing financial support. I was very privileged to benefit from an Otago University Doctoral Scholarship and can only hope that this study has done it justice.

Finally, I must recognise the encouragement, contribution, and sacrifice made by my wife Raewyn throughout the course of my studies. Without her unwavering support and tolerance, this study would not have been possible. Her belief in me at all times has been remarkable. Thank-you love.

iii Table of Contents

Abstract ...... i

Acknowledgements ...... iii

Table of Contents ...... iv

Preface ...... ix

Table of Abbreviations ...... x

Chapter 1 Introduction ...... 1

1.1 The problem ...... 2

1.2 Solution ...... 4

1.3 Research Questions and Hypotheses ...... 4

Chapter 2 The cerebrovascular sequalae of moderate OSA whilst awake ...... 7

2.1 Introduction ...... 7

2.2 Definitions...... 7

2.3 Obstructive sleep apnoea ...... 9

2.4 Phenotypes in OSA ...... 11

2.5 Cerebrovascular pathophysiology ...... 13

2.5.1 Cerebral perfusion homeostasis ...... 13

2.5.2 Intracranial compliance and cerebral autoregulation ...... 13

2.5.3 The vasodilatory response to moderate OSA ...... 17

2.6 Impact of OSA on cerebral perfusion during wakefulness ...... 20

2.6.1 OSA and Cerebrovascular homeostasis ...... 20

2.7 Cerebral perfusion and cognition ...... 23

2.7.1 The neuropsychological sequelae of obstructive sleep apnoea ...... 25

2.8 The selection and use of neuropsychological tests ...... 28

2.8.1 Neurocognitive testing ...... 28

2.8.2 A review of cognitive test batteries for OSA ...... 28

2.8.3 Tests for the neuropsychological testing of obstructive sleep apnoeics ...... 30

iv 2.9 Quality-of-life measures and OSA ...... 35

2.10 Microsleeps and OSA ...... 37

2.10.1 Behavioural Characteristics ...... 37

2.10.2 Neurophysiology of Microsleeps ...... 39

2.11 Positive air pressure therapy for the treatment of OSA ...... 40

2.11.1 Positive air pressure therapy overview ...... 40

2.11.2 PAP and cerebral perfusion...... 41

2.11.3 CPAP and brain function ...... 43

2.12 Arterial spin labelling ...... 44

2.12.1 Overview of the arterial spin labelling process ...... 44

2.13 Summary ...... 46

Chapter 3 Methods ...... 48

3.1 Introduction ...... 48

3.2 Participants ...... 48

3.2.1 Inclusion/Exclusion criteria ...... 48

3.2.2 APAP treatment ...... 49

3.2.3 Recruitment ...... 50

3.3 Apparatus and Tests ...... 51

3.3.1 Polysomnography...... 51

3.3.2 Magnetic Resonance Imaging ...... 51

3.3.3 Eye-video recording during the ASL scan ...... 55

3.3.4 Constant Positive Airway Pressure ...... 56

3.4 Neurobehavioural tests ...... 58

3.4.1 Microsleep and Psychomotor Vigilance Task (PVT) ...... 59

3.4.2 Microsleep testing ...... 64

3.4.3 Psychomotor Vigilance Task ...... 66

3.5 Procedure ...... 67

3.5.1 Polysomnographic testing ...... 67

v 3.5.2 MRI ASL Process ...... 68

3.5.3 Cognitive test procedure ...... 69

3.6 Statistical considerations ...... 69

3.6.1 Statistical power and effect ...... 69

3.6.2 Overview ...... 70

3.6.3 One-tailed tests ...... 71

3.6.4 Significance level and p-values ...... 71

3.6.5 Research questions and statistical tests ...... 73

3.6.6 Effect sizes of OSA on cognition ...... 75

Chapter 4 Results ...... 77

4.1 Introduction ...... 77

4.2 Research participants ...... 77

4.2.1 Recruitment and exclusions ...... 77

4.3 APAP Compliance ...... 79

4.3.1 Usage...... 79

4.3.2 Therapy ...... 80

4.3.3 APAP machine retention...... 81

4.4 Test-retest intervals ...... 83

4.5 Polysomnographic results ...... 84

4.5.1 Apnoea Hypopnoea Index ...... 84

4.5.2 Arousal Counts ...... 88

4.5.3 Oximetry – ST90 ...... 90

4.5.4 Correlation between AHI and other measures ...... 92

4.6 Clinical Results ...... 93

4.6.1 Body Mass Index (BMI) ...... 93

4.6.2 Drowsiness during the arterial spin labelling scan ...... 94

4.6.3 Epworth Sleepiness Scale ...... 95

4.6.4 The Pittsburgh Sleep Quality Index ...... 96

vi 4.6.5 Medical Outcomes Study ...... 99

4.6.6 Demographics ...... 103

4.7 Global cerebral perfusion ...... 104

4.7.1 Global cerebral perfusion ...... 104

4.7.2 The effect of APAP treatment on global cerebral perfusion ...... 105

4.7.3 Global cerebral perfusion and other OSA indices ...... 106

4.7.4 Global cerebral perfusion and cognition ...... 108

4.8 Regional cerebral perfusion ...... 109

4.8.1 Regional cerebral perfusion ...... 109

4.8.2 ST90 ...... 113

4.8.3 Regional cerebral perfusion regions of interest from a previous study ...... 113

4.9 Cerebral perfusion normalization quality...... 114

4.10 Microsleeps ...... 115

4.10.1 Microsleep counts...... 116

4.11 Cognition...... 117

4.11.1 Baseline ...... 117

4.11.2 Does APAP treatment of moderate sleep apnoea improve cognition? ...... 119

4.11.3 Psychomotor vigilance task ...... 124

Chapter 5 Discussion ...... 125

5.1 Cerebral perfusion ...... 125

5.1.1 Is cerebral perfusion reduced in people with moderate OSA severity? ...... 125

5.1.2 Does APAP therapy have an impact on cerebral perfusion whilst awake? ...... 128

5.2 Cognition...... 129

5.2.1 Does PAP therapy have an impact on cognition? ...... 130

5.2.2 Does global cerebral perfusion have an impact on cognition? ...... 131

5.3 Microsleeps ...... 131

5.4 Sleepiness and Quality-of-life measures ...... 133

5.5 Study design ...... 133

vii 5.5.1 Retest reliability ...... 135

5.5.2 Baril et al’s study ...... 136

5.6 Patient demographics recruitment and treatment allocation ...... 136

5.6.1 Recruitment and statistical power ...... 136

5.6.2 Treated or untreated cohort placement ...... 137

5.6.3 Participant demographics ...... 138

5.6.4 Ethnic mix ...... 138

5.7 Individual differences ...... 139

Chapter 6 Concluding summary and outlook ...... 140

6.1 Achievement of objectives ...... 140

6.2 Main findings ...... 140

6.3 Review of hypotheses ...... 143

6.4 Critique of this project ...... 145

6.5 Future directions ...... 147

References ...... 149

Appendices ...... 175

Appendix A Screening checklist ...... 175

Appendix B Clinical participant information sheet ...... 176

Appendix C Control participant information sheet ...... 183

Appendix D Example polysomnography report ...... 189

Appendix E Resmed Airview sleep report ...... 192

viii Preface

This PhD thesis is the result of a research project carried out between April 2014 and December 2020 while the PhD candidate was enrolled in the Department of Medicine, University of Otago, Christchurch. It was written under the close supervision of Professor Richard Jones, Professor Lutz Beckert, Dr. Carrie Innes, Dr. Paul Kelly. The research project was based under the Christchurch Neurotechnology Research Programme, New Zealand Brain Research Institute. Fellow researchers were my four supervisors and Dr Michael Hlavac. The Principal investigator was Dr. Carrie Innes. My role was co-ordinating investigator.

Clinical participants for this study were recruited from referrals to the Christchurch Hospital Sleep Unit for obstructive sleep apnoea and who were assessed as having a moderate level of the sleep disorder but who would not receive treatment. The controls for this study were recruited from the general public. Ethical approval for this study was obtained from the Health and Disability Ethics Committee prior to recruitment and testing.

Constant positive airway pressure treatment was given to a sub-cohort of the clinical group. The equipment used to deliver the treatment was provided by ResMed, Australia. The MRI arterial spin labelling protocols used in the study were written by and their application overseen by Dr. Tracy Melzer who is the MRI Research Manager at the New Zealand Brain Research Institute. All MRI scans were conducted by Pacific Radiology. All level II polysomnographies, were carried out by Registered Sleep Technicians employed by the Christchurch Hospital Sleep Unit except for daily treatment and sleep reports provided by ResMed under their ‘myAir’ facility. Otherwise, the experimental assessment of participants was administered by the PhD candidate throughout the project. The experimental assessment of participants included conducting and performance data recording of SMCTests, cognitive and standard neuropsychological tests. It also included all statistical data analysis, and the subsequent formulation of this thesis.

This thesis is the result of several drafts each of which was reviewed by my supervisors and therefore can be considered collegiate.

Aspects of this research was presented by the PhD student at the Sleep in Aotearoa conference of the Australasian Sleep Association (Christchurch, April 2019) who also attended the Sleep Down Under conference in Brisbane 2019.

ix Table of Abbreviations

AHI Apnoea hypopnea index APAP Auto-titrating positive airway pressure AVLT Auditory verbal learning test ASL Arterial spin labeling CBF Cerebral blood flow CPAP Constant positive airway pressure CVLT Californian verbal learning test DARTEL Diffeomorphic anatomical registration through exponentiated lie algebra EEG Electroencephalogram EOG Electro-oculogram ESS Epworth Sleepiness Scale fMRI Functional magnetic resonance imaging FMRIB Oxford Centre for Functional MRI of the Brain FWHM Full Width at Half Maximum FSL FMRIB Software Library HVLT-R Hopkins verbal learning test revised ICV Intracranial volume MNI Montreal Neurological Institute MRI Magnetic resonance imaging OSA Obstructive sleep apnoea PAP Positive Air Pressure PSQI Pittsburgh Sleep Quality Index PVT Psychomotor Vigilance Task SPGR Spoiled gradient recalled echo SPM Statistical parameter mapping SF-36 Short Form 36 Health Survey Questionnaire ST90 Total Time with oxygen saturation below 90 percent

x Chapter 1 Introduction

Obstructive sleep apnoea (OSA) may be considered the most important of all sleep disorders in which the consequence of non-treatment is often a prolonged assault on the brain (Thomas & Chan, 2013). One possible sequela of that assault along with excessive daytime sleepiness is cognitive dysfunction, which was estimated (C Guilleminault et al., 1976) to adversely impact approximately 80% of all OSA patients (Weaver & George, 2011). It has been postulated that this dysfunction is the result of both permanent and transient grey matter changes to the brain brought about by impairments to neurovascular autoregulation (Daulatzai, 2015; Innes et al., 2015). This hypothesis still needs to be explored further, particular for those people with a moderate level of the disorder (what we refer to as moderate OSA). Doing this is important because cognitive dysfunction can reasonably be expected to jeopardise participation in a full and active life, possibly rendering the patient a burden on family and society. This need not be so, as pointed out by Daulatzai (2015), OSA is a modifiable disease and can be readily treated. However, that treatment does come at a cost. In New Zealand, with its health funding constraints patients with mild to moderate level of OSA severity have difficulty accessing publicly funded treatment (TSANZ, 2004).

This is disappointing because within New Zealand, Maori are disproportionately affected by OSA (Mihaere et al., 2009). With respect to risk factors (e.g., mean neck circumference, current smoker) and symptoms (e.g., witnessed apnoeas. snoring, never waking refreshed, never getting enough sleep, ESS > 10), Maori women and men are worse off in all instances than their non-Maori counterparts (Mihaere et al., 2009). Moreover, they tend to be more likely diagnosed with severe OSA. Furthermore, access to and quality of health care for Māori is less than for non-Māori (Ellison-Loschmann & Pearce, 2006). Clearly, these statistics point towards Maori carrying a greater health-cost burden than non- Maori.

The focus of this research is to develop a better understanding of the pathogenic mechanisms of OSA- related cognitive dysfunction, particularly as it relates to OSA of moderate severity and cerebral perfusion. This should help sufferers of the disorder, family members, healthcare providers, and society overall understand the benefits of treatment against the cost, and lead to the development of new rational therapeutic strategies (Pham & Schwartz, 2015).

This pathology is thought to lead to permanent structural and physiological changes in the brain, as well as to other non-neurophysiological changes, with a consequential negative impact on overall health and, quality-of-life (Daulatzai, 2012; Vaessen et al., 2015). If left untreated the co-morbidities of OSA are wide ranging and pervasive (Pinto et al., 2016). For example, untreated OSA has been associated with cardiac arrhythmias, hypertension, cardiovascular disease, stroke, diabetes mellitus, fatigue and daytime hypersomnolence, along with mood disorders (Aaronson et al., 2014; Aloia & Arnedt, 2012;

1 Beebe, 2005; Beebe et al., 2003; Bucks et al., 2013; Chen et al., 2015; Chen et al., 2011; Daulatzai, 2015; Idiaquez et al., 2014; Jehan et al., 2018; Kendzerska et al., 2014; Lindberg et al., 2012; McEvoy, 2014; Mills & Dimsdale, 2004; Picano et al., 2014; Ramos et al., 2013; Roman et al., 2002; Vaessen et al., 2015; Wallace & Bucks, 2013; Wen et al., 2012). Also, there is a high social cost resulting from OSA related daytime hypersomnolence and fatigue, which is reported to be the most common complaint in people with OSA (Cao et al., 2011).

The public cost must also be considered. A recent study by Walter et al. (2017) into healthcare utilization found that untreated apnoeics had greater health costs than those who were treated, regardless of severity level. Within New Zealand, the societal cost of OSA in people aged 30–60 years has also been estimated to be in excess of the nett cost of treatment, with an estimated incremental net direct medical cost per quality of life year of $94 (range $56–$310) (Gander et al., 2010). A recent population- based study in Lausanne, Switzerland, found approximately 23% of women and 50% of men with a median age of 57 years (IQR 49–68 and 40–85 respectively) to have moderate or severe OSA (Heinzer et al., 2015), which emphasizes that the societal and personal consequences of untreated OSA are far from trivial.

OSA can be treated with CPAP. CPAP therapy, first described by Sullivan et al. (1981), was considered the gold standard for moderate to severe instances of the disorder (Andrade et al., 2014; Buchanan & Grunstein, 2011; TSANZ, 2004). The principal idea is that CPAP applied via a facial mask would act as a pneumatic splint, moving the soft palate and tongue anteriorly away from the posterior oropharyngeal wall, thereby maintaining upper airway patency during sleep, (Andrade et al., 2014; Sullivan et al., 1981). However, during more recent times, the auto-titrating positive air pressure (APAP) machine has been developed based on the principles of CPAP but unlike the constant air pressure characteristic of CPAP, the APAP machine adjusts the air pressure in response to changes in breathing patterns, allowing the pressure to vary across the night. They have an algorithm that increases the air pressure when breathing is compromised and lowers the air pressure following a period of normal respiration. Unfortunately, the initial cost of CPAP or APAP machinery can be expensive for many, and within New Zealand with its funding constraints, those with mild to moderate OSA can experience difficulty accessing PAP treatment and therefore, will remain vulnerable to the morbid effects of the disorder (TSANZ, 2004). This is a concern for the wellbeing of an increasingly aging population, given that one of the many factors increasing OSA vulnerability is age (Punjabi, 2008; Young et al., 2002).

1.1 The problem

To the sequelae of OSA, must be added deteriorated mental health and, the well-recognized, detrimental impact of OSA on cognition (Daulatzai, 2012; Olaithe & Bucks, 2013; Olaithe et al., 2015a; Olaithe et al., 2015b; Wallace & Bucks, 2013). According to Daulatzai (2012), it is neuroinflammation,

2 cerebrovascular endothelial dysfunction, and oxidative stress from OSA-related intermittent hypoxia and sleep fragmentation that underscore this cognitive decline through permanent neurodegenerative changes in the brainstem and neocortex. A proposed mechanism by which this could occur is through hypoperfusion as a consequence of neurovascular pathology resulting from OSA associated hypoxaemia and fragmented sleep (Alosco et al., 2013; Baril et al., 2015; Daulatzai, 2015; Innes et al., 2015; Macey et al., 2008).

Although hypoperfusion is a sequela of OSA, debate remains about OSA severity levels and the manifestation of hypoperfusion (Baril et al., 2015; Daulatzai, 2012; Innes et al., 2015; Yadav et al., 2013). Innes et al. (2015) observed that cerebral perfusion was decreased whilst awake in regions of brains belonging to people with either moderate or severe OSA. Of particular interest, was the finding that these regions of reduced cerebral perfusion are similar to the regions that previously had demonstrated reduced grey matter volume in people with severe OSA (Canessa et al., 2011; Gale & Hopkins, 2004; Morrell et al., 2010; Torelli et al., 2011; Yaouhi et al., 2009). This finding led Innes et al. (2015) to conclude that the blood oxygen deficits from OSA may be evident before, and possibly underlie structural changes in the brain that adversely impact cognition. However, Baril et al. (2015) found that cerebral perfusion was only reduced in those with severe OSA. While this disparity could be the result of study design – Innes et al. used arterial spin labeling (ASL) to quantify cerebral perfusion, Baril et al. used single-photon emission computed tomography – neither result should be dismissed out of hand, thereby leaving open the question of cerebral perfusion risks in patients diagnosed with a moderate OSA severity level.

One way to elucidate this question is to utilize the properties of arterial spin labeling (ASL). ASL is a Magnetic Resonance Imaging (MRI) procedure. Its high spatial resolution capabilities (Petersen et al., 2006), along with its non-invasiveness and ability to quantitatively measure tissue perfusion (Petcharunpaisan et al., 2010), make it ideal for understanding many aspects of cerebral perfusion physiology. ASL’s absolute perfusion quantification feature permits comparisons to be made between multiple measurements such as those of a longitudinal study. Moreover, correlating objective ASL measures of cerebral perfusion against cognition scores from those with OSA both before and after APAP therapy, should contribute to a better understanding of the relationship between perfusion and cognition.

However, the effect of hypoperfusion on cognition is not the only question we hope to answer. Intrusive and unwanted lapses of responsiveness often occur in the real world despite the robust nature of human information processing (Moller et al., 2006). Typically, the outcomes of these lapses are trivial, but they can be of concern in some instances. During those occasions where safety depends on extended periods of unimpaired performance, lapses can lead to severe outcomes i.e., motor vehicle accidents (Poudel et al., 2014). A study by Buckley et al. (2014) compared two types of lapses, attention lapses

3 and microsleeps. The authors concluded attention lapses were the result of resource depletion due to cognitive demands. On the other hand, microsleeps are arousal related lapses characterized by clear overt behavioural signs of either drowsiness, or sleep accompanied by a coincidental response disruption (Buckley et al., 2014; Innes et al., 2013). They are thought to be the result of low arousal ion levels being overwhelmed by the homeostatic drive for sleep (Poudel et al., 2014), which suggests apnoeics will be vulnerable to the deleterious effects of those types of lapses. Furthermore, microsleeps are not infrequent. For example, Peiris et al. (2006) unexpectedly found frequent lapses in a substantial number of healthy non-sleep-deprived participants; as did Poudl et al. Poudel et al. (2014) tested 20 fit and healthy non-sleep deprived young adults and found that the mean microsleep rate per hour was 79 (range 0–190). Although it is well established that attention is not immune to the deleterious impact of OSA (Deering et al., 2016; Simoes et al., 2018), the impact of OSA on microsleeps has had less attention with Embase listing only one reference that addresses the impact of OSA on microsleep (Morrone et al., 2020). Clearly, both sustained attention lapses and microsleeps are frequent and can have serious real-life consequences. Therefore, it is important to clarify the relationship between OSA and these two types of lapses.

1.2 Solution

This research will investigate the effect of moderate OSA on brain structure, cerebral perfusion using arterial spin labelling, neurocognition, and physiology, including microsleeping propensity, both before and following six-months of APAP treatment. The aim of this research was to determine if moderate- severity OSA has an adverse effect on brain function and if so, whether this can be offset/prevented by APAP. Treatment may also reduce OSA-related cognitive impairment, and microsleep-related deaths and injuries.

1.3 Research Questions and Hypotheses

Six hypotheses are proposed:

H1) Perfusion is reduced in moderate OSA: A study by Innes et al. (2015) found that an apnoeic cohort, which consisted of people with either a moderate or severe severity level of OSA, had reduced perfusion in three regions of the brain compared to a non-apnoeic cohort. Whether this difference would be true for a ‘moderate only’ cohort has yet to be demonstrated. It is hypothesized that cerebral perfusion in people with a moderate level of OSA will be reduced while awake compared to perfusion rates in non-apnoeic people. Supportive of this hypothesis are the findings of early transcranial Doppler studies by Placidi et al. (1998) and Franklin (2002) that found cerebral blood flow was impacted by OSA. Also, more recently, studies by Yadav et al. (2015), and Baril et al. (2015) found perfusion changes in people with OSA.

4 H2) Cognition is reduced in moderate OSA: Although several studies have investigated the impact of OSA severity levels on cognition, (Stranks & Crowe, 2016) few have specifically confined their analysis to just those with moderate level OSA. Most studies reviewed cognition in moderate apnoeics in conjunction with mild and severe severity levels and in Stranks and Crowe’s metanalysis, only one of the 19 reviews was limited to moderate OSA. However, that paper only assessed memory (Kloepfer et al., 2009). Our study is confined to an analysis of moderate OSA in conjunction with those with an AHI equal to or less than 5. So, given our study does measure cognition, we are in a rare position of assessing the effects of moderate OSA on cognition. Accordingly, it is hypothesized that moderate severity apnoeics (AHI > 15 < 29) will have lower cognition scores than those with an AHI < 5. The rationale for this position is the number of studies that in addition to either testing mild and severe OSA, have also included moderate OSA and that have found cognitive differences in both verbal and visual memory, attention, and spatial awareness.

H3) Microsleeps are increased in people with moderate OSA. OSA is known to have a deleterious effect on daytime sleepiness, assumed to be because of the increased number arousals from sleep associated with OSA (Slater & Steier, 2012) although hypoxemia (Dewan et al., 2015) and inflammation (Kheirandish-Gozal & Gozal, 2019) cannot be discounted. Microsleeps are thought to be influenced by drowsiness levels with greater propensity related to an increased drowsy state (Innes, Poudel, & Jones, 2013; Poudel, Innes, Bones, Watts, & Jones, 2014). Nonetheless, the link between sleep apnoea and microsleeps has not been thoroughly researched. It is hypothesize that the incidence of microsleeps will be higher in people with a moderate level of OSA compared to people without OSA. This hypothesis will be addressed by comparing measurements of microsleeps taken at baseline and again six-months later from all three research cohorts.

H4) Impaired perfusion when awake can be improved by APAP treatment. The literature on OSA has not satisfactorily explored the effect of APAP treatment of those with moderate OSA (McEvoy, 2014). It has not addressed whether such treatment will prevent or even reverse the deleterious effects of cerebral perfusion seen in sleep apnoea. It is hypothesized that APAP treatment will improve cerebral perfusion in those with moderate levels of OSA while awake at the 6-month follow-up. Supportive of this hypothesis is that OSA may progressively worsen over time and that APAP treatment has been shown in some, but not all, studies to reverse the mild cognitive dysfunction.

H5) Impaired cognition in people with a moderate severity level of OSA can be improved by APAP treatment. The effects of cerebral perfusion on neurocognitive function in people with moderate level of OSA remain poorly understood. It is hypothesized that decreased cerebral perfusion in people with moderate level of OSA will be positively correlated to their cognitive functioning. Evidence from studies into cardiovascular disease suggest a positive correlation between cerebral perfusion and cognitive functioning and changes to total brain volume.

5 H6) Increased microsleeps in people with a moderate severity level of OSA can be reduced by APAP treatment. A consequence of OSA is often excessive daytime sleepiness that may increase propensity for microsleeping. However, the relationship between microsleeping and OSA has not been completely established. It is hypothesized that APAP treatment will reduce microsleeps. Effective APAP treatment should reduce the number of micro arousals that are often associated with the restoration of upper airway patency. This should allow for the restoration of normal sleep architecture and improve daytime sleepiness levels.

6 Chapter 2 The cerebrovascular sequalae of moderate OSA whilst awake

2.1 Introduction

Adequate Cerebral perfusion is important for wellbeing and health outcomes (Azad et al., 2003; De Wit et al., 2018; Jafari, 2018; Wolters et al., 2017). It is, therefore, concerning that cerebral perfusion can be impaired because of intermittent hypoxemia and hypercapnoea; both being hallmarks of OSA (Castello‐Branco et al., 2020; Daulatzai, 2012; Jensen et al., 2018; Nie et al., 2017; Ponsaing et al., 2018; Silverman & Petersen, 2019; Urbano et al., 2008; Wu et al., 2019). This cerebral perfusion impairment can result in hypoperfusion of the brain that negatively impacts the delivery of oxygen and nutrients to brain tissue (Mount & Das, 2020; Torabi-Nami et al., 2015). Furthermore, the pathophysiology of OSA, has been demonstrated to impact cognitive functioning (Aaronson et al., 2014; Aloia & Arnedt, 2012; Beebe et al., 2003; Bucks et al., 2013; Chen et al., 2011; Idiaquez et al., 2014; Wen et al., 2012), which along with excessive daytime sleepiness contributes to a reduced quality- of-life (Vaessen et al., 2015). In particular, concentration, memory, executive functioning (Kylstra et al., 2013; Vaessen et al., 2015), depression, anxiety (Aloia et al., 2004; Olaithe et al., 2015a; Rezaeitalab et al., 2014), and fatigue (Olaithe et al., 2015a) are worrying psychological sequelae for many with OSA. Understanding this impact of OSA on a person’s general life experiences is an important part of case management and of course, research. How that understanding is developed though, does depend on the context of the research and the type of instruments used within it. Therefore, the choice of tests used in research becomes an important assignment.

The literature review explores the effect of moderate OSA on cerebral perfusion during the awake period and our current understanding of cerebral perfusion on cerebrovascular pathology. I review the literature on cognition and OSA, with an emphasis on a moderate level of the disorder. I also cover the various instruments used to measure the neurocognitive sequelae of obstructive sleep apnoea. Also reviewed is the literature concerning microsleeps, the frequency of which has been linked to fatigue and daytime sleepiness, and therefore could be an important metric for OSA. In addition, I look at the arterial spin labelling process used to quantify cerebral perfusion and how it is undertaken. Finally, I review the key literature on the role of APAP therapy in the management of sleep apnoea.

2.2 Definitions

Cerebral autoregulation: cerebral autoregulation is a process that is defined as the ability of the cerebral vasculature to maintain a relatively constant, stable blood flow despite wide-ranging changes in cerebral perfusion pressure (Cipolla, 2009; Filosa & Iddings, 2013; Mount & Das, 2020; Silverman & Petersen,

7 2019; Zeiler et al., 2020). Under normal conditions, this is mediated via changes in arteriolar diameter, which in turn leads to changes in cerebrovascular resistance that is expressed in an equation known as the Hagen-Poiselle equation (Silverman & Petersen, 2019).

Cerebral blood flow: Two terms are used to express the delivery of blood to the brain. Blood flow measured in the arteries is often called CBF, while blood flow measured in the capillaries in the brain parenchymal is normally labelled as perfusion. These terms are often used interchangeably.

Petcharunpaisan et al. (2010) noted that cerebral perfusion can in clinical practice refer to a broad range of quantitative and qualitative measures of blood flow and blood hemodynamic properties, including blood volume, blood velocities and blood transit times. Used quantitatively it refers to the rate at which blood is delivered to tissue, or in other words, the volume of blood per unit of tissue mass per unit of time (ml/g/min) and is essentially the same measure as CBF (Engblom et al., 2017). Typically, CBF in a normotensive person would be 60 ml/100g/min (Desmidt et al., 2018), although for an earlier

estimate, Lassen (1985) place mean≈ normal CBF at 45 – 55 ml/100 g/min.

Additionally, the literature refers to global cerebral perfusion and regional cerebral perfusion. They are different constructs. Global cerebral perfusion is dominated by pressure and clinical drives including the autonomic nervous system, whereas regional cerebral perfusion is inextricably linked to local change in neuron activity, particularly in the Limbic system (Macey, 2015).

Cerebral blood flow velocity: CBF velocity is a measure of the speed at which blood flows though the arteries. Its unit of measurement is cm/s (Desmidt et al., 2018). It is common for transcranial Doppler studies to report CBF velocity but as it is unable to measure the diameter of the artery at the point of insonation and hence cannot quantitatively assess CBF in terms of volume per period of time (Silverman & Petersen, 2019; Sorond et al., 2010). Nonetheless, it is a clinically useful index because the CBF velocity is directly proportional to the rate of blood flow provided the diameter of the vessel remains constant throughout the period of measurement (Durgan & Bryan, 2012).

Cerebrovascular reactivity: Cerebrovascular reactivity is defined as the response of cerebral blood vessels to changes in the partial pressure of carbon dioxide and oxygen (Castello‐Branco et al., 2020).. Cerebrovascular reactivity is also referred to by the literature as a measure of cerebrovascular homeostasis (e.g. Catchlove et al., 2018; Fierstra et al., 2013; Halani et al., 2015; Ryan et al., 2018; Zeiler et al., 2020), and is an indicator of vascular reserve and autoregulatory efficiency (Halani et al., 2015).

Cerebrovascular tone: Cerebrovascular tone is the degree a blood vessel has constricted relative to its maximally dilated state (Klabunde, 2012). Cerebrovascular tone has been referred to as a measure of cerebrovascular homeostasis (Cipolla, 2009; Filosa & Iddings, 2013; Klabunde, 2012).

8 Microsleeps: Microsleeps are arousal-related lapses characterized by clear overt behavioural signs of either drowsiness, or sleep and a coincidental response disruption (Innes et al., 2013; Peiris et al., 2007; Poudel et al., 2014) lasting between 0.5–15 s with lapses greater than 15 s being classified as sleep. The behavioural signs are full or at least 80% eyelid closure accompanied by clear indications of drowsiness – which may include prolonged eyelid closure, slow roving eye movements, head nodding and slumped posture, all of which are sometimes followed by abrupt reactions – coincident with flat responses, where the subject has stopped responding to the task at hand. They are distinct from the microsleep first described by Harrison and Horne (1996) that was based on a Spectral analysis of the EEG.

Obstructive sleep apnoea: Obstructive sleep apnoea (OSA) is a sleep-related breathing disorder that involves a decrease or complete cessation of ventilation despite an ongoing effort to breathe during sleep. It occurs when the muscles relax during sleep, causing soft tissue in the back of the throat to collapse and obstruct the upper airway. This leads to partial reductions (hypopnoeas) and complete pauses (apnoeas) in breathing that to be counted needed to last at least 10s during sleep. Most pauses last between 10 and 30s, but some may persist for one minute or longer. This can lead to reductions in blood oxygen saturation, with oxygen levels measured peripherally falling as much as 40% or more in severe cases (Medicine, 2014).

2.3 Obstructive sleep apnoea

Obstructive sleep apnoea occurs when muscles that normally maintain upper airway patency during respiratory inhalation, become atonic during periods of sleep and cannot counter the collapsing forces associated with ventilation. This results in the pharyngeal Figure 2-1, a section of the airway not supported by cartilaginous tissue, repeatedly becoming either completely or partially blocked specific to the sleep state, often for periods two minutes or more (Cao et al., 2011; Dempsey et al., 2010). In severe cases these obstructive episodes can occur hundreds of times per night. However, even in, what is considered by the American Sleep Association to be moderate cases of this sleep disorder, their occurrence is still between 15 and 29 times per hour of sleep that nonetheless equates to almost 240 times per 8-hour period of sleep (Hudgel, 2016; Ruehland et al., 2009).

These obstructive events are either apnoeas or hypopnoeas. An apnoea is defined as a drop in the peak signal excursion by >90% of the pre-event baseline with a duration > 10 seconds, whilst a hypopnoea is defined as a peak signal excursion drop by > 30% of the pre-event baseline with a duration of > 10 seconds, and a > 3% desaturation from the pre-event baseline or the event is associated with an arousal (American Academy of Sleep, 2020). Hypopnoeas have an equivalent pathophysiology to an apnoea (Daulatzai, 2015) with the likely consequences of these apnoeic/hypopnoeic events being chronic intermittent hypoxaemia, with brain blood oxygen saturation dropping to low levels (Aloia et al., 2004), or intermittent hypercapnoea (Brillante, 2017).

9

Figure 2-1 A sagittal view of the pharynx showing the soft palate or velum, the primary site for OSA.

This in turn results in increased respiratory effort, and sleep disturbances that are thought to promote ventilatory instability (Cao et al., 2011; Younes, 2004), thereby disrupting sleep architecture. Both apnoeas and hypopnoeas are associated with a disturbed sleep architecture, in particular loss of REM sleep and stage three of NREM sleep. Consequently, many of the restorative benefits of sleep are reduced (Daulatzai, 2012; Dempsey et al., 2010). This is reflected in the main symptom of severe OSA being increased daytime sleepiness (Vakulin et al., 2019).

However, a considerable proportion of people with moderate or severe OSA exhibit no significant daytime symptoms (Ryan, 2019). Estimates from recent population studies based on the American Academy of Sleep Medicine (AASM) 2012 criteria are that 49.7% of men and 23.4% of women aged 40 years or more have an AHI > 15 events per hour, but only 2-7% of them complain of sleepiness, i.e. have an ESS > 10 (Adams et al., 2016; Heinzer et al., 2016; Vakulin et al., 2019). This is in stark contrast to the conclusion reached by Weaver and George (2011) who, citing Guilleminault, Hoad and Mitler, (1978) reported that approximately 80% of OSA patients complain of both excessive daytime sleepiness and cognitive impairments, with half of the patients also reporting personality changes.

10 This work will explore the physiological correlations between intermittent nocturnal hypoxia and possible permanent adjustments of the brain. Much is known about the effects OSA interrupting sleep architecture, far less is known on the effect of OSA on the brain during the awake period.

2.4 Phenotypes in OSA

“Zinchuk et al. (2018) vary considerably between individuals (Eckert, 2018). It is, as noted by Zinchuk et al. (2017), a complex heterogeneous condition in which the diverse spectrum of features cannot be captured just by the AHI (See also, Dempsey et al., 2010; Ryan & Bradley, 2005; Subramani et al., 2017). Accordingly, to help make sense of this heterogeneity in OSA, several studies have defined various phenotypes (e.g., Ayas et al., 2015; Jennum & Riha, 2009; Mukherjee et al., 2018; Pien et al., 2018; Subramani et al., 2017; Ye et al., 2014; Zinchuk et al., 2017; Zinchuk et al., 2018). However, the identification of a set of phenotypes useful in studies of cerebral perfusion, cognition, and microsleeps have yet to be elucidated with the majority of OSA-related phenotypes used in clinical settings to target therapeutic interventions.

One definition of phenotype is: “A category of patients with OSA distinguished from others by a single or combination of disease features, in relation to clinically meaningful attributes (symptoms, response to therapy, health outcomes, quality of life)” (Zinchuk et al., 2017, p. 3). However, that definition is primarily written for the clinician. To that end, the definition by Subramani et al. (2017) is more appropriate for research and clinical settings. Subramani et al. defined (p. 3) a phenotype “…is an observable expression of an individual's characteristics that result from the interaction between the individual's genes (genotype) and the environment, without any implication of a mechanism.”

This study is interested in six primary measures: (1–3) the impact of moderate OSA on cerebral perfusion, cognition, and microsleeps, and (4–6) the impact of six-months CPAP treatment on cerebral perfusion, cognition, and microsleeps. The question asked is if these constructs can be explained by a phenotypic approach.

A systemic review of OSA-related phenotypes was conducted by Ramos et al. (2017) who found that approximately half of the articles (8 out of 17) used clinical symptoms to identify a phenotype, reflecting the high use of phenotypes to inform individualized treatments. The next largest selection were articles covering experimental or data driven phenotypes (5 out of 17), while molecular-genetic studies made up the balance (4 out of 17). Of particular interest to this study is the experimental-analytical approach. For example, the Icelandic Sleep Apnea Cohort using cluster analysis (n = 822) with an AHI > 15 identified and described three different phenotypic groups that did not differ in age, sex, BMI, AHI, or oxygen desaturation. One cluster had predominantly insomnia symptoms, cluster two had minimal symptoms compared to the other clusters, while cluster three was characterized by excessive daytime sleepiness (Ye et al., 2014).

11 Schmickl et al. (2018) identified four underlying mechanisms contributing to OSA, the relative contribution of which they say help form an individual’s phenotype. These are 1) a compromised upper airway anatomy, 2) a poor pharyngeal muscle responsiveness, 3) respiratory control instability (high loop gain), and 4) a low arousal threshold (wake up too easily. (See also, Malhotra & Jordan, 2016).

Zinchuk et al. (2018) used polysomnogenic data to identify physiological phenotypes. Using sleep architecture disturbance, autonomic dysregulation, breathing disturbances and hypoxia measures, they identified seven patient clusters that can be used to classify patients into subgroups reflecting different pathophysiological processes (see Table 2-1)

Table 2-1 Descriptions and labels for the seven polysomnographic clusters based on distinguishing features.

Median Cluster label AHI* (events/hour) Conventional OSA severity*

Mild 4 None/mild

PLMS 10

NREM and poor sleep 19 Moderate

REM and hypoxia 19

Hypopnoea and hypoxia 44 Severe

Arousal and poor sleep 68

Combined severe 84

OSA severity definitions: none/mild (AHI <15), moderate (15≤ AHI<30) and severe (AHI ≥30). AHI was not used in generating patient clusters. Median AHIs and severity categories based on median AHI for each cluster are shown for descriptive purposes only (mean AHIs were 7.5, 13.6, 24.0, 25.0, 47.6, 72.6 and 82.4 for clusters A, B, C, D, E, F, and G, respectively). AHI, apnoea-hypopnoea index; NREM, non-rapid eye movement; OSA, obstructive sleep apnoea; PLMS, periodic limb movements of sleep; REM, rapid eye movement (Zinchuk et al., 2018).

All these approaches suggest different ways of formulating and identifying phenotypes. Although most phenotypic studies are used to inform clinical interventions, there is considerable utility in using a phenotypic approach to understand the variation in individual measures of cerebral perfusion, cognition and microsleeps.

12 2.5 Cerebrovascular pathophysiology

The brain’s demand for 15–20% of total cardiac output, despite only contributing approximately two percent of body weight, makes it one of the most highly perfused organs in the body (Bor-Seng-Shu et al., 2012; Cipolla, 2009; Durgan & Bryan, 2012; Jafari, 2018). Moreover, because most of the brain’s energy is obtained from aerobic metabolic processes (Bor-Seng-Shu et al., 2012), this renders the brain susceptible to disruptions in the supply of nutrients and oxygen. As a result, even relatively short disruptions in supply can have significant pathological consequences (Durgan & Bryan, 2012). The brain remains metabolically active during sleep, so maintaining a tight control over blood flow and oxygen delivery, during both sleep and wakefulness is critical (Cipolla, 2009; Klingelhofer, 2012). Moreover, the brain is enclosed in a rigid vault (i.e., the skull) that does not allow for the expansion of brain tissue or extracellular fluids, outside of very narrow limits (Rodríguez-Boto et al., 2015). Accordingly, the need to maintain intracranial pressure within tight limits, yet still provide an appropriate ionic environment for the exchange of water and solutes between blood and brain parenchyma, places special demands on cerebrovascular physiology and on cerebral perfusion homeostasis.

2.5.1 Cerebral perfusion homeostasis

In healthy adults under normal physiological conditions, cerebral perfusion pressure is maintained between 50 and 150 mmHg (Armstead, 2016). Cerebral perfusion pressure is the net pressure gradient that drives oxygen delivery to cerebral tissue (Mount & Das, 2020) and is the difference between mean arterial pressure and venous pressure. However, because venous pressure is directly influenced by intracranial pressure, cerebral perfusion pressure can be considered as the difference in mean arterial pressure and intracranial pressure (Mount & Das, 2020). Intracranial pressure is directly related to intracranial volume with any increase in volume reflected in an increase in pressure (Figure 2-2 next page) (Pinto et al., 2020). As previously mentioned, the cranial vault is a fixed and rigid anatomical structure and, accordingly, its ability to accommodate increases in intracranial volume without excessive increases in intracranial pressure is determined by intracranial compliance (Rodríguez-Boto et al., 2015).

2.5.2 Intracranial compliance and cerebral autoregulation

Intracranial compliance, along with cerebral autoregulation, are key determinants in meeting the special demands of cerebrovascular physiology. Intracranial compliance represents a change in volume per unit change in intracranial pressure and is explained by the Monro-Kellie hypothesis which states that the sum of brain parenchyma, cerebral spinal fluid and cerebral blood volumes is constant and that any increase in volume of any one of the three constituents, must be compensated for by a decrease in one or two of the other constituents (Mokri, 2001). This makes it relevant to OSA pathology, which is

13 known to cause intermittent increases in intracranial pressure because of the associated hypercapnoea, hypoxia, and cerebral vasodilation (Thurtell et al., 2013).

Figure 2-2 The relationship between intracranial pressure and volume. ICP: intracranial pressure. (Nag et al., 2019)

Three stages contribute to intracranial compliance in a process that Tameem and Krovvidi (2013) labelled as volume buffering. In the first stage, an increase in cerebral volume results in cerebral spinal fluid moving into the spinal subarachnoid space, sometimes referred to as the lumbar cistern; this is the principal buffer (Rodríguez-Boto et al., 2015). Secondly, and to a lesser extent, changes to cerebral blood volume can quickly be modified by changes to cerebral venous volume, (Tameem & Krovvidi, 2013). Finally, in the third stage, brain parenchyma can reduce, albeit much more slowly, to accommodate chronic increases in brain volume (Rodríguez-Boto et al., 2015).

In addition to intracranial compliance processes, cerebral autoregulation also plays a key role in CBF homeostasis. Cerebral autoregulation is the ability of the cerebral vasculature to maintain a stable blood flow despite changes in cerebral perfusion pressure, which under normal circumstances, would involve changes in arteriolar diameter, that would drive changes in cerebrovascular resistance (Figure 2-3) (Silverman & Petersen, 2019). In normotensive adults, cerebral perfusion is maintained at approximately 50 mL per 100 g of brain tissue per minute provided cerebral perfusion pressure is between approximately 50 to 160 mmHg (Armstead, 2016; Lassen, 1985). Outside of those limits, autoregulation is lost and cerebral perfusion becomes linearly dependent on mean arterial pressure (Cipolla, 2009). To some extent, reductions in cerebral perfusion resulting from cerebral perfusion pressure falling below the lower limits of autoregulation are compensated for by an increase in oxygen extraction. However, if cerebral perfusion falls too low, hypoperfusion occurs which may eventually result in irreversible tissue damage (Cipolla, 2009).

14

Figure 2-3 Schematic drawing showing the relationship between cerebral perfusion pressure and CBF and the influence of the cerebrovascular autoregulation process.

The impact of OSA on cerebral autoregulation has been the subject of research (e.g., Beaudin et al., 2017; Urbano et al., 2008; Waltz et al., 2016). Urbano et al. (2008) used transcranial Doppler to measure CBF velocities in 28 patients with severe OSA (mean AHI = 78.4 ± 7.1) and 26 controls (Mean AHI = 1.8 ± 0.3) and found that those with OSA had a significantly slower rate of recovery of blood pressure, CBF velocity and cerebrovascular conductance than the control group, indicating the compensatory response to hypoperfusion was impaired. They also found that although OSA patients had reduced CBF velocities when compared to controls (48 ± 3 cm/s and 55 ± 2 cm/s respectively) following an orthostatic task, there was no difference between the two cohorts in their vasodilation response to hypocapnia which was normal.

Beaudin et al. (2017) reviewed current literature on whether OSA had a hermetic effect on vascular health because of OSA’s associated chronic intermittent hypoxia and concluded that patients with severe OSA were at higher risk of vascular disease, but that the data was equivocal for older OSA patients and those with mild OSA. This they attribute to the protection given by advanced age, and short-duration, low-intensity intermittent hypoxia against myocardial infarction and stroke respectively, albeit they concede that additional research is still required to confirm this premise. The second study by Waltz et al. (2016) investigated whether impaired cerebral autoregulation in OSA is further exacerbated by isocapnic-hypoxia and whether this impairment can be improved by CPAP treatment. Their conclusion was that cerebral autoregulation is altered in severe OSA patients during normoxia but not during isocapnic-hypoxia, and that CPAP treatment had no impact on cerebral autoregulation.

15 Table 2-2. Summary of the four cerebral autoregulation mechanisms

Cerebral autoregulation mechanisms

The endothelial response is based on shear stress influencing the endothelium lining to release both vasoconstrictors, e.g., nitric oxide, endothelium derived hyperpolarizing factor, prostacyclin, and prostaglandin, and vasodilators, e.g., E2 endothelial-1, thromboxane A2, and prostaglandin F2. These molecules may act directly on smooth muscles cells (Cipolla, 2009; Silverman & Petersen, 2019; Zeiler et al., 2020).

The myogenic response is a factor of muscular stretch and is an intrinsic property of smooth muscles in both large and small arterioles to constrict in response to increased pressure and dilate in response to decreased pressure. It is understood that through the myogenic tone, transmural pressure influences arterial diameter by acting through smooth muscle contraction or relaxation (Cipolla, 2009; Durgan & Bryan, 2012; Silverman & Petersen, 2019).

The neurogenic mechanism involves direct neural input through the release of various neurotransmitters, such as cholinergic, serotonergic, dopaminergic, and adrenergic/noradrenergic, which have vasoactive properties. For example, acetylcholine and nitric acid are potent dilators while serotonin and neuropeptide Y, stimulate vasoconstriction (Silverman & Petersen, 2019). Moreover, astrocytes and microglia are considered to have vasoactive properties (Attwell et al., 2010; Howarth, 2014; Marina et al., 2020; Silverman & Petersen, 2019).

The metabolic response occurs in smaller vessels that, according to (Silverman & Petersen, 2019), are subject to changes in the local environment and not necessarily be directly influenced by cerebral perfusion pressure. Silverman and Peterson note as an example, that increased concentrations of cerebral carbon dioxide, such as occurs during hypoperfusion, can accumulate causing vasodilation with as little as a 1 mmHg increase in the partial pressure of carbon dioxide resulting in approximate a 4% increase in CBF (Silverman & Petersen, 2019), or as noted by Cipolla (2009) a 5% increase in carbon dioxide inhalation can result in a CBF increase of 50% whilst a 7% increase in carbon dioxide inhalation increases CBF by 100%.

The common link between these three studies is severity level of OSA. However, as acknowledged by Beaudin et al. (2017), despite findings from both animal and human experimental models establishing a detrimental irreversible impact of intermittent hypoxia upon vascular regulation, the link between the uration and frequency of the intermittent hypoxic dose and vascular changes is unknown.

Although research has shed some light on the underpinning mechanisms of cerebral autoregulation, the exact molecular means underlying autoregulation remain elusive (Silverman & Petersen, 2019).

16 Nonetheless, various processes, including endothelial, myogenic, neurogenic, and metabolic responses, have been implicated, albeit the interplay and relative contribution of each of these mechanisms are overly complex and not fully understood (Armstead, 2016; Cipolla, 2009; Silverman & Petersen, 2019). These four mechanisms are briefly described in Table 2-1.

2.5.3 The vasodilatory response to moderate OSA

Under healthy physiological conditions, cerebral autoregulation mechanisms result in marked vasodilation of cerebral arteries and arterioles in response to increased concentrations of carbon dioxide (Silverman & Petersen, 2019) or decreased levels of oxygen (Cipolla, 2009; Johnston et al., 2003). For example, findings have shown that in humans a 5% carbon dioxide inhalation caused cerebral perfusion rates to increase by as much as 50%, and a 7% carbon dioxide inhalation resulted in blood flow increasing by as much as 100% (Cipolla, 2009; Kety & Schmidt, 1948). Hypocapnoea on the other hand has been reported to reduce concentrations of vascular carbon dioxide with a resultant vasoconstriction and reduced cerebral perfusion (Cipolla, 2009). Similarly, acute hypoxia can lead to the dilation of cerebral blood vessels and a concurrent increase in blood flow by as much as 400% (Johnston et al., 2003) via activation of adenosine triphosphate-sensitive K+ channels, increased nitric oxide and adenosine production and changes in capillary density (Cipolla, 2009; Golanov & Reis, 1999; Taguchi et al., 1994).

This vasodilatory and subsequent increase in cerebral perfusion response to hypercapnoea/hypoxia, has also been reported in people with OSA. Balfors and Franklin (1994) reported as early as 1994 that both CBF velocity and mean arterial pressure increased after an apnoea/hypopnoea event when compared to pre-apnoea levels. They tested six patients with OSA using transcranial Doppler ultrasound and found that during an apnoeic/hypopnoeic event there was a concomitant increase of mean arterial pressure and CBF velocity of 11 ± 6% (p < .001) and 15 ± 6% (p < .001) over baseline, respectively. However, immediately after termination of the apnoea/hypopnoea, mean arterial pressure and CBF velocity decreased to a minimum of -8 ± 2% (p < .001) and -23 ± 8% (p < .001), respectively, returning to normal values within 60s, except during repetitive apnoeas, which were associated with prolonged periods of reduced mean arterial pressures and CBF velocities. Also, Durgan and Bryan (2012), reported that CBF velocity in the middle cerebral artery increased by as much as 200% during an OSA episode before recovering to normal levels on termination of the apnoea/hypopnoea event. Coincidentally, they also found that arterial blood pressure increased during the apnoea/hypopnoea before falling below baseline after the apnoea event had terminated (Figure 2-4).

However, there is evidence that the normal vasodilatory response to increased changes in blood levels of carbon dioxide or decreased levels of oxygen can breakdown, resulting in vasoconstriction and reduced cerebral perfusion (Gregori-Pla et al., 2018; Harper et al., 1972; Reichmuth et al., 2009; Silverman & Petersen, 2019; ter Laan et al., 2013). For example, Durgan and Bryan (2012) reported

17 that two weeks of intermittent hypoxia was sufficient to completely abolish the normal vasodilator response. They also reported that cerebral perfusion in patients with OSA decreased during both sleep and wakefulness compared to people without OSA and that these decreases occurred even though end- tidal carbon dioxide levels were significantly higher in those with OSA during both wakefulness and sleep, which is contrary to the normal cerebral autoregulation response (Meyer et al., 1987; Meyer et al., 1980). Netzer et al. (1998), found from their transcranial Dopler study that a reduction in CBF occurred in the middle cerebral artery in 76% (169/223) of observed obstructive hypopnoeas and in 80% (98/123) of obstructive apnoeas.

Figure 2-4 Changes in blood flow velocity in the middle cerebral artery (CBFV; top) and mean arterial blood pressure (MABP; bottom) during an episode of apnoea (shaded in light blue) under healthy physiology (Durgan & Bryan, 2012).

Macey (2015) asked the question, “Whether the resting CBF differences with respect to healthy people are pathological or reflective of normal cerebral autoregulation in the presence of abnormal physiology associated with OSA” (p.1345). He noted that Shiota et al. (2014) and (Meyer et al., 1987; Meyer et al., 1980) had reported that, when awake, the decrease in rCBF observed in patients with severe OSA was reversible when treated and that this was clear evidence that the hypoperfusion in patients with untreated OSA was not due to permanent damage to cerebral vessels. Another study to conclude that OSA-induced brain damage could be reversed was Maresky et al. (2019). They. found that CPAP

18 treatment improved brain microstructural integrity and perfusion and concluded that this is suggestive of repair and healing of OSA-induced brain damage.

While Macey was not able to reach a firm conclusion as to the cause of the improvement, Waltz et al. (2016) suggested that the altered cerebral autoregulation seen in more severe OSA may be the result of an overactive sympathetic system, caused by the repetitive episodes of apnoeas during sleep. Similarly, Ficker et al. (1997) attributed the altered rCBF, seen in the frontal lobe, to repetitive cortical arousals. Jafari (2018) noted that while the exact mechanism of cerebral autoregulation is unclear, it can be explained by either the metabolic or myogenic hypotheses. According to metabolic hypothesis, blood flow and tissue metabolism are tightly bound in such a way that any reduction in CBF results in accumulation of vasodilator factors in the tissue. However, according to the myogenic hypothesis, it is the intravascular pressure as a stimulus that causes the vasoconstriction. A further possible mechanism of autoregulation involves the release of vasoactive factors such as nitric acid (an endothelium-derived relaxing factor), prostacyclin, and endothelium-derived hyperpolarizing factors, and under pathophysiological conditions, endothelium can produce endothelin, a potent contracting factor (Faraci & Heistad, 1998).

According to Chen et al. (2017b), the change to the vasodilatory response may be due to aberrant local perfusion changes induced by frequent hypoxic episodes, blood pressure surges, cerebral autoregulation failure or endothelial changes because of epigenetic processes. This conclusion is in line with Maresky et al. (2019) who proposed that the reduced cerebral perfusion was the result of persistent state of oxygen deprivation damaging cerebral vasculature and loss of autoregulatory mechanisms. However, Reichmuth et al. (2009) attributed the change to increased sympathetic nerve activity as did Macey (2015). In a review of cerebral perfusion and sympathetic tone, M. ter Laan et al. (2013) concluded that the sympathetic nervous system had a significant role in regulating cerebral perfusion during severely challenging circumstances. This conclusion is consistent with a number of other studies that have linked OSA with sympathetic hyperactivity and inflammation of vascular beds (Daulatzai, 2015; Dematteis et al., 2008; Idiaquez et al., 2014; Iturriaga & Idiaquez, 2014; Kato et al., 2000; Somers et al., 1995; Winklewski & Frydrychowski, 2013; Zwillich, 1998), because of systemic hypertension (Daulatzai, 2015; Kato et al., 2000), and autonomic dysfunction (Somers et al., 1995; Zwillich, 1998), including endothelial dysfunction, hypoperfusion, glucose dyshomeostasis, inflammation, oxidative stress, and neurotoxicity (Daulatzai, 2015); all of which are consistent with the previously noted observations of Chen et al. (2017b) and Maresky et al. (2019). In addition, Narkiewicz and Somers (1997); (2003) concluded from their review that this sympathetic nerve response to hypoxia and hypercapnoea, carries over into the wakefulness period, albeit they were unable to explain why.

19 2.6 Impact of OSA on cerebral perfusion during wakefulness

2.6.1 OSA and Cerebrovascular homeostasis

A central question relevant to the current study is: Does OSA influence cerebral vascular homeostasis during the wakefulness period? There are two offshoots to this question based on global cerebral perfusion or regional cerebral perfusion. Global cerebral perfusion regulation is dominated by pressure and chemical drives, with the autonomic nervous system also playing a role through the extensive sympathetic enervation of the cerebral vasculature (Gulbenkian et al., 2001; Macey, 2015). Regional cerebral perfusion on the other hand, is closely linked to changes in neuronal activity (Macey, 2015).

Although the links between severe sleep apnoea and impaired regional cerebral perfusion during sleep are well established (Chen et al., 2017b), those links, whilst awake are not well understood and only a few studies have addressed this important question (Baril et al., 2020; Beaudin et al., 2019; Beaudin et al., 2017; Castello‐Branco et al., 2020; Innes et al., 2015; Ponsaing et al., 2018; Shiota et al., 2014). Moreover, even fewer studies have addressed the impact of OSA on global cerebral perfusion. A search of Embase using as key words “OSA and global cerebral perfusion”, “Obstructive Sleep Apnoea and global cerebral perfusion” and “Sleep apnea and global cerebral perfusion” revealed only two relevant entries (Jensen et al., 2018; Macey, 2015).

The study by Jensen et al. (2018) concluded that OSA does impact global cerebral perfusion during induced hypoxemia. They found that: (1) hypoxia increased global cerebral perfusion in controls, but not in OSA patients, and (2) CPAP treatment normalised global cerebral perfusion response to hypoxia similar to controls. The other entry by Macey (2015) is a commentary covering the drivers of global cerebral perfusion that he records as high sympathetic tone, reduced metabolic demand that he suggests dominate over elevated PaCO2.

But, with respect to regional cerebral perfusion, several researchers have explored the relationship between sleep apnoea and an impaired cerebral perfusion while awake. Meyer et al. (1987) used two processes – Xenon-133 inhalation method, and the stable Xenon computerized tomography – to demonstrate that patients with severe sleep apnoea had reduced cerebral perfusion in grey and white matter during both sleep and while awake. More recently, Joo et al. (2007) Baril et al. (2015); Innes et al. (2015), Nie et al. (2017), Shiota et al. (2014) Yadav et al. (2013) and Chen et al. (2017b) also found that OSA impacted regional cerebral perfusion during wakefulness. This despite the majority of people with OSA being able to breathe normally while awake and being capable of precise ventilatory control with little variation from the norm (Dempsey et al., 2010). Joo et al. in their single photon emission computerized tomography (SPECT) study of 27 patients with severe sleep apnoea, observed regional cerebral perfusion patterns were altered compared to the flow patterns of their non-apnoeic controls. Baril et al. (2015), also using SPECT imaging, detected decreased regional cerebral perfusion in the

20 grey matter of people with severe sleep apnoea but not in people with mild or moderate sleep apnoea (See also Shiota et al., 2014). Nie et al. (2017), likewise found regional cerebral perfusion was reduced in patients with severe sleep apnoea, but in their study, they used arterial spin labelling, as did studies by Innes and colleagues (2015), and Chen et al. (2017b). Innes et al. (2015) found regional cerebral perfusion was decreased in their cohort of participants with either moderate or severe sleep apnoea when compared to their controls as did Chen and colleagues. The cerebral regions where blood flow was reduced are tabulated in Table 2-2.

These results converge to the conclusion that regional cerebral perfusion whilst awake is decreased in people who have severe sleep apnoea. Nonetheless, the question regarding the impact of moderate OSA on regional cerebral perfusion remains open. The first study to definitively compare regional cerebral perfusion in people with moderate OSA was by Baril et al. (2015). In their SPECT study, their cohort of 14 patients with moderate OSA did not differ with respect to regional cerebral perfusion from their controls (n = 20). This they attribute to the AHI not being high enough to breach a threshold above which pathology would occur. Otherwise, the only other studies to include people with moderate OSA were those by Chen et al. (2017b); Yadav et al. (2013) and Innes et al. (2015). These three studies found regional cerebral perfusion was reduced in the apnoeic group when compared to the non-apnoeic group, but these results were in cohorts of both moderate and severe apnoeics, which obfuscates the effect of just moderate OSA on blood flow.

Interestingly, although the studies by Innes et al., Chen et al., and Yadav et al. all used arterial spin labelling and found regional cerebral perfusion changes in their moderate/severe cohorts, the brain regions where the changes were found, differed (See Table 2-2 and Figure 2-5). Nonetheless, a review of the regions where changes in cerebral perfusion did occur revealed that three of the seven studies reviewed, showed reduced cerebral perfusion in the parahippocampal gyrus. Joo et al. (2007) and Nie et al. (2017) both found reduced cerebral perfusion bilaterally, while Innes et al. (2015) found reduced cerebral perfusion unilaterally in the right hemisphere.

21 Table 2-3. Studies and their level of OSA severity, impact on cerebro homeostasis and cerebral regions where cerebral perfusion was reduced.

Study Scan OSA severity Affected cerebral regions type Joo et al. SPECT Severe Bilaterally in the parahippocampal gyri. (2007) Unilaterally in the left lingual gyrus. Yadav et al. ASL Moderate/severe Bilaterally near the corticospinal tracts, (2013) pontocerebellar fibres, and superior cerebellar peduncles. Unilaterally in the right medial lemniscus, right midbrain, right red nucleus, and left dorsal fornix/stria terminalis, left inferior cerebellar peduncle, and left tapetum. Shiota et al. SPECT Severe Bilaterally in the right middle and superior frontal (2014) gyri. Baril et al SPECT Moderate and In the severe cohort only: Bilaterally in the post- (2015) severe central gyri. Unilaterally in the left inferior parietal lobules (supra marginal gyrus), and right praecuneus. Mild and moderate OSA groups showed no differences in regional cerebral perfusion compared to controls. Innes et al ASL Moderate/severe Bilaterally in the cingulate gyri, paracingulate gyri, (2015) and in the subcallosal cortex. Unilaterally in the left frontal cortex, left putamen, right hippocampus, right parahippocampal gyrus, right temporal fusiform cortex, and right thalamus. Nie et al ASL Severe Bilaterally in the parahippocampal gyri. (2017) Unilaterally in the left cerebellum posterior lobe, left medial frontal gyrus and left temporal lobe. Chen et al. ASL Moderate/severe Right putamen/insula, right cerebellum, left thalamus, (2017b) right caudate, right medial, superior and inferior frontal gyrus, right superior temporal gyrus, right fusiform gyrus, left and right cingulate gyrus

ASL: Arterial spin labelling magnetic resonance imaging, BOLD: Blood oxygen level dependent functional magnetic resonance imaging, SPECT: Single photon emission computed tomography. (Baril et al., 2015; Innes et al., 2015; Nie et al., 2017; Shiota et al., 2014; Yadav et al., 2013).

22 In conclusion, there is evidence that sleep apnoea has an impact on cerebral perfusion, not only during hypercapnoea/hypoxia events, but also during the wakefulness period, thereby reducing perfusion. Although some studies consisting of both moderate and severe sleep apnoea, along with those consisting of severe apnoeics demonstrated reduced regional cerebral perfusion, the one study that measured regional cerebral perfusion in a cohort of Moderate OSA, did not. This raised questions of methodological weaknesses, first, although all studies reviewed found cerebral perfusion alterations attributable to sleep apnoea, the cause remains elusive (Cipolla, 2009; Durgan & Bryan, 2012; Urbano et al., 2008). Second, no common cerebral region impacted by reduced cerebral perfusion has been described. Even studies that used the same method of MRI arterial spin labelling, no common cerebral region was found.

2.7 Cerebral perfusion and cognition

Alterations in cerebral perfusion are acknowledged as important contributors to the cognitive dysfunction seen in some people with OSA (Jafari, 2018; Leeuwis et al., 2018; Prilipko et al., 2012; Sforza, 2012; Tarumi et al., 2015; Wolters et al., 2017). For example, Chen et al. (2017b), reported an association between intracranial regional perfusion changes, systemic inflammation and impaired cognitive functioning in patients with OSA (40 ± 8 years). Wolters et al. (2017) also found that hypoperfusion was associated with accelerated cognitive decline and an increased risk of dementia, an association they attributed to hypoxia in older adults (median age 61 years) without OSA. Leeuwis et al. (2018) tested memory, executive functioning attention and language, and found a positive correlation between cerebral perfusion and executive functioning, attention, and memory in older adults (mean age 71 years) also without OSA. Furthermore, Tarumi et al. (2015) in a study of non-apnoeic adults (65 ± 6 years).and Prilipko et al. (2014) in their study of patients (43 ± 8 years) with moderate and severe OSA, found that aspects of executive functioning were related to nocturnal hypoxemia.

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Baril et al. Chen et al. Innes et al. Joo et al. (2015) (2017b) (2015) (2007)

Shiota et al. Yadav et al. Nie et al. (2014) (2013) (2017)

Figure 2-5. Perfusion regions identified by the following colour coded studies:

More specifically, Joo et al. (2007) concluded that the memory, spatial learning, executive functioning and attention deficits, observed in people with OSA could be explained by altered regional cerebral perfusion patterns in the bilateral parahippocampal gyri, right lingual gyrus, pericentral gyrus, and cuneus. In addition, Chao et al. (2010) noted that reduced cerebral perfusion, particularly in the right precuneus, right inferior parietal cortex, right middle cingulum and right middle frontal cortex, was related to the progression of cognitive impairment in people without OSA. Alosco et al. (2013) in their study, not only noted that cerebral perfusion contributed to poorer cognitive function, particularly on tests of memory, and attention/executive function, they also found a specific association between cerebral perfusion of the frontal lobe and performance on a test of executive function. This is an observation supported by Sforza (2012) who concluded that the frontal lobe is more vulnerable to

24 apoptosis because of hypoxemia than to excessive daytime sleepiness and sleep fragmentation, conditions more like to result in attention deficits.

None of the aforementioned studies, however, demonstrated cause and effect, only an association between perfusion and cognition.

Several studies have addressed possible reasons for the association between cerebral perfusion and cognition. Daulatzai (2012) in discussing OSA and cognitive dysfunction noted that impaired oxygen delivery could result in altered neuronal homeostasis, induced pathology, and neuronal degeneration and death. Gozal et al. (2001) found that two days of intermittent hypoxia was sufficient to impair spatial memory in rats. Emamian et al. (2016) following their meta-analysis concluded that progressive changes in sleep quality and structure, changes in blood flow, changes in neurovascular and neurotransmitter cellular redox status and changes in neural regulation are contributing factors. Other studies have attributed the pathogenesis of cognitive impairment to endothelial dysfunction (Hughes et al., 2013; Kelleher & Soiza, 2013; Vendemiale et al., 2013). Yet Obasi et al. (2012) found no association between inflammation and endothelial dysfunction with cognition.

2.7.1 The neuropsychological sequelae of obstructive sleep apnoea

There is little doubt that severe OSA has a negative impact on cognition (Aaronson et al., 2014; Aloia & Arnedt, 2012; Beebe et al., 2003; Bucks et al., 2013; Chen et al., 2011; Idiaquez et al., 2014; Kylstra et al., 2013; Wen et al., 2012), with attention, vigilance, memory, and executive functioning, along with quality-of-life measures being affected (Sforza, 2012; Vaessen et al., 2015). Yet, despite a plethora of quantitative empirical studies, including meta-reviews that have attempted to collate and summarize the neurobehavioural consequences of adult OSA, it remains difficult to determine the exact aetiology of the syndrome because of data heterogeneity. While in part this may be reflective of the inherent nature of OSA, methodological issues are also a significant factor behind this. For example, the various studies investigating the neurobehaviour of those with OSA have often focused on different cognitive domains that are frequently not uniformly defined or have used a variety of different methodologies (Bucks et al., 2013; Tsai, 2010).

Nevertheless, phenomenological reports of cognitive dysfunction by people with OSA have in general been echoed in the empirical literature where it is widely accepted that severe OSA contributes to cognitive impairment (Bucks et al., 2013), mood disorders, and daytime sleepiness (Olaithe et al., 2015a). More specifically, reviews by Aloia et al. (2004); Beebe et al. (2003); Bucks et al. (2013); Fulda and Schulz (2003); Olaithe and Bucks (2013); (Sales et al., 2013); Wallace and Bucks (2013) collectively support the position that vigilance, executive functioning including working memory, and both delayed long-term verbal and visual memory are adversely impacted by OSA. Most studies also found that language and psychomotor capabilities were not impaired, although the data for global

25 cognitive functioning, immediate long-term verbal and visuospatial memory, short-term memory, and visuospatial construction were equivocal. This conclusion is summarized in Table 2-3, which collates a number of studies that have investigated the impact of OSA on cognition. This table is substantially based on the meta-analysis undertaken by Bucks et al. (2013) to which has been added two subsequent studies (Olaithe & Bucks, 2013; Sales et al., 2013), both considered to meet the original inclusion criteria of Bucks et al.

Nonetheless, while the conclusions reached above are reflective of most studies, the extant literature is still debating the presence and extent of those cognitive changes. In particular, questions remain about the contribution of cognitive reserve, co-morbidities, the individual roles of excessive daytime sleepiness versus hypoxaemia, the impact of OSA severity, and the effects of OSA treatment (Lim & Veasey, 2010; Lim & Dinges, 2010; Sforza, 2012; Yaffe et al., 2011).

26 Table 2-4. Effects of OSA on the domains of neurocognitive function.

OSA comparison studies (with norms or controls) Meta-analysis Systemic reviews Number of comparisons with Fulda and Beebe Wallace and Olaithe Aloia Sales et Saunamaki and deficit in OSA Domain Subdomain Modality Schultz et al. Bucks and al. Jehkonen Bucks Attention/Vigilance C+ C+ NA NA C+ C+ NA 4/4 Executive function C+ C+, NA C+ C+ C+ C+ 7/7 N+ Working C- C+, NA C+ NA C+ C+ 5/6 memory a N+ Global cognitive C+ C-, N+ NA NA C- NA NA 2/4 functioning Language C+ C-, N+ NA NA C- NA C- 2/5 Memory Short-term C- NA NA NA NA NA C+ 1/2 Immediate Verbal C- C-, N- C+, N+ NA NA C+ NA 3/6 long-term Visual C- C+, N- C-, N+ NA NA C- NA 2/6 memory Visuo- NA NA C+, N- NA NA NA NA 1/2 spatial Delayed Verbal C+ C-, N+ C+, N- NA C+ C+ NA 5/7 long-term Visual C+ C+, N- NA NA C+ C+ NA 4/5 memory Visuo- NA NA C-, N- NA NA NA NA 0/2 spatial Psychomotor function C- C+, N- NA NA C+ NA C- 2/5 Visuospatial/construction C- C+, N- NA NA C+ C- C+ 3/6 Note. NA indicates that this domain was not assessed in the review. C+ indicates that on balance the authors considered that the OSA group was impaired when compared to controls. C- indicates no difference between OSA group and controls. N+ indicates that the OSA group was impaired when compared to normative data. Conversely N- indicates it was considered that no difference existed between OSA and normative data. a Although not reported separately by Bucks et al, working memory comparisons were able to be extracted from Beebe et al. (2003) and (Olaithe & Bucks, 2013). (Table and data substantially came from Bucks, R.S., Olaithe, M., and Eastwood, P. (2013).

27 2.8 The selection and use of neuropsychological tests

2.8.1 Neurocognitive testing

The results from neuropsychological tests administered to adult patients with OSA have demonstrated that inter-individual variability is complex and far from being straightforward (Aloia et al., 2004; Decary et al., 2000; Denotti et al., 2009; Sforza, 2012). For example, from the review by Aloia of 37 studies published between 1985 and 2002, impairment was found in only 3 of the 7 studies that tested global cognition, 6 of the 8 papers that tested attention and vigilance, 6 of the 9 that tested executive functioning, 7 of the 11 that tested memory, 4 of the 5 that tested construction and 4 of the 5 that tested psychomotor speed showed impairment.

These mixed results, which are in part credited to methodological factors (Beebe et al., 2003), limit our ability to coalesce the OSA literature into a common theory and to develop a clear understanding of its sequalae (Aloia et al., 2004). Those methodological concerns are addressed in the following sections.

2.8.2 A review of cognitive test batteries for OSA

A review of the literature revealed that there is no standardized test battery for the neuropsychological evaluation of OSA patients despite efforts otherwise (Bucks et al., 2013; Decary et al., 2000), and hence each selection must be rationalised. There is also an inconsistent use of cognitive tests (Bardwell et al., 2001; Bucks et al., 2013; Tsai, 2010), with different tests used to test the same cognitive function. Moreover, different researchers use the same tests to test different cognitive functions. This makes comparison of test results between studies difficult. This limits the ability to cross reference studies.

The origin of the tests also brings into question their ecological validity that is, the generalisation of results to real life circumstances (Coolican, 2005). The ecological validity of objective measures of cognition is, according to Vaessen et al. (2015) notoriously low (see also, Burgess et al., 1998; Chaytor & Schmitter-Edgecombe, 2003). Furthermore, most tests that have been used in OSA studies were not constructed to assess the apparent subtle effects of OSA, placing further doubt on their validity. It has been assumed that the neuropsychological tests have sufficient sensitivity and specificity to identify the specific and discrete patterns of the neurobehaviour correlates of OSA (Antic et al., 2011; Quan et al., 2006). Moreover, objective cognitive functioning often correlates poorly with cognitive complaints and QOL measures (Daurat et al., 2010).

Neuropsychological tests are sensitive to cognitive reserves (Lezak et al., 2004; Olaithe et al., 2015a; Tsai, 2010), yet cognitive reserves are not consistently recorded. Cognitive reserve has been proposed as an important buffer of the impact of brain pathology on cognitive functioning (Reed et al., 2010). For example, in a case-controlled study of 46 patients with OSA and 36 normal controls who undertook the Raven’s progressive matrices intelligence test, high-intelligence OSA patients were no different to

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high-intelligence controls in tests of attention and alertness; whereas OSA patients of mid-range intelligence were impaired compared to mid-range-intelligence controls (Tsai, 2010). Finally, not all studies have accounted for test-retest reliability (Aloia et al., 2004), which can result in the inappropriate interpretation of some tests.

2.8.2.1 OSA severity criteria

Inconsistent use of OSA severity criteria was identified by Aloia et al. (2004) who commented that it was a critical factor when reviewing the effects of OSA on cognition. While OSA severity is mostly measured by the AHI, some studies have only counted apnoeas (Aloia et al., 2004) while others have used the respiratory disturbance index (RDI) which has been used interchangeably with the AHI (Weaver & George, 2011). This is problematic as the RDI also includes, in addition to apnoeas and hypopnoeas, other respiratory effort-related arousals (Tsara et al., 2009). Moreover, not all studies have used the generally accepted OSA severity scale of normal ≤ 5, mild 5–14, moderate 15–29, severe ≥ 30 (Verstraeten, 2007). Another confounding factor was that the 1999 Chicago criteria used to identify OSA was changed in 2007 and became slightly more restrictive. A further change to the criteria was made in 2012. Although the impact of those changes was small, they nonetheless weaken between- study comparisons. Lastly, improvements have been made to the sensitivity of polysomnography recording equipment in recent years, which impacts comparisons with earlier results (Heinzer et al., 2015).

It is also possible that the AHI may, by itself, be insufficient to identify clinically significant cases. Clear evidence of a relationship between the severity of OSA as indexed by the AHI and the severity of mood and cognitive functioning has yet to be established (Bucks et al., 2013). Bucks et al.’s conclusion converges with an observation by Saunamaki and Jehkonen (2007) that although cognitive impairment usually worsens with disease severity, the relationship is not linear.

2.8.2.2 Recruitment and method of controlling demographic factors

This was another area criticized by Aloia and colleagues who noted that a lack of control over demographic factors make it difficult for cross-over studies. Their concern is backed up by the inconsistent use of matched controls. Many studies used adjusted published norms while others used comparison groups and others used placebo groups (Saunamaki & Jehkonen, 2007) but, as noted by Tsai (2010), they are not necessarily comparable (Beebe et al., 2003).

Another problematic area is in the recruitment of comparison groups. In some studies, they were recruited from clinical referrals, whereas in other studies they were recruited from the general public. The results of cognitive tests from participants recruited from referrals are not necessarily consistent with those randomly selected from the wider public because of clinical factors, despite having the same severity rating based on AHI scores (Bardwell et al., 2001; Engleman & Douglas, 2004; Weaver &

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George, 2011). Many studies do not factor out comorbidities, such as for example, depression, anxiety, diabetes, and obesity. It is commonsensical when carrying out neuropsychological testing that a patient with comorbid depression, for example, should not be assumed to be comparable to a patient without depression, albeit that they could have the same AHI score.

Finally, it was noted by Beebe (2005) that the small sample sizes in many studies limits statistical power and risk Type II false negative errors being made.

2.8.2.3 Treatment interval and compliance

This was a further concern raised by Aloia et al. In their review, only 11 of 17 CPAP treatment studies measured compliance levels (see also Kylstra et al., 2013) and only four studies examined the impact of compliance on cognition. Intervention periods also varied between studies with a range of one week (Bardwell et al., 2001) to 12 months or more (Munoz et al., 2000). Although individual compliance varied widely in the later study (from 0.4 to 10.95 h per night) it does not appear that anyone was excluded from the study because of it. In the face of mounting evidence that treatment outcomes are correlated to compliance levels, it is important that compliance patterns are clearly enunciated and included in analysis (Aloia et al., 2004; Weaver & Grunstein, 2008).

Some of these methodological concerns are specific to OSA research, while others apply across most research (i.e., sample size, inappropriate use of tests, failure to account for co-founding factors such as comorbidities and cognitive reserves, poor demographic records, disparate metrics and measures, acting in ignorance of standards and norms, etc.). On the other hand, there are methodological issues that are more specific to OSA research, such as the lack of specific neuropsychological tests and overall acceptable test battery. In addition, there is the additional problem of having to place reliance on the AHI to measure OSA severity that by itself seems incapable of consistently separating clinical patient groups. Fortunately, at the individual study level, most of the concerns noted above are manageable using sound research practices.

2.8.3 Tests for the neuropsychological testing of obstructive sleep apnoeics

Building a test battery for the cognitive testing of OSA is an important project. Listed below is a review of the cognitive tests identified in the literature to assess the five cognitive domains associated with OSA. These five cognitive domains are:

2.8.3.1 Attention and concentration

Attention is a multifaceted domain involving concentration, divided attention, vigilance or sustained attention, and alertness with deficits in vigilance, attention, and psychomotor speed most consistently reported as impaired in OSA. Moreover, there appears to be a dose-response effect with higher AHI scores associated with worse cognitive outcomes. Nonetheless, although most studies of Moderate

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OSA patients find impaired performance on at least one measure of attention, studies examining focused and divided attention performance have yielded mixed results (Jackson et al., 2011a).

Typical tests of attention and concentration are:

• Psychomotor Vigilance Test (PVT) – The traditional version of the PVT takes 10 mins to run and is often used in sleep studies as a measure of sustained attention. It is reported as being sensitive to sleepiness and the effects of OSA and treatment (Wilde et al., 2007). The PVT provides two measures: errors of omission and errors of commission.

• Stroop Test – The Stroop test is a popular neuropsychological measure, particularly of concentration effectiveness. It is available in a number of formats although the Dodrill version is the one recommended by Lezak et al. (2004). Only two trials are needed for the colour-word interference to manifest; also because it is the longest version it is considered to be the most sensitive (Lezak et al., 2004). Test-retest reliability is reported as high for reaction times obtained during the card version (Strauss et al., 2005).

• Trail Making Test (TMT) – The TMT is widely used as an easily administered neuropsychological measure of scanning and visuomotor tracking, divided attention, and cognitive flexibility. The TMT is sensitive to a number of neurocognitive impairments and processes (Tombaugh, 2004). The test is administered in two parts, A and B. Part A is typically used as a measure of attention while Part B is used as a measure of executive functioning (Bauer et al., 2012). Test-retest reliability for Part A is estimated at 0.78 (Cangoz et al., 2009).

• Wechsler Adult Intelligence Scale Digit Span subtest (WAIS Digit Span) – The WAIS Digit Span is a popular neuropsychological measure reported in 17 out of the 21 studies reviewed. Although the WAIS Digit Span has a strong short-term memory component, Lezak et al. (2004) consider it to be more strongly associated with attention. There are two variants of the digit span: Digit Span forwards and Digit Span backwards. While both have an attentional component and both depend on short-term memory, each involves different mental activities and each is differently affect by brain damage (Lezak et al., 2004). Digit span is minimally impacted by age. Test-retest reliability coefficients range from .66 to .89 depending on interval length and age (Lezak et al., 2004).

2.8.3.2 Executive functioning

Executive functioning is also multifaceted and complex (Aloia et al., 2004). It includes: initiation, planning, purposive action, self-monitoring, self-regulation, and volition processes (Lezak et al., 2004). According to Saunamaki and Jehkonen (2007) (See also Engleman & Douglas, 2004), the most affected facets of executive functioning because of OSA are: working memory, phonological fluency, cognitive

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flexibility, and planning. Although Gagnon et al. (2014) suggests that all sub-domains of executive functioning are impaired, namely: inhibition, shifting, working memory, generating new information, and fluid reasoning, and problem solving

Tests typically used to test executive functioning in OSA studies (Beebe & Gozal, 2002; Decary et al., 2000; Gagnon et al., 2014; Saunamaki & Jehkonen, 2007) are:

• Mazes – Maze tracing is a task designed to test planning and foresight skills and is sensitive to planning deficits. It has also been reported as significantly correlating with physiological measures of frontal lobe dysfunction (Lezak et al., 2004). Although the Porteus version can take as long as 15 min to complete, the WISC-R and the WISC-III both provide shorter and more easily administered versions that for most clinical purposes , according to Lezak et al. (2004), are a practicable and satisfactory substitute.

• Raven Progressive Matrice – The Raven Progressive Matrice is a measure of reasoning skills. It has good test-retest reliability correlations in the range of .7 to .9. However, its usefulness as a screen for brain damage is limited. Can take up to 60 min to complete (Lezak et al., 2004).

• Stroop Colour Word Test – Although primarily a test of attention and concentration (Lezak et al., 2004), it has been used as a measure of executive functioning because of the response inhibition element to the test (Olaithe & Bucks, 2013). Furthermore, it is highly sensitive to frontal lobe lesions (Decary et al., 2000).

• Tower of Hanoi/Toronto/London – All variants of the Towers test principally measure planning but also rely on working memory, response inhibition, and visuospatial memory for ideal performance (Lezak et al., 2004; Olaithe & Bucks, 2013). Although very similar, the different tests do not measure exactly the same skills and the correlation between performance on the Tower of Hanoi and the Tower of London is low (r = .37) (Lezak et al., 2004) Tower tests appear relatively robust to age and education differences up to 79 years. However, participants older than 79 years are more impaired than those in their twenties and thirties.

• Trail making Test B and B-A – The TMT Part B is used as a neuropsychological measure of executive functioning, particularly complex attention, planning, cognitive set shifting and response inhibition (Cangoz et al., 2009; Olaithe & Bucks, 2013). It has a test-retest reliability estimated to be .67.

• Verbal Fluency – Tests of verbal fluency measure organizational ability and in particular strategic thinking. Age (over 70), education, and gender can influence performance. Test-retest reliability over an interval of 1 year for elderly persons was moderate to high (r = .7 to .71) for letters other than A which was lower (Lezak et al., 2004).

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• Wechsler Adult Intelligence Scale Digit Span backwards subtest – This measure of neuropsychological functioning is a simple measure of mental tracking. Mental tracking involves perception, and complex mental operations including scanning. The Digits Backwards is reasonably robust to age. It is sensitive to different brain disorders, but particularly, those originating in the left hemisphere due to frontal lobe damage (Lezak et al., 2004)

• Wisconsin Card Sorting Task– Designed to measure abstract behaviour and shift of set, this neuropsychological measure has gained widespread popularity as a measure of frontal lobe functioning. Nonetheless, Lezak cautions against using it in longitudinal studies because once solved, it becomes a measure of long-term memory (Lezak et al., 2004). Furthermore, the WCST has been measured against the TMT Part B and found to be a less sensitive measure of cognitive flexibility and ability to maintain set (Bauer et al., 2012)

2.8.3.3 Memory

Results are mixed for the impact of OSA on verbal memory. While Beebe and Gozal (2002) found no significant effect of OSA, this was in contrast to Wallace and Bucks (2013) who found that the ability to recall verbal information immediately after presentation is impaired in individuals with OSA compared to both controls without OSA and cognitive normative data. Wallace and Bucks (2013) suggested that this may be due to methodological reasons involving the separation of verbal memory from short term memory, verbal learning, and cued recall, and because of the larger number of studies included in their review.

Salorio et al. (2002) evaluated 28 adults with OSA and 24 controls aged from 28 to 60 year using the California verbal learning test. The results indicate that the controls recalled more words than those with OSA during the first five trials indicating better word retention. However, delayed recall and recognition trials remained intact. This suggests that encoding rather than maintenance deficits are associated with OSA. The importance of differentiating between recall and recognition was highlighted by Decary et al. (2000) who observed that recognition can be preserved in patients with frontal lobe dysfunction.

Typical tests of verbal memory and learning include:

• Rey Auditory-Verbal Learning Test (AVLT) – This test is sensitive to the effects of age over 70 and gender. However, it has good test-retest reliability with correlations ranging from .61 to .86 during a one-month interval. It has proven useful in delineating memory system deficits in a number of disorders including differentiating between recall and recognition (Lezak et al., 2004).

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• California Verbal Learning Test (CVLT) – This test procedure is like the AVLT and is sensitive to age and gender. It also has after a 21-day interval a good test-retest reliability coefficient of .82 for Total Trials albeit that Total Learning Slope and Total Repetitions were considerably lower (27 and 30 respectively). It can differentiate between recall and recognition.

• Hopkins Verbal Learning Test Revised (HVLT-R) – This is a shorter version of the CVLT and is similarly sensitive to age and gender. It is easy to administer and score and is well tolerated (Lezak et al., 2004). Its test-retest reliability is like CVLT with low coefficients for some facets of the test. Nonetheless, this is still an ideal test for longitudinal studies as there are six alternative interchangeable forms available (Benedict et al., 1998). However, Benedict et al. provide a note of caution; the HVLT-R may lack sufficient difficulty to discriminate young impaired adults from healthy adults. Although the original HVLT lacked a delayed recall trial, the revised version proposed by Benedict et al. does.

2.8.3.4 Visual Memory and Learning

The impact of OSA on visual memory and learning are mixed. Bucks et al. (2013) reported that the impact of OSA on short-term visual memory is equivocal, with one study finding impairment but the other not. The results for long-term visual memory were also equivocal. Four reviews found OSA impacted delayed visual long-term memory, but none found OSA had impacted immediate visual long- term memory. Nonetheless, 15 of 22 studies in this review measured visual memory and learning capability.

Tests typically used to measure visual memory and learning are:

• The Rey Osterreith Complex figure (Rey-O) – This popular test is sensitive to mild neuropsychological impairment in a variety of clinical populations. Participants are first asked to copy the figure and then, 3 or 30 mins later, they are asked without warning to recall as many elements of the figure as they can. Although an early age effect was evident for the 30-minute delayed recall, the age effect in the three- min delay recall scores did not become pronounced until the participants were in their mid-sixties. Gender differences appear not to exist (Lezak et al., 2004). Decary et al. (2000) reported that the Rey-O has a moderate test-retest reliability coefficient of 0.6. Unfortunately, the test-retest interval was not reported. Small (.21) to moderately large (0.7) effect sizes were obtained. In a small study of 35 TBI patients (Wilde et al., 2007), a small effect (0.21) was obtained in the copy sub-test while a greater effect of (- 0.70) was found for the 30-min recall sub-test. These results converge with those of Sales et al. (2013) who found differences between OSA participants and controls for immediate and delayed recall, but not for copy.

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• Corsi Block-tapping Test – This is a neuropsychological test of visual memory. The test consists of nine black blocks fixed randomly to a board. The participant must copy the order in which the examiner taps the blocks. Age effects are not apparent until after 60 when they become increasingly pronounced. Gender differences appear to slightly favour men over women. Moreover, education contributes to performance (Lezak et al., 2004). Castronovo et al. (2014) found differences between OSA and controls at baseline and again at 3-months and 1 year.

2.9 Quality-of-life measures and OSA

OSA has been found to reduce quality-of-life, marital harmony, work performance, health expenditure, and reduced participation in everyday activities (Engleman & Douglas, 2004; Vaessen et al., 2015), even when age and weight have been taken into account (Weaver & George, 2011). OSA has also been found to contribute to a reduction in physical activity, which in turn, influences subjective ratings of impaired sleep, energy, and emotional lability (Verwimp et al., 2013). Moreover, Dutt et al. (2013) assessed quality-of-life using the Sleep Apnoea Quality-of-life Index with 69 apnoeic people and 41 healthy controls and found the apnoeic group had worse experiences across all four Sleep Apnoea Quality-of-life Index domains – daily function, social interaction, emotion and symptoms. Vaessen et al. (2015) undertook a systematic review of subjective measures of cognition and found concentration was commonly impaired as they suspected were memory and executive functioning, but a lack of a validated cognitive complaints questionnaire prevented firm conclusions being reached.

Vaessen et al. (2015) also noted that the more people with OSA complain about sleepiness, the more likely they are to also complain about problems with attention, memory, and executive dysfunction (see also Tsai, 2010). This conclusion aligns with a review by Weaver and George (2011) who reported that approximately 80% of people with OSA complain of both excessive daytime sleepiness and cognitive impairments and 50% have reported personality changes.

Although the link between OSA and quality-of-life measures appears strong, the link between the severity of subjective cognitive complaints and degree of cognitive functioning, measured objectively, may at best be weak (Barbagelata-Aguero & Vizcarra-Escobar, 2013; Dutt et al., 2013; Iacono et al., 2013; Vaessen et al., 2015; Verwimp et al., 2013; Weaver & George, 2011). For example, Chen et al. (2012) found no significant correlation between objective severity measures of attention, memory, and executive function with subjective severity ratings of attention, vigilance, memory, learning, abstract thinking, problem solving, emotional control, and motivation. This led them to conclude that people with OSA may not fully appreciate the degree to which they are cognitively impaired, especially that which relates to executive functioning. This conclusion converges with those of Barbagelata-Aguero and Vizcarra-Escobar (2013) Dutt et al. (2013) and Iacono et al. (2013) who all independently noted

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that a reduced health related Quality-of-life perception was evident across all ages in those with OSA, particularly in the older population, and that it did not necessarily correlate with OSA severity. Misperception of impairment is also supported by an observation of Vaessen et al. (2015) that cognitive complaints are likely to persist after successful CPAP treatment. Taken together, these findings suggest that the complaints are more an indication of psychological distress and do not necessarily reflect clinical symptoms.

Not surprisingly, subjective cognitive complaints have been shown to influence treatment compliance (Olsen et al., 2008; Wells et al., 2007) and have been linked to health services expenditure (Greenberg- Dotan et al., 2007). Importantly, quality-of-life measures have been shown to be sensitive to the effects of CPAP treatment. Avlonitou et al. (2012) measured the quality-of-life status of 50 OSA patients (9 women and 41 men) before and after six months of CPAP treatment using the Sleep Apnoea Quality- of-life Index. They were initially diagnosed using overnight polysomnography as being mildly, moderately, or severely apnoeic, based on conventional criteria. Compliance was monitored electronically and over the six months, CPAP mean usage was 4.5±0.5 h per night. Sleepiness was measured using the Epworth Sleepiness Scale, which showed a decrease over the six months. Furthermore, a comparison between quality-of-life indexes before and after CPAP treatment showed improvements in Sleep Apnoea Quality-of-life Index scores across all four domains – daily functioning, social interactions, emotional functioning and symptoms – regardless of severity and gender. Similarly, an earlier study, by Smith and Shneerson (1995), used the SF-36 to measure quality-of-life pre and post six months CPAP treatment and found it to be sensitive to the effects of sleep disruption in people with OSA and a useful measure of treatment outcome.

Overall, using quality-of-life instruments to profile the experiences of people with OSA does provide a useful patient perspective that is mostly reflective of objectively measured symptomology. There is an important exception however, while cognitive complaints may acknowledge the existence of cognitive dysfunction, they do not necessarily align with performance levels. Quality-of-life measures have been shown to influence health outcomes and to be sensitive to treatment effects. Thus, despite quality-of- life measures being exposed to criticism for reporting on what seems to be the matter, rather than what is, they can be an important and useful measure of patient experience.

Quality-of-life, Health-related Quality-of-life, and Functional Status Questionnaires are oft-used instruments in the assessment of OSA, yet all three focus differently. Whereas Quality-of-life is a measure of everyday living, incorporating elements of material and physical wellbeing; relationships with other people; social, community, and civic activities; personal development and fulfilment; and recreation (Burckhardt & Anderson, 2003), Health-related Quality-of-life is illness centric, with a focus on the domains affected by the illness. On the other hand, the Functional Status Questionnaire characterizes a person’s capacity to fulfil life’s current roles.

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According to (Weaver & George, 2011), these measures can be further categorized as either generic or disease specific. Disease -specific measures of OSA include the Functional Outcomes of Sleep Questionnaire (Weaver et al., 1997) and its shorter version (Chasens et al., 2009), Calgary Sleep Apnoea Quality-of-life Index (Flemons & Reimer, 1998), and OSA Patient-Oriented Severity Index. However, as instruments incorporating disease-specific measures have only recently been constructed, much research has used the Medical Outcomes Study Short Form 36 (e.g., Canessa et al., 2011) or the Nottingham Health Profile (Engleman & Douglas, 2004). The Medical Outcomes Study Short Form 36 measures: physical functioning, mental health, psychological distress, psychological wellbeing, role functioning, social functioning, health perceptions, pain, and vitality (Ware & Sherbourne, 1992) and therefore can be considered a Health-related Quality-of-life measure. Similarly, the Nottingham Health Profile also measures health factors – i.e. pain, energy, sleep, mobility, emotional reaction, and social isolation – and general living factors, which deal with problems regarding occupation, housework, social life, family life, sexual function, hobbies, and holidays (McDowell, 2006).

2.10 Microsleeps and OSA

The maintenance of alertness is important during many daily activities, sometimes being the difference between a safe conclusion to a task or not. Consequently, reducing or eliminating microsleeps during continuous real-life tasks, such as driving, may improve safety. Moreover, according to Poudel et al. (2018), sleep deprivation increases the propensity to microsleep. Therefore, understanding the impact of OSA on microsleep is important, yet a search of Embase found only one paper has addressed this important question (Morrone et al., 2020).

2.10.1 Behavioural Characteristics

Microsleeps have been variously described. Beck et al. (2010) used the term to refer to short periods of sleepiness or sleep between 15–30 s. Anderson et al. (2010) referred to any lapse in responsiveness with eyes closed as a microsleeps whereas a lapse with eyes open is either visual attention or distraction. Levitt (1967) referred to microsleeps as short periods of slow-wave sleep. Attempts have also been made to define microsleeps using strict EEG spectral changes regardless of other overt behavioural indications (Harrison & Horne, 1996; Lim & Dinges, 2008; Peiris et al., 2006). Harrison and Horne proposed that microsleeps are indicated by spectral, topographical, and morphological changes, mainly by the predominance of EEG theta (4–7 Hz) activity. However, as noted by Boyle et al. (2008), there is a difficulty in using only EEG spectral power because muscle and movement artefacts can hinder EEG interpretation and because EEG interpretation is inherently subjective. Golz and Sommer (2008) noted that episodes of theta activity can occur without the necessary coincident lapse in responsiveness, which brings into question the validity of EEG spectral power change as being by itself a sufficient marker for a microsleep. Peiris and colleagues, attempted to use EEG spectral power, normalized

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spectral power and power ratios from the standard EEG bands to detect microsleeps but achieved only modest success. Finally, Sommer et al. (2009) proposed a two-stage methodology for defining microsleeps. The first stage involved the identification of overt behavioural indications of sleep, such as prolonged eyelid closures, slow rolling eye movements, head nodding, slow drifting head movements all of which are often followed by abrupt reactions. These episodes were then assessed using EEG, EOG, and eye tracking bio-signals. Overlaps were defined as microsleeps while non-overlaps were referred to as non-microsleeps.

The microsleeps of interest in this study are arousal-related lapses of responsiveness characterized by clear overt behavioural signs of either drowsiness, or sleep and a coincidental response disruption (Innes et al., 2013; Peiris et al., 2007). Unlike that proposed by Sommer et al. (2009) there is no requirement that specific EEG, EOG bio-signals be present, otherwise they have similar behavioural features. Accordingly, the behaviourally-defined microsleeps have been associated with widespread, increased bilateral activity in the frontal and parietal cortex, particularly the inferior frontal, pre-central, post- central, and superior parietal cortex during fMRI studies (Poudel et al., 2014) They have, by arbitrary definition, a duration of 0.5–15 s with lapses greater than 15 s being classified as sleep. The behavioural signs are full or at least 80 % eyelid closure accompanied by clear indications of drowsiness – which may include prolonged eyelid closure, slow rolling eye movements, head nodding and slumped posture, all of which are sometimes followed by abrupt reactions – coincident with flat responses, where the subject has stopped responding to the task at hand. These behavioural signs are based on the criteria used by Wierwille and Ellsworth (1994) to identify severe drowsiness.

Microsleeps are thought to be the result of low arousal and attention levels being unable to counter the homeostatic drive for sleep, possibly because of low levels of bottom-up exogenous stimulation (Matthews et al., 2002; Poudel et al., 2008). They were studied by Peiris et al. (2006) who during the early afternoon and under soporific conditions, tested the impact of a continuous, monotonous, visuomotor task on responsiveness. In a cohort of 15 healthy, male, non-sleep-deprived volunteers, they unexpectedly found frequent lapses in a substantial proportion of those who participated. Over two one-hour sessions, the average lapse rate – including tracking flat spots, video microsleeps (lapses evident by overt signs of drowsiness but no coincidental flat tracking response), and definite microsleeps – was 39.3 per hour (range 0–141.5 per hour). Notwithstanding, that these figures were observed in seated participants in a quiet room Poudel et al. (2014) also found that microsleeps were common in non-sleep deprived participants, but who were positioned supine in a confined and noisy environment of a fMRI machine. They tested 20 healthy young adults and found that the mean microsleep rate per hour was 79 per hour (range 0–190). Of the 20 participants in this later study, 14 had at least 36 microsleeps during the 50-min tracking task, two participants had 1 and 2 microsleeps respectively, while four participants had none. They attributed the relatively high number of

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microsleeps to several factors, which included the monotonous nature of the continuous visuomotor task, postprandial somnolence, and the supine position of the contestants.

2.10.2 Neurophysiology of Microsleeps

In their study, Poudel et al. (2014), combined both EEG and fMRI in a study that investigated microsleeps during a continuous task. The neural blood-oxygen-level-dependent (BOLD) activity decreased bilaterally in the midbrain, thalamus, posterior cingulate, and occipital cortex along with small areas in the right prefrontal cortex and cerebellum during microsleeps that, with the exception of the occipital cortex, have been associated with OSA (See Table 2-2). Conversely, activity increased strongly in the parietal regions encompassing the bilateral postcentral, superior parietal, and supramarginal cortices and increased less strongly in the bilateral inferior frontal, precentral, parahippocampal, insular, temporal, occipital, and fusiform cortices during microsleeps. Poudel and colleagues also investigated the time course of the BOLD signal in the thalamus and superior parietal cortex, the two structures that showed the greatest decrease and increase respectively during microsleeps. They found that thalamic BOLD activity started to decrease at or near the onset of microsleeps and mirrored the duration of the microsleeps with shorter microsleeps associated with a smaller amplitude decrease. Similarly, the size of the BOLD signal from the right superior parietal cortex also mirrored the duration of the microsleeps and again the shortest microsleeps were associated with the smallest amplitude change. Theta activity in the postcentral EEG correlated positively with the BOLD signal in the bilateral thalamus, basal forebrain, prefrontal, posterior cingulated, posterior parietal, and visual cortices (Poudel et al., 2014).

While sleep deprivation is not necessary for the expression of microsleeps (Innes et al., 2010; Peiris et al., 2006), sleep deprivation has been found to increase microsleeps propensity (Beck et al., 2010; Boyle et al., 2008; Moller et al., 2006; York, 2005). Innes et al. (2013) investigated intra-individual differences in microsleep counts when non-sleep deprived and when sleep deprived. They reported that the number of microsleeps during a 20-min monotonous task increased following sleep-restriction (mean 11.4 vs 27.9, p = .03). However, they found no evidence that the high rates of microsleeps observed in some of their non-sleep-deprived people were due to poor sleep quality, sleep disturbance, circadian type, irregular sleep patterns, low daily sleep duration, or poor sleep efficiency. Yet Morrone et al. (2020), using the presence of theta rhythms without electroencephalographic or electromyographic artifacts to identify microsleeps, found that episodes of microsleeps during the Maintenance of Wakefulness test were more common in participants with OSA, which leaves open the question, does OSA increase our propensity for microsleeping?

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2.11 Positive air pressure therapy for the treatment of OSA

2.11.1 Positive air pressure therapy overview

Positive air pressure (PAP) is the most common first-line treatment for OSA (Avlonitou et al., 2012; Bardwell et al., 2001; Buchanan & Grunstein, 2011; Durgan & Bryan, 2012; Maresky et al., 2019; Spicuzza et al., 2015; Waltz et al., 2016). PAP therapy creates an air-splint to maintain patency of the upper airway (nose, nasal cavity and pharynx) during inhalation. This air-splint is created by a positive

pressure of 0–20 cm H2O. This prevents the airway from collapsing during inspiration when negative pressure is transmitted from the lung to the airway. In other words, the increased luminal pressure counteracts the normal negative air pressure during the inhalation phase of the respiratory cycle.

Continuous positive airway pressure (CPAP), a popular form of PAP therapy has been found to (i ) prevent or reduce high blood pressure (Korcarz et al., 2016), (ii) attenuate OSA moderated arrhythmias, (iii) benefit heart failure patients, (iv) reduce cognitive impairment, (v) attenuate sympathetic drive, and (vi) improve default mode and task-positive network responses (Maresky et al., 2019; Prilipko et al., 2014). Moreover, according to Spicuzza et al. (2015), CPAP treatment reduces the number of apnoeic events to normal or near normal counts (i.e., AHI < 5), improves sleep parameters, and improves nocturnal oxygen saturation levels from the first night of treatment. However, the gains noted above for OSA patients have been found to be short-lived, with many symptoms recurring one to three days following CPAP disruption (see Table 2-4) (Spicuzza et al., 2015). Also, despite CPAP being regarded as a highly efficacious treatment for OSA its effectiveness is limited by poor adherence with many patients refusing the offer of CPAP therapy, or do not tolerate it, or fail to use the CPAP devices properly (Donovan et al., 2015; Ip et al., 2012). Adherence is important, because as pointed out by Donovan et al. (2015) the greater the CPAP usage, the greater the benefits. Adherence has been linked to sleep apnoea severity with higher compliance in those with severe sleep apnoea compared to moderate severity levels (Yetkin et al., 2008). Adherence has also been linked to higher daytime sleepiness (Singhal et al., 2016) and subjective sleepiness (Park et al., 2017).

Auto-titrating positive airway pressure (APAP) has been shown to improve compliance (Smith & Lasserson, 2009) by 13 minutes per night compared to CPAP (Kennedy et al., 2019). APAP differs from CPAP therapy by taking account of changes in airflow resistance when setting the air pressure, thereby accounting for changes in posture, and nasal congestion, whereas CPAP delivers a fixed pressure for the entire sleep period (Ip et al., 2012).

Good adherence with PAP therapy has been defined as the use of PAP for four or more hours in 70% of nights in any given month (Avlonitou et al., 2012; Billings et al., 2020; Golombeck et al., 2019). The longer PAP is tolerated the better the clinical outcome. Avlonitou et al. (2012), argue that PAP should ideally be used for 4.5 hours per night to obtain the full benefits. Similarly, Spicuzza et al.

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(2015) suggest that at least 5 hours usage per night is needed to improve quality-of-life for both patient and bed partner. However, Gaisl et al. (2020), point out, there is still substantial benefit to be gained from even suboptimal PAP adherence, particularly for patients with moderate or severe sleep apnoea.

Table 2-5. Common symptoms associated with OSA (from Spicuzza et al. (2015).

Diurnal Nocturnal Excessive sleepiness Snoring Morning headaches Witnessed apnoeas Depression/irritability Choking at night Memory loss Nocturia Decreased libido Insomnia

2.11.2 PAP and cerebral perfusion

An aim of the current study is to explore the impact APAP has on cerebral perfusion when awake. In a SPECT study, Shiota et al. (2014), found that patients with severe sleep apnoea (AHI 62.7 ± 22.4/h), who used CPAP for at least five hours a night for three or more months, showed a significant improvement in regional cerebral perfusion, particularly in the frontal regions whilst awake. Foster et al. (2007) described that 4 – 6 weeks of CPAP therapy was sufficient to normalize the cerebral perfusion response to hypoxia in people with severe OSA. In their study of eight male patients the cerebral perfusion response to a simulated hypoxic event was reduced by 42% compared to controls. Following CPAP therapy, the hypoxic response was restored to normal levels. They also demonstrated a dose- response relationship: the change in the cerebral perfusion to hypoxia after CPAP was greatest in those with the more severe sleep apnoea. That suggests those patients with more severe sleep apnoea benefit most from CPAP treatment, an observation supported by Shiota et al. (2014). Castronovo et al. (2014) also suggests that CPAP therapy has a beneficial impact on the restoration of OSA-mediated lowering of cerebral perfusion but cautions that even one year of compliant CPAP therapy may be insufficient for cerebral perfusion to be restored to normal physiological levels. In their study of 17 patients with severe sleep apnoea (AHI > 30) and 15 controls (AHI < 5) they measured white matter integrity using diffusion tensor imaging at baseline, after three months, and after one year of CPAP therapy. They reported decreased functional anisotropy values in several parts of the brain: a) the right hemisphere, at the level of subcortical white matter of the superior and inferior parietal lobe, which includes fibres of the superior longitudinal fascicle, b) at the level of deep frontal white matter corresponding to a portion

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of the arcuate fascicle, c) in the left hemisphere in a large area of the subcortical white matter belonging to the superior parietal lobe containing fibres of the superior longitudinal fascicle, as well as the left inferior frontal gyrus, including fibres of the frontal portion of the uncinate fascicle. Although there was limited change in white matter integrity at three months, the change was more notable after one year with ”… an almost complete reversal of white matter abnormalities …” for treatment-compliant patients (Castronovo et al., 2014; p 1).

Maresky et al. (2019) in their small study of seven patients with MRI scans taken pre- and post-six- months CPAP treatment, they showed an improvement in cerebral perfusion in patients with severe sleep apnoea (see Figure 2-6). They argued that OSA creates a state of persistent oxygen deprivation that damages the cerebral vasculature (see also Urbano et al., 2008; Yadav et al., 2013). Maresky et al. (2019) contention is that CPAP treatment neutralizes or improves the underlying hypoxia damaging the cerebral autoregulation process, thereby leading to improvements in cerebral perfusion. Reichmuth et al. (2009) support this conclusion by, using transcranial Doppler ultrasound to demonstrate that CPAP treatment ameliorates impaired hypercapnic vasodilation in the cerebral circulation.

Figure 2-6 Pre-treatment and post-treatment magnetic resonance imaging (MRI) perfusion scans were used to measure cerebral perfusion (LH) and cerebral blood volume (RH) and delta whole-brain maps showing regional increases in cerebral perfusion and CBF velocity after CPAP treatment (from Maresky et al. (2019)).

Not all studies have found improved cerebral perfusion after CPAP therapy. In addition to the study by Waltz et al. (2016) that concluded CPAP treatment does not impact cerebral autoregulation, is that

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conducted by Prilipko et al. (2014). In their study of 23 males with moderate to severe sleep apnoea who were randomized to receive therapeutic or sub-therapeutic (sham) CPAP for two months, they found that although cerebrovascular reactivity was increased with CPAP therapy, there was no correlation between cerebrovascular reactivity and cerebral perfusion and no difference in regional cerebral perfusion between patients and controls. Higher cerebrovascular reactivity has been postulated by Prilipko et al. (2014) to better maintain adequate perfusion in the presence of fluctuations in oxygen and carbon dioxide blood levels.

The recommendation of the American Academy of Sleep Medicine is to treat moderate and severe sleep apnoea with CPAP therapy (Epstein et al., 2009). Although many studies have investigated the impact of CPAP on mild-to-severe, moderate-to-severe, or severe sleep apnoea, no study has investigated only moderate sleep apnoea (i.e., 15 > AHI < 30) on cerebral perfusion.

In conclusion, several studies suggest that CPAP therapy can restore cerebral perfusion when awake to near normal physiological levels. Furthermore, an increased benefit from CPAP therapy is accrued in those with a higher levels of sleep apnoea severity. However, with no studies involving only patients with Moderate OSA, the benefit of CPAP therapy for this category of patients remains to be answered.

2.11.3 CPAP and brain function

An aim of our research was to determine if APAP therapy can offset/prevent adverse effects which moderate OSA might have on brain function. The Apnoea Positive Pressure Long-term Efficacy Study (APPLES) (Kushida et al., 2012) investigated this question via a six-month, randomized, double-blind, two-arm, sham-controlled, multicentre trial. With nearly 1,100 participants given either active (n = 556) or sham CPAP (n = 542) this was a powerful study. The inclusion criteria for this study were a diagnosis of OSA, an AHI > 10, and be aged 18 years or more.

The primary outcomes were three neurocognitive variables, each representing a neurocognitive domain: (1) Pathfinder Number Test-Total Time (PFN-TOTL), which assesses attention and psychomotor function; (2) Buschke Selective Reminding Test-Sum Recall (BSRT-SR), which assesses verbal learning and memory (L/M); and (3) Sustained Working Memory Test-Overall Mid-Day Index to assess executive and frontal-lobe function. Secondary outcomes were seven neurocognitive and two sleepiness measures, the maintenance of wakefulness test (MWT) and the Epworth Sleepiness Scale (ESS) (Kushida et al., 2012).

Kushida et al. (2012); (Kushida et al., 2006) confirmed that there was no difference in AHI at baseline between the two groups. However, despite a significant improvement in AHI levels after two-months (p < .0001) and six-months (p < .0001) of active CPAP therapy there was only a mild transient improvement at two months in executive and frontal lobe functioning for those with severe disease.

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Dalmases et al. (2015) found that elderly adults (65 years and older) with severe OSA improved episodic memory (between-group difference in change, 7.60; 95% CI, 1.66-13.55; p = .014) and short-term memory (between-group difference in change, 1.06; 95% CI, 0.10-2.01; p = .032) and in executive function (speed of mental processing, 5.74; 95% CI, 1.69-9.79; p = .007; mental flexibility, -47.64; 95% CI, -81.83 to -13.45; p = .008), whereas no changes were observed in the control group. This was after three months of CPAP treatment. Whether these were transient improvements was not established. Moreover, the total N for both groups was 33, which is considerably smaller than the APPLES study, albeit all of their subjects had been diagnosed with severe OSA.

Ferini-Strambi et al. (2013) conducted a narrative review of the papers evaluating the impact of CPAP treatment on cognitive performance. They found that in most studies they reviewed, attention/vigilance improved, but changes in global functioning, executive functioning, and memory only improved in about half of the studies. This they attributed to a large variability in inclusion and exclusion criteria, study design, and duration of treatment. Accordingly, they concluded that it was not possible to make definitive conclusions regarding the impact of CPAP treatment on cognition in OSA.

Finally, a study by Maresky et al. (2019) concluded, that in addition to symptom resolution, CPAP treatment may allow for the healing of OSA-induced brain damage by reversing the mechanisms underlying hypoxia, thereby increasing oxygenation. This they report, likely limits reactive oxygen species production thus allowing for the activation of the brains repair mechanisms. However the study by Maresky et al. only had seven participants and their results and conclusions need to be treated with caution.

2.12 Arterial spin labelling

2.12.1 Overview of the arterial spin labelling process

Cerebral blood flow has traditionally been measured using techniques, such as positron emission tomography (PET), single photon emission computerized tomography (SPECT), computed tomography perfusion, and dynamic susceptibility contrast magnetic resonance imaging (see Fantini et al. (2016) for a review). A common feature of many of these methods is reliance on the use of diffusible radioactive tracers that can exchange between the vascular compartment and the tissue of interest, and significantly, have to be exogenously administered (Petcharunpaisan et al., 2010).

Arterial spin labelling (ASL) is a magnetic resonance imaging technique for measuring cerebral perfusion, which was developed by Williams et al. (1992) and does not use diffusible tracers. Instead, it uses magnetic-labelled arterial blood water protons as an endogenous tracer (Ferré et al., 2013; Jezzard et al., 2018; Petcharunpaisan et al., 2010). Accordingly, it is non-ionizing and a completely

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non-invasive technique, making it ideal for longitudinal studies (Wintermark et al., 2005; Xu et al., 2010).

Furthermore, because blood water protons are the tracer, (i) ASL provides an absolute measurement of cerebral perfusion, (ii) any change in cerebral perfusion rates can be expressed in physiologically meaningful units rather than as a percentage change (Borogovac, 2012), (iii) the quick magnetization decay means the scan times can be relatively short, (iv) unlike techniques such as the blood oxygen level dependent procedure, which are considered to mainly express venous flow rates, ASL expresses arterial perfusion, and (v) is a reliable measure (Zhou et al., 2015). Also, according to Borogovac (2012), arterial spin labelling has a higher spatial and temporal resolution than any other current technique; an observation partially disputed by Ferré et al. (2013) who reported that arterial spin labelling has a poorer spatial resolution compared to techniques such as computerized axial tomography perfusion scan. Also, Ambarki et al. (2015) compared arterial spin labelling cerebral perfusion from young healthy adults (age range, 20–30 years) and from healthy older adults (age range, 65–80 years) using pseudo-continuous arterial spin labelling against flow rates from phase-contrast magnetic resonance images. They found that arterial spin labelled blood flow is over-estimated in healthy young adults and under-estimated in the health older adults, bringing into question the accuracy of arterial spin labelled derived cerebral perfusion rates. However, a study by Kaneta et al. (2017) investigating the performance of ASL against SPECT found substantial differences in visual assessments of Alzheimer’s disease between the two methodologies with lower cerebral blood flow attributable to ASL with a post labeling delay of 1.5 s.

Another disadvantage of ASL is a low signal-to-noise ratio related to the subtraction of control images from labeled image. Moreover, ASL has a limited spatial resolution (Ferré et al., 2013) and poor temporal resolution (Petcharunpaisan et al., 2010) that when combined with the low signal-to-noise ratio results in a low contrast-to-noise ratio. As ASL is a subtraction technique, it is sensitive to subject movement. Yet despite these disadvantages, ASL’s noninvasiveness and ability to quantitatively measure tissue perfusion make ASL an ideal technique for research and clinical studies (Petcharunpaisan et al., 2010).

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Figure 2-7. Arterial spin labelling acquisition principles. First the inflowing blood is labelled (A). It is then given time to infuse the brain volume of interest (B), before the images are acquired (C).

The basic ASL process is a differentiating technique in which two signal acquisitions are carried out: one with the labelling of arterial blood water protons interspersed with a control acquisition that is completed without the labelling process. The labelling is done by inverting the blood magnetization using a combination of bursts of a 180-degree radio-frequency pulse and a field gradient in the direction of blood flow (Fantini et al., 2016). This labelling process disrupts the magnetization of the blood water, subsequently reducing magnetic resonance signal strength and image intensity. The labelled protons then migrate via arterial vessels to the brain tissue where they pass from the capillary bed to the extravascular space. Control images, on the other hand, are acquired without arterial labelling. Thus, the blood water protons are relaxed and in equilibrium. By adding a delay between the labelling process and image acquisition, called post-labelling delay (see Figure 2-7, the labelled blood can reach the capillaries where it gives rise to the perfusion-weighted image. Subtraction of the labelled acquisition from the control acquisition suppresses the static tissue signals and the remaining signal is a relative measure of perfusion proportional to cerebral perfusion (Ferré et al., 2013; Petcharunpaisan et al., 2010; Wolf & Detre, 2007).

2.13 Summary

Although there is much still to be learnt about the mechanisms controlling the maintenance of cerebral circulation in OSA (Durgan & Bryan, 2012), there is strong evidence that OSA interferes with the regulation of cerebral perfusion. Most studies discussing the impact of sleep apnoea and its impact on cerebral perfusion while awake have either included moderate with severe, or just severe sleep apnoea. Only one study (Baril et al., 2015) has reported the impact of moderate sleep apnoea on cerebral perfusion – i.e., no significant difference from non-OSA.

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Most studies exploring the impact of CPAP therapy on cerebral perfusion have found that CPAP therapy restored cerebral perfusion to near normal levels. However, this benefit appears to be dependent on severity of sleep apnoea, with a greater benefit accruing to those with more severe sleep apnoea. With no studies investigating CPAP or APAP therapy for patients with only a moderate level of sleep apnoea, the benefit of CPAP/APAP treatment for that group of patients also remains unresolved. Accordingly, research into the impact of treatment on Moderate OSA and on cerebral perfusion during the awake period will make a worthwhile contribution to the management of moderate sleep apnoea, the importance of which should not be underestimated.

A useful tool for investigating the impact of sleep apnoea on cerebral perfusion is ASL. Being a non- invasive procedure, it is ideal for the investigation of perfusion in people who can tolerate the close confines of the scanner. It is particularly useful for longitudinal studies (Ugur et al., 2005).

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Chapter 3 Methods

3.1 Introduction

To investigate the impact of moderate OSA on cerebral perfusion, cognition, and other measures, a longitudinal, intervention study was undertaken in which APAP therapy was administered over six- months to a cohort of participants with moderate OSA, whilst another cohort, also with moderate OSA, was monitored but were not treated. A third group of participants without OSA, a control group, was also tested at baseline and again after approximately six-months to assess any test-retest confounds. Perfusion and cognition scores from all three cohorts at baseline were contrasted against each other and against scores taken at the end of an approximate six-month treatment period. Test measures were compared between baseline and end-of-treatment for the treated cohort and compared to the cohort who were not treated but who, nonetheless, had a similar intervening period between baseline and follow- up tests. The methodology used is described in more detail in the following sections of this chapter.

3.2 Participants

Twenty-four participants with an AHI between 15 and 30 events per hour were pragmatically recruited. They formed the moderate OSA group that was subdivided into either a treated cohort (4 females, 9 males), or an untreated cohort (6 females, 5 males). It was intended that both these two cohorts would consist of 15 participants. However, recruitment proved to be difficult due to lower-than-expected numbers presenting to the Christchurch Hospital Sleep Unit.

In addition, data was collected from a further 7 participants with an AHI < 5, who constituted a control cohort. It was intended to recruit 15 participants to form this group. However, due to the high prevalence of mild OSA in the general population for this age range, only 7 participants – of which 4 were female – met our requirements within the available time.

3.2.1 Inclusion/Exclusion criteria

Membership of the moderate OSA group required that participants had an AHI score between 15 and 30 events per hour as described by Level II polysomnography. They were also required to have been referred to the Christchurch Hospital Sleep Unit by their General Practitioner, and to be naïve to APAP or CPAP therapy.

In addition to having an AHI < 5, membership of the control group was dependent on participants being asymptomatic of OSA (e.g., did not experience excessive daytime sleepiness, or report excessive snoring). Furthermore, they were not patients of the Christchurch Hospital Sleep Unit, or who have previously consulted their General Practitioner about sleep related problems. Control group members

48 were recruited from the general-public through word of mouth, and public presentations of this research project. Some potential members in a list of voluntary research recruits, held by the Otago School of Medicine, were also approached.

The inclusion criteria for both the OSA group and the control group were (i) be 18 – 80 years of age; (ii) have a usual bedtime between 9.00pm and 12.00am; (iii) a usual time in bed of 7 to 9 hours; (iv) have an understanding of verbal and written English; (v) not be receiving or have previously received OSA treatment; (vi) not have a history of physiological, psychiatric, or neurological disorders unrelated to OSA, which could affect sleep behaviour, fatigue, cerebral perfusion or brain structure such as chronic obstructive pulmonary disease, congestive heart disease, diabetes, heart failure, hypertension > 150/90, migraine, myocardial infarction, stroke, previous brain surgery, stroke, and traumatic brain injury; (vii) not be taking medication that could impact cerebral perfusion (e.g., ACE inhibitors, nitrates) or cognition; (viii) not be willing or otherwise unable to remove metal that could be harmful to the participant during a MRI scan or could create MRI artefacts that negatively affect the quality of the scan; and (ix) based on current Sleep Unit criteria, not have any reason for urgent medical treatment.

As a part of the screening process, information on the subject’s current neurological, psychiatric, and sleep status was obtained during personal interviews. A copy of the questionnaire used can be found in Appendix A.

Demographic and other personal information from all participants (age, ethnicity, PSQI, ESS, and SF- 36) were obtained via questionnaires.

All participants were required to have a Level II polysomnography sleep test administered by the CDHB Sleep Unit. This evaluation by the Sleep Unit ensured potential participants in the OSA group met the diagnostic criteria for a moderate level of OSA that, in addition to the presence of sleep disordered breathing, required the presence of other symptoms typical of the disorder but not necessarily excessive daytime sleepiness. It also ensured that members of the control group were correctly placed. Moreover, the Level II polysomnography test minimised the risk of comorbid sleep disorders such as REM sleep behaviour disorder (Kapur et al., 2017) confounding results that would have otherwise been fully attributable to OSA.

3.2.2 APAP treatment

Initially, members of the treated group were offered APAP therapy for six months. Minimum compliance criteria included APAP usage for a minimum period of 4 hours on 5 nights per week (Catcheside, 2010). All APAP users were asked to join ResMed myAir, a support facility provided by ResMed for new users of APAP equipment. With their consent, adherence to APAP therapy was monitored via ResMed’s Airview . Those participants who remained non-compliant after 30 days were,

49 with their understanding, withdrawn from the treated cohort and offered placement in the untreated cohort if that became necessary.

3.2.3 Recruitment

Participants intended for our moderate OSA group were all recruited from patients of the CDHB Sleep Unit who had been previously assessed as having a moderate level of OSA and would not usually qualify for funded treatment with APAP. They were initially approached by the Sleep Unit asking whether they would consider participating in this research project. If the response was agreeable, the prospective participant was then approached by the co-ordinating investigator (RB) who informed the prospective participant of the study objectives, what involvement would mean, and posted out a participant information sheet, to facilitate an informed consent. A copy of that information sheet for membership of the OSA group along with the information sheet for the control group are included in Appendix B. Approximately one week later, the Co-ordinating Investigator contacted the prospective participant, enquired about participation, and completed the consent process. Recruitment was anticipated to take approximately six to nine months, estimated on the number of referrals received by the Christchurch Hospital Sleep Unit at that time.

It was decided to recruit most of the treated cohort first because it was anticipated that this would be the hardest cohort to fill due to the recognized high non-compliance rate for APAP treatment. Initially, participants were not given a choice to which group they were to be assigned. However, the drop-out rate in the first 10 recruits was 40%. That rate was higher than could be afforded. Subsequently, participants were offered a choice of participating in the APAP treatment or the untreated cohort the rationale being that those participants who opted for the treated cohort would be committed participants.

Those that had been placed in the treated cohort and who found within the first month they were unable to tolerate, or for other reasons did not wish to continue using the APAP equipment, were asked if they would consider returning in approximately six months as a member of the untreated cohort if required. This pragmatic approach, although less than ideal (Lurie & Morgan, 2013), was justified on the basis of limited recruitment opportunities to otherwise met our desired populations. Patients recruited explicitly for the untreated cohort were not given a choice to which cohort they could join.”

Participants intended for the control group were approached by the Co-ordinating investigator. An initial contact was first made where study objectives were explained, and participation were told what would be involve. As was the case for the OSA Group, a commitment to participate was not sought at this stage and an information sheet was posted out. Again, approximately one week later a further contact was made and agreement to participate sought.

50

3.3 Apparatus and Tests

3.3.1 Polysomnography.

All participants undertook an overnight Level II polysomnography at baseline of at least six-hours. Sleep was monitored overnight in the participant’s home using a Nox Medical polysomnograph (Model Nox A1, Nox Medical, Reykjavík, Iceland). Central and occipital EEG, ECG, bilateral EOG, submental and tibialis anterior EEG, and body position were recorded using a standard polysomnography montage as recommended by the American Academy of Sleep Medicine (AASM) (Berry et al., 2017). A nasal cannula/pressure transducer was used to measure nasal airflow, whilst oral airflow was measured using a thermistor. Respiratory effort was measured using chest and abdominal piezoelectric belts. A pulse

oximeter (Nonin 3150 WristOx2, Nonin Medical Inc, Plymouth, Minnesota) was used to monitor oxyhaemoglobin saturation (Sao2).

In addition, a detailed sleep history was taken (Epton et al., 2017). This included duration and sleep fragmentation questions; questions relating to snoring and apnoea, restless legs/ periodic limb movements of sleep, and parasomnia; caffeine, alcohol and drug history; weight and weight gain; driving and work-related incidents; and upper airway surgical history. A focused examination was also performed on the nose, mouth, jaw and upper airway, including the Friedman classification of the oropharynx and tonsils (Friedman et al., 2002). In addition, the Berlin questionnaire and the Epworth Sleepiness score were administered.

These tests were repeated six to seven months later for both the Treated and Untreated cohorts. To this end, the Treated cohort were asked to refrain from APAP therapy on the night immediately prior to the second polysomnography. Refraining from the use of APAP on the night of the polysomnography was not expected to disrupt the treatment regimen as an acceptable compliance for the treatment of OSA requires CPAP to be used on at least 70% of nights in any given month (Avlonitou et al., 2012).

3.3.2 Magnetic Resonance Imaging

3.3.2.1 Data acquisition

High-resolution, anatomic images of the whole brain were acquired by Pacific Radiology on a Signa HDxt 3.0 T MRI Scanner (GE Medical Systems, Milwaukee, WI, USA) using an 8-channel head coil. This included a T1-weighted spoiled gradient recalled echo (SPGR) acquisition with: echo time of 2.8 ms, a repetition time of 6.6 ms, inversion time of 400 ms, flip angle of 15 deg, acquisition matrix of 256 × 256 × 170, field of view of 250 mm, slice thickness of 1 mm, voxel size of 0.98 × 0.98 × 1.0 mm3, and a scan time of 5 min 3 s.

51

Whole-brain perfusion was measured quantitatively using a stack of spiral fast-spin-echo-acquired images prepared with pseudo-continuous Arterial Spin Labelling (ASL) with: static background signal suppression, a repetition time of 6 s, echo spacing of 9.2 ms, post-labelling delay of 1.525 s, labelling duration of 1.5 s, eight interleaved spiral arms with 512 samples at 62.5 kHz bandwidth, field of view of 240 mm, 30 phase-encoded 5-mm-thick slices, reconstructed in-plane resolution of 1.88 × 1.88 mm, five excitations, and a scan time of 7 mins giving 70 volumes (scan time/repetition time). The units were mL/100g/min.

3.3.2.2 Scanner relocation

In September 2017, the scanner was relocated from 16 St. Asaph Street, Christchurch 8014 to a new building at Forte Health, 132 Peterborough Street, Christchurch Central City Christchurch 8013, a distance of approximately 1.6 km. To understand the impact of this shift on scan integrity, a test-retest study was completed prior to and post shift. No evidence of any instability between structural T1 scans prior to and post shift was found. Although MRI ASL was not as stable as the T1 scan between tests, there was no evidence of biased signal between locations, or of differences in signal variance. (Melzer et al., 2020)

52 Figure 3-1 GE Signa HDxt 3.0T MRI scanner located at Pacific Radiology, Forte Health, used for the MRI scans.

3.3.2.3 Image pre-processing

Both baseline and follow-up images were treated cross-sectionally as opposed to longitudinal within- subject co-registrations. Pre-processing of acquired ASL data is necessary before it can be analysed. Accordingly, both structural and perfusion MRI data were pre-processed using CAT12 (http://dbm.neuro.uni-jena.de/cat12/) - a toolbox of SPM12 (Wellcome Trust Centre for Neuroimaging, University College London, UK; http://www.fil.ion.ucl.ac.uk/spm) - and custom MATLAB scripts (Mathworks, Natick, MA, USA) as follows. Structural images were: (i) bias corrected, tissue classified, and normalized using linear and nonlinear transformations (DARTEL) within a unified model; (ii) grey- matter segments for each subject were modulated, thereby preserving actual tissue values locally by accounting for the amount of warping; (iii) an intracranial volume (ICV) mask, used to facilitate brain extraction, was calculated using the automatic reverse brain mask method, in which the SPM ICV brain mask in MNI space was inverse normalized to each subject’s space using the DARTEL-derived deformations and nearest neighbor interpolation (Hansen et al., 2015), (iv) modulated, normalized grey matter segments were smoothed with a 10-mm full width at half maximum (FWHM) Gaussian kernel to improve signal to noise ratio and to minimize the effect of any residual misalignment.

Perfusion images were: (i) quantified, co-registered to the SPGR images, brain extracted (using FSL 5.0.2 www.fmrib.ox.ac.uk/fsl), and normalized using the deformation fields generated during segmentation and normalization of the SPGR images. (ii) Normalized perfusion images were smoothed with a 10-mm FWHM Gaussian kernel. (iii) All images were visually inspected for completeness and alignment (see para. 3.3.2.4 and Figure 3-2). (iv) A study-specific grey-matter mask, used to exclude non-grey matter contributions in subsequent analyses, was created by averaging the modulated, normalized grey matter segment from all subjects. (v) Values < 0.10 were excluded from the mask, as were all slices inferior to the superior cerebellum as spiral artefacts were present in this area on perfusion images; (vi) periventricular white matter regions misclassified as grey matter (partial voluming) were manually excluded from the grey matter mask; (vii) grey matter segments were normalized without modulation and smoothed to investigate grey matter concentration.

53 Figure 3-2 From top-left and clockwise: Coronal, sagittal and axial views of a typical normalized/warped cerebral perfusion image before smoothing; as shown in SPM.

3.3.2.4 Quality control

The quantified CBF map for each participant was overlaid on the T1 weighted SPGR structural image and visually inspected for artefacts and errors in registration (Figure 3-3). This was done by verifying if the outside contours of the brain were registered with the inside edge of the skull and if the space between the hemispheres was in the middle of the image.

Figure 3-3. Typical structural images, those on the on the left, overlaid with the quantified CBF images on the right. The z, y, z, co-ordinates for both sets of images are the same at 123, 179, and 93 respectively.

54 3.3.2.5 Statistical image analysis

A general linear model (GLM) ANCOVA was used to investigate baseline voxel-by-voxel differences between the control group and the Moderate OSA group in grey matter volume and concentration, and grey matter perfusion. Age, sex, and BMI were included in the GLM as explanatory variables (EV), along with the voxel-dependent grey matter 4D file, for both the regional grey matter volume/ concentration and perfusion analyses. Furthermore, separate models with and without the additional EVs of drowsiness during the ASL scan and total grey matter perfusion were also run.

To test the treated cohort, and separately the untreated/control group, for change over time, a GLM ANCOVA was used to investigate voxel-by-voxel differences in grey matter volume and concentration, and grey matter perfusion at baseline and follow-up. Age, sex, BMI, and drowsiness were included in the general linear model as explanatory variabless. In addition, APAP usage, i.e. average hours of use per night, was also included in the treated cohort test.

There is mounting evidence (Cielo & Tapia, 2019; Kainulainen et al., 2020; Scott et al., 2014) that increased desaturation severity, and percentage of sleep time with saturation levels below 90% are better markers of OSA severity than AHI. Accordingly, to test whether Total Time with oxygen saturation below 90 percent (ST90) was correlated with changes in regional cerebral blood flow, a general linear model (for regression) was used to investigate voxel-by-voxel correlations, with age and sex included as covariates.

A permutation-based inference tool was used for nonparametric statistical thresholding (“randomise” in FSL 5.0.2; www.fmrib.ox.ac.uk/fsl). For each contrast or regression, the null distribution was generated over 5,000 permutations and corrected for multiple comparisons using family-wise error rate (threshold-free cluster enhancement corrected p<0.05).

3.3.3 Eye-video recording during the ASL scan

3.3.3.1 Eye-video capture

Eye-video, used to rate participant drowsiness (see 3.3.3.2 ), was captured during the ASL sequence using a Visible Eye system incorporating a fibre-optic camera (Avotec Inc., Stuart, FL, USA). The video-capture software recorded 352 × 288-resolution eye-video at 25 fps. The participant looked at a blank screen. During the ASL sequence the participant was instructed to “keep your eyes open except for blinks.” Also, background music was turned off. Although the quality of the screenshots was low (e.g., see Figure 3-4) the resolution was sufficient for the identification of full or partial eye closures, eye blinks, and saccadic eye movements that informed an observer’s subjective assessment of drowsiness.

55 Figure 3-4 A screen shot of a typical frame from the eye video recording used to rate drowsiness levels during the ASL procedure.

3.3.3.2 Eye-video rating of drowsiness

Each participant’s 7-min eye video, recorded during their ASL scan, was visually rated for signs of drowsiness by an experienced rater (RB). Drowsiness was rated in accordance with the visual scoring method proposed by Wierwille and Ellsworth (1994). This rating system is based on observable personal characteristics including eye blinks, eye closures, and saccadic movement. The rating system has 5 levels of drowsiness on an analogue scale of 0 to 100 (not drowsy = 0, slightly drowsy = 25, moderately drowsy = 50, very drowsy = 75, and extremely drowsy = 100). Level of drowsiness was rated first as an average over each min, which were subsequently collated into an overall average rating across the 7 min. Following the lead from a previous study (Innes et al., 2015), the drowsiness rating was done by a single experienced rater (RB). Poudel et al. (2012) noted that interrater reliability of the visually rated drowsiness scores is extremely high with the average raters intraclass correlation coefficient = 0.95, using a 2-way mixed effects model. Poudel, Innes, and Jones also reported that the visually rated drowsiness scores were strongly correlated with the mean percentage eye-closure in both rested (r = 0.75, p = .002), and sleep-restricted subjects (r = 0.86, p = .002).

3.3.4 Constant Positive Airway Pressure

Constant positive airway pressure therapy provided to the treated cohort (see 3.2.2 was administered through an Airsense 10 auto set using either an AirFit N30 full face mask, or an Airfit N20 nasal mask 56 (ResMed Ltd., Bella Vista, New South Wales, Australia). The settings for the APAP machines are listed in Table 3-1.

Table 3-1. APAP settings used in this study.

Value Setting Autoset Mode 15.0 cmH2O Maximum pressure 5.0 cmH2O Minimum pressure Soft Response Automatic Ramp time 4.0 cmH2O Start Pressure On Expiratory pressure relief Ramp only EPR type 3.0 cmH2O EPR level 4 (range 1 to 8, 1 being low) Humidity level

APAP usage was objectively recorded by the APAP machine and reported directly to ResMed via the internet, where available, otherwise usage was recorded onto an integrated secure digital card that is subsequently downloaded. Patient details, the days used, the hours it was used, APAP machine setting including mode, minimum pressure, maximum pressure, and expiratory pressure relief settings were recorded. Therapy settings including median pressure, leaks, number of apnoea and hypopnoea events per hour, and Cheyne-Stokes respiration average duration were also recorded. From this collected information 180-day compliance reports were collated for each participant in the treated cohort. Reported were the number of days APAP was used for four or more hours, the number of days where APAP was used for less than four. Total hours used over the 180-day period, average usage per night over the 180-day period, average usage over the days used, median usage over the days used. An example of a compliance report can be found in Appendix E.

57 3.4 Neurobehavioural tests

Cognition was measured at baseline and again at follow-up. This involved each participant completing a battery of cognitive tasks that was based on analyses by Beebe et al. (2003); Bucks et al. (2013); Fulda and Schulz (2003); Olaithe and Bucks (2013); (Sales et al., 2013); Wallace and Bucks (2013); Weaver and George (2011); and framed within the heuristic model of OSA neurobehaviour proposed by Beebe (2005). It was designed to cover five domains of cognition: attention/concentration, executive functioning, learning and memory, speed of processing and visuospatial (Decary et al., 2000; Lezak et al., 2004). This battery of cognitive tests along with the sequence of completion, and the neurocognitive domains which each test measured, is listed below.

Table 3-2. Cognitive tests undertaken at baseline and at the 6-month follow-up.

Order of Proposed Neuropsychological test Neurocognitive Sub-domain completion domain 1 Rey Complex Figure (Copy) (Osterrieth, Visuospatial 1944; Rey, 1941) construction 2 Rey Complex Figure (Immediate recall) Executive function Planning 3 Verbal Fluency Test (FAS) (Borkowski Executive Verbal Fluency et al., 1967). 4 Verbal Fluency Test (Category) Executive Verbal Fluency 5 Digit Span (Forwards, Backwards, and Attention, Working Short-term Verbal Ordering) (Miller, 1956). Memory 6 Stroop (Colour patch, and Word) Attention Concentration (Stroop, 1935). 7 Stroop (Interference) Executive Inhibition functioning 8 Symbol Digits Modality Test (Smith, Speed of Processing 1973) 9 California Verbal Learning Test (Part 1) Memory Long-term Verbal (Delis et al., 1987). 10 Rey Complex Figure (Delayed recall 30 Memory Long-term Visual min) 11 Trail Making Test A (Reitan, 1958). Attention Concentration 12 Trail Making Test B Working Memory 13 California Verbal Learning Test (Part 2) Memory Long-term Verbal

58 14 Judgement of Line (Benton et al., 1994). Visuospatial construction 17 2D Tracking task Attention Concentration 18 PVT (Dinges & Powell, 1985). Attention Sustained attention

3.4.1 Microsleep and Psychomotor Vigilance Task (PVT)

3.4.1.1 Experimental Setup and Equipment

The microsleep and PVT assessments were conducted in a room measuring approximately 5.5 m x 3.2 m. The room had no windows and, for the purpose of this study, was lit by a single 40-watt incandescent light bulb during the test sessions. Ventilation was provided by an air conditioning unit with the room temperature set at 22 C.

The participant’s workstation consisted of a small desk that faced a blank wall. It held the stimulus visual display unit. The participant sat in a chair placed in front of the workstation with the floor- mounted joystick located between their legs approximately in line with their knees. Approximately 700 – 800 mm in front of and facing the participant was the stimulus visual display unit. The top of the screen was approximately level with the participant’s line of sight. To the left of the participant, and out of their line of sight was located the researcher’s workstation which contained the stimulus computer, researcher’s visual display unit, keyboard, and mouse.

The stimulus visual display unit was a 17-inch LCD flat screen colour monitor manufactured by ViewSonic (Walnut, California, USA). It had a screen resolution set at 1024 x 768 px and was refreshed at 60 Hz.

3.4.1.2 Test program

A Delphi-based computer program SMCTests™ generated the 2D pseudo random visuomotor tracking task used in the identification of microsleeps. It also generated the stimulus used in the PVT. The SMCTests™ program was run on a desktop computer employing a Windows platform. The computer generated and displayed the target disk used in the tracking task (the red disk in Figure 3-9), and sampled the joystick response, which it then displayed as the response disk to provide visual feedback to the participant of their tracking performance (yellow disk Figure 3-9). During the PVT, the program generated the start of a PVT trial by randomly displaying a counter in the centre of the screen that showed in ms the time since the beginning of the trial (see Figure 3-11). The program also stopped the counter when the off on button was pressed on the joystick, calculated reaction times for each PVT trial, and displayed that time on the screen for 1.0 sec to provide feedback on the PVT response. Lastly, the

59 program stored the stimulus and response data from both the tracking and PVT tasks as Matlab ‘.mat’‘.txt’ files for offline analysis.

3.4.1.3 Eye-video recording

The eye-video-recording used in conjunction with tracking response data to identify microsleeps, was made using a Complementary Metal Oxide Semiconductor (CMOS) pin-hole camera, model MO-S408- 3T-C, supplied by Misumi Electronics Corporation (Chungho City, Taipei Hsien, Taiwan, ROC). This small camera was attached to the microphone boom of a headset worn by the participant during the microsleep task and adjusted so a recording of the participant’s left eye could be made.

The video recording was captured through a Winfast VC100 XP video in capture card, (Leadtek, Taipei), and supported by VirtualDub version 1.10.4, a video capture/processing utility for 32-bit and 64-bit Windows platforms licensed under the GNU General Public License (Computer Software, 2012) (Retrieved from https://sourceforge.net/projects/virtualdub/files/virtualdub- win/1.10.4.35491/VirtualDub-1.10.4.zip/download). VirtualDub uses an Audio Video Interleaf (AVI) format with a ‘.avi’ filename extension for its movie files and has a capture rate of 50 fps. For the files to be used by SyncPlayer ™ the AVI format has to be converted to Quicktime h264 ‘.mov’ format using a MatLab Conversion Script. A video frame showing a typical view of a participant’s eye taken during the tracking task is shown in Figure 3-5.

Figure 3-5 A typical eye video of a phasic/transient eye-closure used in conjunction with tracking response data to identify microsleeps.

3.4.1.4 SyncPlayer™.

Off-line analysis of the microsleep task was through SyncPlayer ™. SyncPlayer is a software program written for previous microsleep studies (Buckley et al., 2014; Poudel et al., 2010). It was used to synchronize and display via two separate windows; video recordings of the participant’s eye in the SyncPlayer window along with a graphical representation of the tracking data in the multiple signals window. SyncPlayer uploaded the screen coordinates for both the response and target disks and displayed the x and y-axes of the response signal overlaid on the target signal. It calculated and displayed the resultant speed of the response signal overlaid on the target signal and the tracking error (Figure 3-7).

60 Figure 3-6. Shown are the four response signals to a typical microsleep. Flat tracking response is readily seen for both the X and Y axes. Along with the zero speed indicate the absence of joystick movement. The result is an increase in tracking error evident in the bottom graph.

61 Figure 3-7. Screen shot of the SyncPlayer™ window showing the Multi-signal data view window on the left, along with the SyncPlayer window on the right where marked events are recorded.

In addition to its display function, the program has a function that allows the recording of seven markers used in the definition of a microsleep. The seven markers are listed in Table 3-3 below and were those used previously by Poudel (2010). Each mark locates the event by recording the associated video frame number.

Table 3-3. Description of the seven markers used to record an event. Each mark is made by recording the associated video frame number.

Marked Event Marker Identity Start of poor or flat/incoherent tracking including start of rise in error, or start of Marker 1 speed ~ 0

50% of full eye-closure (where available) based on either video recording or Marker 2 VEOG.

Start of full eye closure (where available) based on either video recording or Marker 3 VEOG.

Completion of full eye closure (where available) based on either video recording Marker 4 or VEOG.

50% of eye open (where available) based on either video recording or VEOG. Marker 5 Full eye open (where available) based on either video recording or VEOG. Marker 6 End or poor or flat/incoherent tracking including end of flat spot, start of speed > Marker 7 0, or start of coherent tracking.

62 3.4.1.5 Floor-mounted joystick

The floor-mounted joystick shown in Figure 3-8 was purpose built by the Department of Medical Physics and Bioengineering, Canterbury District Health Board, New Zealand for a previous study. It has a lateral movement of approximately 30o from the vertical in all directions, which for the average person meant, at the hand position, a circle of movement with a radius of 225 mm. On top of the joystick is the response button for the PVT stimulus (see Figure 3-8). It was used to respond to the PVT stimulus. Its position on top of the joystick was readily accessible by the participant using their thumb. To activate the switch the participant was required to depress the switch approximately 0.5 mm which required them to exert an approximate force of 6 N to overcome the switch’s inherent resistance. As necessary, the hand rest, integrated into the joystick, could be adjusted to accommodate different hand sizes.

Stimulus response button Adjustable hand rest

Adjustment for length

Figure 3-8 The floor-mounted joystick. This joystick is positioned immediately in front of the seated participant such that they can utilise the full circle of movement without obstruction from their upper legs and knees. The height of the joystick and the collar used as a hand rest can be adjusted to suit the user. The button used to respond to a PVT trail is shown located on top of the joystick where it can be easily activated by the thumb. Also shown is an adjustable collar that serves as a hand rest.

63 3.4.2 Microsleep testing

Testing for microsleeps involved assigning a monotonous repetitive tracking task the nature of which would facilitate the identification of ‘no response’. During this task, eye behaviour is observed. Microsleeps are defined as brief periods – longer than 0.5 s but less than 15 s – where there was no motor response coincidental with eye closure and other indications of sleep, i.e. head nodding (Poudel et al., 2008).

3.4.2.1 Tracking Task

The tracking task was the same as that developed by Poudel and colleagues for the detection of microsleeps (Poudel et al., 2008). Its suitability is reflected by its monotonous nature, which is conducive to microsleeps; the ability to detect, identify and measure motor performance with high temporal accuracy; that both eye behaviour and head movement could readily be videoed; and performance metrics could readily be synchronized with both EEG and the video data with millisecond accuracy.

During this task, the participant was required to use a floor-mounted joystick to voluntarily track for 30 min a moving target that tracks, without any ‘flat spots’, across a visual display unit in a 2-D pseudorandom manner – i.e., the target was always moving with a minimum speed. Tracking performance was recorded at a sampling rate of 60 Hz by the SMCTests™ program for later analysis. Any lack of visuomotor responsiveness becomes evident due to either incoherent tracking or the presence of response flat spots attributable to a lack of joystick movement.

The moving target was a yellow disc with a diameter of 7.8 mm. It had a trajectory period of 30 s that was repeated 60 times to extend the task to a 30-min period (Figure 3-9). The trajectory prescribed by the target disk was defined by summing seven sinusoids with frequencies evenly spaced between 0.033 to 0.231 Hz for both the vertical and horizontal vectors of the target. Poudel et al. (2008) considered a bandwidth of 0.231 Hz provides a moderately challenging task during baseline tracking, and a specific set of random-phase shifts in the vertical and horizontal components results in a trajectory that is largely unpredictable. It also results in a target with a velocity range of 63–285 pixels per second. Feedback was provided to the participant by means of the red disk with a diameter of 6.6 mm (Figure 3-9).

64

Figure 3-9 2-D continuous pursuit tracking task. The trajectory of the target (c), traced by the yellow dot, was the result of summing seven sinusoids in two dimensions (a and b). The red dot is the response cursor and provides visual feedback to the performer (from Poudel et al., 2008).

3.4.2.2 Event Classification

Microsleeps were identified and classified using a visual rating protocol developed by Innes et al. (2010), which in turn was based on the work of Peiris et al. (2006). It involves a visual inspection of the recorded synchronized response and video data to identify a set of predefined characteristics. The microsleep events were rated by an expert rater familiar with this rating procedure (RB).

Where flat or incoherent tracking was observed for more than 500 ms, but less than 15 s, and full or partial eye closure (>80%) accompanied by clear behavioural indications of drowsiness, it was recorded as a microsleep.

The distinction between a microsleep and sleep based on duration is arbitrary but, according to Poudel (2010), they are considered physiologically different because the recovery of responsiveness time is much shorter in a microsleep. Therefore, events longer than 15 s, which would otherwise have met the criteria for microsleep, are sleep episodes.

65

Figure 3-10 Example of flat tracking as displayed by SyncPlayer. The top two lines show the x-axes of the target track (smooth line) and the response (flat line). The next two lines are the y-axes. Partial eye closure occurred at 551.7 s coincidental with the beginning of a flat response. Full eye closure occurred at 552.7 s. Eyes began to open at 555.6 s and were fully opened 300 ms later at 555.9. This was immediately followed by a saccade to the target. Motor response began at 556.4 s.

3.4.3 Psychomotor Vigilance Task

The psychomotor vigilance task (PVT) used in this study is similar to the standard PVT proposed by Dinges and Powell (1985). It is a reaction-based task of ten minutes that requires a participant to respond to a visual stimulus, which appears randomly with an inter-stimulus period of 2–9 s. The stimulus used in this study was a numeric counter in the middle of the stimulus visual display unit that changed at each screen refresh (60 Hz) and displayed the time in ms since its appearance (Figure 3-11). The stimulus continued to change until the push-switch on top of the joystick was activated. The stimulus – i.e., the time taken to respond – then remained visible for a further 500 ms to provide feedback to the participant on their performance for that trial.

Since being proposed by Dinges and Powell (1985), the PVT has become a standard assay of attention, particularly in sleep studies (Ratcliff & Van Dongen, 2011; Veasey et al., 2002). During the PVT, several trials are completed in which the participant reacts as quickly as possible to a stimulus. This reaction time can be partitioned into two parts; time due to task demands, i.e., time required to physically complete the task, and time due to attention requirements. An implicit assumption of the PVT is that it is the variability in attention, not task demands, that results in reaction times exceeding an arbitrary 500 ms threshold (e.g. Belenky et al., 2003; Dinges & Powell, 1985).

66 3.4.3.1 Event Classification

The identification of a PVT lapse was objective using system recorded data. This was the time taken to recognize the stimulus plus time taken to depress the response button located on the top of the joystick. By convention, reaction times in excess of 500 ms following the presentation of the PVT stimulus are considered to be lapses of attention (Anderson et al., 2010; Belenky et al., 2003; Chee & Choo, 2004; Graw et al., 2004; Loh et al., 2004; Ratcliff & Van Dongen, 2011; Van Dongen et al., 2003). In this study we have conformed to this convention and adopted the ubiquitous 500 ms as the threshold for determining the presence of an attention lapse.

Figure 3-11 A screen shot of the PVT task as it appears on the stimulus VDU. The white number is the stimulus for the PVT. It first appears as a zero and then counts upwards in ms until the push-switch button on top of the joystick has been pressed.

3.5 Procedure

3.5.1 Polysomnographic testing

All participants were required to have their OSA severity status assessed by a Level II Polysomnography prior to undergoing MRI or cognitive testing (see section 3.3.1 The process required the participant to attend an appointment at the Christchurch Hospital Sleep Unit where a sleep technologist, registered by the Board of Registered Polysomnographic Technologists (BRPT), (New York, Washington), placed the electrodes and sensors necessary for the polysomnography test. The participant then went home set up for a night polysomnography undertaken in their own bed. In general terms this would mean going to bed and arising at their normal times that needed to be at least six hours. The following morning, the participant returned the recorder to the Sleep unit for analysis.

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The polysomnography sleep records were scored manually in accordance with American Academy of Sleep Medicine guidelines (Berry et al., 2017) by a registered polysomnographic technologist employed by the Sleep Unit. The Level II polysomnography provided: sleep statistics i.e., sleep period, total sleep time, sleep efficiency, sleep onset, etc. The polysomnography also reported, sleep staging, ie, the amount and, percentage of total sleep time spent in the sleep stages. sleep summary, arousal statistics, position analysis, periodic limb movement analysis, pulse statistics, oximetry statistics, and respiratory indices – including the AHI score used to determine group membership eligibility (see Appendix D for an example of a sleep report).

In addition to the polysomnography, all participants undertook a series of tasks that included an MRI scan, cognitive tests including a PVT, and a microsleep study. The polysomnography was to be completed and scored prior to the commencement of the remaining tests. However, due to Sleep Unit staff workloads, in some instances this did not happen, but every reasonable effort was made to ensure that the time between all baseline, or follow-up tests did not exceed 4 weeks.

3.5.2 MRI ASL Process

3.5.2.1 MRI data acquisition

All participants underwent a series of MRI protocols at baseline and again following an intervening period of approximately six months. All MRI scans were scheduled for an afternoon session. Each set of scans took approximately 30 minutes to complete. Participants were asked to arrive 15 minutes prior to their appointed time where they were met by the Co-ordinating Investigator, briefed on the procedure, and asked to provide their written consent.

The sequence of scans at both baseline and follow-up included an initial localizer scan followed by a T1-weighted spoiled gradient recalled echo structural scan (T1 SPGR), and then the pseudo-continuous ASL (PCASL). All scans were conducted by registered MRI technologists and clinically reviewed by a Registered Radiologist. No participant had to be withdrawn from participating in the study because of clinical concerns arising out of the scans or because of unusable MRI scans.

3.5.2.2 Observer Rating of Drowsiness

During the ASL sequence, a video recording of the participant’s left eye was made from which a subsequent subjective drowsiness assessment was made by the Co-ordinating Investigator. To facilitate this process, the MRI Technologist instructed the participant to keep their eyes open, except for normal blinks, during the ASL procedure. Furthermore, during this procedure, background music was muted.

68 3.5.3 Cognitive test procedure

Cognitive testing was undertaken at the New Zealand Brain Research Institute rooms. The cognitive tests applied in this study are listed in Table 3-2, along with the neurocognitive domain investigated.

To ensure homogenous circadian phasic and tonic arousal levels, all cognitive testing sessions were scheduled to begin at 1:00 PM. Participants were instructed to eat a normal lunch, but to avoid coffee. Testing took approximately three hours to complete of which the PVT test and Microsleep testing took 45 mins. Cognitive testing was done in a bright, naturally lit room measuring approximately 3m x 2.5m that contained a single desk separating the participant from the Co-ordinating Investigator (RB), who administered the tests. This room, in the New Zealand Brain Research building, Christchurch, was in a quiet part of the building away from most noise.

The session began with the participant completing a Beck Depression Inventory (BDI ii) (García-Batista et al., 2018), the PSQI questionnaire (Buysse et al., 1989), the Epworth Sleepiness Scale (Johns, 1991), and the health-related quality-of-life SF12 form (Huo et al., 2018). Following the completion of those four surveys the participant next completed the battery of cognitive tests in the order listed in Table 3-2.

At the completion of the Judgement of Line task, participants were allowed a 10 min rest period. They were then taken into a separate room, which housed the continuous tracking task used to measure microsleeps, and PVT set-up. Here they completed the 30-minute continuous tracking task and the 10- minute PVT task.

3.6 Statistical considerations

3.6.1 Statistical power and effect

GPower (Faul et al., 2007) was used to calculate the minimum effect size that our analyses were powered to observe. Power calculations required the following inputs: type of statistical test to be used, alpha level (0.5), whether the test would be one or two tailed, the participant numbers (n = 30 for moderate OSA and n = 15 for treated, untreated and controls) and desired power level, which by convention, we used 80% ("Article Summaries for May 2015 Psychosomatic Medicine, Volume 77, Issue 4," 2015; Howell, 2007). Furthermore, according to Brysbaert (2019), an effect size of d = .4 is a good first estimate of the smallest effect size of interest in psychological research.

The following power levels were calculated:

1) To determine if there were regions in the brain which showed lower cerebral perfusion in people with moderate OSA compared with controls at baseline, a t-test (independent groups) with n = 30 vs n = 15 provides 80% power to observe an effect size of 0.80 or greater (alpha level 0.05, one-sided).

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2) To determine if there are regions in the brain which show lower cerebral perfusion in people with moderate OSA after 6 months of APAP treatment compared with people with moderate OSA who have not had treatment, a t-test (independent groups) n=15 vs n=15 will provide 80% power to observe an effect size of 0.93 or greater (alpha level 0.05, one-sided). This is the least powerful comparison.

3) To determine if there are regions in the brain which show higher cerebral perfusion in people with moderate OSA after 6 months of APAP treatment compared with their cerebral perfusion at baseline prior to treatment, a t-test (dependent sample – intraindividual comparison) with n = 15 vs n = 15 provides 80% power to observe an effect size of at least 0.47 or greater (alpha level 0.05, one-sided).

4) To determine if there is an overall effect of group and APAP-treatment on microsleeps, an ANOVA (repeated measures, within-between interaction) includes three groups of n = 15 with two measurements should provide 80% power to observe an effect size of 0.24 or greater (alpha level 0.05), which is less than generally acceptable. (Brysbaert, 2019).

Given that statical power of 80% is generally acceptable (Page, 2014), then the power of the study would be adequate even for the least powerful comparison our study design allowed.

3.6.2 Overview

This was a longitudinal quantitative study to investigate the impact of OSA on cerebral perfusion, cognition, microsleeps and quality of life measures. It involves the collection of cerebral perfusion data and cognitive functioning scores at baseline and again after an intervention period of approximately six-months. It also involved for those in the moderate OSA group a follow-up polysomnography. This second sleep study was done off APAP so that we could get an indication of any change in AHI over that of 6 months earlier at baseline. If APAP was purely a symptomatic treatment, we would have expected no change, unless there was an actual deterioration in the underlying condition of sufficient magnitude as to be detected after only 6 months.

Following the six-month intervention period during which the treated cohort was provided with APAP therapy, the treated cohort underwent a second set of tests that consisted of polysomnography, ASL, cognition, and microsleeps with polysomnography, ASL and cognitive/microsleep tests done on separate days. Participants used APAP on the night immediately prior to their ASL, cognitive, and microsleep tests. On the night of the polysomnography test, they were instructed not to use it. Moreover, in all instances, the second polysomnography was not undertaken on nights immediately prior to the other tests. Accordingly, in those instances where the polysomnography preceded the other

70 tests, treated participants were instructed to continue using APAP on subsequent nights until the other tests were completed (i.e., ASL, cognition, microsleeps).

Kribbs et al. (1993) found that just one night off nasal CPAP was sufficient to reverse all of the sleep and daytime alertness gains derived from CPAP Similarly, Weaver and Sawyer (2010) found that even one night off CPAP was sufficient for the return of symptoms and other manifestations of OSA. Furthermore, CPAP withdrawal has been shown to led to an immediate recurrence of sleep apnoea with increases in apnoea index, arousal index and oxygen desaturation (Grunstein et al., 1996).

Refraining from APAP on the night of the polysomnography was not considered to disrupt the treatment regimen, as an acceptable compliance for the treatment of OSA requires CPAP to be used on at least 70% of nights in any given month (Avlonitou et al., 2012).

Of interest, was the impact of APAP therapy on perfusion, cognition, microsleeps, and quality of life from the treated cohort compared to the results from the untreated cohort. To control for possible practice effects that can occur in a longitudinal study (Caruana et al., 2015), a control group without OSA was established and subjected to the same assessments with the exception of the second polysomnography that was not required from the control group.

3.6.3 One-tailed tests

One-tailed tests

Both one-tailed and two-tailed tests were used in this study. Where one-tailed tests were used, this was on basis of there being no conceivable mechanism for a significant difference being in the direction other than that hypothesized, otherwise two-tailed tests were used (Coolican, 2005).

3.6.4 Significance level and p-values

The significance level applied to statistical tests is α = .05. Fisher (1990) nominated 0.05 as a threshold of significance early in the 20th century. It is a value typically used in social sciences (Coolican, 2005; Moran, 2003). However, the usefulness of a p-value has being questioned because it is prone to being miss- or over- interpreted, leading to serious methodological errors and misinterpretations (Panagiotakos, 2008). Yet, despite the criticisms made, all agree that the significance level for a test should be decided before the data are viewed and compared.

With respect to multiple comparisons, there is an increased risk of making a type I error unless a correction is applied to the p value. Frequently in social science studies, the Bonferroni correction has been employed. However, several papers have criticized the Bonferroni correction as being too severe (Columb & Sagadi, 2006; Moran, 2003), as it can substantially increase the risk of committing a type II error. Benjamini and Hochberg (1995) proposed a methodology to avoid this criticism by controlling

71 the expected proportion of errors among the rejected hypotheses which they called the false discovery rate (FDR). Nonetheless, where the measurements are independent the Bonferroni correction is accurate. However, measurements of CBF, both global and regional, involve multiple comparisons of adjacent voxel that are not independent. Accordingly, where multiple comparisons of perfusion are made in this study, an FDR corrected p value was used (Benjamini & Hochberg, 1995; Columb & Sagadi, 2006).

3.6.4.1 Measures

Five measures were analysed in this study. These measures are shown in Table 3-4, along with their source – i.e., the task that produced them – and their application.

Table 3-4 Measures, their source, and application.

Measure Task Source Application Primary Demographic measures. Participant interviews, Used to identify and define the Polysomnography. three cohorts. Difference between MRI ASL scans. To understand how controlling global cerebral moderate OSA affects perfusion at baseline perfusion rates. and follow-up. Difference between MRIASL scans. To understand how controlling regional cerebral moderate OSA affects perfusion at baseline perfusion rates. and follow-up. Difference between Cognitive tests To understand how controlling cognition scores at including the PVT test moderate OSA affects baseline and follow-up. cognition. To understand how changes in cerebral perfusion affects cognition. Difference between Microsleep task To understand how controlling microsleep scores at moderate OSA affects baseline and follow-up microsleep rates. Secondary Microsleep duration Tracking and dual tasks To test if either task differences or microsleep frequency impact microsleep duration Tracking error Tracking and dual tasks To test task differences and to determine if motor skills reflect microsleep frequency Reaction time PVT and dual tasks To test task differences and to determine if motor skills

72 Measure Task Source Application reflect the frequency of attention lapses

3.6.5 Research questions and statistical tests

Following confirmation that the data is parametric using normality testing feature of SPSS, unpaired t- tests shall be used to determine differences in microsleeps and cerebral perfusion at baseline between controls, people with moderate OSA who are treated, and people with moderate OSA who are untreated. Otherwise, nonparametric statistical methods shall be used such as the Mann-Whitney U test, and the Wilcoxon Signed Ranks test.

Questions that give understanding to the make-up of the three cohorts that comprising the two main groups (moderate OSA and control) along with the research questions arising from the six hypotheses stated in the introduction along with the statistical test are tabulated below Table 3-5.

Table 3-5 Hypotheses and research questions along with their proposed statistical test

Hypothesis and research questions Proposed statistical tests

Do group demographics between the To test for group differences in age Mann-Whitney U moderate OSA group and the control group test was used. differ at baseline? To test for gender differences a Chi square test was used.

Do cohort demographics between the treated To test for group differences in age Mann-Whitney U cohort, the untreated cohort differ at test was used. baseline? To test for gender differences a Chi square test was used.

Does the mean PSQI score between the To test for group differences a Student t-test was used moderate OSA group and the control group differ at baseline?

Does the mean ESS score between the To test for group differences in ESS a Mann-Whitney moderate OSA group and the control group U test was used. differ at baseline?

Does the mean Montreal SF-36 score To test for group differences in SF-36 a Mann- between the moderate OSA group and the Whitney U test was used. control group differ at baseline?

73 Hypothesis and research questions Proposed statistical tests

Does the moderate OSA group experience Mean baseline global perfusion of the moderate OSA reduced cerebral perfusion while awake group will be tested using an independent samples t- compared to the control group? test (one-tailed) to determine if it is lower than the mean perfusion from the control group as hypothesized.

Regional perfusion for the moderate OSA group and the control group will be tested at baseline using FSL’s Randomise independent samples t-test script. This is a non-parametric permutation inference tool used in neuroimaging studies (Winkler et al., 2014).

Are the Cognitive scores of the moderate Baseline cognitive scores from each task for the OSA group less than the scores from the moderate OSA group will be tested to determine if it control group? is lower than the cognitive scores from the control group as hypothesized, using an independent sample t-test.

Does the Moderate-OSA group experience Baseline microsleep counts from the Moderate-OSA more microsleeps than the control group? cohort and the control cohort will be tested using Mann-Whitney U.

Is the mean cerebral perfusion, experienced Differences in the mean global perfusion scores of the by the treated cohort at baseline, increased treated cohort at baseline and follow-up will be tested following six-months of APAP therapy? using a paired sample t-test.

Differences in the regional perfusion scores of the treated cohort at baseline and follow-up will be tested using FSL’s Randomise paired samples t-test script with AHI, drowsiness, age, sex, and gCBF as co- variates.

Is the mean level of cognition, experienced A paired samples t-test will be undertaken to by the treated cohort at baseline, improved determine if the mean cognitive score at follow-up is following six-months of APAP therapy? higher than the cognitive score at baseline.

Is the mean count of microsleeps, A paired samples t-test will be undertaken to experienced by the treated cohort at baseline, determine if the mean microsleep count at follow-up reduced following six-months of APAP is reduced compared to the count taken at baseline. therapy?

Are measures of ODI, ST90, arousal time, Measures of AHI, ODI, ST90, arousal time, and BMI and BMI better correlates of global cerebral will be correlated with global cerebral perfusion. blood flow than AHI?

74 3.6.6 Effect sizes of OSA on cognition

Table 3-6 (Stranks & Crowe, 2016) lists the effect sizes for 13 cognitive domains. Using meta- analysis, Stranks and Crowe investigated the cognitive functioning of 811 patients in 19 studies prior to them receiving any treatment. OSA severity ranged from mild to severe with seven studies being mild to severe, seven studies being moderate or severe and 5 studies being severe only.

75 Table 3-6 Summary statistics for the effects of OSA (listed in order of decreasing weighted negative effect size) on each domain of cognitive functioning.

Cognitive domain Number of Cohen's d 95% CI lower 95% CI upper studies

Non-verbal memory 7 −0.79* −0.97 −0.60

Concept formation 4 −0.78* −1.12 −0.45

Psychomotor speed 7 −0.70* −0.92 −0.49

Construction 7 −0.69* −0.86 −0.51

Executive function 12 −0.64* −0.70 −0.48

Perception 1 −0.61 −1.30 0.07

Motor 6 −0.60* −0.74 −0.46 control/performance

Attention 14 −0.57* −0.66 −0.47

Speed of processing 11 −0.52* −0.69 −0.35

Working memory 8 −0.41* −0.61 −0.22

Verbal memory 10 −0.39* −0.50 −0.28

Verbal 3 −0.38* −0.73 −0.03 functions/language

Verbal reasoning 11 −0.36* −0.51 −0.22

76 Chapter 4 Results

4.1 Introduction

Subject demographics, clinical and polysomnographic measures, brain structure imaging, along with measures of global and regional cerebral perfusion, cognition, and microsleep counts were recorded at baseline and repeated following an approximate seven-month intervention period. During that time, the treated cohort undertook a course of APAP therapy. A summary of the research questions and hypotheses addressed is listed in Methods 3.6.3.

4.2 Research participants

The study design was based on 15 members being recruited for the treated cohort, 15 members for the untreated cohort and 15 members for the control group. One hundred and eleven prospective participants were approached. The final numbers recruited were:

• 13 members in the treated cohort,

• 11 members in the untreated cohort of which four came from the treated cohort on evidence of non-APAP compliance, and

• 7 members in the control group.

The total number was 31 of which the moderate severity group had 24 participants.

To determine if a bias had been introduced into the Untreated cohort because of those who could not tolerate OSA being recruited, their individual results were compared against those results from the other remaining seven members of the Untreated cohort. Comparing the mean and standard deviation for 16 measures of sleep, cognition, microsleeps and QOL taken at baseline showed that in 17 of 64 instances was 1x SD was exceeded and in only one instance was 2x SDs exceeded, this being for microsleeps.

4.2.1 Recruitment and exclusions

Two flow charts show where recruits for the moderate severity group and control group were excluded and the reasons why.

77 Moderate-severity OSA group

CDHB patients with initial diagnosis of Number who declined participation due moderate OSA and who had agreed to to: be contacted re participation N = 78 • Lack of interest = 5 • Reason not given = 6 N = 11 Number who agreed to initial screening. N = 67 Number excluded due to: • Language = 2 • AHI < 15 or > 30 = 29 Number with confirmed moderate- N = 31 severity OSA who completed Baseline tests. Number subsequently lost due to: N 36 • MRI intolerance = 2 • CPAP intolerance = 2 Number who completed Follow-up • Loss of interest = 6 tests. • Medical reasons = 2 N = 24 N = 12

Number in the Treated cohort. Number in the Untreated cohort. N = 13 N = 11

Figure 4-1. Recruitment results for the moderate sleep apnoea group. Note that the medical reasons were not related to any of the studies interventions.

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Control Group

Number of potential controls Number who declined contacted re participation. participation due to: N = 33 • Lack of interest = 2 • Time commitment = 5 N = 7

Number who agreed to initial screening. Number excluded following level N = 26 2 polysomnography due to: • AHI > 10 N = 5

Number with AHI < 10 who completed Baseline tests. N = 21 Number lost due to: • Lack of commitment = 4 • Medical = 1 • Scheduling = 2 Number who completed Follow-up • Withdrawal of consent = 2 tests. N = 9 N = 12

Number with AHI < 5 Number with AHI 5 < AHI < 10 N = 7 N = 5

Figure 4-2. Recruitment results for the control group.

4.3 APAP Compliance

4.3.1 Usage

APAP compliance was monitored in the treated group. The mean number of days between beginning APAP therapy and the follow-up MRI scan was 200.3 days (SD = 23.3, range 191 – 259). The mean number of days between beginning APAP therapy and the follow-up cognitive tests was 205.6 days (SD = 23.9, range 192 – 270).

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The percentage of days APAP was used over the 180-day period preceding the follow-up cognitive testing was 90.8% or 163.5 days (SD = 17.9). Nightly use over the same 180-day period was 6.08 hr (range = 3.97–7.88 h). On the other hand, because of scheduling differences between tasks, APAP mean usage over the 180-day period preceding the MRI scan was 91.8% or 165.3 days (SD = 14.2 days). Nightly use over the 180 days immediately prior to the MRI scan was = 6.13 h (min 4.73 h, max 7.87 h). There was no difference in either the number of days APAP was used (t(12) = -1.18, p = .259), or the nightly hours used (t(12) = -0.92, p = .378) for the two periods. All treated cohort members met compliancy requirements on the day of their ASL scan and cognitive/microsleep tests.

Following the recommendation by Aloia et al. (2004) to compare APAP usage with cognitive results, a Pearson's product-moment correlation was run. There was no statistically significant correlation between hours of APAP use and cognition, r(11) = .24, p = .423 (see Figure 4-9).

Figure 4-3 Scatter plot of the treated cohort’s (n = 13) standardized total cognitive score plotted against their APAP usage (expressed as hours of use).

4.3.2 Therapy

Over the 180-day treatment period preceding follow-up cognitive testing, the mean AHI for those receiving APAP therapy was 3.68 events per hour (range = 1.1–8.9) with a median of 2.6 (see chart of individual scores in ).

80 Figure 4-4 Average AHI events per hour for the treated (n = 13) and untreated (n = 11) cohorts during the 180 days prior to their follow-up cognitive test.

4.3.3 APAP machine retention.

At the conclusion of the data collection phase, those participants in the treated cohort were generously offered the APAP machines by ResMed who provided them for the study. Of the 19 machines assigned to participants three were returned with the 16 remaining machines being retained. Eleven of the 12 comments addressing that offer, expressed a willingness by the recipients to continue using the machines despite this study not being able to demonstrate an improvement in cerebral perfusion, or cognition, or microsleeping. Comments by those retaining the machines indicate a benefit was found in sleep quality for both the wearer and bed partner (see comments in ).

81 Table 4-1. Comments received by 12 participants on being asked if they wanted to retain the APAP machine they were prescribed for their use.

APAP recipient comments

“Now that I have the correct fitting mask, I'm happy to keep it and continue using it. I'm grateful to ResMed for their generous offer and fully accept the terms on which I assume ownership.”

“Thanks. I’ll keep the machine. I’m also happy to participate in any future tests and have a couple of friends who could also be interested in being ‘guinea-pigs’.”

“I appreciate ResMed offering me to keep the machine. I am obliged to keep using it at my wife’s instruction. Apparently, I snore the house down if I don’t.”

“I am still using the machine so would like to keep it. Thanks”

“I’d rather keep the machine please.”

“If at all possible, I would like to keep the APAP machine. I am still using it almost every night and both my wife and I feel that I sleep better when I am using it (hopefully it is still doing me some good).”

“I am using the APAP nightly and getting great benefit from it. I am no longer tired during the day and feeling quite rested after a night’s sleep. Very keen to hang onto it.”

“I'm still using the APAP machine, and feel like I'm getting some benefits from it, so I will gratefully hang on to it.”

“I would like to keep the APAP machine.”

“Ok thanks for that, I am still using the machine and would like to keep doing so as I am sure it is helping me.”

“Thanks for the kind offer, but I’m trying new medication so will return the machine.”

“I continue to use the machine with very good effect. My average nightly sleep time is slowly creeping up to the six-hour mark and I am very grateful! I bought a new nose-only mask which suits me better. At the rare times I don't use it, I remember what a complete pain it is to wake in a panic to start breathing again. So, thank you for this and the opportunity to keep the machine. My head is very much clearer each day and I have the luxury of waking naturally each morning.”

82 4.4 Test-retest intervals

This was a repeated-measures longitudinal study in which the intended interval between test-retest was to be approximately 180 days. However, because limited access to the MRI scanner did not always align with subject availability, the mean interval between the MRI test-retest across all cohorts was 231.9 days, (Min = 171, Max = 434), while the mean interval between the cognitive and microsleep tests was 241.0 days (Min = 192, Max = 349). Note that in all instances the microsleep test was completed on the same day as the cognitive tests. At baseline, the mean number of days between the MRI scan and the cognitive tests was 23.2 days (Min = 1, Max = 232).

Follow-up intervals: The difference in time intervals between the three cohorts, treated, untreated, and control, were explored using a boxplot and the Kruskal-Wallis H test. The boxplots of the test-retest interval times for the three cohorts across MRI scans and the cognitive/microsleep tests are shown in Figure 4-5, followed by Kruskal-Wallis H test results in Table 4-3.

Figure 4-5. Interval in days between MRI scans and cognitive/microsleep tests at baseline and follow- up for the three cohorts.

Of the three cohorts, the highest level of variability and the largest intervals between tests and scans were in the untreated cohort (see Table 4-3). In one instance, it took 434 days before the follow-up MRI scan could be completed. Nonetheless, to determine if there was a difference in the test-retest interval between baseline and follow-up tests, a non-parametric Kruskal-Wallis H test was run. It showed that

83 the test-retest intervals for the three cohorts were not significantly different for either MRI, or cognition/microsleep.

Table 4-2. Cognitive and microsleep tests, and the MRI Scan intervals between baseline and follow- up.

Cohort N Median IQR Kruskal-Wallis H Test (days) Treated 13 235 58.5 Cognitive/microsleep Interval Untreated 11 245 54

2 Control 7 231 43 χ (2) = 3.87, p = .145 Treated 13 213 49.5 MRI Scan Interval Untreated 11 224 64

2 Control 7 210 42 χ (2) = 1.49, p = .474

Overall, the treated, untreated and control intervals between baseline and follow-up for cognition/microsleep (median = 236.0 days, IQR = 53.0) were longer than those for MRI scans (median = 214.0, IQR = 43.0). Nonetheless, the interval difference between the two sets of tests was not statistically significant (Z = 1.21, p = .228).

4.5 Polysomnographic results

4.5.1 Apnoea Hypopnoea Index

Research participants were approached for possible participation in this research based on a two- channel, level III polysomnography, performed as a screening study for clinical indications. Those that agreed to participate underwent a further baseline level II polysomnography. As summarized above, five patients had a higher AHI on polysomnography than with the screening test and as a result were excluded. The baseline descriptive data for the participants included in the moderate sleep apnoea and the control group are shown in Figure 4-6 and tabulated in Table 4-4.

84 Figure 4-6. Apnoea hypopnoea count per hour for the three cohorts taken at baseline. The boxplots show maximum and minimum values along with both upper and lower quartiles as well as median score.

The AHI difference between the moderate group and the control group at baseline was significant (t(29) = 10.90, p = <.001, d = 5.35) however the difference in AHI was not significantly different between the treated cohort and the untreated cohort (t(22) = 0.41, p = 0.689, d = 0.16)

Table 4-3. Group/Cohort AHI statistics taken at baseline.

N Mean SD Minimum Maximum Group/Cohort AHI 24 21.72 4.49 15.6 29.9 Moderate sleep apnoea 13 22.07 4.47 16.3 29.9 Treated 11 21.30 4.70 15.6 27.8 Untreated 7 2.46 2.07 0.2 4.9 Control

The two cohorts treated and untreated, that constituted the group with moderate OSA, underwent a second level II polysomnography following an intervention period of 6–7 months (Table 4-5). The control group did not have a follow-up polysomnography test. It must be noted that APAP treatment

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was to be suspended during the night of the polysomnography. However, in three instances (participants 001, 002 and 077), APAP was used the night of the repeated polysomnography testing contra to instructions. Accordingly, those subject’s follow-up polysomnography results, i.e., AHI, oxygen desaturation index, arousal count and total time below ST90 were excluded from analysis. Also, two untreated participants declined to undergo a second polysomnography (Participants 028, 089) and one untreated subject was unable to get a booking with the Sleep Unit within the studies funding cut-off date (Participant 093).

Table 4-4. Moderate sleep apnoea group AHI statistics at follow-up.

N Mean SD Minimum Maximum Cohort AHI 10 17.92 13.29 5.9 49.7 Treated 8 25.56 10.19 14.9 40.5 Untreated

Figure 4-7 shows the relationship between baseline and follow-up AHI for the treated and untreated cohorts. Four participants from the treated cohort and one subject from the untreated cohort had an AHI at follow-up which would reduce their OSA severity category from moderate to mild. One subject from the treated cohort, and three participants from the untreated cohort had AHIs at follow-up which increased their OSA severity level to severe. The member of the treated cohort whose AHI score increased from 26.3 to 49.7 at follow-up had good compliance with an average daily use of 5 h, 46 min for 168/193 days. However supine sleep increased markedly from 0% at baseline to 70.6% at follow- up which may have accounted for the increase.

The follow-up polysomnography was also compared to the baseline polysomnography to see if any pathological factors had arisen over the intervening months that could impact AHI results. There were none.

86 Figure 4-7. Individual AHI scores for the treated (n = 10) and untreated (n = 9) cohorts at baseline and follow-up. Only the treated members who did not use their APAP machine on the night of the polysomnography are shown. The control group did not do a follow-up polysomnography hence the absence of a line plot for that group. Boxplots show maximum and minimum values along with both upper and lower quartiles as well as median score.

4.5.1.1 AHI differences between groups and between baseline and follow-up

At baseline, the AHI in the moderate group was higher than the AHI in the control group (t(29) = 10.91, p < .001), and at baseline AHI did not differ significantly between the treated and the untreated cohorts

(t(22) = 0.41, p = .686, two tailed).

A Pearson correlation across all moderate sleep apnoea participants indicated there was a positive

association between AHI at baseline and again at follow-up (r(19) = 0.50, p = .030). However, follow-

up AHIs from either the treated cohort (t(9) = 1.22, p = .253), or the untreated cohort (t(7) = -1.03, p = .336) were not significantly different from baseline AHIs (see Table 4-6). Three participants in the treated cohort were excluded because they used the APAP machine on the night of the follow-up polysomnography contra to the study design.

87 Table 4-5. AHI scores for the treated and untreated cohorts at Baseline and at follow-up

Baseline AHI Follow-up AHI Moderate sleep apnoea N = 10 M = 23.44 SD = 4.17 N = 10 M = 18.92 SD = 12.64 Treated N = 8 M = 22.69 SD = 4.75 N = 8 M = 25.56 SD = 10.20 Untreated

4.5.2 Arousal Counts

The number of arousals per hour of sleep for the moderate OSA group and the control group were recorded during their baseline level II polysomnography. The difference in arousal counts between the two groups were tested for equality using the nonparametric Mann-Whitney U test for two independent samples which found a difference between the moderate group (median = 13.1) and the control group (median = 8.4), U = 38.5, z = -2.15, p = .029, r = 0.39 using an exact sampling distribution for U. However, there was no significant difference in arousal counts between baseline and following APAP treatment for the treated cohort, using the non-parametric Wilcoxon Signed ranks test, (z = -0.53, p = .638 two-tailed, r = 0.11). This result is reflected in Figure 4-8. With respect to the treated cohort, the small difference between arousal counts at baseline compared to the follow-up counts indicates that APAP treatment does not have a lasting therapeutic impact on arousal counts beyond the night of treatment.

In keeping with expectations, arousal counts decreased in 8 of the 13 instances for the treated cohort, whilst 3 of the 8 untreated cohort members decreased arousal counts. Furthermore, as can be seen, there are large variances in scores for both cohorts in both directions.

The outlier at follow-up (54 arousals per hour) was the same participant whose AHI score at follow-up increased markedly. Evident at the individual level is the marked heterogeneity of counts within and between participants

The participants were requested to cease APAP treatment on the night of the repeat sleep study, so changes in arousal counts during APAP usage cannot be inferred.

88 Figure 4-8. Arousal counts per hour at baseline and follow-up for the treated (n = 13) and untreated (n = 8) cohorts. The control group did not do a follow-up polysomnography.

Arousal counts were plotted against AHI for all participants (Figure 4-9). A Pearson correlation test between the two factors confirmed a positive correlation between AHI and arousal counts (r(31) = 0.38, p = .036) – as the AHI increased so did the arousal count.

This positive correlation indicates that the moderate sleep apnoea group had a tendency towards more arousals during sleep than the control group which can be seen in Figure 4-9.

With respect to the treated cohort, the almost zero difference between arousal counts at baseline compared to the follow-up counts indicates that APAP treatment does not have a lasting therapeutic impact on arousal counts beyond the night of treatment.

There was no difference between baseline and follow-up arousal counts for the treated and untreated cohorts. However, there was considerable individual differences with two treated members increasing their arousal counts significantly at follow-up. It is noted that that the member with the greatest increase in arousal count at follow-up also had the greatest increase in their follow-up AHI count.

89

Figure 4-9. Arousal counts and apnoea/hypopnoea index scores at baseline for treated (n = 13) and untreated (n = 11). The fitted line indicates a positive correlation between the two factors.

Correlations between arousal counts, Epworth Sleep Quality results, Pittsburgh Quality-of-life indices and Medical Outcomes study, including all eight subtests, were tested using the Pearson correlation test.

Only arousal counts and Pittsburgh Quality-of-life scores were moderately correlated (r(31) = 0.37, p = .039).

4.5.3 Oximetry – ST90

SpO2, also known as oxygen saturation, is a measure of the amount of oxygen-carrying haemoglobin in the blood relative to the amount of haemoglobin not carrying oxygen. Time spent with peripheral oxygen saturation levels below 90% was measured during the polysomnography at baseline and at follow-up for the two cohorts that constitute the moderate sleep apnoea group, and at baseline for the control group. A higher number is associated with more time spent with suboptimal blood-oxygen levels.

90

Figure 4-10. Total oxygen saturation time spent below 90 percent for the two cohorts that constitute the moderate sleep apnoea group, and the control group, all at baseline.

The median baseline value for the control group was 12.2 (IQR = 27.6), which was slightly, but not significantly higher (U = 70.00, z = -0.66, p = .532) than the median value for the moderate sleep apnoea group (Median = 11.8, IQR = 28.4.

Spearman’s rho was used to correlate Total time ST90 against arousal counts, ESS, behavioural microsleeps, drowsiness, global cerebral perfusion, PSQI and SF-36 at baseline (Table 4-7). None of the correlations were significant.

Table 4-6. Spearman’s rho correlations between ST90 against AHI, arousal, ESS, microsleeps, global cerebral perfusion, PSQI, and SF 36.

AHI Arousa ESS MSleep gCBF PSQI SF-36

l

rs .27 0.03 0.27 0.25 -.02 0.20 -0.10 ST90 p .144 .895 .135 .173 .920 .275 .584

Spearman’s rho was also used to calculate the correlation between ST90 and composite cognitive score at baseline. The result approached significance (rs(30) = -0.33, p = .07) with a negative correlation indicating that greater time spent with reduced oxygen while asleep is associated with a lower cognition score when awake.

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Baseline and follow-up counts for ST90 when SP02 fell below 90% are presented in Figure 4-11. The treated member (participant 013) whose SPO2 count went from 20 at baseline to nearly 30 at follow-up and the untreated member (participant 091) score went from just over 2% to 20% had their scores checked for data entry eerrors and conformed as correct. Their scores were also compared with their cognitive scores. Participant 013 had an overall cognitive score of 0.57 SD while participant 091 had a follow-up score for cognition at -0.09 SD and was the lowest of the follow-up scores for the untreated cohort. A review of the five cognitive domains showed that participant 91 had the lowest “speed of processing” score (-1.79) and scond lowest “executive function” score of -1.57 SD. It is also worth noting that the participant with the second highest overall cognitive score at follow-up also had a moderately low ST90 score of 1.2%. The participant with the highest cognitive score at follow-up did not have an oximetry test done at follow-up.

Figure 4-11 Total saturation time below 90% (min) for the treated (n = 13) and untreated (n = 11) cohorts at baseline and follow-up. An increased score indicates a worsened state.

4.5.4 Correlation between AHI and other measures

Spearman correlations indicate that AHI was positively associated with arousal counts, ESS, and PSQI scores, but not with Microsleeps, or SF-36 score at baseline (Table 4-7). However, because the SF-36 instrument is more than a measure of sleepiness, the possibility needed to be explored that some of the items constituting the SF-36 might obfuscate valid associations. Accordingly, the correlations between AHI and each of the eight items that comprise the SF-36 (i.e., physical functioning, role-physical, bodily

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pain, general health, vitality, social functioning, role-emotional, and mental health) were calculated (Table 4-8).

Table 4-7. Spearman-rho correlations between AHI and number of arousals, ESS, Microsleep counts, PSQI scores and the SF-36 ratings.

Arousal ESS Micro- PSQI SF-36

sleeps

rs .36 * .58 ** .11 .43 * -.09 AHI p .048 .001 .554 .015 .630

Note: N = 31 in all instances.

*Correlation significant at 0.05 level, ** correlation significant at 0.001 level

Table 4-8. Spearman-rho correlations for AHI against the eight SF-36 items.

Physical Role- Bodily General Vitality Social Role- Mental

Function Physical Pain Health Function Emotion Health

rs -0.24 0.05 0.27 -0.36 -0.37 -0.29 -0.32 -0.32 AHI p .099 .405 .071 .023* .021* .055 .039* .041*

* Correlation significant at 0.05 level (1-tailed). ** Correlation significant at the 0.01 level (1-tailed).

Thus, while the overall score for SF-36 was not significantly correlated with AHI, when the SF-36 is broken into its component parts, four of the components were significantly associated with AHI – General Health, Vitality, Role Emotion, and Mental health. That is, an increase in AHI is associated with poorer health, loss of vitality, increase in emotional problems, and poorer mental health.

4.6 Clinical Results

4.6.1 Body Mass Index (BMI)

To understand the BMI profiles of the moderate sleep apnoea and the control groups, BMI data were collected from all participants at the time of their sleep study. Because the control group did not have a follow-up sleep study, only one set of BMI data collected at baseline was available for that group. Otherwise, two sets of BMI data were available for the two cohorts that made up the moderate sleep apnoea group.

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A boxplot of the BMI scores showing the distributions for the two cohorts that constitute the moderate sleep apnoea group and the control groups at baseline is shown in Figure 4-12.

Figure 4-12. Body mass index scores at baseline for the three cohorts. Box plots show maximum and minimum values along with both upper and lower quartiles as well as median score.

A Mann-Whitney U test found there a difference between the BMI scores for the moderate sleep apnoea group (mean rank = 18.29) and the control group (mean rank = 8.14) (U = 29.00, z = -2.60, p = .006).

Differences between follow-up and baseline BMI scores within the treated and untreated cohorts were tested using the Wilcoxon Signed Ranks test. The BMI median scores for the treated cohort at baseline and follow-up were 27.7 and 28.6 vs 30.2 and 31.8 for the untreated cohort. However, the differences were not significant (z = -0.13, p = .918, and z= -1.12, p = .313, for treated and untreated cohorts respectively) indicating there was no significant change in a person’s BMI score during the period between baseline and follow-up tests.

4.6.2 Drowsiness during the arterial spin labelling scan

The drowsiness of all participants, as proposed by Wierwille and Ellsworth (1994), was rated during the ASL sequence of the MRI scan both at baseline and at follow-up. The correlation between

drowsiness during the MRI scan and AHI was not significant (rs(31) = 0.31, p = .850 two-tailed).

Drowsiness ratings taken at baseline during the MRI scan by AHI at baseline.

94 The distribution of drowsiness levels between the two groups, moderate sleep apnoea and control, were tested for differences using the Mann-Whitney U test. This showed that the moderate sleep apnoea (Median = 13.5) and the control group (Median = 7.0) were not significantly different (U = 65.5, z = - 0.88, p = .391). These two results suggest that drowsiness levels during ASL cannot be primarily attributed to OSA status.

A scatter plot of drowsiness levels during the ASL scan and global cerebral perfusion reveals, at most, a weak correlation between these two factors (Figure 4-13), (rs(31) = -0.32, p = .083).

Figure 4-13. Scatter plot with fitted line of “drowsiness” levels by “global cerebral blood flow” at baseline for all three cohorts..

4.6.3 Epworth Sleepiness Scale

Longer-term sleepiness for all participants was subjectively estimated at baseline and again at follow- up using the ESS instrument, in which a higher score indicates greater sleepiness. The distribution of baseline scores for the treated, untreated and control cohorts are shown in Figure 4-14

The median ESS score of the moderate group at baseline was 13.00 (IQR = 6, which was higher than that for the control group of 7.00 (IQR = 8). (U = 13.00, z = -3.37, p < .001, d = 1.51).

To determine if APAP treatment had any influence over ESS scores, the treated cohort ESS scores at follow-up were compared to the cohort’s baseline scores using a Wilcoxon Signed-Rank test. This 95 showed that APAP treatment of at least six months resulted in an improvement in ESS scores for this cohort (median = 8.0, IQR = 4.0 and median = 13.0, IQR = 4.0 respectively, z = -2.94, p = .003) Conversely, there was no difference in self-reported sleepiness between baseline and follow-up in the untreated cohort (median = 14.0, IQR = 5.0 and median = 13.0, IQR = 8.0 respectively, z = -1.01, p = .312) [Figure 4-14].

Attributing the improved ESS result following APAP treatment to a test-retest effect can be discounted because there was no difference in the follow-up and baseline results in the untreated cohort which would have been expected if a test-retest effect existed.

Evident in Figure 4-14 are the number of treated participants who improved their ESS score, some quite markedly. However, for the untreated and controls the overall shift in the ESS was not so large, albeit that even in those two cohorts there were some large individual changes in both directions. Again, the heterogeneity of the scores both within and between participants is evident.

Figure 4-14 Epworth Sleepiness Scale scores at baseline and follow-up for the treated, untreated and control cohorts (n = 12, 11, 7 respectively). The smaller the Epworth Sleepiness Scale number the better the sleep.

4.6.4 The Pittsburgh Sleep Quality Index

The PSQI was completed by all participants at baseline and again at follow-up. Figure 4-15 shows the distribution of baseline scores for the moderate sleep apnoea group and the control group where a higher

96

score is an indication of poorer sleep quality. The group with moderate OSA had a mean PSQI score of 15.29 (SD = 3.59), whereas the control group’s score was 12.14 (SD = 0.86). The Kolmogorov-

Smirnov test of normality indicated that the PSQI scores followed a normal distribution (D(24) = 0.16, p

= .140, and D(7) = 0.26, p = .149, respectively). Accordingly, the baseline differences in PSQI scores were tested using the parametric independent-samples t-test. This test found that the scores for the

group with moderate OSA and the control groups were different (t(29) = 2.18, p = .019, one-tailed, d = 0.94).

Figure 4-15. Global Pittsburgh Sleep Quality Index scores for the three cohorts taken at baseline. A high score indicates a worse result.

The group difference in PSQI scores was also reflected in a moderate positive correlation between AHI

and PSQI scores (r(31) = 0.48, p = .003, d = 1.09) (Figure 4-16). However, with respect to the baseline PSQI scores between the treated cohort (M = 15.69) and the untreated cohort (M = 14.82), there was no significant difference (t(22) = 0.59, p = .564, d = 0.25).

Comparisons between baseline and follow-up showed that the treated cohort had a better PSQI at follow-up (See Table 4-10). In contrast, there was a trend for the untreated cohort to have a worse score at follow-up. However, the results show that the PSQI scores for the control group did not change.

97 Figure 4-16. Total PSQI scores and AHI at baseline for the three cohorts. The lower the score the better the result.

Table 4-9. Mean PSQI scores for the three cohorts, treated, untreated, and control, at baseline and at follow-up along with SD values and t-tests.

N Baseline mean Follow-up mean P value two- Cohen’s d

(SD) (SD) tailed 13 15.69 (3.82) 14.23 (3.19) .046 0.44 Treated 11 14.82 (3.43) 16.27 (3.61) .070 0.35 Untreated 7 12.14 (2.27 11.57 (2.15) .103 0.55 Control Of interest was whether there was an interaction between the three treatment categories and baseline/follow-up as suggested in Figure 4-17. A 2x3 ANOVA found that the interaction between

treatment category and order of PSQI tests i.e., baseline/follow-up, was not significant, F(2, 52) = 1.20, p = .309, partial ŋ2 = .041.

98

Figure 4-17. Plot of baseline and follow-up PSQI scores for the treated (n = 13) and untreated (n = 11) cohorts and the control group (n = 7). A higher score is an indication of poorer sleep quality.

At the individual levels, it was evident there were large movements in some individual scores (Figure 4-17). For example, one treated participant had an improved standard score of 5.94 at follow-up, whilst another had an improved score of 3.95. Yet one treated participant was worse at follow-up with a z score of -3.06. Overall, nine of 13 treated participants (69%) improved their PSQI score, while only three participants of the 11 members of the untreated cohort (27%) improved their result. With respect to the control group, five participants improved, one stayed the same, and one got worse.

4.6.5 Medical Outcomes Study

The Medical Outcomes Study 36-item short-form health survey (Ware & Sherbourne, 1992), was completed by all participants at baseline and again at follow-up. A higher score indicates a better health state. Figure 4-18 shows the distribution of mean scores for the moderate sleep apnoea group and the control group taken at baseline where the median SF-36 score for the control group is higher than the score for the moderate sleep apnoea group. Descriptive statistics for each of the eight items that make up the SF-36 can be found in Table 4-11

99

Figure 4-18. The distribution of median scores from the SF-36 Health Survey for the moderate sleep apnoea and the control groups measured at baseline where the higher score indicates better health status. This is the median score of the eight items that constitute the survey.

4.6.5.1 SF-36 baseline results

The Mann-Whitney U was used to test for group differences between moderate sleep apnoea and control, across the eight items comprising of the SF-36. The results from those tests are tabulated in Table 4-11 in which at baseline, the overall quality-of-life measure for the moderate sleep apnoea group was not as good as that of the control group. At the item level, this was true for: Physical Functioning, General Health, Vitality, and Social Functioning. Conversely, the mean ranking of scores for Role- Physical, Bodily Pain, Role-Emotion, and Mental Health were not different across the two groups.

100 Table 4-10. Group test summary (moderate sleep apnoea and control) of the eight items that comprise the SF-36 Health Survey including the Median score at baseline.

Moderate group Control group SF-36 Item Median IQR Median IQR P-Value Cohen’s d (one- tailed 74.0 25.0 90.0 10.0 .024 * 0.76 Physical Functioning 75.0 50.0 100.0 25.0 .043 * 0.66 Role Physical 74.0 29.0 51.0 43.0 .108 0.47 Bodily Pain 59.5 33.0 92.0 23.0 .002 ** 1.17 General Health 50.0 24.0 70.0 25.0 .021 * 0.78 Vitality 87.5 47.0 100.0 0.0 .024 * 0.76 Social Functioning 83.3 58.0 100.0 0.0 .062 0.51 Role Emotion 74.0 35.0 80.0 32.0 .107 0.44 Mental Health 68.57 22.43 80.38 12.0 .018 * 0.83 SF-36 Total

*Significant at p < .05, ** significant at p < .01

4.6.5.2 SF-36 follow-up results

The overall SF-36 descriptive statistics for the treated cohort at baseline and at follow-up are shown in Table 4-12

Table 4-11. Descriptive statistics for SF-36 for the treated cohort at baseline and at follow-up.

N Mean SD Treated cohort 13 66.09 17.09 Baseline 13 76.31 15.45 Follow-up

Treated cohort baseline and follow-up overall SF-36 mean scores were tested using paired samples t-

test and were found to be different (t(12) = -1.98, p = .035, one-tailed, d = 0.58). The one-tailed t-test was justified on the basis of our hypothesis that APAP treatment would lead to an improved score.

The separate item scores comprising the SF-36 were also tested to determine which, if not all improved because of APAP therapy. The separate item results of paired samples t-tests for the treated cohort are 101 tabulated in Table 4-13 which shows that Vitality, Role-Emotional, and Mental Health were improved at follow-up, whilst for all other items there were no significant changes.

Table 4-12. T-test results from the treated cohort. Tested were the mean differences between pre- and post-treatment scores of the eight items, which comprise the SF-36.

Test Statistic T(12) p Value (One-tailed) Cohens d SF-36 Item -0.30 .384 0.11 Physical Functioning -1.40 .094 0.39 Role-Physical -0.90 .199 0.25 Bodily Pain -0.62 .273 0.17 General Health -2.03 .033 * 0.56 Vitality -0.39 .353 0.11 Social Functioning -2.74 .009 ** 0.76 Role-Emotional -2.02 .033 * 0.56 Mental Health * Significant at p < .05, ** significant at p < .01

To determine if a test-retest effect obfuscated any change, the difference in the scores between follow- up and baseline for the untreated/control cohorts combined were also tested (any significant difference must be attributable to a possible test-retest order effect). The mean scores were 71.22 (SD = 18.19) at baseline and 73.85 (SD = 20.27) at follow-up. This difference was not significant (t(17) = -1.06, p = .153 (one-tailed). However, when the improvement in the treated score is compared to both the untreated and control group scores at baseline and follow-up both the treated and control group scores improved while the untreated score did not (see Figure 4-19). The sameness in improvement for the treated cohort and the control group shown in the plot strongly suggests the improvement in the treated cohort is the result of a test-retest effect. However, any firm conclusion cannot be made because the large variation in individual scores evident, could obfuscate true results.

At the individual level, 46% of treated participants improved over the treatment period whilst 2% improved in the untreated cohort and 43% of controls improved. With the exception of one treated participant and one untreated participant who had z-scores of 2.55 and -1.79 respectively, the differences were within + 1 z-score (see Appendix F)

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Figure 4-19 -Three items in the SF-36 for which the improvements were statistically significant for the treated cohort. They are Vitality, Role-emotional, and Mental health, measured at baseline and again at follow-up for the three cohorts. (In all cases a higher score indicates an improved result. At follow- up, the population in each cohort was treated = 13, untreated = 11, and control = 7.

4.6.6 Demographics

The demographics of the moderate sleep apnoea group and the control group, along with the demographics of the treated and untreated cohorts that make up the moderate group are listed in Table 4-1.

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Table 4-13. Group demographics for control and Moderate OSA groups as well as the two cohort, Treated and Untreated, that comprise the moderate OSA group.

Cohort N Sex Median Age Ethnicity Group (F) (Range) AHI <5 7 4 62 years (42–73) European 7 Control AHI 15–29 24 10 59 years (32–72) European 23 Maori 1 OSA Asian 1 Treated 13 4 59 years (43–69) European 12 Maori 1

Untreated 11 6 62 years (32–79) European 11 Asian 1

The median age difference between the two groups was not statistically significant (Z = 0.92, p = .365, exact two-tailed). Also, the mean rank ages of the two cohorts that make up the moderate group were not significantly different (Z = 2.32, p = .820, exact two-tailed). Furthermore, there was no significant 2 difference between the female-to-male ratios in the moderate group and in the control groups (χ (1, N =

31) = 3.84, p = .250). Finally, there was no significant difference between female-to-male ratios in the 2 treated and untreated cohorts (χ (1, N = 24) = 1.39, p = .100).

4.7 Global cerebral perfusion

4.7.1 Global cerebral perfusion

To ensure the appropriateness of parametric testing of global cerebral perfusion using an independent samples t-test, the distributions from the moderate sleep apnoea group and from the control group were subjected to the Kolmogorov-Smirnov and the Shapiro-Wilk tests of normality. The results indicated that parametric t-test could be used.

Moderate sleep apnoea and control group perfusion rates were measured to establish if a difference in global perfusion existed between the two groups at baseline. Any group difference in baseline global perfusion would need to be quantified to establish if APAP had an intervention effect. The mean global perfusion and distribution for the two groups at baseline are presented in Figure 4-20. Differences were tested using the student’s t-test. There was no difference in global perfusion scores between the

moderate sleep apnoea group and the control group at baseline (mean 49.26, vs 48.73, t(30) = 0.126, p = .899; d = 0.06).

Global perfusion was not correlated with AHI ( r (50) = -0.14, p = .34) and as can be seen in Figure 4-20 the relationship is not significant.

104

Figure 4-20 Global cerebral blood flow plotted against Apnoea Hypopnoea Index at baseline for the three cohorts.

4.7.2 The effect of APAP treatment on global cerebral perfusion

To determine if APAP treatment changed global cerebral perfusion, a paired samples t-test was conducted between baseline and follow-up results for the treated cohort. This showed that baseline global cerebral perfusion (M = 51.64, SD = 8.56) and the follow-up result (M = 48.56, SD = 7.42) were not significantly different (t(12) = -1.53, p = .150 (two-tailed, d = 0.36). That is, there was no improvement in global cerebral perfusion.

The difference in baseline and follow-up mean global cerebral perfusion rates for the two cohorts not treated (untreated cohort and control group combined) were also tested. A significant difference would be an indication of some intervening factors that may have obfuscated any change in the treated cohort.

Again, the difference was not significant (M = 47.34, vs 45.24, t(17) = -1.21, p = .242 two-tailed), indicating that there was no order effect between baseline and follow-up testing on global cerebral perfusion rates.

A line graph (Figure 4-22 showing the individual change in gCBF for the treated and untreated cohorts at baseline and follow-up. Cohen’s d for the Treated cohort between baseline and follow-up was 0.38, Cohens d for the Untreated cohort was 0.11. Figure 4-21 shows that 4 of 13 treated cohort members improved their gCBF rates, and 5 of 9 untreated cohort members improved. Large variations in

105 individual scores are evident with z-scores ranging from 1.27 to -1.87 in the treated cohort and from 1.51 to -2.03 in the untreated cohort.

Figure 4-22 Global cerebral blood flow values for the treated and untreated cohorts at baseline and follow-up.

4.7.3 Global cerebral perfusion and other OSA indices

Correlations between global cerebral perfusion and arousal counts, ESS scores, PSQI scores, Microsleep counts, and the SF-36 score were calculated using Spearman-rho, Table 4-14. None of the correlations were statistically significant, albeit that the correlation between global cerebral perfusion and arousal counts unexpectedly approached significance (p = .064).

106 Figure 4-23 Arousal counts and global cerebral perfusion at baseline for all participants.

Table 4-14. Spearman-rho correlations between global cerebral perfusion, other indicators of OSA, and sleep quality measures at baseline.

Arousal ESS results Microsleep PSQI SF-36 score

counts counts rs 0.34 -0.22 0.07 0.11 -0.13 Global cerebral perfusion p .064 .226 .725 .552 .495

* Correlation significant at 0.05 level (2-tailed). global cerebral perfusion: global cerebral perfusion

The number of days separating the polysomnography, from which the arousal count was obtained, and the MRI scan was 77.2 (SD = 71.3). To determine if this could have obfuscated the relationship between global cerebral perfusion and arousal counts, a Pearson’s partial correlation was run. It showed that the relationship strengthened slightly rpartial(29) = 0.37 vs rs(29) = 0.34, and became statistically significant (see Table 4-14). Conversely, the mean number of days separating the day the ESS, Microsleeps, PSQI

107

and SF-36 were done, and the day of the MRI scan was 23.2 (SD = 42.9). This interval had no material impact on the statistical significance of the correlations, albeit that the correlation between arousal counts and global cerebral perfusion became significant (Table 4-15).

Table 4-15. Pearson’s partial correlation accounting for the length of time separating the tests.

Arousal ESS results Microsleep PSQI SF-36 score

counts counts

rpartial 0.37 * 0.17 0.13 0.06 -1.8 Global cerebral perfusion p .042 .369 .487 .736 .353

* Correlation significant at 0.05 level (2-tailed). (global cerebral perfusion: global cerebral perfusion)

4.7.4 Global cerebral perfusion and cognition

Baseline measure of global cerebral perfusion was compared to cognition using Spearman’s rho and was found not to be significant across the moderate OSA and control groups for any of the cognitive domains tested or for the overall composite cognitive result (See Table 4-16). A scatter plot of global cerebral perfusion and composite cognition score is shown in Figure 4-24 where the heterogeneity of the data is evident.

Furthermore, the composite cognitive score and the scores from the five domains tested for the moderate OSA group and the control group did not differ at baseline (see 4.11.1

Table 4-16. Spearman’s rho correlation results for global cerebral perfusion, the five cognitive domains tested and the composite cognitive test result.

Attention Executive Memory Speed of Visuo Composite

function processing spatial cognition

0.20 -0.08 -0.20 0.07 0.15 0.09 rs(31) .282 .893 .279 .709 .420 .648 p =

108

Figure 4-24 Global cerebral perfusion and cognition at baseline for the three cohorts

4.8 Regional cerebral perfusion

4.8.1 Regional cerebral perfusion

Differences in regional cerebral perfusion rates between the moderate sleep apnoea group and control group were tested at baseline. Using threshold-free cluster enhancement analysis between participants, with AHI, age, sex, and global cerebral perfusion as explanatory variables along with the voxel dependent grey matter, no differences were found in regional cerebral perfusion after multiple- comparisons correction. Moreover, using the four explanatory variables of AHI, drowsiness during ASL, sex, and age, as used by (Innes et al., 2015) there were still no differences between the moderate OSA group and the controls (p > .05).

Differences in regional cerebral perfusions between the treated cohort at baseline and at follow-up were also tested as above. Figure 4-25 shows a positive effect of APAP therapy on regional perfusion in 22 axial views taken from the treated cohort. No statistically significant regions of improved cerebral perfusion were identified, however, two clusters of voxels (right cerebral cortex/cingulate gyrus region, and the right angular/supramarginal gyri (Harvid Oxford cortical structure map; Figure 4-26) approached significance (p < .10). In addition, no significant differences were found in regional cerebral perfusion between the baseline and follow-up for the untreated and the control group combined (p > .05).

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Treated cohort

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Untreated cohort

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Control group

Figure 4-25 These three multi-sliced-axial views of the human brain come from the group analysis of the treated cohort, untreated cohort and the control group. They show differences in regional cerebral perfusion at follow-up over baseline. The measurement used is the students t-statistic where orange/yellow represents regions of the brain where changes in perfusion were had increased at follow- up, whilst blue represents regions where changes in perfusion had decreased. The larger differences are represented by the colour yellow or green, red and blue represent small changes. There were no statistically significant regions of difference.

With respect to the ASL difference between follow-up and baseline for the treated cohort, the peak t- test score was 4.06 – the peak t is the highest t-score of a defined cluster of voxels, in this case the whole brain. This set of values was similar to that found in the studies by Baril et al. (2015) that ranged from

112 3.6 to 5.4 (n = 33), Baril et al. (2020) that ranged from 3.9 to 4.6 (n = 36), Innes et al. (2015) whose peak t values ranged from 3.9 to 5.4 (n = 38), and Nie et al. (2017) whose peak t scores ranged from 2.4 to 6.0 (n = 60). Nonetheless, the difference was not significant (p = .09, d = 1.13).

Figure 4-26 This corrected probability map of the treated cohort with age, sex, body mass index, and drowsiness as explanatory variables shows the location of two regions where differences in cerebral perfusion between baseline and follow-up approached significance. These are in the right cerebral cortex/cingulate gyrus region, and in the right angular/supramarginal gyri (Harvid Oxford cortical structure map).

4.8.2 ST90

ST90 was regressed against perfusion rates to determine if it could differentiate cerebral regions between moderate OSA and controls. However, threshold-free cluster enhancement analysis, with age and sex as co-variates, found no significant differences (p > .05).

4.8.3 Regional cerebral perfusion regions of interest from a previous study

Baseline perfusion data from three regional clusters, previously found by Innes et al. (2015) to have significant perfusion differences, were tested for differences between the moderate sleep apnoea and the control groups from this study. Those regions are: (1), bilaterally, the paracingulate gyrus, anterior

113 cingulate gyrus, and subcallosal cortex, including the left putamen and left frontal orbital cortex (2) the right-lateralized, posterior temporal fusiform cortex, parahippocampal gyrus, and hippocampus and, (3). the right thalamus.

Multiple-comparisons correction using threshold-free cluster enhancement analysis did not show any of those three regions of interest having significant perfusion differences between the two groups in this study (p > .050).

Table 4-17. Participant demographics and clinical measures from the study by Innes et al. and our study,

Innes et al. (2015) This study

19, (2) 24 (8) OSA n (female) 19 (4) 7 (4) Control n (female) 57yr, (41–70) 58yr, (32 – 79) OSA mean age (range) 50yr, (41 – 81) 61yr, (42 – 73) Control mean age (range) 29.3, (22.9 – 37.5) 30.2, (22.6 – 37.5) OSA mean BMI (range) 27.5, (2.0 – 32.6) 24.5, (22.7 – 25.9) Control mean BMI (range) 30, (15 – 53) 22, (16 – 29) OSA mean AHI (range)

4.9 Cerebral perfusion normalization quality

Part way through data collection, the MRI scanner was relocated. To demonstrate the quality of the ASL procedure following that shift, normalisation processing of perfusion data, cerebral brain perfusion images, in subject space, from four participants are provided (Figure 4-27). These four participants were selected because their baseline scan was done prior to the scanner being relocated to its current site, whilst their follow-up scan was done at the new location, thus permitting a visual comparison of the ASL data pre- and post-relocation. The images, post-relocation, appear similar in quality to those taken pre-relocation, thereby supporting the contention that there was no degradation of scan quality because of the shift that may have obfuscated any differences in baseline group regional perfusions.

Moreover, the images are representative of the overall quality of the ASL procedure and the underlying data. They show that the scans are complete with good structural separation evident. There was no evidence of degraded scan quality taken overall that could have obfuscated any differences in baseline group regional perfusions.

114 In addition, the Cat 12 pre-processing steps produce an image quality report at the segment/normalisation stage. For both pre- and post-move, 65% were assessed as B, 33% assessed as B- and 2% as C+. Although these ratings are internal to the software program, they do indicate a level of consistency across results.

Figure 4-27 Normalized cerebral perfusion images from four participants pre-MRI scanner relocation (left-hand images) and post-relocation (right-hand). The three slices in each sequence come from z = 30, 50, and 70 co-ordinates, and are taken in subject space.

4.10 Microsleeps

The number of microsleeps that occurred during the microsleep trial for each participant were collected at baseline and again 6–7 months later at follow-up.

115 4.10.1 Microsleep counts.

The counts of microsleeps are shown in Figure 4-28). There was a wide variability in the counts. One subject experienced 126 microsleeps during the 30-min trial, with a mean microsleep duration of 6.6 s (min = 0.9 s, max = 109. 7 s). Conversely, 18 of the 31 participants had no microsleeps at baseline and 19 of 26 participants had no microsleeps at follow-up. A review of individual counts found that the untreated subject who experienced 126 microsleeps at baseline had 8 counts at follow-up. Conversely, another untreated subject experienced 56 microsleeps at baseline, and 59 at follow-up.

Figure 4-28 Microsleep counts during the 30 min trial. Shown are the baseline and follow-up counts for the three cohorts, treated (n = 13), untreated (n = 11) and controls (n = 7 at baseline with the data from two treated cohort members lost at follow-up, one untreated cohort member lost at baseline and follow-up and one control member lost at follow-up).

An independent samples Mann-Whitney U test was applied to the distributions of microsleep counts for moderate sleep apnoea (median = 0.5) and control groups’ (median = 0.0), which showed no significant difference (U = -1.29, p = .253, d = 2.1). Moreover, a Kruskal-Wallis test of the follow-up microsleep counts also confirmed there were no significant differences in microsleep counts across the 2 three cohorts, treated (Median = 0.0), untreated (median = 0.0) and control (median = 0.0), (χ (2) = 2.68, p = .262).

116 Evident at the individual level was the heterogeneity of the data with one untreated participant reducing microsleeps between the two trials from 126 at baseline to 8 at follow-up. However, most participants (N=22), had two or less microsleep episodes with 17 of that 22 having none.

4.11 Cognition

4.11.1 Baseline

There was no difference in cognition – as measured by the mean, normalized and standardized test scores of Attention, Executive Function, Memory, Processing speed, and Visuospatial construction – at baseline between the moderate sleep apnoea group and the control group using the Mann-Whitney U Test (U = 119.0, p = .104, d = 0.62), nor was there a difference in cognition between the treated and the untreated cohorts (U = 68.0, p = .865, d = 0.08), or between the treated and control cohorts (U = 64.0, p = .157, d = 1.30) These results are graphically represented in Figure 4-29 which shows the overall cognitive scores for each of the three cohorts at baseline, albeit that the control group overall had slightly higher cognitive scores than the two cohorts constituting the moderate sleep apnoea group.

The five cognitive domains that constitute the overall cognitive score were tested for group differences at baseline using the Mann-Whitney U Test. There was no significant difference between the moderate sleep apnoea group and the control group in any of the five cognitive domains. However, two of the neurocognitive tests used in the assessment were significantly different. These were the FAS part of the Verbal fluency test used to help assess Executive functioning, and the copy part of the Rey Complex Figure Test used to help assess Visuospatial Construction.

117 Figure 4-29 Median standardized cognitive scores at baseline for the moderate sleep apnoea group, which consists of the treated and the untreated cohorts, and the control group.

Table 4-18. Mann-Whitney Test results of the distribution of cognitive scores for each of the five cognitive domains used to assess overall cognition between those with moderate sleep apnoea and controls at baseline. Also shown are the overall average scores for each of the five cognitive domains.

Neuropsychological test Mann- Z p Neurocognitive Whitney U domain test Average overall 92.00 0.38 .729 Attention Digit Span (Forwards, and Backwards) 77.5 -0.31 .764 Digit Span (Ordering) 75.0 -0.43 .695 Stroop Colour-Patch 105.5 1.03 .317 Stroop Words 117.0 1.57 .127 Trail Making Test A 98.0 0.66 .532 Average overall 118 1.61 .115 Executive functioning Rey Complex (Immediate recall) 109.5 1.21 .234 Stroop Interference 87.0 0.14 .908 Trail Making Test B 96.0 0.57 .595 Verbal Fluency (FAS) 136.5 2.49 .011*

118 Verbal Fluency (Category) 91.5 0.36 .729 Average overall 101.00 0.80 .444 Memory CVLT (Free Recall) 74.5 -0.45 .661 CVLT (Short-Delay Free Recall) 87.0 0.14 .908 CVLT (Long-Delay Free Recall) 64.5 -0.93 .365 CVLY (Yes/No Recognition) 112.5 1.37 .182 Rey Complex (Immediate recall) 109.5 1.21 .234 Rey Complex (Delayed recall) 428 1.55 .121

Average overall 112.5 1.35 .182 Speed of processing Symbol Digits Modality Test 102.5 0.89 .391 Stroop Interference 87.0 0.14 .908 Stroop Words 117.0 1.57 .127 Trail Making Test A 98.0 0.66 .532 Average overall 119.0 1.65 .104 Visuospatial construction Judgement of Line 89.0 0.24 .835 Rey Complex (Copy) 133.5 2.34 .017*

* Significant at p < .05

4.11.2 Does APAP treatment of moderate sleep apnoea improve cognition?

To test for an improvement in cognition following approximately six-months of APAP treatment the non-parametric Wilcoxon Signed Ranks test was used to test for differences between baseline and follow-up scores (see Table 4-19). Although there was no difference in the composite cognitive score, when the five cognitive domains were tested separately, the memory domain was found to be different following treatment.

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Table 4-19. Wilcoxon Signed-Ranks test results for differences between baseline and follow-up cognitive domain scores for the treated cohort.

Wilcoxon z Effect size p (one-tailed) Cognitive domain -.66 -0.13 .265 Attention -.77 -0.15 .233 Executive Function -2.83* -0.55 .001 Memory -1.01 -0.20 .170 Speed of Processing -.18 -0.04 .446 Visuospatial Construction -0.73 -0.14 .249 Cognition Overall

Individual results fluctuate widely as can be seen in the lie chart Figure 4-30. A feature of six charts that comprise this figure, is the variation in scores across all three cohorts with large improvements and decreases in results evident.

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Figure 4-30 Baseline and follow-up results for the five domains that made up the overall cognitive score these being attention, executive function, memory, speed of processing, and visuospatial construction. In addition, the cognition overall chart is included.

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To examine if the improved memory results reflected a test-retest order effect, the results from those participants not treated, i.e., the untreated cohort combined with the control group, also the untreated cohort, and the control group separately, were tested using the Wilcoxon Signed Ranks test. Evident in Figure 4-31 is that the median score for all three cohorts improved between baseline and follow-up. However, only the treated cohort difference was significant even though the median score for the control cohort showed the most improvement at follow-up. However, the control group had the greatest spread along with low numbers (n = 7) resulting in insufficient power to reach significance. Yet the tighter spread at follow-up for the treated cohort meant the improvement in this cohort was significant. Despite the lack of significance in the untreated and control cohorts, the higher follow-up scores across all three cohorts indicates a strong test-retest influence. Accordingly, we reject the hypothesis that the treated cohort memory scores would change between baseline and follow-up.

Figure 4-31 Standardized and normalized memory scores for each of the three cohorts at baseline and follow-up.

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Table 4-20. Wilcoxon Signed Ranks Test results for differences between baseline and follow-up for Memory overall and the six cognitive tests used in the assessment.

Baseline Follow-up Z P Treated cohort (N = 13) median Score median Score

0.2 0.9 -2.83 .001** Overall 1.4 2.0 -2.83 .001** CVLT Free Recall 0.0 0.7 -2.53 .004** CVLT Short Recall 0.0 0.7 -2.52 .004** CVLT Long Recall -1.0 1.0 -2.68 .002** CVLT Yes/No 0.1 1.1 -1.50 .071 Rey Complex Immediate -0.3 0.8 -2.03 .021* Rey Complex Delayed Baseline Follow-up Z P Untreated cohort (N = 11) median Score median Score

0.6 0.8 -1.16 .139 Overall 1.7 2.1 -0.92 .196 CVLT Free Recall 0.0 0.7 -1.73 .049* CVLT Short Recall 0.3 1.0 -2.06 .021* CVLT Long Recall 0.0 0.0 -0.07 .496 CVLT Yes/No 0.4 1.1 -0.85 .213 Rey Complex Immediate 0.4 0.8 -0.62 .289 Rey Complex Delayed Baseline Follow-up Z P Control group (N = 7) median Score median Score

0.3 1.4 -1.10 .164 Overall 1.3 2.0 -1.37 .094 CVLT Free Recall 0.0 0.7 -0.68 .281 CVLT Short Recall 0.0 1.3 -1.48 .094 CVLT Long Recall 0.7 0.0 -1.63 .094 CVLT Yes/No 0.6 1.6 -1.78 .047* Rey Complex Immediate 0.7 1.2 -1.10 .148 Rey Complex Delayed

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4.11.3 Psychomotor vigilance task

Participants completed a standard 10 min Psychomotor vigilance task (PVT) as part of the cognitive test at baseline and at follow-up. The PVT objectively assesses fatigue-related changes in alertness associated with sleep loss (Basner et al., 2011). Therefore, it should provide a useful insight into a moderate apnoeic’s ability to sustain attention in comparison to controls. It was not included in the combined cognitive test analysis because normative data was not available.

Comparing the count of attention lapses, defined as a reaction time of 500 ms or more, there was no difference between those with moderate sleep apnoea and the control group (U = 81.0. z = -0.15, p = .908). This result is reflected in the analysis of reaction times between the two groups that also showed no difference (U = 75.0, z = -0.43, p = .695).

Individual counts are plotted in Figure 4-32. This chart is characterized by large variance in individual scores with one member of the control group improving their score by 32 counts. While most participants showed some improvement, one untreated and two treated participants had a poorer performance at follow-up compared with baseline.

Figure 4-32 Psychomotor vigilance task (PVT) counts during the 10 min trial. Shown are the baseline and follow-up counts for the three cohorts, treated (n = 13), untreated (n = 11) and controls (n = 7).

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Chapter 5 Discussion

Our study investigated global and regional cerebral perfusion, cognition, and microsleeps in people with moderate OSA before and following APAP therapy. Overall, six hypotheses were formed, none of which were subsequently supported by the results of this study. Initially our study hypothesized that cerebral perfusion and cognition would be reduced in people with moderate OSA and that microsleeps would be higher at baseline compared to participants without OSA. However, we found no significant differences in either global cerebral perfusion, regional cerebral perfusion, cognition, or microsleeps at baseline between participants with moderate OSA and controls. The next three hypotheses, largely contingent on there being a difference in cerebral perfusion at baseline between the moderate -OSA group and the control group, were that six months of APAP therapy would improve cerebral perfusion, cognition, and microsleeps. Despite no significant difference in cerebral perfusion at baseline, the impact of 6–7 months of APAP therapy was nonetheless analysed in case the low population, particularly in the control group (n = 7), reduced the power of the between-subject t-test to identify regions of reduced cerebral perfusion at baseline. However, in keeping with a lack of difference at baseline 6–7 months of APAP therapy (1) did not significantly alter cerebral perfusion, (2) did not improve cognition, and (3) did not change microsleeps.

5.1 Cerebral perfusion

5.1.1 Is cerebral perfusion reduced in people with moderate OSA severity?

Cerebral perfusion is significantly distorted in severe OSA (Winklewski & Frydrychowski, 2013). Previous studies have found that global (Urbano et al., 2008) and regional cerebral perfusion (Chen et al., 2017b; Innes et al., 2015; Yadav et al., 2013) is reduced during the awake period in mixed cohorts of moderate and severe apnoeics. Whether this finding holds true for a cohort of people with just moderate OSA has yet to be clearly established. Only one other study has addressed this question and did not identify any regions of reduced cerebral perfusion in people with OSA of moderate severity (Baril et al., 2015). Not finding an effect does not necessarily falsify a premise, it may just mean that the observations were not detailed enough or that the sample size was too small to identify differences. Given the high prevalence of moderate OSA (Heinzer et al., 2015) and the deleterious effect of the sleep disorder, one of the objectives of our study was to look more carefully at cerebral perfusion. For example, is cerebral perfusion reduced, whilst awake, in people who have moderate OSA? Also, if we pay particular attention to people with moderate OSA (Ryan, 2019), given OSA severity increases with age (Aloia et al., 2004), we may be able to reduce the risk of downstream morbidities. All of which is predicated on poor CBF leading to brain injury and ensuing problems as opposed to poor CBF occurring

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with or after brain injury. Moreover, the fact that moderate OSA does not appear to impact cerebral perfusion could argue for this phenomenon not being a precursor to brain injury and cognitive problems.

The results from studies that included both moderate -OSA and severe-OSA patients in their cohorts (Chen et al., 2017b; Innes et al., 2015; Yadav et al., 2013) raised the question that participants with moderate OSA would show regional hypoperfusion. However, our study found no evidence to refute the null hypotheses that cerebral perfusion in those people with moderate OSA differ from controls either globally or regionally. This remained so for regional cerebral perfusion even after considering AHI, drowsiness levels during an MRI scan, age, and sex as covariates.

Of the seven studies reviewed that had investigated the impact of OSA on cerebral perfusion whilst awake (Baril et al., 2015; Chen et al., 2017b; Innes et al., 2015; Joo et al., 2007; Nie et al., 2017; Shiota et al., 2014; Yadav et al., 2013), only the study by Baril et al. (2015) directly addressed whether regional cerebral perfusion was altered in those participants with moderate of OSA. In their SPECT study, Baril et al. (2015) found no significant difference between their cohort of 14 patients with moderate OSA and their cohort of 20 controls without OSA. However, they did find a cerebral perfusion difference between their cohort of severe apnoeics and controls, as did all the studies reviewed that included patients with severe OSA. This may suggest that the level of OSA severity is a determinant factor in the manifestation of the disorder, and that moderate OSA is associated with insufficient pathology to significantly reduce regional cerebral perfusion while awake.

To confirm this premise, our study tested for cerebral perfusion differences in three regions that Innes et al. (2015) had described in their mixed cohort of people with either moderate or severe-OSA. By selecting regions a priori, the number of multiple comparisons would increase the power of the t-test to detect differences and decrease a type II error. The study by Innes et al. was selected because the methodologies were consistent with our study, and because of ease of access to local data. They found cerebral perfusion differences in their cohort of people with either moderate or severe OSA (1) bilaterally in the paracingulate gyrus, anterior cingulate gyrus, and subcallosal cortex, including the left putamen and left frontal orbital cortex, (2) right-lateralized, in the posterior temporal fusiform cortex, parahippocampal gyrus, hippocampus, and thalamus. However, our study of only moderate OSA found no change in perfusion in any of those regions of interest.

Given the ASL procedures were the same in both the Innes et al. (2015) study and ours, the reason for the different result most likely had to lie either in demographic or clinical factors. A comparison of the data between the two studies showed that they differed in number of controls and the male/female ratio, mean age of the controls, and BMI for the controls. Overall, the level of OSA severity, as measured by the AHI, was higher in the Innes et al. study. All these variables have been linked to differences in global cerebral perfusion (Chen et al., 2017b; Cosgrove et al., 2007; DuBose et al., 2018) and, therefore, either separately or combined, could be the reason for the different results. It is also worth noting that

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ASL scans are noisy and prone to artifacts (Hales et al., 2020) meaning that CBF measurements are inherently variable. Nonetheless, our results are consistent with the premise that the pathophysiology of patients with moderate OSA does not significantly reduce regional or global cerebral perfusion whilst awake.

Science’s understanding is quite advanced in the neurophysiology of cerebral perfusion, which is dependent on intracranial compliance, cerebral autoregulation, and sympathetic tone to maintain cerebral blood flow homeostasis, much is still to be understood. Although cerebral autoregulation has been shown to be affected by severe OSA (Beaudin et al., 2017; Urbano et al., 2008; Waltz et al., 2016), the impact of moderate OSA was equivocal with no evident impact on the efficacy of cerebral autoregulation found. This observation is supported by Waltz et al. (2016) who concluded that one month of effective CPAP treatment had no impact on cerebral autoregulation regardless of OSA severity levels (see also Beaudin et al., 2017).

However, although studies of people with moderate OSA versus controls, like this study and Baril et al. (2015) could find no difference in cerebral perfusion rates, either globally or regionally other studies, which recruited participants with more severe OSA (i.e., Innes et al., 2015; Joo et al., 2007; Yadav et al., 2013), did find regional differences.

Despite an incomplete understanding of the pathogenesis of OSA (Maresky et al., 2019) and how cerebral autoregulation works (Fantini et al., 2016), this study postulated that any adverse effect of moderate OSA on cerebral perfusion can be adequately compensated for by the normal homeostatic processes responsible for the maintenance of cerebral perfusion, such as intracranial compliance, cerebral autoregulation, and sympathetic tone (Macey, 2015). This postulate is supported by the current study and that of Baril et al. (2015) and others (Chen et al., 2017a; Joo et al., 2007; Nie et al., 2017; Shiota et al., 2014; Yadav et al., 2013) that all tested either moderate/severe or severe and found cerebral perfusion was reduced, whereas the current study and that of Baril et al. (2015) that tested cohorts with a less severe diagnosis of OSA, did not. This evidence supports the hypothesis that the autoregulatory system can adequately manage disruptions to CBF caused by instances of mild or moderate OSA but not in severe instances of OSA. This is further supported by the low correlation between AHI and gCBF in our study. Accordingly, it is proposed that if the OSA is severe enough the autoregulatory system will break down and allow pathological changes to CBF to occur.

Although global cerebral perfusion did not differ in our study between people with moderate OSA and controls, we went on to compare arousal counts, along with ESS, ODI, PSQI, microsleep counts, and SF-36 scores in case any of these variables were more sensitive to changes in cerebral perfusion than AHI. Only arousal counts were found to be significantly and positively associated with global cerebral perfusion (rp = 0.37, p = .042). The significance of this finding is unclear as the exact role of arousal

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levels on the pathogenesis of OSA, if any, has yet to be resolved (Eckert & Younes, 2014; Wali et al., 2020; Yue et al., 2009).

5.1.2 Does APAP therapy have an impact on cerebral perfusion whilst awake?

Another objective of our study was to clarify whether PAP treatment would improve cerebral perfusion in those with moderate OSA during the awake period. This objective was originally set contingent on finding a difference in cerebral perfusion at baseline between moderate OSA and controls, a difference subsequently not found. Nonetheless, the question relating to whether PAP therapy improves cerebral perfusion whilst awake remains unresolved with several studies finding an enduring beneficial impact of PAP therapy on cerebral perfusion in patients with severe OSA (Kim et al., 2017; Maresky et al., 2019; Shiota et al., 2014), while other studies found that PAP does not make a difference (Prilipko et al., 2014; Waltz et al., 2016). The importance of this question lies in the financial cost of the treatment and because of the inconvenience and social cost of PAP usage, neither of which is trivial.

Our study investigated whether 6–7 months of PAP provided as auto-titrating, a symptomatic treatment, would improve cerebral perfusion rates over baseline results using both within and between comparisons. In line with expectations based on our finding of no difference in perfusion between people with moderate OSA and controls at baseline, our study found no changes in global or regional cerebral perfusion rates while awake within the treated cohort following 6–7 months of treatment. This same result was found in the untreated cohort of people with moderate OSA. Together these results suggest that cerebral perfusion remains unimpaired in people with a moderate level of OSA severity.

These results differ from those of Kim et al. (2017). Their study tested 30 male participants with severe OSA (AHI = 63.4 + 17.5) and 26 male participants without OSA (AHI = 3.99 + 1.5). Compared to controls, those with severe OSA had a significantly lower rCBF. Furthermore, six-months or more of CPAP treatment partially reversed rCBF in the limbic and prefrontal regions, with a complete reversal in the medial orbitofrontal, angular, and cerebellar regions. Differences in rCBF between studies were considered by Kim et al. (2017) to be due to differences in clinical characteristics of patients in each of the studies, including level of disease severity.”

Our study did not measure cerebral perfusion during actual PAP usage, so we cannot confirm that PAP prevented reductions in global and/or regional cerebral perfusion during PAP usage. However, PAP treatment was effective in reducing OSA severity during treatment with AHIs dropping to a mean of 3.68 (range 1.1–8.9). Accordingly, the efficacy of PAP treatment for decreasing AHI is not in question. Moreover, internight variability of AHI has been found not to change significantly in people with moderate OSA over two nights of at-home polysomnography (Sforza et al., 2019). Also, the AHI did not change significantly in our untreated cohort over the 6-month experimental period.

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5.2 Cognition

Another objective of our study was to assess the impact of moderate OSA on cognition. Despite a plethora of studies exploring the impact of OSA in general, or moderate and severe OSA, or severe OSA on cognition, this question, specific to moderate OSA only, has been little explored. For example, a meta-analysis by Stranks and Crowe (2016) listed only one of 14 studies that had considered moderate OSA as a separate cohort (Kloepfer et al., 2009). Kloepfer et al. found that procedural and declarative memory was impaired in that cohort. Beebe et al. (2003) in their meta-analysis of 31 studies had no studies of only moderate OSA albeit, that studies by Engleman et al. (2000); Kim et al. (1997); Redline et al. (1997), and Monasterio et al. (2001) all considered mild and moderate severity. or mild and severe OSA. Accordingly, the opportunity was taken in our study to specifically address the question: does OSA of moderate severity have an impact on cognition?

To test cognition, five cognitive domains were tested: attention/vigilance (Aloia et al., 2004; Bawden et al., 2011; Decary et al., 2000; Harvey, 2019; Jackson et al., 2011b; Quan et al., 2006), executive functioning (Aloia et al., 2004; Bawden et al., 2011; Decary et al., 2000; Harvey, 2019; Jackson et al., 2011b; Olaithe & Bucks, 2013), memory (Aloia et al., 2004; Bawden et al., 2011; Decary et al., 2000; Harvey, 2019; Jackson et al., 2011b), processing speed (Aloia et al., 2004; Harvey, 2019; Quan et al., 2006), and visuospatial construction (Aloia et al., 2004).

Our study found no difference in cognition between the moderate OSA group and the control group at baseline as reflected by the total aggregate score from the five domains tested. Furthermore, none of the cognitive domains tested showed any differences between groups, including the PVT test (lapses and reaction times), which were analysed separately. By comparison, several studies have found cognitive deficits (Aloia et al., 2004; Beebe et al., 2003; Bucks et al., 2013; Stranks & Crowe, 2016). However, the finding of cognitive deficits is not universal. Other studies have reported an effect of OSA on psychomotor vigilant tasks. Lee et al. (2010) found that performance on a PVT by participants with an AHI > 10 (mean PVT counts = 1.75) was not significantly different to the PVT counts by participants with an AHI < 10 (mean = 2.63). Sforza et al. (2004) on the other hand, found that vigilance performance could be impacted by OSA but only at AHI levels greater than 50. With respect to executive functioning, language, memory, psychomotor functioning and construction, the results are less equivocal, with the review by Bucks et al. (2013) finding a number of studies in their meta-review linking OSA to cognitive deficits, as did Aloia et al. (2004). The review by Aloia et al. (2004) also examined the association between cognitive status and OSA severity and noted that of the 11 studies they reviewed, 10 had a significant association between OSA severity and cognitive performance. Accordingly, they concluded that before cognitive deficits could manifest, some threshold of apnoea severity might require being breeched. The lack of a significant difference in cognitive scores between those with moderate OSA and controls in our study is consistent with that premise.

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When individual responses are reviewed, wide variability becomes evident across the five cognitive domains with extreme results occurring in executive functioning, speed of processing and visuospatial construction (see Figure 4-30 ). Moreover,

5.2.1 Does PAP therapy have an impact on cognition?

We explored the hypothesis (Hypothesis 2) that cognitive performance would be impaired between controls without OSA and those with moderate OSA at baseline, and whether APAP treatment could improve cognition. This was done in case variances within the baseline groups obfuscated a real difference between the two groups resulting in a false negative.

At baseline, there was no difference in composite cognition between the moderate OSA group and controls, or between the treated and untreated cohorts, or between the untreated cohort and control group. This also extended to the five cognitive domains when tested separately at baseline.

At follow-up, no improvement in the composite cognition score was seen in participants with moderate OSA who received six-months of APAP treatment. However, when the five cognitive domains were considered separately, semantic and visual memory showed a statistical improvement in the four components of the CVLT tested – Free recall, Short delay recall, Long delay recall, and the Yes/No recognition test, all of which are measures of semantic memory (Lezak et al., 2004). In addition, a statistical improvement was observed in the delayed recall trials of the Rey-Osterrieth Complex Figure test, a measures of visual memory (Lezak et al., 2004).

The exact mechanisms behind statistical improvements in semantic and visual memory experienced by the treated cohort over the treatment period cannot be attributed to improved cerebral perfusion, which was not impaired at baseline in our study, and which did not change following APAP treatment. However, they could be attributable to improved sleep quality and quality-of-life, which improved. They could also be attributed to a test-retest effect for the CVLT and Rey-Osterrith complex despite some studies (Berry et al., 1991; Meyers & Meyers, 2008; Woods et al., 2006) reporting good test-retest characteristics for those tests. Strong support for a test-retest effect comes from the observation in our study, that the median memory scores for all three cohorts were higher at follow-up with the greatest increase being in the control group, albeit the differences, apart from the treated cohort, were not significant, possibly because of the relatively low number of subjects a wide spread of the data in the control group. It is of course possible that untreated OSA patients do not “learn” as well as do treated patients, and hence do not show a test-retest improvement. However, the absence of differences in memory scores between the OSA group and controls at baseline indicates that memory was not impaired in people with moderate OSA at baseline and therefore would be unlikely to improve. Consequently, it is concluded the statistical improvement seen in the memory score for the treated group is most likely attributable to a test-retest effect. Also, the absence of any differences in memory scores between the

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moderate OSA group and the control group at baseline would indicate memory was not impaired in people with moderate OSA and therefore would be unlikely to improve.

These findings are comparable to that obtained in the APPLE study by Kushida et al. (2006). In their large 6-month, randomized, double-blind, two arm, sham-controlled, multicentre trial found that only executive and frontal lobe function were impaired in those with severe OSA at two-months but not at six-months between the two arms of their study (sham or active CPAP). They found no difference with respect to attention and psychomotor function and learning and memory. However, this weak impact on cognition does not appear to be attributable to AHI per se, but to the severity level of oxygen desaturation. Kushida et al. (2012) found that after adjusting for level of education, ethnicity, and gender, there was no association between AHI and neurocognitive performance. However, they did find that the severity of oxygen desaturation was weakly associated with worse neurocognitive performance on some measures of intelligence, attention, and processing speed.

5.2.2 Does global cerebral perfusion have an impact on cognition?

Leeuwis et al. (2018) in their study involving a cohort of 452 participants (214 Europeans, 151 South Asian, 87 African Caribbean) from the SARE study (Tillin et al., 2012) used 3T pseudo-continuous arterial spin labelling to estimate whole-brain, haematocrit corrected CBF. They also measured global cognition and three cognitive domains (memory, executive functioning/attention, and language). They found higher cerebral blood flow was associated with higher cognitive performance on executive functioning, attention, and memory, but not language.

Nonetheless, in our study, baseline measures of global cerebral perfusion and composite cognitive score or its five domain scores were not correlated because of small effect sizes and small population of 7 for the control group (Brysbaert, 2019). The composite cognition score had an r = 0.09, while attention had an r = 0.20, followed by visuospatial cognition with azsn r = 0.15. On the other hand, memory had a negative effect size of r = -0.02 and executive functioning an r of -0.08, albeit none of these correlations were significant. Compared to a meta-analysis of cognitive scores and effect sizes (Stranks & Crowe, 2016), attention and executive functioning were both outside their 95 percentile ranges, while memory was within. Stranks and Crowe did not specify an effect size for visuospatial construction.

Despite the absence, of an impairment in either cerebral perfusion or cognition at baseline between moderate OSA group and the control group, we were still able to explore, at least to a limited extent, the question of an association between perfusion and cognition.

5.3 Microsleeps

Our study tested the number of microsleep people with moderate OSA had during a 30min monotonous task. Microsleeps are obtrusive lapses of responsiveness that are driven by sleepiness levels and can

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occur in both sleep and non-sleep-deprived people (Innes et al., 2010; Peiris et al., 2006; Poudel et al., 2014). They differ from Harrison and Horne’s definition (1996) which is EEG based. Microsleeps, as defined by Peiris et al. are thought to emanate from a homeostatic drive for sleep/rest and a complex interaction between the brain’s arousal and attention systems (Buckley et al., 2014). Given the link between OSA with sleep loss in general and thus an increased homeostatic drive for sleep, microsleep counts can be expected to be higher in people with moderate OSA than in those people without OSA.

Contrary to expectations, moderate OSA had no impact on the number of microsleeps that occurred. Our study counted microsleeps at baseline and at follow-up for the three cohorts; treated, untreated and controls. We found no significant difference in microsleep counts between those with moderate OSA and controls. This also extended post-treatment both within and between cohorts. Accordingly, we found no improvement in microsleep counts because of APAP treatment for those with moderate OSA.

These results were surprising given the association between microsleeps and 1) sleep restriction and/or 2) subjective sleepiness (Innes et al., 2013). Our data indicates a wide variation in microsleep counts, with one participant experiencing 126 microsleeps during the 30-min trial, yet 18 subjects had none. The untreated person who experienced the high microsleep count, improved greatly over the 6–7 -month period and at follow-up had only 8 microsleeps. It is likely that this large variability in scores both at baseline, and between baseline/follow-up, reduced the power of the test to detect differences.

Thirteen of our participants experienced microsleeps (42%) and 18 did not in a 30-min tracking task. This is less than the 70% of participants who, in a study by Poudel et al. (2014), had at a mean rate of 79 microsleeps/hour during a 50-min tracking task. However, another study in non-sleep-deprived participants (Buckley et al., 2014) found that 11 of the 23 participants (48%) experienced microsleeps, with an average count of 2.26 for the 30-min task. Finally, a study by (Innes et al., 2013) investigated microsleeps in a non-sleep restricted cohort found that 9 of the 16 participants (56%) averaged 11.4 microsleeps during a 20-min session. Why fewer participants in the current study had microsleeps than the three above studies is not known. The protocol used to classify microsleeps was the same in all 4 studies but there were differences in tasks and/or conditions. Task durations differed with Poudel et al.’s study lasting 50 min compared to the current study (30 min). Also, in both Innes et al.’s and Poudel et al.’s studies, the participants were lying supine in an MRI scanner, and the joystick was smaller hence smaller movements. In contrast, the tasks in both (Buckley et al., 2014) and the current studies were identical (30 min), participants were seated, and they controlled a floor-mounted joystick. Consequently, there was not a great difference in proportions of participants who had microsleeps – i.e., 48% and 42% respectively.

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5.4 Sleepiness and Quality-of-life measures

At the conclusion of the data collection phase, participants in the treated cohort were offered to keep the APAP machines provided to them for the study. Of the 19 machines allocated, three participants returned the machines and 16 retained them. Eleven participants who provided feedback kept the machines despite our study not showing an improvement in cerebral perfusion, or cognition, or microsleeping. Comments by those retaining the machines indicated a benefit in sleep quality for both the wearer and bed partner.

The comments supported a subjective improved sleep quality and improved quality-of-life that were also reflected by improvements in Epworth Sleepiness Scores, PSQI, and the Medical Outcome Study SF-36 results. Those results show that at baseline, the moderate OSA group experienced worse sleep and quality-of-life than the control group. At the end of 6–7 months of APAP treatment, sleep and quality-of-life measures improved. Improvements did not occur in the untreated cohort and control group.

These improvements cannot be the result of any measurable changes in cerebral perfusion, which remained unchanged following APAP therapy. They were also not correlated to ST90, another recognised covariant of OSA (Wali et al., 2020). However, these improvements could be the result of a reduced AHI. Although there was no enduring improvement in AHI as the result of 6–7 months of APAP therapy, nightly monitoring showed AHI levels did drop to non-apnoeic levels on the nights APAP was used (medium = 2.6), resulting in improved sleep architecture, sleep physiology and reduced fatigue the next day. Also, in this study, improving PSQI scores were related to reduced arousal levels that were shown in our study to reflect AHI scores. Another possibility is a placebo effect that is a beneficial health outcome resulting from a person’s anticipation that an intervention will help. It is possible that due to a placebo effect, APAP users may actual or perceived improvements in subjective sleep and general quality-of-life that cannot be directly attributed to the APAP therapy itself.

Despite the possibility of a placebo effect needing to be considered, the subjective quality-of-life improvements were associated with changes to OSA severity. This highlights the importance of sleep quality in the manifestation of symptoms associated with OSA.

5.5 Study design

A primary purpose of our study was to understand the impact of PAP therapy over time on cerebral perfusion, cognition, and microsleeping in a cohort of apnoeics with moderate OSA. At the time this study was commenced; it was not known just what the impact of moderate OSA would have on cerebral perfusion Baril’s (2015) study had not been published and two studies (Innes et al., 2015; Yadav et al., 2013) had shown reduced regional cerebral perfusion in mixed cohorts of moderate and severe OSA.

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Furthermore, the study by Innes et al., had not been able to find regions of reduced perfusion in their cohort of severe OSA and it was not until they combined both moderate and severe OSA that a significant effect was revealed. Thus, feeling reasonably confident of finding an effect of just moderate OSA on regional perfusion we undertook a longitudinal study to compare perfusion at baseline with that after APAP intervention. A key question with any longitudinal study is the length of time over which the study is conducted before baseline measures are repeated. One of the determinants of that question, is the efficacy of the treatment, in this case how well OSA responds to APAP therapy. Another determinant is the responsiveness of the pathology to the treatment, e.g., how quickly do OSA-related cognitive deficits respond to APAP treatment? Also, it must be considered whether any of the functions being measured were impaired at baseline or whether the sample size was too small to enable the detection of differences.

Constant positive airway pressure therapy is highly efficacious, with improvements in AHI scores during sleep often occurring on the first night of treatment (Sutherland et al., 2018). Typically, symptoms return to pre-treatment severity levels following the cessation of treatment (Schwarz et al., 2018; Young et al., 2013). However, according to Kohler et al. (2011), two week CPAP withdrawal can lead to a rapid recurrence of OSA (on the second night), a return to subjective sleepiness, and is associated with impaired endothelial function, increased urinary catecholamines, increased blood pressure, and an increase in heart rate. Consequently, it must be acknowledged that with-holding treatment on the night of the polysomnography could be detrimental to the patient’s health.

Also, of importance, is the question: How long does PAP need to be used before an improvement in cognitive function can be documented? Bardwell et al. (2001) demonstrated that one week of CPAP treatment was sufficient to improve overall cognition in their participants with severe OSA, albeit that none of the cognitive domains when tested separately were significantly improved. Munoz et al. (2000) demonstrated CPAP-derived changes to daytime functioning after twelve-months of treatment. Others, such as Canessa et al. (2011) and Castronovo et al. (2009), used a three-month intervention period to demonstrate improved cognition and daytime functioning respectively in severe apnoeics, as did Aloia et al. (2003), Gale and Hopkins (2004), and Zimmerman et al. (2006), whilst Joseph et al. (2009) and Kushida et al. (2012) based their longitudinal studies on a six-month intervention. Overall, cognition may improve sometime between one-week or 12 months of treatment.

The final determinant was the level of impairment at baseline. If a dependent variable is not impaired at baseline, then treatment is not required. In our study, it was reasonable to assume cerebral perfusion, cognition, and microsleeps would be impaired based on available literature at that time. Accordingly, and noting that most of the studies mentioned above involved patients with severe OSA, we chose an intervention period of 6 months. It was reasonable to expect those 6 months of APAP therapy would

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provide sufficient time for the manifestation of cognitive and microsleep changes yet would not be too onerous for those participants who would be treated.

5.5.1 Retest reliability

A consideration in repeated measures studies is test-retest reliability. Test-retest reliability is the degree to which test scores remain unchanged when measuring a stable individual characteristic on different occasions (Vilagut, 2014)

The PSQI has a high retest reliability and a good validity for patients with primary insomnia according to Backhause et al (2002), as does the SF-36 (Anderson et al 1996 and Ruta et al. 1994).

Melzer et al. (2020) undertook a study on test-retest effect of a relocation. As part of that study, they also did a test-rest study at the new location which was 9 weeks apart; what they termed a within-site repeat. They found little difference between scanner relocation and within-site results which appeared to have a negligible effect on variability of the global MRI metrics tested including cortical grey matter perfusion.

With respect to microsleeps, Peiris et al. (2006) tested 15 male participant on two occasions spaced at least one week apart (mean 17, range 7-50). They found there was no difference in the number of participants who experienced microsleeps, 5 vs.8, McNewman’s test p = .250. Also there was only a marginal increase in rate of definite microsleeps, 10.8 vs 19.6 h-1, p = .085. In a repeated-measures design study, interindividual differences in microsleep rates have shown substantial agreement across two sessions (single-rater intraclass correlation (2,1) = 0.72, p<0.001) (Innes et al., 2013).

Table 5-1 Cognition retest reliability

Test Coefficient Reference CVLT 0.76 – 0.89 (Alioto et al., 2017) Digit Span 0.83 (Waters & Caplan, 2003) Verbal Fluency FAS 0.47 (Ross, 2003) Judgement of Line 0.84 – 0.90 (Spencer et al., 2013) Rey Complex figure 0.76 – 0.89 (Woodrome & Fastenau, 2005) Rey complex interrater o.80 – 0.99 (Woodrome & Fastenau, 2005) Symbol Digit Modality 0.70 (Pereira et al., 2015) Stroop 0.85 (Martínez-Loredo et al., 2017) Trail Making A and B 0.76 – 0.89 (Wagner et al., 2011)

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Retest reliability coefficients were also obtained for the eight cognitive tests used in this study (Table 2-6). With the exception of the Verbal Fluency test, which was moderately poor, all had good retest reliability coefficients.

5.5.2 Baril et al’s study

The study by Baril et al. (2015) also investigated as a standalone group, patients with moderate OSA. Like this study, they found no significant difference in cerebral perfusion either global or regional in their cohort of moderate OSA when compared to their control group. This raised the question, “what did our study add?” Baril used SPECT to investigate regional perfusion in mild, moderate and severe OSA patients whereas this study used ASL to test for cerebral perfusion in moderate OSA. Kaneta et al. (2017) in their study which compared ASL with SPECT found that ASL with a post delay of 1.5 s produced a better-quality image overall than SPECT (84,9% vs 66.7%). A comparison of Baril et al’s results to other SPECT studies showed that Baril’s findings were different with reduced perfusion found in the outer neocortex, which raised the question “what did they do differently to other studies?”.

This study, on the other hand, used MRI ASL, and used an ASL protocol that in a previous study (Innes et al., 2015) had found perfusion changes in regions consistent with other studies. Moreover, our intention to the very end was to recruit 30 participants with moderate OSA and 15 without OSA. This would have given us more power than Baril et al. who recruited 14 participants with a moderate level of severity and 20 controls. Finally, this study also sought to contribute to our understanding with respect to cognition and microsleeping, constructs not considered by Baril et al.

In summary, these proposed differences show that this study should have complimented that of Baril et als., by using a supposedly better scanning protocol (Kaneta et al., 2017), proven methodology (Innes et al., 2015), having more power (n = 45 vs n = 34), and more coverage (cerebral perfusion, cognition and microsleeps). The lower than desired participation in this study impacted the power of this study.

5.6 Patient demographics recruitment and treatment allocation

5.6.1 Recruitment and statistical power

Final recruitment numbers were less than targeted because recruitment of participants with moderate OSA was much slower than anticipated. In addition, several potential participants with moderate OSA on initial screening turned out to have more severe OSA on undergoing a formal Level II polysomnography sleep study to estimate their AHI at baseline. Conversely several participants (n = 5) had lower AHI scores thus placing them in the mild OSA category. Recruitment of controls was also slower than expected as not many people were willing to commit for the six-month period of the study. Also, several participants without overt symptoms of OSA, were found to have OSA when tested formally for a place in the control group. This included several participants placed in the mild OSA 136

category (n = 10). Those factors, along with attrition from both the clinical and control groups during the six-month period, led to slow recruitment and the nonachievement of targeted numbers. The less- than-desired recruitment numbers meant there was an imbalance between controls (n = 7), and moderate OSA group with n = 13 in the treated cohort and n = 11 in the untreated cohort. This imbalance has negatively impacted the power of the statistical tests. For example, the power to detect an effect size of d = 0.4 (Brysbaert, 2019), with probability of an α = 0.05, and n = 24 and 7 for a two tailed Mann Whitney U-test for between moderate OSA and controls was between 22% and 68% depending on how well the two groups were matched. What was hoped to be achieved apriori was a power between 33% and 83%; again, dependent on how well the two groups were matched.

5.6.2 Treated or untreated cohort placement

Ideally, this study design would have been a randomized controlled trial (RCT with a placebo treatment and subjects blinded to treatment allocation, as was done by Kohler et al. (2011). However, Schwarz et al. (2018) cautioned that RCTs of CPAP usage in OSA are not only time consuming but their findings are often inconclusive or limited because of suboptimal CPAP adherence by CPAP-naïve patients with OSA. In a RCT, participants would have been randomly assigned to the treated or untreated cohorts and those participants who found they could not tolerate APAP treatment placed in an “Intention to Treat” sub-cohort of the treated cohort. However, that ideal design was beyond the funding/scope of this study. Instead, it was decided to rely on the order patients presented to the Sleep Unit for placement in the two cohorts with the treated cohort mostly filled first. Accordingly, moderate OSA participants were first assigned a place in the treated cohort.

Several participants (n = 4) had difficulty adjusting to the intrusiveness of APAP therapy and did not reach ideal compliancy targets (four or more hours over 70% of nights in any given month, (Billings et al., 2020). They were withdrawn from the treated cohort and provided there had been no more than 30 days since they had begun APAP and with their consent were offered an interim placement in the untreated cohort. This was done on the understanding that if recruitment for the untreated cohort was not met by conventional means, they would be asked to undergo the follow-up tests. This decision was made knowing there was a possibility of bias being introduced into follow-up perfusion, cognition, ESS and microsleep scores, but countering those concerns were slow recruitment opportunities and additional sleep studies both of which we could not afford. By now a substantial cost had been incurred: (1) they had completed all baseline tests including Level 2 polysomnography testing, (2) cognitive testing had been completed and, (3) they had undergone an MRI ASL scan. Subsequently, four participants who could not tolerate APAP treatment were recruited into the untreated cohort.

It is noteworthy that none of the treated participants who had been offered a choice of placement were non-compliant, whereas 40% of those that were not given a choice, were.

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5.6.3 Participant demographics

The problems with recruitment led to cohorts not being perfectly matched. Although there was no statistical difference in age or sex between the moderate OSA group and the control group or between the treated and untreated cohorts, there were differences in actual numbers, particularly in the ratio of female to male where there were more women than men in the untreated and control cohorts but more men in the combined treated and untreated cohort compared to controls. Furthermore, the narrow range for the control group (22.7 – 25.9) that was much narrower than the range for the OSA group (22.6 – 37.5) could be a potential confounding factor.

Whether these differences are sufficient to result in biased findings is difficult to assess. However, it is known that global cerebral perfusion is higher in women than in men but differences are less clear with respect to regional cerebral perfusion (Cosgrove et al., 2007). Given the higher ratio of females to males in the control group compared to the moderate OSA group, this may have introduced a bias towards finding a difference – i.e., the possibility of a false positive.. However, the question regarding sex differences in cognitive performance is complex (Jäncke, 2018; Lezak et al., 2004). In general, the literature tends to support better performance by women on verbal tasks and better performance by men on non-verbal and visuospatial tasks (Coltheart et al., 1975; Lezak et al., 2004; Schaie, 1994). But, as pointed out by Lezak et al., this conclusion is controversial. To overcome this controversy, our study used cognitive data that had been normalised for sex, age, and BMI, apart from the PVT task where normalised data was not available.

5.6.4 Ethnic mix

Recruitment of moderate sleep apnoeics was driven by referrals from the Sleep Unit. Of those 79 referrals, one was female Māori, three were male Māoris, and one was female Pacific Islander, one was female Asian, and one was male Asian. Thus, by far, most participants were of European descent. When compared to the ethnic mix for Canterbury, European New Zealanders were over-represented while Māori and Pacific New Zealanders were under-represented in this study. Of the 31 active participants, there was one female Māori, one male Māori and one male Asian, the rest being of European descent. According to Mihaere et al. (2009), Māori, because of greater BMI and neck circumference, are more likely than non-Māori to report having OSA risk factors and symptoms and were 4.3 times more likely to have an AHI ≥ 15 (95% CI = 1.3–13.9) after controlling for sex and age. Thus, there should have been a greater number of Maori participants then was the case.

A review of the exclusion criteria for this study noted that participants were required to have been referred to the Christchurch Hospital Sleep Unit by their General Practitioner. Low referral numbers of Māori and Pasifika people from General Medical Practitioners probably accounts for the low representation. Furthermore, according to Frith and Cant (1985), evidence from sleep clinics in New

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Zealand suggest that Māori patients often present with more severe OSAS and therefore would be more likely to be ineligible for this study, as would be the case for Pasifika people (Frith & Cant, 1985).

Table 5-2 Ethnicity mix for Canterbury and New Zealand for 2020/21.

Canterbury New Zealand

Other 87.4% 76.7%

Māori 9.8% 16.6%

Pasifika 2.8% 6.7%

5.7 Individual differences

Individual differences were assessed at baseline and between baseline and follow-up differences. To do this, z scores were calculated for all results. An examination of the individual data was carried out to identify any underlying patterns or subgroups that may not have been evident from the group analyses. Clear was the heterogeneity seen at the group level also occurs between and within individual participants. Moreover, whilst much of the data showed individual and group differences, none revealed any striking subgroups of value that pointed towards useful phenotypes. This also applied to two of the phenotypes –, minimal symptoms, and excessive daytime sleepiness – nominated by Ye et al. (2014) and for which we had data – we did not collect insomnia data..

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Chapter 6 Concluding summary and outlook

6.1 Achievement of objectives

Our study aimed to clarify the impact of moderate OSA on cerebral perfusion, cognition, and microsleeps before and after APAP treatment. Previous studies had shown reduced cerebral perfusion and cognition in cohorts consisting of moderate and severe apnoeics, or severe apnoeics. However, whether this would remain true for just moderate OSA was unresolved.

To achieve this objective, we formed six hypotheses. Three hypotheses were that moderate OSA adversely influenced perfusion, cognition, and microsleeping. Our study, consequent to finding an influence, then hypothesized that APAP treatment would result in an improvement in perfusion, cognition, and microsleeps. Despite these hypotheses being rejected, the main findings are informative and contribute to our understanding of cerebral blood flow and cognition in people with moderate OSA.

6.2 Main findings

In our study in which all participants had sleep apnoea of moderate severity, we found no evidence of reduced of reduced cerebral perfusion, either globally or regionally. This concurs with the only other study which has investigated the effect of moderate severity sleep apnoea on cerebral perfusion (Baril et al., 2015). The significance of our finding becomes apparent when the results from studies involving people with severe sleep apnoea are compared with the results of our work. The finding by other studies of regional differences in cerebral perfusion in their participants with either moderate/severe OSA or severe OSA suggest that a certain level of obstructive sleep apnoea severity, as measured with AHI, is necessary before cerebral perfusion is impacted. This conclusion is supported by findings that cerebral autoregulation is disrupted in cohorts with both moderate and severe OSA but which is maintained in people with just moderate OSA (Waltz et al., 2016). The sympathetic nervous system seems to have little influence on cerebral autoregulation except under challenging circumstances (ter Laan et al., 2013). Accordingly, it is suggested that homeostatic processes (vasomotor, chemical, metabolic), responsible for maintaining cerebral perfusion within normal limits, are effective in people with mild or moderate OSA These processes seem to deteriorate in people with severe OSA resulting in pathological changes.

These pathological changes can manifest as impaired cognition, which has been shown to be impacted by severe obstructive sleep apnoea (Kushida et al., 2012). The impact of just moderate OSA on cognition has rarely been explored. Studies that have explored its effects on cognition have produced ambivalent results (Aloia et al., 2004; Bucks et al., 2017; Kushida et al., 2012).

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Our study found no difference in cognition between participants with moderate OSA and the control group at baseline. Several important study factors may have led to this result, including small sample size, demographic differences, recruitment bias, differences in fatigue levels because of individual differences in personality/interest/motivation trait complexes, along with participant wellbeing (Ackerman & Kanfer, 2009). However, steps were taken to limit these confounding factors, principally using normalised data, selection criteria and methodology that saw all participants tested at the same time of day and in the same sequence. Moreover, recruitment was undertaken based on order of presentation to the Sleep Unit. This would mitigate some recruitment bias as order of presentation would be somewhat random in terms of many demographic factors. There was no placebo effect at baseline. It is also possible that the sample size was too small to reach sufficient statistical power.

There was also no difference found in cognitive scores between the treated cohort and the untreated cohort at follow-up. Again, there are important study factors that could have contributed to this result. In addition to those mentioned above, there is also a placebo effect. Kushida et al. (2012) managed this confound using a sham CPAP device, as have other studies (Baillieul et al., 2021; Pepin et al., 2016). However, this was not an option in this study because of cost. Other studies investigating CPAP treatment and cognition have not used sham CPAP to manage any placebo effect. These included Canessa et al. (2011); Castronovo et al. (2014); and Dalmases et al. (2015) and Xu et al. (2017)

Our study also investigated the association between global cerebral perfusion and cognition. No evidence of an association between those two measures was found at baseline, which is consistent with the absence of any reduced regions of cerebral perfusion. This absence of any correlation between global cerebral perfusion and cognition also extended to the five sub-domains of cognition tested in this study.

The absence of any difference in cerebral perfusion and cognition at baseline also extended to microsleeps. Our study investigated the effects of moderate severity sleep apnoea on behavioural microsleeps. Based on behavioural microsleep counts no difference in microsleeps was found between people with moderate OSA and controls at baseline or between baseline and follow-up in any of the three cohorts tested. In our opinion the most likely explanation for the absence of a difference is the small cohort size, which meant we had insufficient power to detect differences.

Our study also compared sleep quality measures using the Epworth Sleepiness Scale, PSQI, and Medical Outcomes Study with indicators of sleep apnoea severity levels such as AHI, arousal counts, cognition, global cerebral perfusion, microsleeps, and ST90 levels. The AHI correlated best with measurements of sleep quality and quality-of-life indices. As the AHI increased, sleep quality and

quality-of-life measures worsened. Arousal counts correlated with the PSQI (rs = 0.37, p = .039) showing that as arousal counts dropped, sleep improved. Otherwise, cerebral perfusion, cognition, microsleeps, and ST90 levels were not associated with changes in sleep and quality-of-life indices. At

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baseline, arousal counts were also correlated with AHI (rs = 0.38, p = .036) as was the PSQI (rs = 0.48, p = .003). This correlation does not confirm a causation, however in the given setting of our study, it is most likely that the correlation between arousal counts and the PSQI was driven by obstructive sleep apnoea severity as measured by the AHI.

Our study could not detect any impact of moderate OSA on cerebral perfusion or cognition. Still 16 (85%) out of 19 participants were keen to retain the APAP machines. Given the low adherence rate in the general population for APAP compliance (Wolkove et al., 2008), this high retention rate suggests that the users gained benefits that were elusive to our measurements (Engleman et al., 1996). A review of the comments justifying why the participant’s wanted to keep their APAP machines, all point to perceived improved sleep, and quality-of-life.

On treatment with APAP, the Epworth Sleepiness Scale, PSQI, and the SF-36 all improved as expected. These results are supportive of treating, if not to improve cerebral perfusion or cognition, but to improve sleep and quality-of-life.

To conclude:

1) At baseline, no difference was found in global or regional cerebral perfusion between our participants with moderate OSA severity and our controls.

2) Following 6–7 months of APAP treatment, no global or regional cerebral perfusion differences were detected.

3) No difference in cognition was detected between our participants with moderate OSA and our controls.

4) Following 6–7 months of APAP treatment, cognition did not improve.

5) No association was found between cerebral perfusion and cognition at baseline.

6) At baseline, no difference was detected in microsleep counts between our participants with moderate OSA and our controls.

7) At baseline, Epworth Sleepiness Scale, PSQI, and SF-36 were worse in our participants with moderate OSA compared to our controls.

8) Following 6–7 months of APAP therapy the Epworth Sleepiness Score, PSQI, and SF- 36 scores improved in the treated cohort.

9) The main study limitations were sample size, especially the size of the control group, recruitment and allocation bias, the absence of a placebo control group, and ethnicity bias.

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6.3 Review of hypotheses

This study proposed six hypotheses. Each is critiqued with the benefit of hindsight.

(H1) Cerebral perfusion is reduced in moderate OSA: It was hypothesised that cerebral perfusion in people with moderate OSA would be reduced while awake compared to perfusion rates in non-apnoeic people. This hypothesis was based on previous observations of people with moderate-severe sleep apnoea that suggested reduced cerebral perfusion can occur in people with moderate OSA.

We found no significant differences in cerebral perfusion when people with moderate OSA were compared to controls (the absence of a difference does not necessarily mean there was not a difference just that it was not found). At the time this hypothesis was formed, we did not have the benefit of Baril et al.’s (2015) study who explored the same research question and reached similar results.

H2) Cognition is reduced in moderate OSA: It was hypothesised that people with moderate OSA would have reduced cognition. The rationale for this position were studies with participants that had mild and severe sleep apnoea, These studies would have also included moderate OSA and have found cognitive differences in both verbal and visual memory, attention, and spatial awareness. Nonetheless, contrary to our hypothesis, when our participants with moderate OSA were tested, none of the five domains of cognition were reduced compared to our controls at baseline.

Although this hypothesis was not supported by our findings, it was nonetheless, a valid question to ask as the relationship between obstructive sleep apnoea, severity and cognitive performance, according to Bucks et al. (2013) still needed to be resolved, and only few studies have investigated the impact of moderate OSA on cognition. This research question is consistent with observations that cognitive impairment is associated with sleep apnoea severity (Aloia et al., 2004).

H3) Microsleeps is increased in people with moderate OSA: Obstructive sleep apnoea is known to cause daytime sleepiness. The mechanism is assumed to be due to arousals from sleep associated with sleep apnoea (Slater & Steier, 2012) although hypoxemia (Dewan et al., 2015)and inflammation (Kheirandish-Gozal & Gozal, 2019) cannot be discounted.. Microsleeps are thought to be influenced by drowsiness levels with greater microsleeps related to increased levels of drowsy (Innes, Poudel, & Jones, 2013; Poudel, Innes, Bones, Watts, & Jones, 2014). It was hypothesized that the incidence of microsleeps would be higher in people with moderate OSA.

This hypothesis was rejected because we found no difference in microsleep counts at baseline. However, the substantial variability in microsleep counts in the same cohort of participants with moderate OSA affected the power to detect differences, if any. In retrospect, the population size of our study was insufficient to resolve this hypothesis.

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H4) Impaired cerebral perfusion in people with moderate OSA can be improved by APAP treatment: The effect of APAP treatment on perfusion in those with moderate OSA has not been adequately explained. Our hypothesis was that APAP treatment would reduce OSA severity that in turn would improve cerebral perfusion. Although this hypothesis was based on the premise that cerebral perfusion is impaired in people with moderate obstructive sleep apnoea, our study still explored the effect of APAP treatment on cerebral perfusion. This study found that following 6–7 months of compliant APAP therapy, cerebral perfusion did not change. This means we either did not detect a clinically meaningful difference in cerebral perfusion, or APAP therapy has no effect when the baseline perfusion level is within the normal reference range of 50 mL per 100g of brain tissue per minute.

H5) Impaired cognition in people with moderate OSA can be improved by APAP treatment: The independent effects of cerebral perfusion on neurocognitive function in people with moderate OSA remain poorly understood. Evidence from studies into cardiovascular disease suggest a positive correlation between cerebral perfusion and cognitive functioning. Other studies have reported that cerebral perfusion is negatively impacted by OSA. Accordingly, it was hypothesized that a reduction in OSA severity would improve cerebral perfusion and consequently, cognition.

An underlying assumption of this hypothesis was that cognition would be different between moderate OSA and controls at baseline, an assumption that was subsequently found not to be the case. Nonetheless, the hypothesis was still useful as the results were consistent with not finding either a difference in cerebral perfusion or a cognitive difference at baseline thereby lending weight to the premises that moderate OSA does not have a significant effect on either cerebral perfusion or cognition.

H6) Increased microsleeps in people with a moderate severity level of OSA can be reduced by APAP treatment: A frequent consequence of obstructive sleep apnoea is excessive daytime sleepiness which may increase the propensity for microsleeps. However, the relationship between behavioural microsleeps and OSA has not been established in the literature. It was hypothesized that APAP treatment would reduce microsleeps. Effective APAP treatment should reduce the number of micro- arousals that are associated with upper airway patency – a major factor in the aetiology of OSA. Reducing sleep arousals should allow for the restoration of normal sleep architecture and improve daytime sleepiness levels. However, our study found no association between moderate OSA and microsleeps, at either baseline or follow-up.

In summary, all six hypotheses have been rejected. Even in retrospect they are valid hypotheses, clinically relevant and informed by the literature. While our study has had negative results, it is possible that either studying participants with severe OSA or a larger cohort participating with moderate OSA would have assisted in detecting signals supporting the hypotheses. We are reassured regarding the quality of our local research as other researchers studying small cohorts of participants with moderate OSA have reported similar results. That our results are supportive of other studies (Aloia et al., 2004;

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Baril et al., 2015), makes a strong contribution to our understanding of moderate OSA consequences. In particular, the role of cerebral autoregulation has been raised as a possible factor that explains why people with moderate levels of OSA can maintain good cerebral blood flow.

6.4 Critique of this project

The objectives of this study were to determine the effects of moderate OSA and six months of APAP treatment on cerebral perfusion, cognition, and microsleeps.

The lower than desired recruitment numbers in our study would have had a negative impact on the ability to detect differences between cohorts, if such differences existed. Our recruitment of clinical participants was impacted by substantially fewer moderate OSA patients presenting to the Christchurch Hospital Sleep Unit than expected. This could have been resolved by recruiting from the general-public, however, that would have incurred addition costs the study was not funded for. Accordingly, recruitment was best done through the Canterbury District Health Board Sleep Unit who had previously tested the potential member as meeting the “moderate OSA” criteria using either Lvl II or Lvl III polysomnography. Furthermore, the rationale for our study was that moderate OSA was rarely tested as a standalone group and given the number of international and national epidemiology studies pointing towards an increase in OSA prevalence, understanding the deficits incurred by people with moderate OSA was important. Therefore, recruiting participants with a severe level of OSA would not compliment the study’s objective. Finally, at the time we set study parameters, recruitment was expected to be straightforward given the number of moderate OSA presentations to the Christchurch Hospital Sleep Unit at that period.

Funding for this study had to be used within a specified time-period that because of slow recruitment for the moderate OSA group was at risk of expiring. This meant that 10 potential controls had to have their Level II polysomnographs done without first screening them with much less expensive Level III polysomnographs. Subsequently, there were 7 controls who met our specification of an AHI < 5 and a further 7 recruits with AHI ≥ 5. The recruitment number of 11 for the untreated cohort was also less than the 15 planned, as was the 14 members of the treated cohort.

The self-selection of one member of the treated cohort was a potential source of bias in this study, as was allowing four participants who were unable to tolerate APAP treatment to change to the untreated cohort. It is acknowledged that this was a design flaw that has decreased the scientific rigour of our study and therefore potentially reduced our ability to interpret the findings from the 6-month follow-up component of our study. However, it was done on the basis that the risk of bias to perfusion, microsleep and cognition results would be less than the risk of low sample sizes in the experimental groups due to recruitment and withdrawal from the study by participants who were not committed i.e., attrition bias (Asendorpf et al., 2014). Furthermore, this assessment was made within the context of the study having

145 already invested substantial time and funding. However, this potential bias only applied to the results following the treatment period. The results at baseline were without bias because the assignment to either the treated or non-treated cohorts took place following those tests.

Several procedures could have been employed to minimise the biases of a longitudinal study at the trial planning and trial implementation phases (Coolican, 2005; Pannucci & Wilkins, 2010). At the planning stage, bias can be attributed to flawed study design, selection bias and channelling bias. This study used validated methods for collecting MRI (Innes et al., 2015), cognitive (Lezak et al., 2004) and microsleep data (Buckley et al., 2014; Poudel et al., 2008). Bias can also be attributed to selection bias. However, that bias can be reduced by selecting participants from the same general population, which is what this study did. We also tried to match participants for age and sex. Finally, at the planning stage there is channelling bias. This can be avoided by assigning participants to study cohorts using rigorous criteria. This our study did by using order of presentation to the Sleep unit. However, the transfer of three participants, who could not tolerate APAP treatment, from the treated cohort to the untreated cohort could have introduced some selection bias. At the trial implementation stage, Pannucci and Wilkins (2010) listed six sources of bias Those biases are, interviewer bias, chronological bias, recall bias, transfer bias (attrition bias), misclassification of exposure and outcome, and performance bias. To prevent Interviewer bias this study’s interviewer interaction with participants was standardized. However, being blind to the participant did not occur. Chronological bias can be reduced by using a prospective process, as was done in this study. To reduce recall bias this study used mainly objective data sources although the inclusion of quality-of-life instruments, such as ESS, PSQI, and MS 36-SF, were subjective and a possible source of bias. Nonetheless, those results were able to be validated against other published data which gave a degree of confidence. Outcome and exposure misclassification can occur if the exposure itself is poorly defined. In this study all the exposures, tests, scans, and polysomnographies are standardized processes, so outcome and exposure misclassification should not have occurred. Finally, there is performance bias. To minimise bias introduced by performance bias there was only the one investigator who conducted all of the cognitive and microsleep. In addition, the MRI scans, and polysomnographies were all conducted by registered technicians, thereby minimising bias. Furthermore, performance bias on the part of the participant could have been prevented by the use of a placebo APAP machine (Rodway et al., 2010). However, this was an expense that could not be afforded.

It is noted that the test results from the four members who were transferred from the treated cohort to the untreated cohort were not significantly different to the results from the other members of that cohort. In only one instance was two standard deviations exceeded and that was for microsleeps. Accordingly, confidence remains high that no bias was introduced because of those transfers. Furthermore, because of the steps taken as outlined above, study bias would not have been excessive,

Another area of improvement would be to quantify the participants sleep state prior to the MRI scan by administering the Karolinska Sleep Study and the Stanford Sleep Study. Blood pressure should also

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have been recorded as hypertension is understood to impact CBF (Innes et al., 2015). Furthermore, according to Fantini et al. (2016), a change in posture and exercise can also influence CBF. These tests were omitted because the complexities of cerebral autoregulation were not fully appreciated at the time the study was being designed.

Financial management constraints (a deadline for spending set by the financial year) in conjunction with slower than expected recruitment, meant we ended up making a considerable investment in each control, only to find some were unsuitable because of AHI levels > 5 that placed them in the mild- obstructive sleep apnoea category. Ideally, each control should have undergone a much cheaper level- 3 polysomnography confirming their non-apnoeic status before asking them to commit to the study. Only then should they have undertaken the level-2 polysomnography that because they are reliant on sleep technologists to first set up and then to process are much more expensive. However, adding the level 3 polysomnography to the process, would also add extra time to the recruitment; time that was not available.

Another area of improvement was the length of time between tests that varied widely. We were unable to make advanced bookings for polysomnography tests, MRI scans, and cognitive testing. Moreover, MRI scans and cognitive testing were reliant on polysomnography test results that were reliant on Sleep Unit staff members working outside of normal worktime and consequently, not always available.

Nonetheless, our study benefitted from the dedication of the Sleep Unit staff members in processing the sleep studies. It also benefitted from the helpful approach shown towards the study by members of Pacific Radiology, who were responsible for conducting the MRI ASL studies. Using these two professional bodies was a strength of this study as it ensured no participants were lost to the study because of unusable data. A further strength came from the diversity and contribution of its research members with both clinical and research advocates freely contributing. The diversity of ideas has given the study depth.

6.5 Future directions

There are indications from our study and those of others that cerebral perfusion is related to sleep apnoea severity levels. Is there a severity level above which normal homeostatic processes regulating cerebral perfusion are no longer effective, thus resulting in disrupted cerebral perfusion? The validity of this question is unchanged. Our study added to the literature by not detecting an impact in people with sleep apnoea of a moderate level as has previously being published.

The identification of phenotypes useful to researchers would help to make better sense of the data heterogeneity. Currently, most phenotypes are for clinicians. Although the APPLES research project

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(Kushida et al., 2012) has made a valuable contribution, more work needs to be done to validate those results and to identify other useful phenotypes for research settings.

It is also noteworthy that the long-term consequences of OSA have yet to be fully elucidated. Although it is suggested in the literature that OSA contributes to neurodegenerative dementias such as Alzheimer’s, Parkinson’s and dementia with Lewy bodies (Andrade et al., 2018; Kaminska et al., 2015; Manni & Terzaghi, 2015; Meng et al., 2020; Sun et al., 2020) more work is required to identify and confirm pathological causeways linking the two constructs.

A useful study would be to investigate the impact of a much broader range of AHI scores then just those defining a moderate level of sleep apnoea, on perfusion, cognition, and microsleeps. One objective would be to identify a threshold at which changes to perfusion manifest. Part of this proposed study could be to review different measures of severity i.e., ST90, ODI, hypoxic burden, as better measures of severity than breathing patterns reflected in the AHI (Terrill, 2020). This understanding could lead to revised severity classifications where mild, moderate, or severe categories are reflective of clinical consequences and not arbitrarily set limits. As our understanding of OSA pathology improves, these broad-based severity classifications will lose their clinically relevance (Won et al., 2018). The refinement of a clinically relevant threshold at which point OSA impacts cerebral perfusion, cognition, microsleeps, or quality of life, will lead to improved clinical interventions.

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Appendices

Appendix A Screening checklist

OSA PARTICIPANT Name:

Screening at first contact (i.e., before First Session if possible) Ensure that the following criteria are met:

Date of birth ______Over 18? YES / NO

When do you normally go to bed?

(Is this time between 10 pm and 12 pm?) YES / NO

When do you normally get out of bed in the morning? (Is the time between going to bed and getting up between 7 – 9 hours?) YES / NO Do you drink 4 or more caffeinated drinks (like coffee, coke, or tea) per day? YES / NO

Do you suffer from claustrophobia? YES / NO

Do you have any metallic implants? (May affect your ability to undertake and YES / NO MRI)

Do you have metallic braces or jewellery, such as a bracelet, ring, or piercing YES / NO that you can’t take off? (May affect your ability to undertake and MRI) Have you ever had brain surgery, stroke, or brain injury (including concussion)? YES / NO

Have you ever been diagnosed with a psychiatric or neurological disorder YES / NO (including migraine)?

Have you ever been diagnosed with a sleep disorder? YES / NO

Do you smoke? If so number per day YES / NO

Address:

Cellphone number:

Email address:

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Appendix B Clinical participant information sheet

Clinical Participant Information Sheet

Can Early Treatment Prevent Sleep Apnoea-Related Brain Damage?

Principal Investigator:

Dr Carrie Innes Neuroscientist Department of Medical Physics and Bioengineering, Canterbury District Health Board C/ New Zealand Brain Research Institute 66 Stewart Street, Christchurch 8011 Phone: 03 378 6096 Email: [email protected]

Primary Researchers:

Russ Buckley PhD Candidate

University of Otago C/ New Zealand Brain Research Institute 66 Stewart Street, Christchurch 801 Phone: 03 378 6906 Email: [email protected]

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Professor Richard Jones Director, Christchurch Neurotechnology Research Programme

Department of Medical Physics and Bioengineering, Canterbury District Health Board C/ New Zealand Brain Research Institute 66 Stewart Street, Christchurch 8011

Phone: 03 378 6077 Email: [email protected]

Professor Lutz Beckert

Head, Department of Medicine University of Otago, Christchurch PO Box 4345, Christchurch 8140

Phone: 03 641 8461 Email: [email protected]

Dr Michael Hlavac Sleep and Respiratory Physician Respiratory Services, Canterbury District Health Board Private Bag 4710, Christchurch 8011 Phone: 03 364 1914 Email: [email protected]

Dr Paul Kelly Team Leader Sleep Unit, Canterbury District Health Board Private Bag 4710, Christchurch 8011

Phone: 03 378 6239 Email: [email protected]

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Introduction

This study will look at the effects of moderate obstructive sleep apnoea (OSA) and how this might change after treatment. We will investigate the structure of the brain, blood flow, your ability to think clearly, and how sleepy you get. Most previous research has only included people with severe OSA. However, we are interested in looking at people with more moderate levels of OSA severity.

You are invited to take part in a research study to measure the impact of six months Continuous Positive Airway Pressure (CPAP) treatment for moderate OSA. During the study, we will check how well you sleep. We will then assess your ability to stay alert – Daytime Sleepiness Assessment. We will also check your mental ability. Finally, we will take a series of pictures of your brain. This will allow us to measure the brain’s blood flow and structure. Overall, these tests will be completed twice; initially and again six months later. Between the two sets of tests, you will be required to undertake APAP treatment.

Before you make a decision, it is important that you understand what is involved in this research.

Study Procedures

Where will the study be conducted?

This study will be conducted at your home, the New Zealand Brain Research Institute at 66 Stewart Street, Christchurch, and Hagley Radiology (in the same building as the Brain Research Institute next to Hagley College).

How are participants chosen for this study?

People with moderate OSA will be chosen from people referred to the Sleep Unit at the Christchurch Hospital. They will then be screened to ensure they meet important selection criteria, such as normal sleep habits, minimum age, etc.

What will you have to do if you participate in this study?

This is a study that measures changes over a six month period. So at the beginning of the study you will complete:

Overnight ‘Sleep Recording’ in your own home, unless you have recently completed one.

Daytime Sleepiness Assessment

Set of Mental Tasks

MRI scan

Six months later we repeat the same tests.

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In between you will complete a six-month treatment period using Continuous Positive Airway Pressure. This is the normal treatment for OSA. The treatment involves an air-pump that delivers a gentle flow of air via a flexible hose and mask which you wear while you sleep. The air pressure helps to keep your airway open allowing you to breathe and sleep more easily.

During this period, we will monitor your treatment and provide support. We will record the number of hours you use the APAP machine each night and the number of days each week it is used. It is expected that you will comply with the treatment by using the APAP machines for a minimum 4 hours per night on at least 5 out of seven nights.

The APAP machines and masks we will use are manufactured by ResMed. The APAP machine is an Airsense 10 Autoset with humidifier. These are premium machines for the treatment of sleep apnoea. Although you will be offered every support and encouragement to use these machines, they are not for everyone and some people have difficulty tolerating them. If the compliance levels noted above cannot be met after a reasonable length of time we will have to withdraw the machines so they can be used by other participants.

The APAP machines have been lent to the research project by the manufacturer and at the conclusion of the research project we are expected to return them. However, for those who have benefitted from the treatment, we expect to be able to offer you an option that will allow the APAP treatment to be continued.

Unless you have recently undergone a sleep study you will need to do so so we can confirm your level of OSA. The Sleep Recording will mean coming into Christchurch Hospital on a Wednesday evening. There a sleep technologist will attach sensors to your head and chest that will record information about your breathing patterns, heart rhythms, and brain activity. You will also have a band-aid like sensor wrapped around your finger to measure the amount of oxygen in your blood. The sensors are all attached and removed painlessly. You are welcome to have family/Whanau support present throughout the attachment of recording sensors. The sleep technologist will not monitor the recording nor be present during the night.

After the Sleep Recording, you will be asked to come to the New Zealand Brain Research Institute to pick up a small sleep monitor, a sleep diary, and a set of questionnaires about your sleep habits. The small sleep monitor looks like a wristwatch and measures time spent asleep or awake. You will be asked to fill in the diary and wear the small sleep monitor over the next week.

You will then come back to the New Zealand Brain Research Institute. There you will complete a set of mental tasks followed by a daytime sleepiness assessment. On that day you should eat and drink and take any medication as per normal. However, you should not consume any food or drinks which contain

179 alcohol or caffeine (or any other stimulants or depressants). You should also not smoke cigarettes for at least one hour before your appointment.

You will be asked to complete a set of mental tasks that test attention, memory, planning, and mental dexterity. These tests are expected to take about 1 hour. Following those tests and after a short break, we will ask you to undertake a Daytime Sleepiness Assessment This session will also take around 1 hour. It will consist of a 10-minute reaction time task and a 50-minute tracking task. During this last task your eyes will be videoed and your tracking responses will be recorded so we can tell if you briefly fall asleep.

MRI Study

Finally you will need to come to Hagley Radiology (in the same building as the Brain Research Institute) on a Saturday afternoon for a 20-min MRI scan to measure the blood flow in your brain. You should eat and drink and take any medication as normal. However, you should not eat or drink anything that day, which contains alcohol or caffeine (or any other stimulants or depressants). You should also not smoke cigarettes for at least one hour before your appointment.

Benefits

As a participant in this research study, there may be no direct benefit for you; however, information from this study should help other people in the future.

Risks

MRI is a safe and well established technique. It does not use harmful radiation. However, the strong static magnetic field it uses can in some cases be a danger if any metallic objects, such as a bracelet, necklace, watchs, hair clips, or electronic heart pacemakers are worn inside the scanner. The MRI technician will ask you to remove anything metallic that could be dangerous. If it cannot readily be removed, you will not be allowed to continue to participate any further.

In the unlikely event of a physical injury as a result of your participation in this study, you may be covered by ACC under the Injury Prevention, Rehabilitation and Compensation Act. ACC cover is not automatic and your case will need to be individually assessed. If your claim is accepted by ACC, you still might not get any compensation. This will depend on a number of factors such as whether you are an earner or non-earner. ACC usually provides only partial reimbursement of costs and expenses and there may be no lump sum compensation payable. There is no cover for mental injury unless it is a result of physical injury. If you are covered by ACC, this may affect your right to sue the investigators.

If you have any questions about ACC, contact your nearest ACC office or the investigator.

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Clinical Assessments

Although blood flow and structural images of your brain will be recorded during the study, these will not be viewed by a clinical neurophysiologist or radiologist. The research is not a clinical diagnostic facility and as such does not inspect the MRI data for abnormalities. However, from time to time an anomaly may be observed on an MRI scan. We can only indicate that further advice might be sought. The presence or absence of an unusual reading is not an indication of the presence or absence of disease. If an unusual observation is made, we will inform you of this and, if you agree, inform your Doctor.

Remuneration

You will not be paid a wage for taking part in this study. However, any expenses incurred in travelling to and from the Institute and any parking charges for the purpose of this study will be reimbursed by a fixed $75 paid via cheque for the initial assessment and the a further $75 will be paid at the completion of the six-month assessment.

Confidentiality

Your data will be only be made available to the researchers listed on page 1. However, if you agree we will store your data for future research where it could be useful in other New Zealand-based microsleep or brain blood flow studies. These studies would first need approval from a New Zealand accredited ethics committee before going ahead. You may still take part in this study if you do not consent for us to use your data this way.

Participation

Taking part in this study is voluntary. If you decide to take part, you can change your mind at any time and withdraw. Your decision to withdraw will not change any present or future relationships with the Christchurch Hospital, and the New Zealand Brain Research Institute or their associated organisations.

Further Information:

If you have any questions about this study, please contact Russ Buckley or one of the other researchers by phone or email. If you have any questions or concerns about your rights as a participant you can contact an independent health and disability advocate. This is a free service provided under the Health and Disability Commissioner Act.

Telephone (NZ wide): 0800 555 050

Free Fax (NZ wide): 0800 2787 7678 (0800 2 SUPPORT)

Email: [email protected]

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If you have any questions or concerns about your rights as a participant in this research study from a Maori perspective, you may wish to consult Nga Ratonga Hauora Maori, Maori Health Services, you can contact them directly on telephone 03 364 0640 ext. 88797 or through Te Komiti Whakarite on telephone 03 364 0640 ext. 86160 or email [email protected]

Note:

This study has been approved by the Health and Disability Ethics Committees and by Te Komiti Whakarite (Canterbury District Health Board Research Consultation with Maori committee).

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Appendix C Control participant information sheet

Control Participant Information Sheet

Can Early Treatment Prevent Sleep Apnoea-Related Brain Damage?

Principal Investigator: Dr Carrie Innes Neuroscientist Department of Medical Physics and Bioengineering, Canterbury District Health Board C/ New Zealand Brain Research Institute 66 Stewart Street, Christchurch 8011 Phone: 03 378 6096 Email: [email protected]

Primary Researchers:

Russ Buckley

PhD Candidate

University of Otago C/ New Zealand Brain Research Institute 66 Stewart Street, Christchurch 801 Phone: 03 378 6906 Email: [email protected]

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Professor Richard Jones Director, Christchurch Neurotechnology Research Programme Department of Medical Physics and Bioengineering, Canterbury District Health Board

C/ New Zealand Brain Research Institute 66 Stewart Street, Christchurch 8011 Phone: 03 378 6077 Email: [email protected]

Professor Lutz Beckert Head, Department of Medicine

University of Otago, Christchurch PO Box 4345, Christchurch 8140 Phone: 03 641 8461 Email: [email protected] Dr Michael Hlavac Sleep and Respiratory Physician Respiratory Services, Canterbury District Health Board Private Bag 4710, Christchurch 8011 Phone: 03 364 1914 Email: [email protected] Dr Paul Kelly Team Leader Sleep Unit, Canterbury District Health Board Private Bag 4710, Christchurch 8011 Phone: 03 378 6239 Email: [email protected]

Introduction

Obstructive sleep apnoea (OSA) is a sleep disorder that affects respiration. It occurs during sleep and causes the upper airway to close temporarily. It affects approximately 20% of women and 50% of men. OSA has been associated with excessive daytime sleepiness and an increased risk of accidents. In addition, it has been associated with heart disease, diabetes, and dementia. It is readily treated by Continuous Positive Airway Pressure (CPAP), a treatment not freely available to all.

This research study aims to measure the impact of PAP treatment for people with moderate severity obstructive sleep apnoea (OSA). We will investigate the structure of the brain, blood flow, the ability 184 to think clearly, and sleepiness in people with moderate OSA both before and after treatment. You are invited to take part as a member of the control group against which measurements from those who have OSA can be compared. This is important for the integrity of the study.

To undertake the role of a control it is important that you do not have OSA. Therefore, the first step will be to complete a sleep study so we can confirm you do not have OSA. We will then measure how well you can stay alert – Daytime Sleepiness Assessment. We will take a series of pictures of your brain. This will allow us to measure the brain’s blood flow and structure. Finally, we will measure your cognitive ability at base line and again six months later.

Before you decide, it is important that you understand what is involved in this research.

Study Procedures

Where will the study be conducted?

This study will be conducted at your home (sleep studies), the New Zealand Brain Research Institute at 66 Stewart Street, Christchurch (Cognitive testing), and Hagley Radiology for the brain scan (in the same building as the Brain Research Institute next to Hagley College).

How are participants chosen for this study?

People without OSA will be chosen from an existing database of healthy volunteers held by the University of Otago, and closely match to the clinical group for gender and age.

What will you have to do if you participate in this study?

This is a study that measures changes over a four to six-month period. So at the beginning of the study, you will complete:

• Initial Overnight ‘Sleep Recording’ in your own home. If this indicates that you do not have obstructive sleep apnoea, then a more thorough sleep study will be undertaken.

• Daytime Sleepiness Assessment

• Set of Mental Tasks to measure cognition

• MRI scan

Four to six months later we will repeat the tests.

Sleep Study

The initial sleep study is a simpler and more economical test conducted in your own home. It involves wearing a small light weight recorder while you sleep. If the results of this study indicate you do not have OSA, then we would schedule a more thorough sleep study. This study will mean coming into 185

Christchurch Hospital on a Wednesday evening. There a sleep technologist will attach sensors to your head and chest that will record information on your breathing patterns, heart rhythms, and brain activity. You will also have a band-aid like sensor wrapped around your finger to measure the amount of oxygen in your blood. You then go home to sleep. The sensors are all attached and removed painlessly. You are welcome to have family/Whanau support present throughout the attachment of recording sensors. The sleep technologist will not monitor the recording or be present during the night. Two sleep studies are necessary because of funding constraints. The more thorough sleep study provides the best information, but it is expensive.

Cognitive testing

After the sleep study. You will be asked to complete a set of standard mental tasks that test attention, memory, planning, and mental dexterity. The results will form a benchmark against which we can compare the results from the OSA group. These tests are expected to take about 1 hour. Following those tests and after a short break, we will ask you to undertake a Daytime Sleepiness Assessment This session will also take around 1 hour. It will consist of a 10-minute reaction time task and a 50-minute tracking task. During this last task your eyes will be videoed, and your tracking responses will be recorded so we can tell if you briefly fall asleep.

On that day you should eat and drink and take any medication as per normal. However, you should not consume any food or drinks which contain alcohol or caffeine (or any other stimulants or depressants).

MRI Study

Finally you will need to come to Hagley Radiology (in the same building as the Brain Research Institute) on a Saturday afternoon for a 20-min MRI scan to measure the blood flow in your brain. You should eat and drink and take any medication as normal. However, you should not eat or drink anything that day, which contains alcohol or caffeine (or any other stimulants or depressants).

Benefits

As a participant in this research study, there may be no direct benefit for you; however, information from this study should help other people in the future.

Risks

MRI is a safe and well established technique. It does not use harmful radiation. However, the strong static magnetic field it uses can in some cases be a danger if any metallic objects, such as a bracelet, necklace, watchs, hair clips, or electronic heart pacemakers are worn inside the scanner. The MRI technician will ask you to remove anything metallic that could be dangerous. If it cannot readily be removed, you will not be allowed to continue to participate any further.

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In the unlikely event of a physical injury as a result of your participation in this study, you may be covered by ACC under the Injury Prevention, Rehabilitation and Compensation Act. ACC cover is not automatic and your case will need to be individually assessed. If your claim is accepted by ACC, you still might not get any compensation. This will depend on a number of factors such as whether you are an earner or non-earner. ACC usually provides only partial reimbursement of costs and expenses and there may be no lump sum compensation payable. There is no cover for mental injury unless it is a result of physical injury. If you are covered by ACC, this may affect your right to sue the investigators.

If you have any questions about ACC, contact your nearest ACC office or the investigator.

Clinical Assessments

Although blood flow and structural images of your brain will be recorded during the study, these will not be viewed by a clinical neurophysiologist or radiologist. The research is not a clinical diagnostic facility and as such does not inspect the MRI data for abnormalities. However, from time to time an anomaly may be observed on an MRI scan. We can only indicate that further advice might be sought. The presence or absence of an unusual reading is not an indication of the presence or absence of disease. If an unusual observation is made, we will inform you of this and, if you agree, inform your Doctor.

Similarly, a sleep study will also be completed. If the results indicate that you have moderate OSA you will be invited to participate in the study as a member of the OSA group. If the results indicate that your level of OSA is severe then the Sleep Unit will discuss those results with you.

Remuneration

You will not be paid a wage for taking part in this study. However, any expenses incurred in travelling to and from the Institute and any parking charges for the purpose of this study will be reimbursed by a fixed $75 for the initial assessment and a further $50 will be paid at the completion of the six-month assessment.

Confidentiality

Your data will be only be made available to the researchers listed on page 1. However, if you agree we will store your data for future research where it could be useful in other New Zealand-based microsleep or brain blood flow studies. These studies would first need approval from a New Zealand accredited ethics committee before going ahead. You may still take part in this study if you do not consent for us to use your data this way.

Participation

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Taking part in this study is voluntary. If you decide to take part, you can change your mind at any time and withdraw. Your decision to withdraw will not change any present or future relationships with the Christchurch Hospital, and the New Zealand Brain Research Institute or their associated organisations.

Further Information:

If you have any questions about this study, please contact Russ Buckley or one of the other researchers by phone or email. If you have any questions or concerns about your rights as a participant, you can contact an independent health and disability advocate. This is a free service provided under the Health and Disability Commissioner Act.

Telephone (NZ wide): 0800 555 050

Free Fax (NZ wide): 0800 2787 7678 (0800 2 SUPPORT)

Email: [email protected]

If you have any questions or concerns about your rights as a participant in this research study from a Maori perspective, you may wish to consult Nga Ratonga Hauora Maori, Maori Health Services, you can contact them directly on telephone 03 364 0640 ext. 88797 or through Te Komiti Whakarite on telephone 03 364 0640 ext. 86160 or email [email protected]

Note:

This study has been approved by the Health and Disability Ethics Committees and by Te Komiti Whakarite (Canterbury District Health Board Research Consultation with Maori committee).

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Appendix D Example polysomnography report

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Appendix E Resmed Airview sleep report

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Appendix F. Individual z-scores Z-scores for Baseline results

Raw AHI Arousals/hourTT<90% ESS gCBF PSQI Global scoreMOSA36SF GlobalMicrosleeps score PVT Composite AttentionComposite ExecComposite MemorySpeed of ProcessingComposite Vis 18.4 -0.53 -0.29 -0.49 1.33 -0.68 -2.05 0.23 0.46 -0.94 0.57 -0.30 0.00 0.74 16.3 -1.65 0.33 0.41 1.51 0.16 -0.10 -0.22 -0.13 -0.62 -0.21 -0.52 0.50 -2.27 26.4 -1.28 -0.17 0.63 0.97 0.72 0.83 0.31 0.46 -0.49 -0.26 0.60 0.50 0.18 29.9 0.18 -2.40 -0.94 0.17 -1.52 -0.05 0.23 0.31 0.32 0.39 0.49 -0.50 0.47 27.3 -3.35 0.52 -1.39 1.18 -2.08 0.00 0.23 0.46 -0.72 0.24 0.33 1.00 -0.05 19.2 -0.05 0.64 -0.27 1.41 -0.68 0.11 0.31 0.31 0.32 -0.12 0.55 1.00 0.28 23.4 0.22 0.18 -0.04 -0.54 1.28 0.72 0.27 0.16 1.23 0.31 -0.39 3.00 0.02 21 -0.15 0.67 0.18 -0.80 0.44 -0.36 0.31 0.31 0.14 0.51 0.84 0.00 -0.10 26.3 -1.29 0.69 -0.27 0.03 -1.52 -1.22 0.31 -0.42 -0.01 -0.41 0.22 -0.50 -0.40 19.4 -0.41 0.31 0.18 0.38 -1.24 0.85 0.31 -0.13 -0.31 0.19 0.16 0.50 0.66 17.8 0.22 0.69 -0.27 -0.38 -0.40 0.85 0.23 0.46 -0.13 1.22 1.14 3.00 0.55 17 1.29 0.63 -0.04 -0.42 0.72 -1.77 0.15 -0.56 -1.00 -0.74 -0.47 -1.00 -0.83 24.5 -0.39 0.54 -1.17 -1.05 0.72 0.00 0.31 0.46 -0.97 1.00 0.68 -0.50 0.47

19.2 1.03 0.37 -0.72 -2.07 -0.40 1.17 0.31 0.46 0.35 -1.09 -0.33 1.00 0.11 27.8 0.47 -0.98 -1.17 -0.63 -2.08 -1.03 -4.87 -1.44 0.08 0.11 0.35 2.50 0.22 15.6 0.33 0.12 -1.17 -1.22 -0.12 -0.24 0.31 0.46 -0.87 -0.18 -0.01 -1.00 0.69 17.1 -0.06 0.32 -1.39 -0.01 0.72 0.58 -1.99 -1.29 0.36 1.12 0.58 1.00 -0.49 27.6 0.48 -2.54 -1.39 -1.03 -1.24 -2.28 -0.35 0.46 -1.63 0.70 0.88 0.50 0.06 24.6 0.12 0.47 -0.04 -0.14 0.44 -0.69 0.27 0.46 0.97 1.37 0.84 1.50 0.74 27.8 0.75 -0.45 -0.27 -0.33 0.72 0.93 0.31 0.46 0.76 -0.19 -0.28 -1.00 -0.03 19.8 -1.49 0.66 -0.49 0.73 0.44 0.93 0.31 0.31 -0.77 -0.11 -0.64 -0.50 0.32 18.1 0.94 0.67 1.98 0.60 1.28 0.67 0.31 0.46 -0.03 0.11 0.70 0.50 0.49 17.6 0.14 0.27 0.18 1.91 -0.12 -1.27 0.31 0.16 0.04 0.58 1.26 0.00 -0.15 19.1 0.33 -3.25 -0.27 -0.60 -0.40 -1.34 0.06 0.46 0.33 0.04 -0.66 1.00 -0.56

0.3 1.08 0.70 1.75 -0.74 1.00 0.07 0.31 0.46 0.21 1.55 0.73 1.00 0.18 4.9 -0.68 -0.17 1.08 -1.90 1.00 1.07 0.31 0.46 -0.13 0.81 -0.42 -1.00 -0.23 0.2 0.48 -0.20 0.18 0.97 0.44 0.40 0.31 0.31 0.74 0.79 0.31 1.50 0.69 4.8 1.27 0.65 0.18 0.31 1.28 1.01 0.23 -0.56 -0.36 0.04 -0.06 0.50 0.81 2.7 0.86 0.70 1.98 -0.54 0.16 1.17 0.31 0.46 0.65 1.73 0.98 0.50 -0.02 3.6 0.45 0.31 1.08 0.89 -0.40 0.42 0.27 -4.64 0.56 0.53 0.77 1.50 0.95 0.7 0.70 0.01 1.98 0.90 1.28 0.64 0.31 0.46 0.00 0.42 0.22 1.00 0.41

Key No fill Yellow Orange Red

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Z-scores for Difference between Follow-up and Baseline

Raw AHI Arousals/hourTT<90% ESS gCBF PSQI Global scoreMOSA36SF GlobalMicrosleeps score PVT Composite AttentionComposite ExecComposite MemorySpeed of ProcessingComposite Vis 0.64 0.19 0.94 -0.58 -0.30 -0.54 -0.14 -0.29 0.55 0.31 0.82 -0.38 -0.83 1.94 -0.37 0.15 0.48 0.55 -0.30 1.30 -0.39 0.41 1.33 0.85 0.53 -0.18 0.53 -0.06 -0.55 -0.30 -0.34 -0.29 0.10 0.21 -0.04 0.13 -0.25 -0.53 -1.40 0.43 0.87 5.94 0.33 -0.19 -0.96 -0.02 0.13 0.51 -0.06 -0.89 0.81 0.85 -0.11 1.45 -1.87 2.94 -0.56 -0.36 -0.34 -0.23 -1.29 -0.42 -0.49 1.03 -0.47 0.31 -0.25 0.64 -0.84 -1.06 0.57 -0.30 -0.13 -0.29 0.51 0.89 -0.27 0.40 0.13 0.69 0.24 0.84 -0.65 0.94 -0.07 -0.24 0.07 -0.80 0.34 0.74 -0.71 -1.10 0.67 0.05 -0.20 0.03 0.81 -3.06 0.59 -0.30 -0.34 -1.87 -2.41 -0.88 -2.39 0.52 -2.51 -2.92 -0.20 -0.18 0.29 1.94 0.88 -0.30 0.90 -0.99 0.69 0.92 -0.03 0.15 0.75 -1.98 0.13 -0.59 0.24 2.94 0.22 0.00 0.48 0.40 -0.06 0.29 0.64 -0.24 -0.18 0.88 3.94 -0.42 -0.30 -0.34 0.88 -0.67 -1.04 0.11 2.16 -0.09 -5.99 -0.15 -0.59 0.47 1.94 2.55 -0.07 0.00 0.89 1.66 0.99 1.12 -2.15 1.20 0.42 -0.14 2.88 1.27 -1.06 -0.56 -0.30 -1.37 2.10 0.52 -0.47 1.20 0.26

-0.15 -0.11 -2.51 -0.38 0.59 -4.06 0.00 -0.30 -0.34 0.06 1.81 0.16 0.27 -0.26 -1.49 0.36 0.78 -1.20 0.28 -0.06 0.69 6.36 -0.34 -0.39 0.23 -0.49 -0.66 0.52 0.00 0.84 0.29 -0.38 1.51 -2.06 -0.48 0.00 -0.54 2.35 2.11 -0.04 2.66 1.48 -1.35 0.40 -0.02 -0.18 -2.03 -3.06 -1.79 -0.47 1.31 0.48 0.08 0.75 0.70 -1.22 -1.21 -1.37 1.74 1.86 0.51 -0.06 -0.08 0.55 -0.75 1.07 -1.71 -1.89 0.49 0.41 0.65 0.04 -0.07 0.23 -0.05 0.94 -0.10 -0.24 -0.34 -1.22 -0.61 -0.79 -0.95 0.80 -0.64 -0.03 0.37 2.07 0.37 0.94 0.10 -0.30 -0.54 0.04 0.23 -1.14 0.96 -0.03 -0.04 0.93 -0.23 -0.59 -1.02 -2.06 -0.60 -0.30 -0.13 1.80 1.41 0.03 1.79 -1.71 0.00 0.56 -0.18 -1.61 -0.14 -5.06 -0.29 -0.30 -0.54 0.45 0.03 -0.97 -0.25 -1.04 -0.18 -0.02 -2.98 -0.79 0.28 1.94 -0.33 0.00 0.07 -2.21 -1.97 -0.60 -2.47 1.83 0.00 0.84 3.20 -0.59 0.43 -4.06 -0.35 0.04 -0.54 -0.50 0.43 3.19 -0.28 -0.76

-0.59 -0.14 0.94 0.63 -0.30 -0.34 -0.58 0.52 0.40 -0.47 1.11 Z-score within + 1 0.23 0.28 0.94 -0.09 -0.30 -0.54 -0.14 -0.22 -1.26 -0.16 -0.05 Z-score >1 -0.99 0.43 -0.06 -0.20 0.00 -0.13 -0.15 0.05 0.84 -0.24 -0.91 Z-score >3 0.43 -0.45 -1.06 -0.19 -0.19 0.90 0.04 -0.30 -0.33 -0.30 0.23 Z-score >4 -0.59 -0.33 0.94 -0.22 -0.30 -0.54 -0.31 -0.43 -0.78 -0.07 -0.95 -0.79 -0.02 0.94 0.75 -0.24 6.45 -0.73 -0.15 0.35 -0.24 -0.42 -0.99 0.16 0.94 0.50 -0.30 -0.34 -0.24 -0.43 -1.04 0.20 -0.53

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