Translational Measures of the Effects of Insufficient Sleep on Cognition in the

Rat

Sally Loomis

Thesis submitted to the University of Surrey for the degree of Doctor of Philosophy in Biochemistry and

Physiology

Faculty of Health and Medical Sciences

May 2019

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For Toby, Mimi, Henry and Bo

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Abstract

Insufficient sleep is highly prevalent and associated with deficits in functional wakefulness. Thus, a need remains for pre-clinical evaluation of sleep restriction to develop countermeasures for functional deficits. The overall aim of this thesis was to evaluate translational methods to assess the cognitive consequences of sleep loss in the rat.

We first compared the effects of 11-h sleep restriction induced by three novel non-invasive protocols on attention using a Simple Response Latency task (SRLT). Wakefulness was enforced by cylinder rotation following a Constant, Decreasing or ‘Weibull’ (i.e., modelled on EEG-driven sleep restriction) protocols. While all protocols resulted in sleep loss and attentional deficits, differences in sleep recovery and functional alterations were identified, with the Decreasing and Weibull methods inducing attentional deficits similar to those observed in humans. Many behavioural tasks use food as a reward in rodents, thus we next assessed the interaction of food and sleep restriction. Food-restricted rats displayed resilience in SRLT performance to the effects of 11-h sleep restriction compared to ad libitum-fed rats. By contrast, motivation for food reward value was not altered in a progressive ratio task. We then evaluated the effects of pharmacological treatments to counteract the effects of 11-h sleep restriction. The drugs showed distinct pro-vigilant profiles, with caffeine and modafinil displaying beneficial effects on SRLT performance. A non-pharmacological counter-measure (naps) was unsuccessful in alleviating functional deficits induced by sleep loss. Finally, we applied oxygen amperometry, as a surrogate of neuroimaging, and measured oxygen consumption in the nucleus accumbens during the SRLT. However, data interpretation was limited due to throughput capabilities.

Overall, the data indicated the sleep restriction methodologies provide a translational platform to develop novel pro-vigilant compounds that improve sustained attention. Careful choice of methodologies (i.e., sleep restriction protocols; reward) is important when studying functional deficits induced by sleep loss in rodents.

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Acknowledgments

First and foremost a sincere THANK YOU to my Surrey University supervisor Dr. Raphaelle Winsky-Sommerer who went above and beyond to help me finish this thesis.Dr. Gary Gilmour and Prof. Derk-Jan Dijk, for their help and overwhelming patience.Dr. Andrew McCarthy for his contribution to the analysis of the EEG data presented here but more so his encouragement to believe I could ever complete this thesis.For help with the amperometry work, a huge Thank you to Michael Conway and Dr. Jennifer Li.Julia Eaton, Julie Foss and Yvonne Thomas for so much help in the lab, my inane whinging and putting up with my randomness, not only during my PhD but many years prior – Thank You

Tim. For being there every step of the way, for your continued and immense support throughout. Thank-you, you are my rock. Tamara. Teşekkür ederim, her zaman güzel ve beni devam ettirmemi teşvik eden bir şey söyledin.

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Finally, and most importantly, my four fantastic children Toby, Mimi, Henry and Bo for putting up with my stresses (and there were a lot) for so many years during this process. You are each amazing, I love you dearly and I am so lucky to have you all.

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Declaration

I, Sally Loomis, declare that this thesis and the work presented in it is entirely my own, and except where otherwise stated as below, is a result of my own original research.

I confirm that:

1. This work was done wholly or mainly while in candidate for a research degree at this University;

2. Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any institution, this has been clearly stated;

3. Where I have consulted the published work of others, this is always clearly attributed;

4. Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work;

5. I have acknowledged all main sources of help;6. Where the thesis is based on work done by myself jointly with others, I have made

clear exactly what was done by others and what I have contributed myself;

Contribution for results chapters are as follows:

Chapter 3 - Experimental work was entirely conducted by Sally Loomis, EEG automated analysis and Weibull modelling was conducted by Dr. Andrew McCarthy. Chapter 4 - Experimental work was conducted by Sally Loomis, except for concurrent feeding studies that were designed by Sally Loomis and Sonia Nestorowa but conducted by Sonia Nestorowa. EEG automated analysis was conducted by Dr. Andrew McCarthy.Chapter 5 - Experimental and data analysis was conducted by Sally Loomis. Dr. Andrew McCarthy contributed to the EEG analysis.Chapter 6 - Experimental work was entirely conducted by Sally Loomis. Dr. Jennifer Li and Michael Conway contributed to O2 Amperometry analyses.

7. Parts of this work have been published before submission;

Loomis, S., McCarthy, A., Baxter, C., Kellett, D.O., Edgar, D.M., Tricklebank, M. and Gilmour, G., 2015. Distinct pro-vigilant profile induced in rats by the mGluR5 potentiator LSN2814617. Psychopharmacology,232(21-22), pp.3977-3989.

McCarthy, A., Loomis, S., Eastwood, B., Wafford, K.A., Winsky‐Sommerer, R. and Gilmour, G., 2017. Modelling maintenance of wakefulness in rats: comparing potential non‐invasive sleep‐restriction methods and their effects on sleep and attentional performance. Journal of sleep research, 26(2), pp.179-187.

Loomis, S., McCarthy, A., Dijk, D-J., Winsky‐Sommerer, R. and Gilmour, G., 2018. Hunger-induces functional resilience to sleep restriction in rats. Submitted to Sleep October 2019

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Signed:……………………………………………………………………………………………………………………..

Date:…………………………………………………………………………………………………………………………

ContentsAbstract................................................................................................................................................ iii

Acknowledgments................................................................................................................................ iv

Declaration............................................................................................................................................v

Guide to Figures....................................................................................................................................xi

Guide to Tables...................................................................................................................................xiii

Abbreviations......................................................................................................................................xiv

Chapter 1 - General Introduction..........................................................................................................1

1.1 Overview of sleep..................................................................................................................1

Sleep in humans and rats...............................................................................................................1

Regulation of sleep........................................................................................................................5

Neurochemistry of sleep-wake cycle.............................................................................................6

Sleep for the brain and the body...................................................................................................8

Consequences of insufficient sleep..............................................................................................10

1.2 Attention and insufficient sleep...........................................................................................11

Attention as a cognitive measure................................................................................................11

Neural circuitry involved in attention and sleep deprivation.......................................................13

The effects of sleep restriction on attention in humans..............................................................14

Sleep restriction effects on attention in rodents.........................................................................18

Why attention tasks are sensitive to sleep deprivation...............................................................20

1.3 Objectives............................................................................................................................21

Aims of the thesis........................................................................................................................22

Chapter 2 - Methods............................................................................................................................23

2.1 Subjects...............................................................................................................................23

2.2 Surgical procedures.............................................................................................................23

Electroencephalogram/Electromyogram/Event Related Potentials............................................23

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Amperometry..............................................................................................................................24

2.3 Sleep restriction methodology.............................................................................................26

Recording environment...............................................................................................................26

Biofeedback sleep restriction......................................................................................................27

Non-biofeedback sleep restriction...............................................................................................27

2.4 Automated EEG/EMG data collection, sleep staging and analysis.......................................28

2.5 Event Related Potentials (ERPs)...........................................................................................29

2.6 Amperometry data collection and analysis..........................................................................30

2.7 Behavioural tasks.......................................................................................................................31

Simple Response Latency task (SRLT), a sustained attention task...............................................31

Progressive Ratio (PR) to assess motivation................................................................................32

Concurrent Fixed Ratio 5 (CFR5) task to assess effort-related choice behaviour.........................33

2.8 Physiological measures........................................................................................................34

Body temperature and locomotion measurement......................................................................34

Corticosterone measurement......................................................................................................34

2.9 Drug formulation and administration..................................................................................34

2.10 Statistics...............................................................................................................................34

Chapter 3 - Evaluation of a Non-Invasive Sleep Restriction Method to Assess the Effects of Insufficient Sleep on Sustained Attention in the Rat...........................................................................35

3.1 Introduction.........................................................................................................................35

Sleep restriction methods in rodents...........................................................................................35

Vigilance tasks as a functional readout of sleep restriction.........................................................36

Chapter aims................................................................................................................................38

3.2 Methods..............................................................................................................................40

3.2.1 Animals and housing conditions..................................................................................40

3.2.2 Surgical procedures......................................................................................................40

3.2.3 Sleep restriction protocols...........................................................................................40

3.2.4 Simple Response Latency task (SRLT)...........................................................................42

3.2.5 Locomotor activity, body temperature and corticosterone levels...............................42

3.2.6 EEG/EMG analyses.......................................................................................................42

3.3 Results.................................................................................................................................43

3.3.1 Validation of a standard sleep restriction protocol using SRLT...........................................43

3.3.2 Characterisation of wakefulness induced by the “gold standard” EEG-triggered (“Biofeedback”) sleep restriction.................................................................................................46

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3.3.3 Comparison of three non-invasive non-bio-feedback sleep restriction protocols on sleep/wake parameters...............................................................................................................47

3.3.4 Comparison of a Biofeedback and non-Biofeedback sleep restriction protocols on SRLT and physiological parameters......................................................................................................50

3.3.5 Effects of sleep restriction protocols on subsequent recovery sleep...........................52

3.3.6 The effect of using an 11-hr non-invasive Weibull sleep restriction protocol during 5 weeks on SRLT in rats..................................................................................................................55

3.4 Discussion............................................................................................................................57

Comparison of different sleep protocols on sleep parameters....................................................57

Stress measurements and locomotion on sleep protocols..........................................................58

Comparison of sleep protocols on functional impairments using SRLT.......................................59

Limitations of the study...............................................................................................................60

Comparisons with human literature............................................................................................61

Chapter 4 - Motivation for Food as a Confounding Factor in the Assessment of Sustained Attention following Insufficient Sleep in the Rat.................................................................................................62

4.1 Introduction.........................................................................................................................62

Motivation...................................................................................................................................63

Assays for measuring motivation in rodents................................................................................64

Motivation in rat sleep restriction studies...................................................................................65

Effect of motivators in human sleep deprivation studies............................................................66

Chapter aims................................................................................................................................67

4.2 Methods..............................................................................................................................68

4.2.1 Subjects and housing conditions..................................................................................68

4.2.2 Surgical procedures......................................................................................................68

4.2.3 Sleep restriction protocols...........................................................................................68

4.2.4 Behavioural tasks.........................................................................................................68

4.2.5 Data and statistical analyses........................................................................................70

4.3 Results.................................................................................................................................71

4.3.1 Effect of time of day and feeding status on SRLT performance..................................71

4.3.2 Effect of feeding regimen on sleep, wake, physiological parameters and SRLT following an 11-h Biofeedback sleep restriction..........................................................................73

4.3.3 Effect of feeding regimen on motivation assessed by the progressive ratio task following 11-h sleep restriction...................................................................................................83

4.3.4 Effect of feeding regimen and sleep restriction in the CFR5 assay..............................84

4.4 Discussion............................................................................................................................88

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Circadian testing times on sustained attention............................................................................88

Sleep restriction and feeding regimen on sustained attention....................................................89

Sleep restriction and feeding regimen on progressive ratio........................................................91

Reward value and sleep restriction using a CFR5 task.................................................................92

Limitations of the study...............................................................................................................93

Chapter 5 - Assessing Pharmacological and Non-Pharmacological Countermeasures for Sleep Loss in Rats......................................................................................................................................................95

5.1 Introduction.........................................................................................................................95

Pharmacological countermeasures of excessive daytime sleepiness.....................................95

Non-Pharmacological countermeasures......................................................................................97

Aims of chapter..........................................................................................................................99

5.2 Methods............................................................................................................................100

5.2.1 Animals and housing conditions................................................................................100

5.2.2 Surgical procedures.................................................................................................100

5.2.3 Sleep restriction methodology................................................................................100

5.2.4 Simple Response Latency task (SRLT)....................................................................101

5.2.5 Drugs.........................................................................................................................101

5.2.6 Data analyses............................................................................................................101

5.3 Results...............................................................................................................................103

5.3.1 Effects of Modafinil, Amphetamine and Caffeine on sleep-wake parameters following 11-h sleep restriction.................................................................................................................103

5.3.2 Effects of pharmacological compounds on PVT performance following 11-h sleep restriction.................................................................................................................................111

5.3.3 Use of Naps as a non-pharmacological countermeasure to potentially improve SRLT performance following 11-h non-biofeedback sleep restriction.................................114

5.4 Discussion..........................................................................................................................117

Comparison of pharmacological countermeasures following sleep restriction...................117

Non-pharmacological countermeasures following sleep restriction....................................119

Future Work and Limitations..................................................................................................121

Chapter 6 - Effects of Sleep Restriction on the Nucleus Accumbens using in vivo Amperometry......122

6.1 Introduction.......................................................................................................................122

Oxygen amperometry................................................................................................................122

Sleep restriction, performance and the Default Mode Network coherence..............................123

The role of the nucleus accumbens in reward and motivational behaviour..............................124

Aims of chapter.............................................................................................................................125

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6.2 Methods............................................................................................................................126

6.2.1 Subjects and housing.................................................................................................126

6.2.2 Surgical procedures....................................................................................................126

6.2.3 Amperometric technique...........................................................................................126

6.2.4 Sleep restriction methodology...................................................................................126

6.2.5 Behavioural tasks.......................................................................................................127

6.2.6 Histology....................................................................................................................127

6.2.7 Data and statistical analyses......................................................................................128

6.3 Results...............................................................................................................................129

6.3.1 Overall oxygen responses within the nucleus accumbens during the behavioural SRLT task 129

6.3.2 Oxygen response in the nucleus accumbens during specific SRLT events..................130

6.3.3 SRLT outcomes following 11-h non-biofeedback sleep restriction in rats used for amperometry recordings...........................................................................................................133

6.3.4 Correlations between SRLT outcomes and the oxygen responses within the nucleus accumbens following 11-h sleep restriction..............................................................................136

6.4 Discussion..........................................................................................................................138

Oxygen amperometric effects in reward driven tasks...............................................................138

Oxygen amperometric effects on task performance following sleep restriction.......................139

Limitations of in vivo amperometry...........................................................................................140

Translational implications of in vivo amperometry....................................................................141

Chapter 7 - General Discussion..........................................................................................................143

Comparison of sleep deprivation and induced performance deficits in rats and humans.............144

Nature of the sleep manipulation..............................................................................................144

Nature of the behavioural assessment......................................................................................145

Interaction of motivation in assessing the effect of sleep in cognitive tasks.............................147

Translational value: pharmacology and napping in sleep studies..............................................148

Neuroimaging contributions to understanding cognitive function and sleep............................149

Taking into account circadian rhythms when assessing the impact of sleep restriction on behaviour...................................................................................................................................150

Effects of sleep deprivation on cognitive function.........................................................................151

Measuring more than attention................................................................................................151

Summary of findings and further work..........................................................................................153

References.........................................................................................................................................155

Appendix...........................................................................................................................................187

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Kaplan-Meier Estimates.................................................................................................................187

Weibull Distribution.......................................................................................................................188

Guide to Figures

Figure 1.1: Hypnograms obtained from a human young male healthy participant and a Wistar adult rat.

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Figure 1.2: The sleep/wake cycle in a rat. 8

Figure 1.3: Schematic overview of the top-down and bottom-up control of attention. 13

Figure 1.4: Distribution of 5th and 95th percentile reaction times in the psychomotor vigilance task over 88 hours of continuous total sleep deprivation in humans. 16

Figure 1.5: Psychomotor vigilance task in rats subjected to 24hrs sleep deprivation using wheel turning or gentle handling.

19

Figure 2.1: Position of EEG/ERP electrodes.24

Figure 2.2: Carbon paste electrodes used for amperometry.25

Figure 2.3: Amperometry surgery.26

Figure 2.4: Sleep restriction chamber.27

Figure 2.5: Progressive ratio task to assess motivation.33

Figure 3.1: Simple Response Latency task in rats and psychomotor vigilance task in humans. 37

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Figure 3.2: Sleep restriction protocol. 41

Figure 3.3: Dose response for 6, 8 and 10 hours EEG biofeedback sleep restriction on SRLT.44

Figure 3.4: Performance in SRLT following an 11-hr EEG Biofeedback sleep restriction. 45

Figure 3.5: Effects of time of day on SRLT performance.46

Figure 3.6: Effects of EEG-biofeedback induced sleep restriction on wakefulness. 47

Figure 3.7: Comparison of sleep restriction protocols with non-sleep restricted controls (blue) and biofeedback protocol (dark red).

49

Figure 3.8: Comparison of sleep restriction protocols on rat SRLT performance during baseline (“pre”), following sleep restriction (“test”) and recovery (“post”) days. 51

Figure 3.9: Effect of sleep restriction protocols on physiological parameters.52

Figure 3.10: Effects of sleep restriction protocols on subsequent sleep.54

Figure 3.11: Effect of 11-h non-invasive Weibull sleep restriction protocol applied once a week during 5 weeks on SRLT parameters.

56

Figure 4.1: Effect of time of day on SRLT performance in food restricted and ad libitum fed rats. 73

Figure 4.2: Effects of feeding regimen on sleep during an 11-h bio-feedback sleep restriction protocol.

74

Figure 4.3: Time-course of time spent awake before, during and after the 11-h sleep restriction. 75

Figure 4.4: Time course of wakefulness following the 11-h EEG Biofeedback induced sleep restriction.

75

Figure 4.5: EEG power density during the 40-min Simple Response Latency task.76

Figure 4.6: Physiological parameters during an 11-h sleep restriction.77

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Figure 4.7: Effects of feeding status on SRLT performance following 11-h of biofeedback sleep restriction.

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Figure 4.8: Cohen’s d effect sizes of SRLT parameters.80

Figure 4.9: Effect of food restricted and ad libitum feeding on Event Related Potentials (ERPs) to the magazine light (imperative cue) during the SRLT.

81

Figure 4.10: Correlations between time spent awake during the SRLT and performance parameters.

82

Figure 4.11: Effect of feeding regimen on progressive ratio task.84

Figure 4.12: Effect of feeding regimen to satiety assessed during the CFR5 task.85

Figure 4.13: Effect of sugar pellet availability on performance in the CFR5 task. 86

Figure 4.14: Effect of sleep deprivation on CFR5 task.87

Figure 5.1: A simplified mechanism of action for Modafinil.96

Figure 5.2: The effect of pro-vigilant compounds on wakefulness in rats. 104

Figure 5.3: Effects of Modafinil on non-REM Sleep in 11 h sleep-restricted rats.105

Figure 5.4: The effect of Amphetamine on non-REM Sleep in 11 h sleep-restricted rats. 106

Figure 5.5: The effect of Caffeine on non-REM Sleep in 11 h sleep-restricted rats.107

Figure 5.6: Time course of the effects of Modafinil on REM sleep in 11 h sleep-restricted rats. 109

Figure 5.7: Effects of Amphetamine on REM sleep in 11 h sleep-restricted rats.110

Figure 5.8: The Effect of Caffeine on REM sleep parameters in 11 h sleep-restricted rats. 111

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Figure 5.9. Pro-vigilant drug effects on SRLT response parameters in 11 h sleep-restricted rats. 113

Figure 5.10. Effect of a 15-min and 30-min nap on SRLT following an 11-h non-biofeedback sleep restriction in food restricted rats.

115

Figure 5.11: Effect of a 15-min and 30-min nap on SRLT in ad libitum fed rats following an 11-h non-biofeedback sleep restriction.

116

Figure 6.1: Simplified view of the mesolimbic reward pathway.125

Figure 6.2: Experimental protocol for sleep restriction. 127

Figure 6.3: CPE placement confirmed by histology. 128

Figure 6.4: Oxygen responses during SRLT on baseline (pre) day.129

Figure 6.5: Oxygen responses during the SRLT tasks following 11-hr sleep restriction. 130

Figure 6.6: Amperometry readouts within the nucleus accumbens during specific SRLT parameters on baseline (pre-test) day.

131

Figure 6.7: Amperometry readouts during specific SRLT events following an 11-hr Weibull non-invasive sleep restriction.

133

Figure 6.8: Effect of a 11-h sleep restriction on SRLT in all ad libitum-fed rats instrumented for amperometry.

134

Figure 6.9: Effect of 11-h sleep restriction on SRLT in ad libitum fed rats included in the amperometry.

135

Figure 6.10: Correlations between the AUC of oxygen response in the nucleus accumbens and SRLT performance parameters.

136

Figure 6.11: Correlations between X-Peak in the nucleus accumbens and SRLT performance parameters.

Appendix Figure A1: Fitting a Weibull distribution model to Biofeedback sleep restriction wake bout data

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Guide to Tables

Table 4.1: Time of day testing schedule.69

Table 4.2: Statistical analysis of feeding condition and sleep restriction on SRLT parameters 79

Table 5.1: Effects of Modafinil, Amphetamine and Caffeine on sleep parameters during recovery in sleep-restricted rats.

108

Table 6.1: Statistical F and p-values for AUC and X-Peak for pre Day and test Day. 132

Appendix Table 6A1: Kaplan Meier probability estimates of sleep attempts.187

Abbreviations

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AASM American Academy of Sleep MedicineACC Anterior Cingulate CortexACh AcetylcholineAP AnteroposteriorARAS Ascending Reticular Activating SystemASR Automated Sleep RestrictionBOLD Blood-Oxygen-Level-DependentCFR5 Concurrent Fixed Ratio 5 TaskCIT Constant Interval TurningCORT Corti costeroneCPE Carbon Paste Electrode5-CSRTT 5 Choice Serial Reaction Ti me TaskCT Circadian TimeDA DopamineDIT Decreasing Interval TurningDL DorsolateralDV DorsoventralDMN Default Mode NetworkECG ElectrocardiographyEDS Excessive Daytime SleepinessEEG ElectroencephalogramEMG ElectromyogramEOG ElectrooculographyERP Evoked Response PotentialfMRI Functional Magnetic Resonance ImagingFR Fixed RewardHis Hi stamineHz Hertz - 1 Cycle per second i .e. 4Hz (4 cycl es per second)GABA γ-aminobutyri c acidIL1 Interleukin-1IL6 Interleukin-6LC Locus CoeruleusLDT Lateral Dorsal TegmentalLMA Locomotor Acti vityML Medial LateralNAc Nucl eus Accumbens

NE NorepinephrineNREM Non-Rapid Eye MovementPFC Pre-frontal CortexPET Pos itron Emission TomographyPPT Pedunculopontine TegmentalPR Progressive RatioPVT Psychomotor Vigilance TaskREM Rapid Eye MovementRIT Random Interval TurningRT Reaction TimeSAT Sustained Attention TaskSCN Suprachiasmatic NucleusSD Sleep DeprivationSerotonin (5-HT) 5-hydroxytryptamine

SRLT Simple Response Latency TaskSWS/SWA Slow Wave Sleep/Slow Wave ActivityTIB Time in BedTMN Tuberomammillary NucleusTNF Tumor Necrosis FactorTOT Time on TaskTSD Tota l Sleep DeprivationVLPO Ventrolateral Preoptic NucleusVTA Ventra l Tegmental AreaWM Working Memory

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CHAPTER ONE

Chapter 1 - General Introduction

Sleep deprivation is known to have deleterious consequences on cognitive functioning and long term adverse health outcomes in humans (Van Dongen et al., 2003) (Alhola and Polo-Kantola, 2007) (Luyster et al., 2012). To develop preclinical research in this field, cognitive consequences of sleep loss in rodents need to be compared to humans and assess similarities in order to assure that more compounds tested pre-clinically translate to human efficacy. The aims of this thesis are to; (i) develop novel non-invasive sleep restriction manipulations in rats that can produce sustained attentional deficits equivalent to those assessed by the psychomotor vigilance task (PVT) in sleep deprived humans; (ii) to investigate the interaction between the motivation for sleep and food intake on behavioural performance measures; (iii) to evaluate countermeasures for insufficient sleep using pharmacological and non-pharmacological interventions; and (iv) to examine the use of oxygen amperometry as a translational biomarker to assess sustained attention outcomes in the presence of sleep restriction.

1.1 Overview of sleep

Sleep in humans and rats

Electroencephalography (EEG) is used as the standard methodology to measure sleep-wake cycles in an objective manner. Initial recordings were reported in animals by Caton in 1875 (Caton, 1875), and subsequently by Hans Berger in 1929 using non-invasive measurements to obtain EEG recordings in humans (Berger, 1929). EEG measures electrical brain activity recorded by electrodes located above or within the cortex. Sleep studies routinely use polysomnography which incorporates EEG measures simultaneously with other

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recordings to give a comprehensive characterisation of sleep/wake cycles in humans and rats. For the recording and visualisation of the human EEG the potential differences between two electrodes located on the scalp is amplified and displayed. Standardisation and comparability of recordings obtained in various laboratories is achieved by placement of electrodes according to the 10-20 International System of Electrode Placement (Jasper, 1958). Rat EEG is measured using skull screws placed for instance above the frontal and occipital cortex (George and Charles, 2007). Polysomnography readouts can encompass electroencephalogram (EEG), muscle activity, electromyogram (EMG), electrocardiography (ECG) and eye movement electrooculography (EOG). Early experiments demonstrated that sleep was not a continuum of one state but rather consisted of stages (Loomis et al., 1937). In 1968, Rechtschaffen and Kales refined these stages to develop a 5 sleep staging scale that standardised human EEG scoring (Rechtschaffen and Kales, 1968). This was superseded in 2007 by the scoring rules of the American Academy of Sleep Medicine (AASM) which distinguish only 4 sleep stages. Non-Rapid Eye Movement (NREM) consisting of Stages N1, N2 and N3 and Rapid Eye Movement (REM) termed stage R. NREM sleep encompasses “deep slow wave” sleep (N3), and “light” NREM sleep. Stages N1 and N2 represent lighter sleep states. The transition from the eyes-closed alpha rhythm to an irregular waveform of mixed theta-activity (4-8 Hz) defines the onset of sleep in the human. Contained within the EEG readout of the NREM N2 sleep state are transient components such as K-complexes and sleep spindles. K-complexes are defined as a single large amplitude wave <2 Hz which has a brief negative (upward deflection) peak followed by a slower positive (downward deflection) peak. Sleep spindles were first identified by Loomis in 1937 and are short bursts of activity (~0.5 secs) at ~12-14Hz and often follow K-complexes (Loomis et al., 1937). Stage N3, represents delta activity or slow wave sleep (SWS), a deeper level of sleep characterised by low-frequency waves (0-2 Hz). REM sleep (Stage R) sometimes termed paradoxical sleep, presents as low amplitude, high frequency, waves more characteristic of wake (and stage N1 theta activity). In contrast to wakefulness, during REM sleep the EMG is generally inhibited, displaying muscle atonia, and includes the so-called bursts of rapid eye movements. In rats, sleep is also subdivided in NREM and REM sleep but NREM sleep is not divided into stages as for humans (Figure 1.1).

2

In adult humans, sleep is in general monophasic consisting of one major sleep period. In the nocturnal rat, sleep occurs primarily during the light period (inactive phase). In both the light and the dark phases, the sleep pattern in the rat is polyphasic with shorter bouts of sleep interspersed with wakefulness. The sleep state cycles between NREM and REM stages throughout the night with 60-90 minutes cycles in humans versus 10-15 minutes in rats (Carskadon and Dement, 2005), but the relative proportions of each stage changes as sleep progresses. Approximately a fifth of human sleep consists of REM sleep and occurs predominantly in the latter part of the night (Figure 1.1a). In rats, during the light (sleep) phase around 65% of the time is spent in NREM sleep, 15% in REM sleep and wakefulness accounts for the remaining 20% (Shea et al., 2008).

Some differences exist between the evaluation of sleep in rodents and humans with respect to the frequency range of the EEG signal. In humans, EEG frequency bands are usually defined as Delta (δ) activity: 0.5-2 Hz, Theta (θ) activity: 4-8Hz; Alpha (α) activity: 8-13Hz and Beta (β) activity: 14-30Hz. In rats the effective range is 0.1-20 Hz, divided into the same four bands but measured as; Delta (δ) activity: 0.1-3.9Hz; Theta (θ) activity: 4-8.9Hz; Alpha (α) activity: 9-11.9Hz and Beta (β) activity: 12-20Hz. Higher frequency, Gamma (γ) activity between 30-50Hz is generally not included in analyses as it is mostly masked by background noise from the EMG and other sources. These frequency ranges can vary slightly between studies. The epoch length used for classification of sleep-wake state in rats is usually a 4 or 10 second epoch, while in human studies the epoch length is longer, i.e., a 20 or 30 second epoch is used as standard. Epochs that contain more than one single stage of sleep-wake state are termed transition epochs. In humans the subdivision of NREM sleep into N1, N2 and N3 stages are thought to reflect increasing depth of sleep; in rats the depth of NREM sleep is quantified by power density in the delta frequency range.

Importantly, the physiological characteristics of sleep, the mechanisms governing the process and the consequences of sleep restriction seem well conserved between rodents and humans (Dijk, 2009) (Phillips et al., 2010). This conservation of sleep characteristics between rodents and humans implies that pre-clinical rodent sleep restriction experiments can be considered a valuable model to measure the impact of sleep restriction.

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N3

N2

N1

REM

WAKE

1 2 3 4 5 6 7 8

Time (h)

(a)

NREM

REM

WAKE

1 2 3 4 5 6 7 8

Time (h)9 10 11 12

12-h Hypnogram 7AM-7PM

NREM

REM

WAKE

405 10 15 20 25 30 35

Time (min)45 50 55 60

Sleep/Wake States 10-11AM

SRLT

Tes

ting

NREM

REM

WAKE

405 10 15 20 25 30 35

Time (min)45 50 55 60

Sleep/Wake States 3-4PM

(b)

(c) (d)

Figure 1.1: Hypnograms obtained from a human young male healthy subject and Wistar adult rat during a natural sleep period). (a). A hypnogram obtained

from a polysomnography recording during a baseline night in a young male and healthy

4

participant. Data are shown for the Central C3 derivation (data from Surrey Sleep Research Centre ). (b). Characteristic hypnogram obtained in a food-restricted male Wistar rat across a 12-h light phase (in-house data). Data presented in 10-s epochs.

Inserts (c & d) show a zoomed in image of 1-h time point between 10-11am (c) and 3-4pm (d).

Regulation of sleep

In humans, sleep propensity is known to be under the control of two processes, the homeostatic need for sleep (process S), reflected by slow wave activity (SWA) also referred to as delta activity, and the endogenous circadian process (process C) (Borbély, 1982) (Borbély et al., 2016). Process ‘C’ or circadian rhythmicity is governed by the circadian clock in the suprachiasmatic nucleus (SCN) located within the hypothalamus. Process C rhythmically modulates the propensity to sleep and remains closely linked to environmental variables such as the light-dark cycle. In contrast, process ‘S’ increases during wakefulness and decreases during non-rapid eye movement (NREM) sleep. Process S also modulates the depth of sleep reflected by SWA. When an individual has been deprived of sleep, process ‘S’ rises to levels above those seen during a baseline night (Deboer, 2018). A third interactive process was later introduced to account for the effect of sleep inertia (process “W”) (Folkard and Åkerstedt, 1992). Sleep inertia is described as a transient period of low arousal that produces a temporary decrement in subsequent performance immediately following wakening (Tassi and Muzet, 2000). In this context, arousal is considered to be the shift from a deep sleep NREM (N3) state to a lighter NREM (N1 & N2) sleep state, or from sleep to wakefulness, while vigilance is associated with alertness and functioning during wakefulness. Low arousal and low vigilance ‘states’ are often difficult to disentangle behaviourally. Sleep inertia is important as it can affect cognitive functioning for up to a few hours post-waking and is particularly relevant in the context of napping (Hilditch et al., 2017). Prior sleep deprivation will increase sleep inertia as will the sleep stage preceding wakening, for example waking from slow wave sleep (SWS).

Effects of sleep restriction on subsequent sleep and waking function have been studied for many years in both rats and mice (Rechtschaffen et al., 1989) (Revel et al., 2009) (Colavito et al., 2013). In rodents, recovery sleep from sleep restriction is similar to recovery sleep in humans, i.e., SWA during recovery sleep is elevated above baseline (Weber and Dan, 2016).

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Forced desynchrony protocols have been used in humans to study the interactions between circadian and homeostatic processes. The robustness of homeostatic control of slow wave activity can be shown via studies that manipulate the endogenous circadian rhythm. In the original study by Dijk in 1995, subjects were maintained in a controlled environment void of external cues for 33-36 days and maintained on an imposed sleep–wake periodicity 28-h long in order that sleep periods would occur across all phases of the circadian cycle. This protocol allowed separation of the sleep-wake cycle from the circadian rhythm and showed that SWA in humans is primarily under homeostatic rather than circadian control and REM sleep is more governed by circadian processes (Dijk and Czeisler, 1995) (Dijk, 2009). It has been postulated that the homeostatic processes of REM and NREM sleep are independent (OCAMPO‐GARCÉS and Vivaldi, 2002). However in rats using a selective 2-h REM sleep

deprivation designed to maximise the elimination of circadian influences the researchers concluded

that homeostatic regulation of REM sleep is positively coupled with the homeostatic regulation of

NREM sleep (Shea et al., 2008). Later studies in rats have also shown that EEG in the frequency 7-25Hz during NREM sleep appears more modulated by circadian rhythm and less influenced by sleep homeostasis (Yasenkov and Deboer, 2010) (Skorucak et al., 2018).

Neurochemistry of sleep-wake cycle

Waking and sleep states are regulated by several neurotransmitter systems. The key modulatory neurotransmitters include acetylcholine (ACh), the monoamines: norepinephrine (NE), serotonin (5-hydroxy-tryptamine, 5-HT), histamine (His), dopamine (DA) and the hypocretin (orexin) neuropeptides. GABA (γ-amino-butyric acid) and glutamate have been proposed to also be part of the main regulators of the sleep/wake cycle (Scammell et al., 2017). Manipulations that interfere with these neurochemical systems lead to sleep-wake disorders and functional changes of wakefulness and sleep (Holst and Landolt, 2018). Control and release of these neuromodulators is via an ascending reticular activating system (ARAS). The ARAS consists of a network of individual nuclei expressing distinct neurotransmitters that promote arousal ascending from the brainstem and posterior hypothalamus into the thalamus, hypothalamus, and the basal forebrain. From here this pathway then divides into a ventral pathway that projects into the cortex via the hypothalamus and basal forebrain releasing aminergic neurotransmitters and a dorsal pathway which projects to the

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thalamus to regulate cortical activity and release acetylcholine. This dorsal pathway involves the laterodorsal tegmental (LDT) and pedunculopontine tegmental (PPT) nuclei and enables thalamic processing of signals related to sensation, motor responses and cognition. The basal forebrain also contains cholinergic GABA and glutamate-producing neurons that project into the cerebral cortex from the medial septum back to the substantia innominata (Zant et al., 2016) and create arousal by reducing activity of inhibitory neurons in the cortex. The monoaminergic neurons are fundamental to the wake promoting system and strongly inhibit the REM sleep cycle. Norepinephrine is produced predominantly in the locus coeruleus (LC). Neurons from this region receive inputs from the brainstem, prefrontal cortex and other arousal and project across the central nervous system (Luppi and Fort, 2011). Serotonin (5-HT) is produced in the dorsal raphe and median raphe nuclei. 5HT neurons have been thought to influence wake promotion as 5HT receptors agonists promote wake and suppress REM sleep; however, 5HT has also been shown to increase sleep propensity (Monti, 2011). Increases in dopamine through compounds that increase synaptic dopamine release such as amphetamine and modafinil (Loomis et al., 2015) exert a wake-promoting effect whereas dopamine antagonists that decrease dopamine concentrations induce sedation. Histamine is produced in the tuberomammilary nucleus (TMN), (an area in the posterior hypothalamus), and plays an important role in transitioning between sleep and wakefulness. Histamine neurons are active in wake and silent during sleep (Takahashi et al., 2006). Optogenetic silencing of TMN neurons during wake promotes the rapid onset of slow-wave sleep (Fujita et al., 2017), and administration of histamine antagonists induce sedation (Reiner and Kamondi, 1994).

Orexin A and Orexin B are excitatory neuropeptides synthesized in neurons within the lateral hypothalamus. These neurons project to the brain stem and hypothalamic arousal centres. Orexin is involved in the maintenance of wake and transitioning from sleep to wake (Blouin et al., 2013). Two orexin receptor subtypes (OX1R, OX2R) mediate the activity of orexins, OX1R is selective only for orexin A but OX2R is non-selective acting as a receptor for both orexin A and B. As well as involvement in sleep-wake regulation, orexin release is also associated with changes in ghrelin, leptin and glucose levels which are major contributors to maintaining energy homeostasis (Chieffi et al., 2017). Orexinergic neurons are also known to stimulate the dopaminergic pathways via excitatory projections to

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the ventral tegmental area (VTA) and are therefore integral in the regulation of motivation and reward (Nevárez and de Lecea, 2018).

As the need for sleep increases, neurons in the ventrolateral preoptic nucleus (VLPO) become more active and release GABA and galanin and inhibit the ascending arousal system (Luppi et al., 2017). The VPLO is most active during sleep while receiving inhibitory signals from each of the major monoaminergic nuclei during wakefulness. GABAergic neurons in the hypothalamus are most prominent in co-ordinating the inhibition of wake-promoting brain regions and thereby elicit sleep. During NREM sleep the inactivity of the cholinergic neurons in the LDT and PPT reduces excitatory signalling from the thalamus to cortex and thereby facilitates the appearance of low frequency EEG patterns such as delta. As sleep progresses a subset of these cholinergic neurons are reactivated to elicit thalamic and cortical activation seen during REM sleep (Figure 1.2). Using optogenetics, the role of these REM sleep active cholinergic neurons were examined (Van Dort et al., 2015). It was shown that these neurons are involved in the initiation of REM sleep but not the maintenance of REM sleep, as they increased the frequency but not the duration of REM sleep episodes (Weber and Dan, 2016). The muscle atonia associated with REM sleep is also produced by these cholinergic neurons, predominantly glycinergic and GABAergic via a descending pathway that runs from the sublaterodorsal nucleus down to motor neurons in the spinal cord (Brooks and Peever, 2008) (Luppi et al., 2017).

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Figure 1.2: Predominant neurochemicals and pathways involved in the modulation of the sleep/wake cycle. The neurochemicals that promote wakefulness are shown on the left. The neurochemicals involved in NREM and REM sleep are shown on the right. Red arrows indicate direction of transitions in wake/sleep cycle. Wake can

only transition into NREM sleep (with exception of narcolepsy that transitions directly from wake to REM). From NREM sleep the animal will either return to wake or enter into

the deeper REM sleep state. REM sleep can transition either back to NREM sleep or wake. Neurochemical information taken from (Scammell et al., 2017). Abbreviations:

NA-Noradrenalin, LC-Locus Coeruleus, Raphe- Dorsal raphe and median raphe nuclei, DA-Dopamine, VTA-ventral tegmental area, Ox-Orexin, LH-lateral hypothalamus, Hist-Histamine, TMN-tuberomammillary nucleus, BF-Basal Forebrain, VLPO-ventrolateral

preoptic area, MnPo-median preoptic nucleus, PZ-Parafacial Zone, SLD-sublaterodorsal nucleus, MCH-melanin-concentrating hormone, PPT -Pedunculopontine, LDT-Laterodorsal

tegmental nuclei, DPGi/LPGi-dorsal/lateral paragigantocellular reticular nuclei.

Sleep for the brain and the body

To date no unequivocal explanation as to the function of sleep has been identified, yet sleep must be fundamental to overall survival as during sleep an organism relinquishes the ability to eat, drink and reproduce, whilst rendering itself vulnerable to predation (Assefa et al., 2015). The notion that sleep is vital is supported by evidence in rodents that lack of sleep proves fatal after only relatively short periods. In an extreme sleep deprivation experiment conducted on rats by Rechtschaffen and Bergmann, rats died within 16-21 days when subjected to 70-90% sleep loss. Intriguingly whilst rats exhibited many symptoms such as weight loss, increased food intake, poor skin condition, elevations in body temperature and heart rate and decreases in thyroid hormones, pathological analysis could not establish the cause of death (Rechtschaffen and Bergmann, 1995).

Theories about the function of sleep include management of energy balance, regulation of the immune system (Imeri and Opp, 2009), synaptic downscaling (Tononi and Cirelli, 2006), neurogenesis (Meerlo et al., 2009) (Fernandes et al., 2015) and reversal or removal of neurotoxic waste products (Xie et al., 2013), among others. In addition, the more traditional idea that sleep restores the brain globally has been replaced with the hypothesis that sleep can be a local phenomenon whereby different areas of the brain may be more asleep than others depending on their prior activity during wake, a concept referred to as

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“use-dependent” regulation (M Krueger and Tononi, 2011) (Vyazovskiy et al., 2000).

As mentioned, sleep may serve to restore energy balance in the brain. When awake the brain consumes energy but during sleep, particularly NREM sleep, there is a reduction in glycogen use compared to wakefulness (Petit et al., 2015). Glycogen and adenosine are known to be predominant regulators of sleep, and it has been shown that adenosine accumulates in response to cerebral glycogen utilisation during wake resulting in both an increased drive to sleep and an increase in NREM upon entry to sleep (Benington and Heller, 1995). This increase in adenosine is thought to be part of the homeostatic process that promotes sleepiness. However whilst conservation of energy appears to feature routinely in theories surrounding sleep function this may not be considered a primary function as REM sleep also shows increases in brain metabolism albeit less than during wakefulness (Schmidt, 2014).

Immunity is known to be compromised in the event of sleep loss (Irwin et al., 2006) and similarly sleep is compromised in the event of an immune reaction (Imeri and Opp, 2009). Therefore, it could be hypothesised that sleep is required for healthy immune function. Increases in response t0 partial sleep deprivation are shown in systemic inflammatory markers, interleukin-1 (IL-1), interleukin-6 (IL-6) and tumor necrosis factor (TNF) (Irwin et al., 2006) (Vgontzas et al., 2004). These inflammatory markers known as cytokines are well characterised as sleep regulatory substances (Clinton et al., 2011). In contrast, IL-6 has been reported to show a decrease after 40 hours of total sleep deprivation (Frey et al., 2007). In rats, measurements of the same key inflammatory markers (IL-1, IL-6 and TNF) following 72-h of REM sleep deprivation were increased (Yehuda et al., 2009) as was IL-6 after 96-h REM sleep deprivation (Neto et al., 2010). Despite the inconsistent results it is apparent that sleep loss affects physiological factors that would contribute to the development and progression of certain metabolic diseases, the consequences of which can lead to the increased risk of cardiovascular disease, arthritis, diabetes and cancers to name but a few.

The synaptic homeostasis hypothesis proposes that sleep is required for synaptic downscaling to occur. During wakefulness, information is acquired, and synaptic strength is potentiated; during sleep, the interaction with the environment ceases and synaptic potentiation does not occur. Synaptic downscaling can then progress during slow wave sleep effectively resetting synaptic strength to

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baseline prior to waking (Tononi and Cirelli, 2006) (Raven et al., 2018). Synaptic downscaling is in keeping with the concept that sleep restores energy balance whereby the majority of energy consumed by the brain is due to repolarisation of neurons following postsynaptic potentials. It is also in line with the notion that synaptic strengthening uses physical space. The synaptic homeostasis hypothesis may also provide an explanation as to why cognitive impairments occur following loss of sleep particularly memory formation (Raven et al., 2018).

During sleep, the glymphatic system (a series of fluid filled channels) allows for elimination of toxins

(Underwood, 2013). This hypothesis that sleep serves to remove toxins, has been demonstrated in experiments on mice using 2-photon imaging showing that during sleep there is a dramatic increase in interstitial space (60%). This increase in space is proposed to allow more ‘room’ for the cerebrospinal fluid that circulates throughout the brain to interact with the interstitial fluid and hence increases removal of possible neurotoxic interstitial proteins (Xie et al., 2013).

The hypotheses presented above are by no means an exhaustive review of theories on sleep function and the true function of sleep remains elusive. Nevertheless, it is widely acknowledged that sleep is crucial for brain function and that sleep for the organism as a whole, preserves energy and provides a restorative period with positive outcomes. However, how this is mechanistically achieved and whether this occurs at a cellular, molecular level or both remains to be fully elucidated.

Consequences of insufficient sleep

In humans, an average sleep period of more than 7 hours a night is reported as a requirement to protect against sleep debt (Panel et al., 2015). Intra-individual differences, gender and age however all play an important role with regard to sleep requirements and hence what constitutes sufficient sleep time (Badr et al., 2015). However, it has been postulated that only 4.5-6 hours of sleep is necessary for restoration and any additional sleep would not decrease sleep debt further (Horne, 1988). It is estimated that approximately 20-30% of adults are not getting enough sleep to such a degree that it impacts their daily functioning (Hafner et al., 2017), and the overall number of hours sleep per night attained by adults has dropped significantly in recent years (Liu, 2016). The inability to obtain a sufficient amount of sleep is due to a variety of reasons. These include

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unavoidable domestic responsibilities and shift-work demands (Short and Banks, 2014), requiring the ability to maintain functional during periods more naturally associated with sleep (Åkerstedt and Wright, 2009). Sleep loss and disturbance also present as a co-morbidity in many diseases including psychiatric disorders (Goldstein and Walker, 2014), chronic pain and neurodegenerative diseases, such as Parkinson’s and Alzheimer’s disease (Schreiner et al., 2019, Peter-Derex et al., 2015). Extensive social, internet and television usage have also been implicated in reducing sleep time (Basner and Dinges, 2009).

Sleep deprivation is reported to induce impairments in cognitive functioning including attention, learning and memory formation (Walker, 2004) (McCoy and Strecker, 2011, Harrison and Horne, 2000), as well as impacting broad aspects of bodily function, including endocrine and cardiovascular parameters (Spiegel et al., 1999) (AlDabal and BaHammam, 2011) (Mullington et al., 2009). Studies ascertaining the need for and addressing the function of sleep, often focus on changes in functional and physiological variables when subjects are deprived of sleep. The proposition that the impairment of waking function is a contributing factor to sleep loss related accidents has prompted an emergence of research into the effects of sleep deprivation (Leger, 1994) (Åkerstedt et al., 2011). The negative effects on waking performance can be critical in professions such as in healthcare or the military, where extended periods of attention and high-level cognitive functioning are required for safety (Balkin et al., 2004) (Dinges, 1995). The impact of these deleterious effects of insufficient sleep on both physiological and psychological health, lead to an economic burden and hence there remains an unmet clinical demand to prevent or treat the consequences of insufficient sleep. However the far-reaching effects of sleep loss on cognitive performance are only beginning to be understood from a scientific perspective (Diekelmann, 2014) (Jackson et al., 2013).

1.2 Attention and insufficient sleep

Attention as a cognitive measure

Attention is a form of cognition by which we actively process a limited portion of information from the extensive amount of information available through our senses, our stored memories and other cognitive processes (Oken et al., 2006). Intact attention is required for higher cognitive processes such as cognitive flexibility and executive function. Attention can be divided into four sub-domains: 1) Selective attention, the process by which only one stimulus is

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focussed on, excluding others, 2) Vigilant or sustained attention, where attention is focused on particular stimuli for prolonged or continuous periods; 3) Divided attention, the highest level of attention where attention is focused despite distractors and/or having to attend to multiple tasks and 4) orienting (switching attention) allowing a shift of attention from one focus to another (Ma et al., 2015). Components of attention are also modulated by the physiological state of the participant such as vigilance and alertness or sleepiness and fatigue. Sleepiness reflects the desire by the subject to sleep and fatigue represents the loss of ability to perform a task or a requirement of increased effort to perform a task. Alertness is sometimes wrongly defined as wakefulness. Unlike wakefulness that is the state of not being asleep, alertness is synonymous with vigilance and should be more accurately described as the process of maintaining close and continuous attention. Furthermore, the alert subject is in a state of readiness to respond to a given stimuli. Hence vigilance as described above requires sustaining attention to a task for a prolonged period of time (Davies and Parasuraman, 1982). A diminution in ability to maintain performance as time on task increases is referred to as a vigilance decrement (Mackworth, 1948). There are two proposals to account for this vigilance decrement, either reduced arousal or a depletion of cognitive resources as time progresses (Davies and Parasuraman, 1982). The process of acquiring and maintaining alertness in the face of prolonged dull tasks has been a much studied subsection within sleep research (Harrison and Horne, 2000). The processes of controlling of attention are often referred to by two distinct processing streams: top-down (cue-driven attention) and bottom-up (sensory-driven attention) processing (Sarter et al., 2001) (Sarter et al., 2016). Top-down knowledge driven processes that enhance neuronal processing of relevant stimuli facilitate the distinction between signal and noise and thereby direct subjects towards the relevant signal. This is thought of as a more “voluntary” process. In addition, top-down processing enables development of strategies towards the expectations of the signal. To achieve this, top-down processes rely primarily on anterior and posterior attention systems, as conceptualised by Posner and Peterson (Posner and Petersen, 1990) (Petersen and Posner, 2012). Frontal and parietal regions (prefrontal cortex and parietal lobes) are consistently activated

in various tasks involving spatial attentional tasks. Evidence for this view has been derived from imaging studies showing sequential activation of the fronto-parietal-sensory region (Corbetta, 1998). The basal forebrain cholinergic projections are essential for

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activating top-down processes (Ballinger et al., 2016). In contrast, the attentional function of the bottom-up process is to focus and detect the inherent qualities (saliency) of the stimulus and place it within context. This “involuntary” system relies on the visual cortex, as well as temporal and parietal regions, to visualise, recognise and locate stimuli respectively (Figure 1.3). Buschman supported this theory in a study using Rhesus monkeys (Macaca mulatta) where they directly compared the neural activity of frontal and parietal cortices rather than independently. As hypothesised, they showed that prefrontal neurons reflected the target location during top-down attention, whereas neurons in the parietal cortex were involved in the earlier signalling during bottom-up attention (Buschman and Miller, 2007). However, this finding has subsequently been disputed by Katsuki 2012 in Rhesus monkeys where detection of a salient signal was only defined by bottom-up factors. Their results showed contrary to previous theories that the pre frontal cortex has an earlier involvement in response to salient stimuli (Katsuki and Constantinidis, 2012).

Figure 1.3: Schematic overview of the Top-Down and Bottom-Up control of attention. Top down processes rely on knowledge-based mechanisms that enhance only relevant stimuli that will bias the subject toward a signal. Bottom-up processes use characteristics and context of the stimuli, allowing easier detection due to the

salience of the signal. These processes are not dichotomous but work in conjunction to optimise attention.

Neural circuitry involved in attention and sleep deprivation

As eluded to in the previous section, attention does not imply a singular mechanism; rather, it is a complex system presiding over a number of distinct

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neuronal circuits. Brain regions identified and associated with anatomical attention components are reported to be under the control of two separate but interrelated cortical systems (Posner and Dehaene, 1994). The first is the anterior network involved in selective attention that includes the prefrontal cortex, anterior cingulate (ACC) and basal ganglia. The second is a posterior network involved in switching attention which includes the superior parietal lobes, superior colliculus and pulvinar nuclei. These two systems are connected via efferent projections from the prefrontal cortex to the anterior cingulate, then via afferent projections from the anterior cingulate to the superior parietal lobes. The introduction of non-invasive neuroimaging methods, such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and various versions of functional MRI (fMRI), have aided significantly in elucidating the effects of sleep deprivation on brain metabolism and neural activation. MRI allows anatomical changes in the brain to be made visible in vivo, whereas PET and fMRI more specifically measure cerebral blood flow. Human fMRI experiments showed that when attention is required in the context of significant mental effort, such as target detection or conflict resolution, frontal areas and in particularly the ACC are activated (Weissman et al., 2004). Therefore, this attentional system is concerned with working memory, planning, switching, and inhibitory control. Orienting of attention that guides a subject towards a stimulus can be triggered by either presentation of a stimulus or equally as a result of the subject voluntarily shifting attention. Arterial spin labelling (ASL) perfusion fMRI permits non-invasive measures of absolute cerebral blood flow (CBF) that are tightly coupled to regional brain function providing a method to quantify changes in neural activity. ASL and fMRI neuroimaging studies in humans (selected participants were right-handed) have shown that performing an attentional task engages right-lateralised fronto-parietal network, including the anterior cingulate cortex, middle prefrontal gyrus, thalamus and inferior parietal cortex (Drummond and Brown, 2001, Lim et al., 2010). Using blood-oxygen-level-dependent (BOLD) fMRI, subjects more vulnerable to the negative cognitive effect of sleep deprivation showed reduced activation in these fronto-parietal regions corresponding with lapses during an attention task. Resilient subjects seemed less affected in fronto-parietal areas whilst also preserving activation in the visual cortex and thalamus, despite equivalent activation on baseline nights. Vulnerability was assessed on performance accuracy after sleep deprivation (Chee and Tan, 2010). Using ASL to characterise time on task (TOT) effects after

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sleep loss, Rao showed that reaction time performance changes mirrored brain activity in a right thalamus parietal-cingulate-prefrontal network both at rest and during the task. By contrast, mental fatigue was associated with a reduced global blood flow change throughout the task which correlated with performance decline (Rao et al.). Additional research using ASL to measure resting CBF changes after partial sleep deprivation compared drowsy and non-drowsy subjects. In drowsy participants reduced fronto-parietal CBF was observed, while non-drowsy participants maintained fronto-parietal CBF and increased CBF in basal forebrain and cingulate regions (Poudel et al., 2012).

The effects of sleep restriction on attention in humans

A hallmark of sleep deprivation is a pronounced deficit in attention. The psychomotor vigilance task (PVT) was originally designed by Dinges in 1985 and was developed to measure vigilant attention in human studies of sleep loss and performance capacity (Dinges and Powell, 1985). PVT has been utilised in numerous sleep deprivation protocols. When compared with 25 other objective and subjective measures, PVT showed the most pronounced sensitivity to the effects of sleep deprivation (reduced time in bed for 7 days) (Balkin et al., 2004). PVT requires the subject to respond in a timely manner to a salient signal where the attentional requirements are not affected by spatial orientation or executive decision-making. It tests simple reaction times to a visual cue occurring at variable inter-stimulus intervals (2-10 secs). In humans total and partial/chronic sleep loss causes consistent deficits (Short and Banks, 2014). Firstly, and most pronounced is a slowing of reaction times. On average, an alert human would have a reaction time of around 220-ms; when sleep-deprived the reaction time is increased to around 300ms. Further indicators of a sleep deprived state are an increase in lapses (> 500 ms) and errors of commission. Errors of commission indicate a response either when no stimulus is presented or equally a response to the wrong stimulus possibly reflecting a compensatory effort to resist sleep. Finally, an enhancement of TOT detriments is observed, i.e., a diminution of performance across the course of the task (due to fatigue, boredom or diminishing motivation) and an increase in omissions. Importantly, the PVT has no learning effect and therefore can be administered repeatedly with little or no practise effects (Basner et al., 2017). However, the relevance of a 10-min laboratory-controlled PVT test when “real-life” vigilance/attention is required for many hours should be questioned. Therefore, despite the robustness of PVT

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deficits to measure the effects of sleep deprivation it is beneficial to also measure performance on complex tasks (Beebe et al., 2010). In a study directly comparing PVT and a working memory task (WM, 4 item recall test), 4-5hrs of sleep restriction per night for one week led to a significantly increase in lapses and reaction times by day 2 in the PVT. By contrast, the WM task was not significantly affected across any of the test days (Dinges et al., 1997). These findings support the theory that more cognitively challenging tests are less affected by sleep deprivation (Lo et al., 2012). It could be argued that these more engaging tests of cognition that include aspects of executive function and working memory may be more relevant to actual work settings or ‘real life’ situations than attention alone. However, these deficits in tasks involving learning and memory or executive function are not consistently observed and often depend upon the protocol of sleep loss applied (Slama et al., 2017) . Furthermore, few studies have directly compared across cognitive domains within the same study (Lo et al., 2014). PVT shows increased sensitivity over other tasks following sleep loss, with attention deficits becoming apparent after one full night of total sleep deprivation (Short and Banks, 2014). Basner 2011 investigated which PVT parameters were most significant in predicting consequences of sleep deprivation. Calculated effect sizes showed lapses and false starts’ were the variables considered the most indicative of impairment caused by acute sleep deprivation, whereas mean reaction time was the most affected outcome using chronic sleep restriction (Basner and Dinges, 2011). Reaction times are often divided into the following two categories: 1) the 10th percentile (fast responding) informs about the sensorimotor capacity of the subject; 2) the 90 th percentile (slow responding) is more prone to sleep deprivation effects and includes lapses (Figure 1.4). Graw 2004, investigated the effects of low and high sleep pressure on RT in humans. Low sleep pressure was induced by imposing an ultradian sleep-wake cycle of 150 minutes awake and 75 minutes asleep throughout a 40-h period. The high sleep pressure condition was a 40 hours TSD protocol. Changes in the 90th percentile (slow responding) were consistently significant and the 10th percentile did not change significantly. They therefore concluded differences between the fastest and slowest RT are most sensitive in detecting changes of sleep pressure. This was corroborated in a comparative study using partial and total sleep deprivation on human PVT performance where the slowest

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responses deteriorated during both sleep conditions compared to baseline values (Lo et al., 2012)

Figure 1.4 – Distribution of 5th and 95th percentile reaction times in the PVT over 88 hours of continuous total sleep deprivation in humans. With increasing

time awake the left peaks representing the faster response times (5th percentile) shows a reduced number and slowing of responses, whilst the right peaks showing the slower responses (95th percentile) increase in frequency and reflects an increase in lapses

during the PVT (Lim and Dinges, 2008).

In human studies, two types of sleep loss protocols are used; total sleep deprivation (TSD), that consists of one continuous extended period of wake during the usual sleep period (e.g., night shift), and partial sleep deprivation which consists of insufficient sleep over an extended time period. In addition, selective sleep deprivation can be applied to one particular sub-state of sleep, e.g., SWS or REM sleep. Cognitive deficits consistently showed a faster onset with TSD when compared to extended chronic sleep loss in humans (Jewett et al., 1999b). Total sleep deprivation taken across the breadth of human literature generally refers to wake periods that are longer than 45-hr. This protocol shows reversible but reproducible deficits on sustained attention tasks (Durmer and Dinges, 2005). Chronic partial sleep deprivation can be used to study not only the acute effects of partial sleep loss but is also interesting from the perspective

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of adaptation to reduced sleep. A study by Belenky and colleagues used 7 days with 3-, 5-, 7- or 9-hour time in bed (TIB), followed by 3 days of 8 hours TIB recovery. As expected the results showed that the 9-hour group showed no deficits in RT and lapses on either test or recovery day. In contrast, the 3-hour group showed slower RTs and an increase in lapses which became more pronounced across the 7 days. For the 5- and 7-hour group, RT and lapses initially increased but then stabilized (Belenky et al., 2003). Results from a comparison of total and chronic sleep deprivation showed that halving the typical 8-hr night sleep for 2 weeks significantly induced cognitive deficits that compare to 2-night of total sleep deprivation (Van Dongen et al., 2003). Chronic partial sleep deprivation is more likely to be relevant to translation to real life situations and it is easy to surmise how modern lifestyles would have an impact on the ability to achieving less than the adequate amount of sleep each night for optimum cognitive functioning.

Human studies on cognitive recovery from total sleep deprivation, as measured by improved performance in attention tasks such as PVT, show a faster recovery than recovery from chronic partial sleep deprivation. One full night of sleep has been proposed to fully restore cognitive deficits imposed from total sleep deprivation (Drummond et al., 2006) and prior extended sleep (“banking sleep”) has been shown to protect against attentional deficits induced from total sleep deprivation (Arnal et al., 2015). Whereas restoration of performance on cognitive tasks require longer periods of recovery sleep following chronic partial sleep loss (Banks et al., 2010). Studies postulate that the slower recovery is due to an adaptation to maintain performance albeit at a reduced level over the week of sleep loss and full cognitive recovery to baseline (pre-sleep deprived state) is slower to restore (Belenky et al., 2003). Recovery sleep following a period of sleep deprivation shows specific characteristics. Sleep latency (time to fall asleep) is shortened while the amount of SWS and REM-sleep is increased and NREM sleep is often shortened. This means the time required to recover sleep loss is less than the initial amount of sleep lost. Jewett studied the effects on PVT following 8, 5, 2 hrs of sleep and no sleep where SD was restricted to the middle of habitual sleep period. Subjects were tested 2 hrs after waking in order to reduce the sleep inertia confound. They also concluded that the relationship between the decrement in PVT performance and the hours of sleep lost could be described by a saturating exponential function. This may explain why recovery sleep does not necessarily

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equate to lost sleep with regard the number of hours in order for normal functioning to be restored. According to this view the first few hours asleep play the most important role in recovery of function, although different cognitive domains may recover at different rates (Jewett et al., 1999b).

Sleep restriction effects on attention in rodents

Rat Psychomotor Vigilance Task

In contrast to humans, there is a paucity of studies investigating the effects of sleep deprivation on a vigilance task in the rat. In 2008 Christie developed an analogue of the human PVT to be used in rodents. The ‘rodent PVT’ (rPVT) uses operant responding to a visual stimulus presented with a variable inter trial interval. In this test the animal receives a food pellet following the response. The rPVT allows assessment of the same outcomes as in the human task, i.e., reaction time, trial number, premature responding, lapses or omissions. Lapses were defined post hoc as response latencies greater than two times average baseline response latency; omissions were defined as a failure to respond to the stimulus within a predetermined time of 3s. Baseline response latencies in rats are slower than in humans, with on average of approximately 600-ms, increasing to around 800-1000 ms after sleep deprivation (Christie et al., 2008). Using the rPVT protocol, a 24-hr enforced activity sleep deprivation protocol was applied, where activity wheels were rotated at a ratio of 3-s 0n/12-s off. In accordance with human data, an increase in latencies and lapses was found, while no effects were observed on premature responses (Figure 1.5). Importantly, rats were not asleep during the task following sleep deprivation, showing that the deficits were due to a reduced vigilance. A more recent study combined rPVT, sleep deprivation and included a separate cohort of rats instrumented for EEG measurements experiencing the same SD procedure (Oonk et al., 2015). In accordance with the Christie 2008 study, increases in lapses and premature responding together with a decrease in the number of trials were shown (Figure 1.5). These reported outcomes in PVT were reproduced in a chronic sleep restriction protocol used in rats subjecting them to 148-h of a 3-h enforced activity sleep restriction with 1-h sleep opportunity cycle. After 28-h, reaction latencies and omissions had increased, and trial number had decreased. Interestingly these deficits in PVT had returned to baseline levels after 52-h and remained stable thereafter indicating an adaptation to the sleep restriction (Deurveilher et al., 2015).

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Figure 1.5: PVT in rats subjected to 24hrs sleep deprivation using wheel turning or gentle handling. Left: data from Christie 2008, using 24hrs of wheel

turning induced SD in Fischer-Norway rats and showing a slowing of reaction time and an increase in lapses (Christie et al., 2008). Right: data from Oonk 2015 using Long-Evans male rats and 24 hr gentle handling SD method and showing an increase in lapses and premature responses with a concomitant decrease in correct trials (Oonk et al., 2015).

The 5-Choice Serial Reaction Time Task

Another translational attention task used in conjunction with sleep deprivation, the 5-Choice Serial Reaction Time Task (5-CSRTT), could be of interest in elucidating specific aspects of attention in rats. Its human equivalent from which it was developed is the Continuous Performance Test (CPT). Choice reaction time tasks are considered more informative than vigilance tasks as they also require a component of decision making. In rats, the 5-CSRTT allows measurements of selective attention, vigilance, impulsivity and motivation. The human CPT similarly measures sustained and selective attention and impulsivity. As for the PVT, a response to a stimulus light is required but the light may be presented in any one of 5 chambers. Only one study to date reports the effects of sleep deprivation on 5-CSRTT in rats. Male Long Evans rats were sleep deprived for 4, 7 and 10 hrs using a gentle handling technique. Following 10 hrs sleep restriction significant effects on reaction times, correct responses and omissions were noted as would be expected from an attentional task. However, as observed in the PVT experiments performed by Christie 2008, no effect was apparent on aspects of the task that reflect inhibitory control such as premature

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responding or perseveration characterised by continued responding in the absence of a stimulus (Cordova et al., 2006).

Motivational reward in rodent attention tasks

One major difference between human and rat PVT studies is the utilisation of either water or food deprivation to provide a motivational aspect in rodent operant tasks. This may be a possible confound in rodent studies. An advantage of such operant paradigms in studying animal behaviour is the exclusion of human involvement. However, when assessing the effects of sleep deprivation on performance the question of how these differing biological drives of hunger/thirst and sleep interact to modulate performance should be addressed. Unlike rodents, human subjects are able to adhere to instructions, are intrinsically motivated to complete the task and are reinforced through constant feedback as the results are displayed on the screen during the test. Some studies in humans have specifically shown that feedback or simple monetary rewards can override the effects of sleep deprivation (Massar et al., 2016). The effect of motivation in attention tasks in rodent sleep restriction experiments will be addressed in Chapter 4 of this thesis.

Why attention tasks are sensitive to sleep deprivation

The consensus that sleep deprivation more readily affects performance in theoretically un-engaging tasks such as PVT affords researchers a simple behavioural task to study sleep loss outcomes. Attention is transient, and can come and go over seconds and hence may explain why deficits are more measurable in tasks that require quick, simple responses rather than more complex tasks assessing memory or executive function that require longer concentration spans (Lo et al., 2012).

Theories to explain why data are more robust and reproducible in simple stimulus driven attention tasks have been proposed. Firstly, in a study addressing the question of the relevance of laboratory based tests to work related tasks, a more complete testing regime covering attention and higher cognitive processing using an acute SD protocol was examined (Pilcher et al., 2007). It was postulated that the greater decline seen in vigilance tasks over more complex tasks may be due to a ‘controlled attention model’ that predicts that the task characteristics help to maintain performance (Kane and Engle, 2002). In this respect the more complex tasks encourage the participants to remain attentive and engaged, whereas in contrast, the vigilance tasks

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intrinsically less interesting and engaging. Despite these results, Pilcher concluded it is difficult to distinguish between vigilance and other cognitive tasks as there is a considerable degree of overlap. This contradicts the “neuropsychological” model based on neuroimaging and clinical data that suggests sleep deprivation leads to a temporary “functional lesion” (Harrison and Horne, 2000). It was shown that neural activity decreased in the pre-frontal cortex (PFC) when performing tasks following sleep deprivation. This implied sleep deprivation specifically caused a decrease in neural activity in the PFC and therefore following sleep deprivation any tasks whether attention or executive function that require activity in this area of the brain would be disrupted.

A further hypothesis, that may account for reduced cognitive functioning and a deficit in attention following sleep deprivation, is the lapse hypothesis whereby a subject under sleep deprived conditions may experience “microsleep”, a low arousal state where α waves decrease and SWA increases. A lapse can be caused by a microsleep leading to an inability to respond to stimuli (Dorrian et al., 2005). However, microsleeps and lapses are separate constructs (Innes et al., 2013). Microsleeps are generally defined as periods of unresponsiveness lasting between 0.5 and 15s (Buckley et al., 2016). Microsleeps can occur either with eyes open or eyes closed. Attentional lapses in humans are more usually defined as a response >500 ms and can occur during microsleeps (Dinges, 1992). The effect of lapses and microsleeps was directly compared during three tasks: PVT, a 2 D tracking task and a combination of both, termed the “dual task”. The dual task that represented a more cognitively challenging task showed that attention lapses were more frequent but microsleeps were reduced. The authors conclude higher cognitive load causes an increase in lapses but microsleeps are more evident in the simple less complex tasks (Buckley et al., 2016). Lapses in the waking state are caused by state instability. This implies performance is variable in the sleep deprived state because of the competing effects of sleep initiation and wake-promoting factors. The pressure to fall asleep overrides the ability to stay awake and a momentary protrusion (0.5-15 sec) of sleep into the waking state leads to the performance decrements.

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1.3 Objectives

Sleep homeostasis is affected by both acute and chronic sleep restriction, however these two different forms of sleep restriction are likely to differentially affect behavioural responses. Rodent studies to date have primarily focussed on effects of total sleep deprivation and often include only outcomes on simple attentional tasks. There is a scarcity of rodent studies that include direct comparison of total and chronic partial sleep restriction and examine the differential rates of recovery of cognition. Gaining further insights would be beneficial in evaluating the effects of long-term sleep loss on performance that reflects modern changes in social or work imposed sleep patterns (St Hilaire et al., 2017), or chronic sleep loss that prevails co-morbidly with other disorders such as neurodegeneration, pain and psychiatric illnesses. Hence, it would be beneficial for pre-clinical research to use rodent protocols that applied both total and chronic sleep restriction that could then be combined with other domains of behaviour to address the outcomes of functional deficits beyond attention extending to memory and executive functioning. These more complex tasks would be of additional value in understanding the cognitive decline resulting from sleep restriction. Finally, pre-clinical work could help to addresses the influence and interaction of extraneous physiological factors on sleep such as hunger, pain and stress especially in combination with chronic sleep loss which remains unclear (Weber and Dan, 2016).

Aims of the thesis

Human and rodent studies have established that sleep deprivation leads to deficits in sustained attention and that these deficits can be reliably and robustly assessed by the psychomotor vigilance task. This implies the rat equivalent of the human PVT to be a worthwhile translational assay to consider in the context of pre-clinical sleep research. The overarching aim of this thesis is to build on current research and to further examine effects of sleep restriction on cognition with a specific focus on the domain of attention in rats. The research aims to investigate how these effects may translate to human data at the behavioural and physiological level. Ultimately this should lead to a pre-clinical platform for the development of pharmacological treatments for the cognitive deficits due to sleep loss. To achieve this, four main aims were developed:

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1) To establish to what extent various sleep restriction methods are effective in inducing loss of sleep and activate sleep homeostatic processes and to what degree do these methods lead to changes in measures of sustained attention in the rat.

2) To assess the translational value of the rodent psychomotor vigilance task, which includes a food reward, by evaluating effects of food-related motivational aspects on the observed consequences of insufficient sleep on sustained attention.

3) To evaluate pharmacological (modafinil, amphetamine and caffeine) and non-pharmacological countermeasures (napping) for the cognitive effects of sleep restriction in rodents and how does this translate to human findings.

4) To investigate whether other approaches to study the effects of insufficient sleep hold promise as translational biomarkers. These other approaches are oxygen amperometry, which is analogous to fMRI in humans, and measurements of evoked potentials during an attention task in sleep deprived rodents.

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CHAPTER TWO

Chapter 2 - Methods

2.1 Subjects

All experimental protocols described in this thesis were approved by the local Animal Welfare and Ethical Review Body and carried out in accordance with the UK Animals (scientific Procedures) Act 1986. Facilities were also accredited by the Association for Assessment and Accreditation of Laboratory Animal Care. Housing rooms were temperature-controlled (21± 1° C) and maintained on a 12: 12 h light/dark cycle. Adult, male Wistar rats (Charles River Laboratories, Margate, UK) were used as subjects. Water was available ad libitum. Animals were either given food ad libitum or food restricted to >85% of their normal diet as specified for each study. If drugs were administered, subjects were given at least 7 days “washout” preceding and following any treatment.

2.2 Surgical procedures

Electroencephalogram/Electromyogram/Event Related Potentials

All animals were surgically prepared at the Charles River Laboratories prior to arrival and weighed approximately 250-300 g at time of surgery. Rats were anaesthetized (2% isoflurane in 100% oxygen) and surgically prepared with a cranial implant that permitted long-term electroencephalogram (EEG), electromyogram (EMG) and event related potentials (ERP) recordings. Body temperature and locomotor activity were monitored via a miniature transmitter (Minimitter PDT4000G, Philips Respironics, Bend, OR, USA) surgically placed in the abdomen during the same anaesthetic event. The cranial implant consisted of stainless steel screws (2 frontal [+3.5 anterior-posterior (AP) from bregma, ±2.0 mediolateral (ML)]) and 2 occipital [-6.4 AP, ±5.5 ML]) for EEG recording (Figure 2.1). A rat atlas was used as reference for the surgery coordinates (George and Charles, 2007). For EMG recordings, two teflon-coated stainless

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steel wires were positioned under the nuchal trapezoid muscles. All leads were soldered to a miniature connector (Microtech, Boothwyn, PA) and gas sterilized with ethylene oxide prior to surgery. The implant assembly was affixed to the skull by the combination of EEG recording electrodes, cyanoacrylate applied between the hermetically sealed implant connector and skull, and dental acrylic. An analgesic (buprenorphine 0.05 mg/kg) was administered subcutaneously pre-operatively, at the end of the surgery day and the morning of the first post-operative day. To provide additional pain relief, Metacam (meloxicam) 0.15 mg/kg was administered orally for 6 days post-surgery. An antibiotic (Ceporex (cephalexin) 20 mg/kg) was administered orally 24h before and again immediately before surgery, and twice daily for 7 days after surgery. At least 3 weeks were allowed for recovery after being exposed to food restriction or behavioural testing.

Figure 2.1. Position of EEG/ERP electrodes (George and Charles, 2007)

Amperometry

All animals were surgically prepared at the Charles River Laboratories prior to arrival and weighed approximately 340-510 g at time of surgery. Animals were anaesthetised with isoflurane (2% isoflurane, 1L/min O2). Following midline incision, the skull was cleaned and dried, and positions of electrodes were stereotaxically determined, using the bregma as a reference point. The skull surface was roughed up with a drill bit (“golf balling”) to improve contact

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between glue/dental cement and the skull. Six screws were inserted to the skull to ensure the longevity of the head cap. Rats were implanted with carbon paste electrodes (CPEs) for oxygen amperometry (Figure 2.2). Rats were implanted unilaterally in the nucleus accumbens (AP +1.9 mm, ML- 0.8 mm, DV -7.2 mm from the dura, tooth bar set at -3.3 mm below the inter-aural line)

A B Working Electrode

Reference Electrode

AuxciliaryElectrode

Figure 2.2. Carbon paste electrodes used for amperometry (A) Schematic diagram of carbon paste electrodes (CPEs) used to measure tissue oxygen. A reduction potential of oxygen (-650mV) is applied to the CPE, any oxygen present on the tip is reduced and

the resulting flow of electrons is recorded by the CPE as a measurement of tissue oxygen. (B) CPEs were made from Teflon®-coated silver wire (200µm bare diameter, 270 µm coated diameter). Working CPEs are implanted in specific brain regions, while

reference electrodes are implanted into the cortex and auxiliary electrodes are wrapped around a skull screw

Once the CPE’s had been implanted and cemented, the auxiliary electrode was wrapped around one of the posterior skull screws. The reference electrode was inserted into the posterior cortex by 2 mm in depth and secured with glue and cement. The electrode placement for a CPE implantation surgery is shown in Figure 2.3. After electrodes were cemented into place, the gold sockets of the electrodes were inserted into a six-pin plastic socket (‘pedestal’; Cat. # MS363, Plastics One, Roanoke, VA, USA). Glue was used to secure the gold sockets of the four electrodes within the pedestal, which was then cemented to the skull. The entire pedestal was encased in dental cement (Meadway Rapid Repair, Mr. Dental Suppliers Ltd., UK) to make a robust head cap. Finally, a plastic dust cap (Cat. # 363DC, Plastics One, USA) was screwed onto the pedestal. Pre-and post-operative Rimadyl (Carprofen 5mg/kg; Pfizer Inc) was administered subcutaneously. There was a post-operative recovery period of two weeks before food restriction or testing commenced.

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A B

Figure 2.3. Amperometry surgery. (A) Schematic diagram showing electrodes placement (B) Six-pin plastic pedestal used to house the gold sockets. CPE: carbon paste

electrodes, AUX: auxiliary electrode, REF: reference electrode.

Before any studies began, the electrodes were calibrated in vivo to assess responsiveness to the inhalation of different concentrations of oxygen in freely-moving rats. Animals were placed individually in test chambers and a constant -650 mV potential applied to CPEs to allow signal settling for 15 minutes. The rats were then exposed to oxygen gas (approximately 200 psi) at their nose for 30 seconds and the O2 signal was allowed to decay back to baseline levels. This process was repeated three times with oxygen and then replicated three times with nitrogen gas.

2.3 Sleep restriction methodology

Recording environment

Individual sleep restriction chambers consisted of a rotatable cylinder (39.7 cm diameter by 32.1 cm length, 637.2 cm floor space) constructed of cylindrical plexiglass rods, allowing animal waste to be collected below the chamber (Figure 2.4). The cyclinder was positioned horizontally inside a Plexigass frame (637.2 cm2 floor space). In the chamber, a cranial implant was connected to ultra-low-torque slip-ring commutators (Hypnion, Inc., Lexington, MA, USA) by metal coil reinforced flexible cables, allowing free, unrestrained movements. Each chamber was provided with an infrared light source and digital video camera to ensure sleep restriction protocols were running correctly and allow a minimum of twice daily remote visual monitoring for animal welfare. Water was available ad

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libitum and food depended on the feeding protocol. A 24-hr light-dark cycle (LD 12:12) using fluorescent light was maintained and monitored throughout the study. Light intensity averaged 35-40 lux at mid-level inside the cage.

Figure 2.4. Sleep restriction chamber. Plexiglas rotating chamber with the commutator attached to the head implant to allow continuous long-term EEG/EMG

recordings.

Biofeedback sleep restriction

All animals underwent baseline recordings of sleep-wake patterns. Sleep and wake scoring was performed automatically in real time using SCORE-2004TM (Van Gelder et al., 1991). Vigilance states were classified on-line for 10-second epoch as NREM sleep, REM sleep, wake, or theta-dominated wake using EEG period and amplitude feature extraction and ranked membership algorithms. Individually taught EEG-vigilance-state templates and EMG criteria for REM sleep differentiated states (see Section 2.4 for details).

SCORETM automated sleep restriction was driven by the real time evaluation of sleep/wake states indicated by SCORE2004™. Following detection of an NREM or REM sleep epoch, the program activated a motor to roll the cylindrical chamber around its axis for 8 seconds at a rate of 11.5 cm/sec (265° of rotation per epoch). The wheel turns in a random direction to prevent habituation. This protocol initiated the righting reflex of the rats and effectively disrupted their NREM/REM sleep. A safety mechanism prevents continuous turning in the event of problems. In addition to frequent on-line inspection of the EEG and EMG signals throughout the sleep restriction, quality control of the sleep restriction protocol was assured after the experiment by individual visual examination of raw EEG and EMG signals and statistical assessments.

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Non-biofeedback sleep restriction

Three non-biofeedback sleep restriction methods, namely Constant, Decreasing and Weibull were devised for non-invasive chamber turning based upon historical data obtained using the Biofeedback protocol described above. In all non-biofeedback sleep restriction protocols, the chamber turned in a pseudo-random direction per trigger event to prevent habituation.

Constant interval: The average time between wheel turns was calculated from an EEG-biofeedback protocol of a sample of previously implanted animals across 11 hours of sleep restriction. On average 651.91 wheel turns were initiated across the 11-h equating to approximately 1 turn per minute. To implement this strategy every 6 epochs the motor was initiated, and the cylindrical chamber turns in the same way as during biofeedback sleep restriction.

Decreasing interval: The numbers of wheel turns were not consistent across time and a linear regression was fitted to the total turns per hour. Significantly fewer turns are required to maintain wakefulness at the beginning of the sleep restriction period than at the end. By reducing the interval between turns over time it was possible gradually increase the stimulus in line with sleep pressure.

Weibull distribution: The interval between attempts to enter sleep can be modelled using survival analysis. The estimated model can be used to derive the probability of transition from one state (i.e., wakefulness) to another (sleep) within a specified time interval. To approximate these transition probabilities and hence estimate when the wheel should most likely turn, the distribution of intervals between sleep attempts were measured over time. A Weibull distribution was fitted to these data on an hourly basis and parameters for shape and amplitude were generated using JMP statistical software. These parameters were entered into a random number generator to create a wheel turn pattern. Additional restrictions were placed upon the model to prevent continuous turning and no turning for greater than 3 minutes. Total number of wheel turns per treatment was kept equal (confirmed by T-test), (see Appendix for details).

All sleep restriction protocols were run in a crossover study design and each animal received all treatments. The subjects were randomly assigned to a treatment sequence. Subjects were allowed at least 7 days “washout” preceding and following treatments with no duplication. Prior to any study, all subjects were sleep deprived for 5 hours to eliminate unpredictable changes due to novel exposure to the procedure.

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2.4 Automated EEG/EMG data collection, sleep staging and analysis

The system amplified EEG (X10,000, bandpass 1-30 Hz [Grass Corp., Quincy, MA]; initial digitization rate 400 Hz), integrated EMG (bandpass 10-100 Hz, RMS integration), and recorded telemetered body temperature and non-specific locomotor activity (LMA) with a maximum capacity recording of 16 rodents simultaneously.

EEG/EMG recordings were analysed for a 12-h baseline period, 11-h sleep restriction period and 13-h recovery following the Simple Response Latency task. Vigilance states, i.e., waking, NREM sleep and REM sleep, were assessed using SCORE2004™. Scoring was based on a combination of the salient features of both the electroencephalogram and muscle tone. The following features from each 10-second epoch were first generated: EEG amplitude and zero-crossings, EEG harmonic amplitude and frequency, integrated EMG tone; locomotor activity, drinking and feeding activity. These parameters were matched to individual scoring templates for each animal using the SCORE2004™ algorithm as previously described ((Van Gelder et al., 1991) to determine vigilance state. Frequent signal inspection was performed to ensure data quality and effective sleep state determination. Data were digitized and collected to permit off-line verification of vigilance state scoring, additional data quality control, and EEG spectral analysis. The power spectrum in each 10-s epoch was determined using a fast Fourier transform. The spectrogram was then sub-divided into the following bands: delta 0.1-3.9 Hz, theta 4.0-8.9 Hz, alpha 9.0-11.9 Hz, beta 12.0-20.0 Hz. The state specific time series of EEG power in each band was calculated for all EEG-defined epochs devoid of artefacts. NREM sleep delta power was quantified as the mean power between 0.5 and 4.0 Hz during NREM sleep.

The primary sleep variables were duration of NREM sleep, REM sleep, total sleep time per hour and wake. Wake and sleep continuity during sleep restriction and subsequent recovery periods were assessed by computing bout length and/or using survival analysis. Bout length was defined as a continual episode of the specified arousal state delimited before and after by three or more consecutive 10 s epochs. All bouts commencing in a given hour were averaged (arithmetic mean) and expressed as minutes per hour.

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2.5 Event Related Potentials (ERPs)

Measurement of event related potentials were recorded during the SRLT. Stimulus cues were time-locked to the EEG recording with the use of Transistor-Transistor Logic (TTL) signals supplied from the MedPC hardware (Med Associates, UK) into the EEG data acquisition hardware (DNR-12 RACKtangle. United Electronic Industries, USA). Grand average event related potentials were calculated offline using python 3.6. Data from 50ms immediately prior to the stimulus to 1000 milliseconds after the stimulus were analysed. ERPs were grouped according to sleep/wake staging and only ERPs where the animal was confirmed to be awake by EEG measure were included in the analysis. The amplitude and latency of the ERP components were identified manually as local maxima/minima occurring between 50-150, 110-210 and 160-360 ms post tone stimulus for P1, N1 and P2 respectively.

2.6 Amperometry data collection and analysis

Changes in extracellular tissue oxygen were measured using constant potential amperometry with carbon paste electrodes (Lowry et al., 1997). A potential of -650 mV was applied to carbon paste electrodes allowing the electrochemical reduction of dissolved O2 at their tip. Prior to implantation, all CPEs were calibrated in vitro in a glass cell containing 15 ml phosphate buffer solution (0.01 M), pH 7.4, saturated with nitrogen (N2) gas, atmospheric air (from a RENA air pump), or pure O2 (compressed gas) at room temperature. The concentrations of dissolved O2 were taken as 0μM (N2-saturated), 240μM (air-saturated), and 1,260μM (O2-saturated), respectively. During O2 signal recording, the head mounted 6-pin socket was connected to a low-noise, four-channel potentiostat (EA164 Quadstat, eDAQ Pty Ltd, Australia) via a flexible screened 6-core cable mounted through a swivel (both Plastics One Roanoke, VA) in the ceiling of the cage to allow free movement of the animal throughout the test chamber. A 16-channel e-corder (ED1621, eDAQ Pty Ltd, Australia) was used for analogue/digital conversion before data was collected on a PC running Chart v5 (both eDAQ Pty Ltd, Australia). Amperometric recordings were recorded at 1000 Hz and analysed at a down-sampled frequency of 200 Hz. Gemport (in house video recording software) was used to record videos for movement and continuously monitor animals during the test period.

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For the amperometry study, 32 animals prepared by Charles River were included. Animals pre-trained in the SRLT were subjected to either no sleep restriction or 11-h sleep restriction using the Weibull protocol as described above and placed in operant test chambers modified to acquire amperometry readouts. A constant -650 mV potential was applied to CPEs to allow signal settling for 10 minutes before the recording session began on each day. Throughout test sessions, oxygen levels were measured in each brain region for three SRLT measures: the number of correct responses, premature responses and number of omissions.

At the end of the study, brains were removed and placed in 10% (w/v) buffered paraformaldehyde and shipped for histological processing (Neuroscience Associates Inc., Knoxville, TN) which involved 40 μm coronal sectioning of implanted regions and staining with thionin. Microscopic assessment was then performed to confirm CPE placement with reference to a rat brain atlas (George and Charles, 2007). Animals with inaccurate placements were excluded from subsequent analyses. Animals with poor signal quality or signal losses were also excluded from further analysis prior to data processing.

For each electrode, linear interpolation was used post-collection to replace occasional missing data points and a bi-quad Butterworth filter (high-pass 0.1 Hz) was used to suppress fast noise-related artefacts. Data were normalized by subtraction of the 60-s average pre-session value from each data point in the series, thereby compensating for absolute differences in baseline between channels. Finally, a boxcar-averaging algorithm was applied to down-sample the data, keeping a single average from multiple 0.5-s non-overlapping windows. The time-courses of absolute regional [O2] levels were plotted in 1 minute bins, and the area under curve (AUC) was calculated for the total 30 minutes. Functional connectivity data were analysed in the 0.01 – 0.1 Hz range, from a period 600-s pre-session start to 1800-s post-session and averaged in 300-s time bins. To achieve this, the session start-aligned series of N data points from both brain regions were padded with N/4 additional points to eliminate edge-artefacts which are produced when applying a low-pass filter to data with a mean offset from zero. Butterworth noise filtering was applied as before for each frequency F of interest and then data was correlated in a series of half-overlapping windows of length 2/F, excluding padded sections of data. Pearson’s r was generated as an index of functional connectivity between the filtered signals at a given time for that frequency, to build a correlation spectrum over time for multiple

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frequency bins. An average r value was calculated for each pair of regions on each day, and the within-network/between-network comparison and correlation matrix was shown as an average.

2.7 Behavioural tasks

Rats were tested in operant test chambers (Med Associates; ENV 008), made of two aluminium walls and two Perspex walls situated in sound and light attenuation boxes. The floor consisted of 19 stainless steel bars (diameter 0.48 cm, spaced 1.6 cm apart) and each chamber had a house light (100mA, Med Associates; ENV 215M) located 1.8 cm from the ceiling on the opposite wall to a food magazine incorporating a magazine light, two retractable levers were situated to either side of the food magazine and a pellet dispenser for reward (sucrose pellet) delivery. Infrared beam breaks in the magazine recorded the number of nose pokes.

Simple Response Latency task (SRLT), a sustained attention task

SRLT testing was conducted as described previously (Loomis et al., 2015), in standard operant chambers equipped with dummy commutators so that EEG implant tethers could be connected to prevent disturbing the implanted animals during testing. Levers were not used during these studies. Briefly, a house light acted as the preparatory cue, followed by a variable interval (range 4-6s), after which the magazine light (imperative cue) was illuminated. A period of 10-s allowed the rat to perform a nose poke to receive a food reward. An interval of 20-s was imposed between trials (see Figure 3.1 of Chapter 3). Criterion performance of successful training was determined by >75% trial completion efficiency across five consecutive days. Experimental session data were recorded by programs written in-house using MedPC IV software (Med Associates, UK). Rats were subjected to 40-min of SRLT on three consecutive days (pre-, test- and post-sleep restriction sessions). On pre- and post-days, rats were tested between ZT2 and ZT4, while on test day rats were tested immediately after the 11-h sleep-restriction period (i.e., ZT11). The SRLT was performed to characterise a number of parameters, including the number of trials completed, response errors (i.e., premature responses and omissions) and reaction times. Head Entries, 5th percentile and 95th percentile reaction times,

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premature response latencies during the session were also collected but analysed only in preliminary experiments.

Progressive Ratio (PR) to assess motivation

Progressive Ratio (PR) testing was conducted in standard operant chambers and data recorded using in house programmes within MedPC IV software (Med Associates, UK). During PR testing, two active retractable levers were accessible and located either side of a recessed magazine where food pellets (Noyes, 45 mg, Formula P) were delivered from an automatic dispenser. PR uses an increasing schedule of food reinforcement. No specific stimulus was associated with each component. During each component, the size of the FR increases but reward delivery remains constant at 1 food pellet. The progressive ratio schedule was based on the following exponential progression (5 x e0.2n)-5, where n is the position in the sequence of ratios (Richardson and Roberts, 1996). Each component ends after one reinforcement with a 15-s time-out between components. The session ends after 20 components, or 30 min, whichever occurs first. Breakpoint is defined as the number of lever responses made to obtain the last reward and is used to index reward strength. The PR protocol is illustrated in Figure 2.5.

As for SRLT, for 3 consecutive days (Pre, Test and Post) rats were removed from sleep restriction wheels and placed immediately into operant boxes. Rats were subjected to a maximum of 30 minutes PR. On test day, PR was performed immediately after the 11-h sleep restriction (ZT 11), while on pre and post days rats were tested at ZT 2-5. Breakpoint and press rate were recorded.

Fixed Ratio Components: 1, 2, 4, 6, 9, 12, 15, 20, 25, 32, 40, 50, 62, 77, 95, 118, 145, 178, 219, 268

HOUSELIGHT ONLEVER ACTIVE

MINIMUM RESPONSE RATE

HOUSELIGHT OFFFOOD PELLET

20sPresentation Blackout

Fixed Ratio Component

Fixed Ratio Component

15sTime Out

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Figure 2.5 Progressive Ratio Task to assess motivation. Following a 20-s lights off period the houselight is illuminated, the levers presented, activated and the fixed ratio component started (the fixed ratio component is an exponential rate of lever presses required to achieve a food pellet reward). On completion of each component a food pellet is delivered, and the house light goes off. This was followed by a 15-s time out

period prior to presentation of the next component.

Concurrent Fixed Ratio 5 (CFR5) task to assess effort-related choice behaviour

Concurrent fixed ratio 5 (CFR5) testing was conducted in standard operant conditioning chambers (Med Associates). Rats were trained to lever press on a continuous reinforcement schedule (fixed-ratio/ FR1, 30min sessions), where one lever press resulted in one sugar pellet (Noyes, 45 mg, TestDiet, UK). Animals were randomly assigned to the left or right lever, and this assignment was maintained through the subsequent stages of training. Rats were then switched to a concurrent fixed-ratio 5, (CFR5), schedule for five weeks (30 min sessions). After reaching stable levels of lever presses, animals were moved to the final CFR5/chow-feeding (CFR5) procedure. In this procedure, weighed amounts of laboratory chow pellets (BioServ, between 15-20g) or sugar pellets were concurrently available during the CFR5 programme. Freely available chow or sugar pellets were provided in a bowl fixed to the cage floor opposite the pellet magazine. A black Perspex grid floor cover (made in-house) was used to prevent chow/sugar pellets from falling between the gridded operant chamber floor. After task completion, remaining chow/sugar pellets (including spillage) were collected and weighed. Rats subjected to sleep restriction in the CFR5 task were tested for 3 consecutive days (Pre, Test and Post), where 11-h sleep restriction was administered on test day. Data were recorded as number of lever presses and amount of food (chow or sugar pellets) consumed.

2.8 Physiological measures

Body temperature and locomotion measurement

Body temperature and locomotor activity were monitored via a miniature transmitter (Minimitter PDT4000G, Philips Respironics, Bend, OR, USA) placed in the abdomen during the EEG surgical procedure. Locomotor activity was automatically recorded as counts per minute, and body temperature was

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recorded each minute. Locomotor activity was detected in both horizontal and vertical planes by a customized telemetry receiver (ER4000, Minimitter Inc., Bend, OR, USA) beneath the cage. Telemetry measures (locomotor activity and body temperature) were not part of the SCORE vigilance-state determination algorithm; thus, sleep-scoring and telemetry data were concurrent but independent measures.

Corticosterone measurement

Urine collection was performed as previously described (Loomis and Gilmour, 2010). Disposable collection trays were fitted beneath the gridded floors of each operant box. Rats were left to void naturally throughout the SRLT. Any animals that did not produce sufficient urine for analysis were excluded from analysis. Samples were frozen at -20°C and analysed within one week of collection. Corticosterone was measured using a commercially available ELISA kit (Immunodiagnostic Systems Ltd., UK). Corticosterone was expressed relative to creatinine excretion to correct for individual urine production rates across days. Creatinine measurements were assayed using mass spectroscopy (Greendale Laboratories, UK). CORT measurements are expressed as ng CORT/µmol creatinine.

2.9 Drug formulation and administration

Formulation always occurred immediately before treatment. Compounds were weighed using a Mettler Toledo AB104-S analytical balance (d=0.1 mg). Compounds were formulated with an appropriate vehicle as per details in Chapter 5. Compounds were administered as detailed in Chapter 5 and according to rat weight on dosing day.

2.10 Statistics

Details of all statistical analyses are provided in the Methods section of Results Chapters (Chapters 3-6).

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CHAPTER THREE

Chapter 3 - Evaluation of a Non-Invasive Sleep Restriction Method to

Assess the Effects of Insufficient Sleep on Sustained Attention in the

Rat

3.1 Introduction

Reduced sleep has an impact on both health and quality of life for individuals and imposes substantial economic costs to society (Hafner et al., 2017). Restricted sleep time is known to have deleterious effects both at the functional and physiological levels (Balkin et al., 2008). Whilst some sleep loss may arise from medical conditions, it can also be due to family, societal or vocational demands. These demands are becoming a more widespread problem due to the growing 24-hour society (Hasler et al., 2005) (Chatzitheochari and Arber, 2009) (Andersen and Tufik, 2015). A validated pre-clinical sleep restriction method combined with a functional readout from a rodent cognitive task would provide a useful platform to screen putative wake-promoting compounds, and ultimately identify novel therapies to counteract cognitive impairments (Loomis et al., 2015) (McCoy and Strecker, 2011). Whilst numerous validated methodologies have been developed to deprive rodents of sleep, most are associated with a number of limitations that restrict the high throughput screening necessary for well-powered pre-clinical studies in rodents.

Sleep restriction methods in rodents

A commonly used non-invasive sleep restriction method is the procedure of gentle “handling” whereby rodents are kept awake by laboratory personnel via introduction of objects, movement of bedding, noise or cage movements (Tobler et al., 1997). This technique has proven effective in suppressing REM sleep and over 90% of NREM sleep (Franken et al., 1991). However, this protocol is labour

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intensive as it requires the constant presence of laboratory personnel and thereby limits high throughput (Colavito et al., 2013).

Another long-standing protocol uses the disc-over-water technique to specifically deprive REM sleep in rodents. It relies on the loss of muscle tone during REM sleep, causing the animal to drop from a small raised holding platform into a bath of water beneath and awaken (Mendelson et al., 1974). While muscle atonia is only achieved during REM sleep, it has been reported that this procedure also induces some loss of NREM sleep (Machado et al., 2004). A potential drawback of this technique is the confound of thermoregulation effects through continual wetting (Hanlon et al., 2010) and elevation of plasma corticosterone and adrenocorticotrophic levels indicative of increased stress levels (Suchecki et al., 1998) (Andersen et al., 2005). Pre-clinical sleep restriction techniques that elevate stress may contribute to subsequent functional impairment resulting from sleep loss and therefore should be taken into consideration when interpreting the findings.

Alternative methods have been developed to deliver automated movement stimuli, causing enforced activity of the animal via rotation of a platform or the housing chamber, and thereby inducing a righting reflex (Leenaars et al., 2011). While most protocols using enforced activity impose rest-activity cycles at a constant interval, the number of stimuli applied shows a large range across studies (i.e., from 60 to 720 stimuli per hour) (McCoy et al., 2013), (Baud et al., 2013) (Stephenson et al., 2015). Furthermore, the delivery of waking stimuli at a constant interval is often misaligned with sleep need, as the requirement to sleep becomes more intense with time spent awake. A comparison of enforced activity protocols with a constant and incrementally decreasing inter-stimulus interval have concluded that this protocol may more appropriately replicate the increasing sleep pressure as restriction continues (Leenaars et al., 2011).

Another sleep restriction method to induce automated enforced activity utilises real-time sleep-state determination based on the electroencephalogram (EEG). Upon detection of the animals’ entry into NREM sleep, a waking episode is initiated via enforced movement of a platform or housing chamber. As this technique uses online EEG readout to trigger the waking stimulus, it was coined as “Biofeedback” (Wurts and Edgar, 2000). However, a significant downside is that it requires invasive instrumentation of animals to record the EEG/EMG and

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software to record and analyse online data output. This methodology is thus labour intensive to perform surgeries and subsequently expensive.

Vigilance tasks as a functional readout of sleep restriction

The relationship between insufficient sleep and impairment of sustained attention in rodent and human studies is well established (Christie et al., 2008) (Deurveilher et al., 2015) (Dinges et al., 1997) (Jewett et al., 1999a) (Gao et al., 2018) (Lo et al., 2012). The psychomotor vigilance task (PVT) has been used in many studies to evaluate the effects of sleep deprivation in humans and was originally developed to measure vigilance via response time to a salient yet unpredictable signal (Dinges and Powell, 1985). PVT has the advantage that it can be administered repeatedly without practise effects (Basner et al., 2017). An equivalent task, the “rat PVT” also known at the Simple Response Latency task (SRLT), has been developed and assessed in rodents (Davis et al., 2016) (Deurveilher et al., 2015). A validation study in the context of sleep deprivation suggested that the rat SRLT is an acceptable homologue of the human PVT and therefore provides a valid translational assay to study the impairment induced by sleep restriction and sleep disorders (Christie et al., 2008). While the human PVT and rodent SRLT show some differences in their protocols (Figure 3.1), they allow to assess the comparable outcome measures following sleep restriction, such as lapses, reaction times, premature responses and time on task effects (Davis et al., 2015).

HOUSELIGHT ON

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Figure 3.1. Serial Latency Response Task in rats and Psychomotor Vigilance Task in humans. A) Rats are given a preparatory cue in the form of a houselight.

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Reaction time is measured from magazine (stimulus) light illumination to first head entry or “nose-poke” response by the rat (test duration: 30 – 40 minutes). B) In humans, reaction time is measured from start of counter until the counter is stopped by the

response of the subject (test duration: 10 minutes).

PVT and SRLT measure three main outcomes: reaction time, omissions and effects of Time on Task. A pronounced effect of sleep restriction on PVT in humans and SRLT in rodents is a slowing of overall reaction times (Nir et al., 2017) (Doran et al., 2001b) (Christie et al., 2008) (Deurveilher et al., 2015). Reaction times are sometimes further divided into two categories: 1) fast responses corresponding to the 5-10th percentile of the distribution and reflecting the sensorimotor capacity of the subject; and 2) slow responses distributed in the 90-95th percentile. Sleep restriction appears to have greater effect on slow responses i.e. those in the 90th percentile in rodents (Graw et al., 2004), whereas in adolescent humans fastest reaction time also show a deterioration although slow responses were not reported (Louca and Short, 2014). In humans, PVT reaction times of more than 500 ms are termed lapses, which are also included in the 95th percentile (Lim and Dinges, 2008) (Lee et al., 2010). Following sleep restriction, omissions, which reflect a failure to respond to the stimulus, significantly increase in humans and rats (Lim et al., 2010) (Deurveilher et al., 2015). Finally, both human PVT and rodent SRLT measurements show an enhancement of Time on Task decrements, reflecting worsening of performance across the course of the task due to fatigue, boredom or reduced motivation (Lim and Dinges, 2010a), (Davis et al., 2015). Time on Task is a useful parameter as it seems more sensitive to sleep manipulations than other measures that report performance for the total test period (Doran et al., 2001a) (Wesensten et al., 2004). One further measure often reported in rodent SRLT is premature responding that reflects either a response when no stimulus, is presented or a response to the wrong stimulus (Oonk et al., 2015). Premature responding may be associated with impulsivity caused by sleep loss possibly suggesting a compensatory effort to resist sleep. In human PVT studies, responses taken before the presentation of the stimulus are termed “errors of commission” (Lim and Dinges, 2008).

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Chapter aims

To address the potential drawbacks of presently used sleep restriction protocols in rodents, the overarching aim of this chapter was to develop and compare non-invasive automated protocols using enforced activity to achieve sleep restriction leading to significant functional deficits.

To achieve this:

1) We first established the duration of sleep restriction required to achieve a consistent functional deficit in the SRLT, i.e., a reduction in trials and an increase in omissions and reaction time. We also assessed which SRLT parameters in rats showed impairment similar to human PVT outcomes following sleep restriction.

2) We also investigated whether performing SRLT testing at different zeitgeber times would have an impact on the functional deficit.

3) We developed three algorithms for applying chamber rotation waking stimuli to induce sleep restriction. Three protocols were coined “Constant interval” (sleep chamber rotating once every minute), “Decreasing interval” (increased wheel rotations linearly in proportion with sleep restriction time) and “Weibull distribution” based on historical EEG-biofeedback data to accurately reflect number and timings of attempts to enter into sleep across a 11-hour sleep restriction. We compared the effectiveness of the three non-invasive protocols with the Biofeedback method of sleep restriction, on sleep and functional deficit using SRLT following sleep restriction. In addition, body temperature, locomotion and stress hormones levels were measured.

4) Finally, we assessed the use of the Weibull protocol over 5 weeks to assess whether this protocol could produce functional deficit following repeated use.

Part of this Chapter was included in the following publication:

Mccarthy A*, Loomis S*, Eastwood B, Wafford KA, Winsky-Sommerer R, Gilmour G. Modelling maintenance of wakefulness in rats: comparing potential non-invasive sleep-restriction methods and their effects on sleep and attentional performance. Journal of Sleep Research. 2017 Apr;26(2):179-187. * Contributed equally to this work.

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3.2 Methods

3.2.1 Animals and housing conditions

The study was conducted in sound-attenuated, light and temperature-controlled recording rooms. Following surgery, animals were singly housed in standard home cages. After recovery from surgery and during behavioural training, rats were food restricted to 85% of their ad libitum feeding weight. When a stable behavioural baseline in the SRLT was achieved, rats were transferred to sleep restriction chambers and returned to an ad libitum food regime. In addition to the main study, historical data from 42 rats that underwent EEG-driven Biofeedback sleep restriction were used and compared to data from 31 non-sleep restricted rats. These data were used to devise the three non-invasive sleep restriction protocols presented in this Chapter.

3.2.2 Surgical procedures

Adult, male Wistar rats (n = 16, approximately 270-300 g at time of surgery, Charles River Laboratories, Margate, UK) were implanted with electrodes for long-term EEG and EMG recordings (see Chapter 2 section 2.2).

3.2.3 Sleep restriction protocols

Sleep restriction was carried out as described in Chapter 2, sections 2.3. The four sleep restriction protocols were run in a crossover study design carried out across 5 weeks allowing each animal to randomly receive all treatments: control treatment (no sleep restriction), 11-h sleep restriction using Biofeedback, Constant, Decreasing and Weibull treatments (Figure 3.2). The subjects were randomly assigned to a treatment sequence. Subjects were given at least 7 days “washout” preceding and following treatments with no duplication of treatments. Prior to the study, all subjects were sleep restricted for 5 hours to eliminate unpredictable changes due to novel exposure to the procedure.

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ZT12

Sleep RestrictionZT0 – ZT11

ZT0 ZT0ZT0ZT12 ZT12

ZT2.5Behaviour“PRE DAY”

ZT11Behaviour“TEST DAY”

ZT2.5Behaviour

“POST DAY”

Figure 3.2. Sleep restriction protocol. Sleep restriction was initiated at light onset for 11 hrs (ZT0 – ZT11). Rats were subjected to the 40-min SRLT test immediately at end of sleep restriction period (ZT11). “Pre” and “post” day testing was carried out at ZT2.5.

Four different protocols were used to induce sleep restriction. Firstly, an EEG/EMG (Biofeedback) based method used real-time ongoing sleep/wake monitoring to prevent sleep. Detection of NREM or REM sleep epochs acted as closed-loop feedback via which the SCORE2004™ program activated a motor to roll the cylindrical chamber around its axis for 8 seconds (265° of rotation at 11.5 cm/s), thereby initiating the righting reflex and waking the rat. A safety mechanism was in place to block directly consecutive trigger signals, preventing the chamber from continuous movement. Three other algorithms were devised for chamber turning based upon historical Biofeedback protocol data. These three non-invasive (i.e., non-biofeedback) protocols were driven by pre-determined sequences that activated the chamber for an equivalent number of times across the sleep restriction period as the Biofeedback protocol did. The three algorithms used were: “Constant”, where the chamber was triggered at a constant rate of 1 turn per min; “Decreasing”, where the interval between chamber turns decreased linearly in proportion to time spent in sleep restriction (fitted model: Y= -5X + 80, i.e., an initial wheel turn interval of 80 seconds that declined each hour by 5 seconds); and “Weibull”. The Weibull protocol was developed using parametric survival analysis to model the probability of transition from wakefulness to NREM sleep within a specified time interval. To approximate these transition probabilities and hence estimate when the wheel should be triggered turn, the distribution of intervals between sleep attempts was assessed using EEG/EMG datasets from former 11 hours Biofeedback sleep restriction studies. Weibull modelling included a random component that reflected increases in homeostatic sleep pressure over time by progressively increasing the number of waking stimuli in an unpredictable manner. In all sleep

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restriction protocols, the chamber turned in a pseudo-random direction per trigger event to prevent habituation.

3.2.4 Simple Response Latency task (SRLT)

SRLT tests were conducted as described in Chapter 2, section 2.7. The number of trials completed and response errors (premature responses and omissions) were computed. Omissions were defined as any reaction exceeding 10 s in length. Reaction times were recorded in addition to time on task effects. For details of SRLT statistical analysis see section 3.2.6 below.

3.2.5 Locomotor activity, body temperature and corticosterone levels

Locomotor activity (LMA) and body temperature (BT) were collected throughout the study. Corticosterone levels were assessed as described in Chapter 2 (section 2.8) by urinalysis during the SRLT. Any animal that did not produce sufficient urine was excluded from the corticosterone dataset. Samples were frozen at -20°C and analysed within one week of collection.

Statistics were conducted using Statistica. An ANOVA was conducted using a general linear model approach with “Treatment” as an independent variable. Significant Main Effects were followed by planned comparisons (univariate test of significance) against the control group.

3.2.6 EEG/EMG analyses

EEG/EMG recordings were analysed during a 12-h baseline period, 11-h sleep restriction period and 13-h recovery following the Simple Response Latency task as described in Chapter 2 (section 2.4).

Statistical analyses were performed using SAS (version 9.2, SAS Institute, Inc., Cary, NC) and JMP (version 8, SAS Institute, Inc., Cary, NC) statistical software packages. Sleep-wake and SRLT variables were analysed using a mixed effect model and programmed using the mixed procedure in SAS. The corresponding measures in the 24-h pre-treatment interval acted as a baseline for comparison. Sleep restriction ‘Treatments’ and baseline measures were fixed effect variables,

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with the baseline variables acted as a covariate to account for inter-animal differences. In cases where treatments were administered at different dates, this was also included in the model.

Survival analysis was performed on calculated wake bout lengths, where each bout was assigned to the hour in which it began and bouts were pooled within-subjects for each given hour under sleep restriction. No data was censored in the study. Kaplan-Meier curves were generated using SAS 9.2 (SAS Institute Inc., Cary, NC) (See Appendix).

3.3 Results

3.3.1 Validation of a standard sleep restriction protocol using SRLT

Effect of sleep restriction duration on SRLT in rat

To establish the amount of sleep restriction required to produce consistent and reproducible behavioural deficits, the effects of 6, 8 and 10 hours of EEG triggered Biofeedback sleep restriction on SRLT were assessed. Significant effects on trials, omissions and median magazine latency were shown after 10 hours of sleep restriction. No significant effects on these parameters were seen following 6 or 8 hours of sleep restriction (Fig. 3.3a-b and d). Premature responses did not show any differences following any duration of sleep restriction (Fig. 3.3c). There was a significant time on task effect following 10 hours of sleep restriction such that compared to the control treatment, at 20 and 30 minutes reaction time events dropped from 20.5 ± 0.53 to 17.43 ± 1.25 at 20 minutes and 21.07 ± 0.40 to 10.93 ± 2.42 at 30 minutes into the task (Fig. 3.3e). There was no significant alteration in corticosterone levels for any of the sleep restriction durations (Fig. 3.3f).

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Figure 3.3. Dose response for 6, 8 and 10 hours EEG Biofeedback sleep restriction on SRLT. (a) Number of Trials, (b) Number of Omissions, (c) Number of Premature Responses, (d) Median Magazine Latency, (e) Time on Task (Number of Reaction Time Events), x-axis shows time in minutes across SRLT, Corticosterone

measurements. Control (NSR) group (blue; n=14), sleep restricted (SR) group (red; n=14). Main effects shown as SR and Time (6, 8 & 10-h). Asterisks refer to planned

comparisons of sleep restriction condition to the Control condition, ***p < 0.001. Data represented as mean ± SEM.

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Effect of 11-hr sleep restriction using the EEG Biofeedback protocol on performance in SRLT

Based on results from the section above, a minimum of 10 hrs sleep restriction is required to induce functional deficit in SRLT. Thus an 11-h duration for sleep restriction was chosen in the following studies. We assessed the following SRLT parameters following 11-hr sleep restriction using EEG Biofeedback: One-way ANOVA statistical analysis showed trials and head entries decreased significantly (Fig. 3.4a-b). Omissions and premature reaction times significantly increased whereas premature responding and magazine reaction time latencies did not show any significant differences between the non-sleep restricted and the sleep-restricted groups (Figure 3.4c-h). Analysis of time on task effects showed that the number of reaction time events decreased significantly across the 40 min task time. Although there was a slowing of reaction times at 30 and 40 minutes, no significant difference between the two groups was observed (Figure 3.4i-j).

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Figure 3.4. Performance in SRLT following an 11-hr EEG Biofeedback sleep restriction. (a) Number of Trials, (b) Head Entries, (c) Number of Premature Responses, (d) Number of

Omissions, (e) Premature Response Latency, (f) Magazine Response Latency (5th

percentile), (g) Median Response Latency, (h) Magazine Response Latency (95th

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percentile), (i) Time on Task (Number of Reaction Time Events), (j) Time on Task (Response Latency. Control group (NSR; blue), sleep deprived (SR; red) (n=8). Asterisks refer to planned comparisons of sleep restriction condition to the Control condition, * p

<0.05; ***p < 0.001. Data represented as mean ± SEM.

3.3.1c Effect of time of day on performance assessed by SRLT

In the standard protocol developed in our laboratory, rats are routinely tested in the SRLT at ZT11 on test days, while tests on pre and post days are performed at ZT2.5. Thus, we wanted to ascertain that the deficits in performance observed during sleep restriction studies are due to the sleep restriction protocol rather than a time of day effect. To determine whether time of day influences SRLT test day results, rats were tested at ZT2.5 and ZT11 in the absence of any sleep restriction. No significant effects of test time were found on SRLT parameters (Figure 3.5).

Figure 3.5. Effects of time of day on SRLT performance. Test at ZT 2.5 (light blue bars) or ZT 11 (dark blue bars) on SRLT in rats (n = 16) not subjected to sleep restriction. (a) Number of Trials, (b) Number of Omissions, (c) Number of Premature Responses, (d)

Median Magazine Latency. Data represented as mean ± SEM.

3.3.2 Characterisation of wakefulness induced by the “gold standard” EEG-triggered

(“Biofeedback”) sleep restriction

Historical data obtained from rats over several 11-hr EEG-triggered “biofeedback” sleep restriction experiments were analysed to develop the non-invasive sleep restriction algorithms used in this chapter. The EEG-triggered Biofeedback protocol maintained wakefulness for between 40 and 60 min per hour throughout the 11-h period during which the number of sleep attempts progressively increased (Figure 3.6a). Wake bout length (Figure 3.6b) showed a

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progressive non-linear decay such that average wake bout length significantly decreased from ZT1 to ZT11 (p<0.01).

We next analysed hourly hazard ratios to assess the likelihood of a wake bout ending at any given time. Figure 3.6c shows the likelihood of falling asleep had significantly increased by 3 hours compared to the first hour using the Biofeedback protocol. By the 11th hour of Biofeedback protocol the hazard ratio had significantly increased.

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Figure 3.6. Effects of EEG-Biofeedback induced sleep restriction on wakefulness. (a) Wakefulness during the 12 h baseline, 11 h sleep restriction and 13 h

recovery period in mins per hour (red, n = 42) versus control condition (blue, n = 31). Dark phases are indicated by black boxes on the lower x-axis. During application of the Biofeedback protocol, wakefulness was maintained at, or above, levels measured during the prior 12 h (dark) period. Sleep attempts progressively increased over the course of

sleep restriction. (b) Mean waking bout lengths. Wake bout length decreased throughout the sleep restriction period. (c) Hazard ratio of waking bouts, increased as a function of

time spent in sleep restriction. *p < 0.05; **p < 0.01; ***p < 0.001.

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3.3.3 Comparison of three non-invasive non-bio-feedback sleep restriction protocols

on sleep/wake parameters

All three non-invasive sleep restriction methods delivered a similar number of total chamber rotations, which did not differ significantly from the number of rotations received by rats in the Biofeedback protocol. For the Constant protocol, 652 chamber triggers occurred across the sleep restriction period, amounting to approximately 1 turn per minute. Biofeedback, Decreasing and Weibull methods initiated a similar number of turns across the 11-h but had longer intervals between turns at the beginning of the protocol and shorter intervals towards the end of the restriction period (Fig. 3.7a).

During sleep restriction, all sleep parameters showed significant differences between the non-sleep restricted control group and the four sleep restriction protocols. A significant difference in total sleep (ANCOVA, F4,38.1 = 45.61, p<0.0001); NREM (ANCOVA, F4,39.1 = 37.28, p<0.0001); REM (ANCOVA, F4,39.7 = 103.53, p<0.0001); total bouts (ANCOVA, F4,37.4 = 60.68, p<0.0001); sleep bout length (ANCOVA, F4,39.3 = 37.81, p<0.0001) was present between the sleep-restricted conditions. REM sleep decreased significantly (39 ± 3, 42 ± 3 and 41 ± 3 min) relative to controls under the Constant, Decreasing and Weibull protocols, respectively. Relative to the non-restricted control group, 125 ± 16, 152 ± 17, and 127 ± 16 minutes of NREM sleep were lost under the Constant, Decreasing and Weibull protocols respectively, which represented a total of 38 ± 5, 47 ± 5 and 39 ± 5% of the time spent in NREM sleep during the control condition (Figure 3.7c-d). Average sleep bout numbers were significantly different for all non-invasive sleep restriction protocols relative to the control condition. For the Biofeedback protocol, the total number of bouts decreased, whereas it increased for the Constant, Decreasing and Weibull protocols (Figure 3.7e). However, a significantly greater number of sleep bouts were observed during the Constant protocol compared to the Decreasing and Weibull protocols. For all non-invasive sleep restriction protocols, average sleep bout lengths were significantly shorter than during the control condition (Figure 3.7f).

Whilst the amount of REM sleep lost was consistent between the non-invasive and Biofeedback protocols, none of the non-invasive protocols were as effective at restricting NREM sleep as the EEG driven Biofeedback protocol. Compared to the Biofeedback protocol, the Constant, Decreasing and Weibull protocols allowed an additional 76 ± 16, 79 ± 17 and 74 ± 17 min of NREM sleep to be

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obtained. In addition, all non-invasive protocols led to significantly more and longer sleep bouts than the Biofeedback protocol (Fig. 3.7d).

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Figure 3.7. Comparison of sleep restriction protocols with non sleep restricted Controls (blue) and Biofeedback protocol (dark red). (a) Average interval between chamber

rotations per hour experienced by animals throughout the 11 h period of sleep

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restriction; (b) Total sleep time; (c) Time spent REM sleep; (d) Time spent in NREM sleep; (e Total number of sleep bouts; (f) Average sleep bout length. Statistical significance

refers to planned comparisons of each treatment group with the Control (*), Biofeedback (BIO; ^) or Constant (#) conditions, where: ***/^^^/### = p < 0.001. Data represented

as mean ± SEM.

3.3.4 Comparison of a Biofeedback and non-Biofeedback sleep restriction protocols on

SRLT and physiological parameters

3.3.4a Effects on performance assessed by SRLT

Trials, premature responding, omissions and median latency showed no significant differences between protocols on baseline “pre” or recovery “post” days (Figure 3.8a-d). On test day (i.e., following sleep restriction), compared to the control group, trial number was significantly decreased for Biofeedback, Weibull and Decreasing protocols (ANCOVA, F1,14= 35.71, p = <0.001). However, the Constant protocol was not significantly different from the control but showed a significantly increased trial number compared to the EEG-induced Biofeedback sleep restriction method (Figure 3.8a). Omissions were significantly increased for all four-sleep restriction protocols compared to the non-sleep restricted control group (ANCOVA, F1,14= 34.13, p = <0.001). However, the Constant protocol resulted in significantly lower number of omissions compared to the EEG-induced Biofeedback sleep restriction method (Figure 3.8c). Premature responding (ANCOVA, F1,14= 3.31, p=0.09) and median magazine latency (ANCOVA, F1,14= 1.13, p = 0.306) measures showed no significant differences between any of the protocols tested compared to the non-sleep deprived control group (Figures 3.8b & d).

Relative to the control undisturbed condition, all sleep restriction protocols resulted in the number of completed trials becoming significantly less frequent over the course of the task (ANCOVA, F4,42.5= 5.56, p<0.0011). (Figure 3.8e). Decreasing and Weibull protocols showed similar declines in performance over time compared to the Biofeedback protocol. In contrast, trial completion rate following the Constant protocol decreased to a lesser extent and showed significant increases in the number of trials compared to the biofeedback protocol at 30 and 40mins. No significant effects were shown for reaction time latency across the 40-minute task time (Figure 3.8f).

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Figure 3.8. Comparison of sleep restriction protocols on rat SRLT performance during baseline (“pre”), following sleep restriction (“test”) and recovery (“post”) days. (a)

Number of Trials; (b) Premature Responses; (c) Omissions; (d) Median Response Latency. Control (no sleep restriction; blue) and sleep restriction conditions (red). (e)Comparison of sleep restriction protocols for time on task on test day only; Number of Reaction Time

Events; (f) Response Latency. n=16. Asterisks refer to planned comparisons of sleep

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restriction condition to the Control condition, ***p < 0.001. Carots refer to planned comparisons of treatment conditions to the Biofeedback condition, ^^^ p < 0.001. Data

represented as mean ± SEM.

3.3.4b Alterations of locomotor activity, body temperature and corticosterone following EEG-

Biofeedback and non-Biofeedback sleep restriction protocols.

During sleep restriction, all four protocols significantly increased locomotor activity and to a similar extent (all p<0.001, T58 > 3.65) relative to the Control group (Figure 3.9a). Nonetheless, no significant differences in core body temperature were observed (ANCOVA, F4,53 = 0.84, p = 0.504; Figure 3.9b). Urinary corticosterone levels were significantly increased compared to the control group following the Constant and Decreasing protocols (p<0.001, T34 = 3.7 and p = 0.027, T34 = 2.24, respectively), while Biofeedback and Weibull protocols did not induce any significant changes in corticosterone levels (p = 0.082, T34 = 1.75 and p = 0.483, T34 = 0.69, respectively; Figure 3.9c).

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3.3.5 Effects of sleep restriction protocols on subsequent recovery sleep.

Following the 11-h sleep restriction and subsequent behavioural testing, sleep was monitored for 36 hours. Total recovery sleep was significantly different between the sleep restriction conditions (ANCOVA, F4,40.1 = 38.14, p = <0.001)

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and only total sleep occurring during the first 12-h dark phase following sleep restriction was defined as rebound sleep (Figure. 3.10a-b). Relative to the control condition during which rats slept ad libitum, total sleep time in the first 12-h dark phase was increased following Constant, Decreasing and Weibull induced sleep restriction respectively (Figure 3.10b). Relative to the EEG-induced Biofeedback sleep restriction method, the Constant and Weibull models induced significantly less recovery sleep (p<0.001, T55 = -4.6 and p<0.01, T55 = -2.73 respectively). The Decreasing protocol did not induce any significant difference compared to the EEG-induced Biofeedback sleep restriction on total sleep time during the first 12-h of recovery (p>0.1, T55 = -1.58). This recovery sleep consisted of both NREM and REM sleep. NREM and REM sleep were significantly different between the sleep restriction conditions (NREM; ANCOVA, F4,56 = 9.10, p = <0.001) (REM; ANCOVA, F4,56 = 19.95, p = <0.001). Rats subjected to Constant, Decreasing and Weibull induced sleep restriction protocols gained more NREM and REM sleep during the first 12-h dark phase following sleep restriction (Figure 3.10d&f) than the control rats (planned comparisons all p = <0.001). Relative to the EEG-induced Biofeedback sleep restriction method, the Constant model induced significantly less NREM recovery sleep (p = 0.04), whereas REM recovery sleep was not significantly different for any of the non-invasive protocols.

Sleep after sleep restriction showed greater continuity and depth, as reflected in average sleep bout length and EEG delta power in NREM sleep. Average sleep bouts after the 11hr sleep restriction compared to the control group were all significantly higher for the Biofeedback, Constant, Decreasing and Weibull protocols respectively Figure 3.10d). EEG delta power in NREM sleep was also significantly increased during the recovery period with 126 ± 2, 119 ± 2, 124 ± 3 and 121 ± 2 % increases in power observed compared to the non-sleep restricted control condition after Biofeedback, Constant, Decreasing and Weibull protocols, respectively (Figure 3.10f). The Constant protocol induced a significantly smaller increase in NREM sleep EEG delta power relative to the Biofeedback protocol (p<0.01, T55 = 2.81).

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delta power from NREM sleep respectively during baseline, sleep restriction and recovery period, (Graphs b, d, f, h & j) Total sleep, NREM, REM time, average sleep bout length and EEG delta power from NREM sleep respectively for the first 12 hour dark phase

following sleep restriction. Asterisks refer to planned comparisons of sleep restriction

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conditions to the Control condition (*p < 0.05; **p < 0.01; ***p < 0.001). Carots refer to planned comparisons of treatment conditions to the Biofeedback condition (^p < 0.05;

^^p < 0.01; ^^^p < 0.001. Data represented as mean ± SEM.

3.3.6 The effect of using an 11-hr non-invasive Weibull sleep restriction protocol

during 5 weeks on SRLT in rats

To maximise throughput and reduce costs in rodent sleep experiments it would be advantageous to

be able to reuse behaviourally trained rats assuming no diminution of SRLT deficits. To this extent we wanted to examine the effects of repeated use of the non-invasive sleep restriction protocol on performance in the same cohort of rats. The results above showed the Weibull and Decreasing protocols to be the most similar to the Biofeedback sleep restriction. The Weibull protocol was chosen as we felt it more closely resembled EEG monitored Biofeedback sleep restriction. Rats were sleep restricted using an 11-h Weibull protocol once per week for 5 weeks and SRLT performance was assessed.

A significant decrease in trials completed was observed for weeks 1 and 2 compared to their baseline values (ANCOVA, F5,90 = 4.58, p = <0.001). However, for weeks 3, 4 and 5, trial numbers had significantly increased compared to week 1 (ANCOVA, F4,75 = 3.81, p = 0.007; Fig. 3.11a). Omissions showed a similar pattern, with an increase on week 1 and week 2, but by weeks 3, 4 and 5 omissions were not significantly different to baseline levels (ANCOVA, F5,90 = 5.16, p = <0.001) and were significantly lower than week 1 (ANCOVA, F4,75 = 4.78, p = 0.002; Fig. 3.11b). Premature responses, a measure of impulsivity, remained unchanged compared to baseline on week 1 (ANCOVA, F5,90 = 3.90, p = 0.003; Fig. 3.11a). however, a significant increase was seen on weeks 2, 3 and 4 (ANCOVA, F4,75 = 2.53, p = 0.047; Fig. 3.11c). Compared to week 1, the number of premature responses significantly increased on week 3. Reaction times remained faster from week 3 to week 5 compared to both baseline and week 1 (ANCOVA, F5,87 = 15.38, p = <0.001; ANCOVA, F4,72 = 15.91, p = <0.001 respectively). No significant difference in reaction time was observed between baseline and week 1 (Fig. 3.11d).

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premature responses, (d) Median magazine latency. Asterisks refer to planned comparisons of sleep restriction compared to baseline, where *p < 0.05; **p < 0.01; ***p < 0.001. Carots refer to planned comparisons of treatment groups to week 1, where ^ p

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3.4 Discussion

This chapter aimed to identify alternative non-invasive, sleep restriction protocols with comparable functional impairments as the EEG-based “Biofeedback” sleep restriction protocol, thus removing the need for surgical intervention required for EEG recordings. This would allow an increase in throughput for pre-clinical studies. Several variables were of interest for this comparison, including, the functional impact of sleep restriction, the amount and quality of sleep loss during restriction, measures of physiological activation/stress, and the amount and quality of recovery sleep observed in the period following sleep restriction. Not all protocols performed equivalently to the “gold standard” EEG-driven Biofeedback protocol. Overall, Decreasing and Weibull protocols were superior to the Constant protocol at producing functional deficits compared to EEG driven Biofeedback sleep restriction and showed an increased need for recovery sleep. These findings show alternative non-invasive enforced activity protocols in rodents may offer translational methods that compare to human sleep deprivation studies using psychomotor vigilance testing as a functional readout (Lo 2012).

Comparison of different sleep protocols on sleep parameters

EEG Biofeedback sleep restriction allows application of minimal waking stimuli only upon initiation of a sleep bout (Wurts and Edgar, 2000), thus reducing the rodents’ ability to maintain prolonged sleep bouts. Initial experiments showed that over an 11-hr sleep restriction period, the standard EEG-Biofeedback protocol progressively reduced the capacity of rats to maintain bouts of wakefulness. Sleep attempts increased in proportion to the time spent in sleep restriction; an effect comparable to the increased sleep propensity, measured using the multiple sleep latency test, as a consequence of increased homeostatic sleep pressure in humans (Bonnet and Arand, 2003).

All sleep restriction protocols had broadly similar effects on sleep-wake profiles during the restriction period, markedly reducing total sleep time, fragmenting normal sleep patterns, and effectively abolishing REM sleep. However, for all parameters, except for REM sleep, non-invasive protocols were not as impactful as the Biofeedback protocol. All three non-invasive protocols displayed less total sleep and NREM sleep loss compared to the Biofeedback protocol. Similarly,

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sleep bout lengths were longer in all three non-invasive protocols in comparison to the Biofeedback protocol. EEG Biofeedback data showed that entry into sleep (sleep attempts) were absent. By contrast, all non-invasive protocols led to increased number of sleep attempts compared to the non-sleep deprived group. The absence of sleep attempts is likely an artificial readout and reflects the fact that during the Biofeedback protocol animals do not exhibit sufficiently long NREM or REM sleep epochs for them to be scored as sleep attempts before they are woken up again. Although in theory the number of awakenings during the sleep restriction period could be used as a proxy for sleep attempts. Finally, when specifically comparing the three non-invasive protocols, the Constant protocol allowed a significantly greater number of sleep attempts than either the Weibull or Decreasing protocols, likely reflecting the relative physiological inaccuracy of the Constant protocol in modelling the non-linear increase in homeostatic sleep pressure that occurs with time spent awake (Daan et al., 1984). For this reason, a constant sleep restriction protocol is unlikely to be as effective in comparison to the other two non-invasive sleep restriction protocols used in this study.

Variables relating to recovery sleep following the sleep restriction period showed that, compared to the Biofeedback protocol, the Decreasing protocol produced the most similar outcomes on recovery sleep parameters, increasing total sleep time, and average sleep bout length and delta power. The Weibull protocol resulted in significantly less total sleep time during recovery than the Biofeedback control but did not differ from the Biofeedback group for average sleep bout length and delta power. Finally, the Constant protocol resulted in both less total sleep time during recovery and less delta power than the Biofeedback protocol. This range of differences in recovery sleep parameters induced by the sleep restriction protocols may suggest that it is not only the total amount of sleep that is lost that is important for determining subsequent recovery, but also the manner in which this sleep disruption occurs. In this regard, the present data are in agreement with studies of sleep fragmentation in humans highlighting that sleepiness may be driven more by the level of sleep loss or fragmentation as opposed to the means by which the sleep is disturbed (Martin et al., 1999) (Bonnet and Arand, 2003).

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Stress measurements and locomotion on sleep protocols

Few rodent sleep restriction studies report corticosterone measurements, an indicator of stress during sleep deprivation. Of those reporting corticosterone measures, activity wheel and carousels appear less stressful as sleep restriction methods than treadmills (McCoy and Strecker, 2011). Corticosterone measurements reported in this Chapter showed that Biofeedback and Weibull protocols did not significantly increase urinary corticosterone, whereas Decreasing and Constant protocols did. Interestingly the Constant protocol had the largest effect on urinary corticosterone levels despite producing the least sleep and functional deficits. The Constant protocol is amongst the most commonly used enforced activity sleep restriction protocol described in the literature (Stephenson et al., 2015) (Baud et al., 2013) (Leenaars et al., 2011), yet the corticosterone levels produced by this method were similar to a sub-maximal dose (<0.5 mg/kg) of nicotine (Loomis and Gilmour, 2010). It could be speculated that the Constant protocol was ultimately the most stressful protocol because animals can utilize the fixed inter-stimulus interval to initiate more sleep bouts and achieve a slightly deeper level of sleep each time, before being roused again by the righting reflex. While these findings should certainly be replicated with a more robust measurement of plasma corticosterone (as well as control for the circadian phase at testing), they are suggestive of a basic biological difference between the Constant sleep restriction protocol compared to the other protocols.

Other physiological readouts showed that locomotor activity increased to a similar degree by all sleep restriction protocols, whereas the body temperature of sleep-restricted animals did not increase above that of the Control group. The increased activity level of all animals was therefore likely a consequence of the chamber turning technique used, where the number of chamber triggers were equal and hence activity was balanced across each non-invasive sleep restriction group to match that of the Biofeedback control. This increase in locomotion however possibly induced fatigue that may have consequences in the subsequent operant SRLT (Colavito et al., 2013).

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Comparison of sleep protocols on functional impairments using SRLT

Functional capacity was assessed with a rat version of the human PVT, a task well known to be sensitive to the effects of sleep restriction. A progressively increasing failure to respond in a timely fashion to an imperative cue, as measured by PVT, is a hallmark effect of the sleep-deprived state both in rodents and humans (Christie et al., 2008) (Cordova et al., 2006) (Oonk et al., 2015) (Jewett et al., 1999b) (Cohen et al., 2010). In this Chapter, we showed that, with 11 hours of EEG-Biofeedback sleep restriction, rats lost around 3 h of non-REM sleep and 40 min of REM sleep. Significant deficits to perform a Simple Response Latency task as indexed by impairments in trials completed, omissions and progressive task disengagement were shown. These findings replicate previous work (Loomis et al., 2015), and show similarities with the well-described effects of sleep deprivation in humans on psychomotor vigilance test performance (Lo et al., 2012) (Van Dongen et al., 2003). Two further measurements are widely reported in human PVT literature as showing pronounced deficits following sleep restriction, namely premature responses and median magazine latency whilst these measures did not consistently show significance in our experiments, for translational discussion, these parameters will be included in subsequent data analysis.

Accordingly, in the sleep restriction comparison study there was an overall reduction in trials for Biofeedback, Decreasing and Weibull protocols compared to the non-sleep deprived control group and omissions were significantly increased for all treatment groups. However, we failed to show any effect on latency to respond and premature responding. Inconsistent effects on premature responding in rodents have been previously reported. Using a 3s on/12s off enforced locomotion sleep restriction protocol (Christie et al., 2008)did not show an increase in premature responding. In contrast to another study in the rat using a 24-h gentle handling sleep restriction protocol, premature responses were increased (Oonk et al., 2015), implying the induction of a change in premature response rate by sleep restriction may depend on assay protocols employed.

Time on task is often considered a relevant parameter for measuring disengagement from the task across time. Our results demonstrated that for sleep restriction beyond 10 hours, rodents disengage progressively across the duration of the SRLT task. Accordingly, Decreasing and Weibull protocols

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induced deterioration of performance similar in magnitude to the deficit produced by the Biofeedback protocol. By comparison, while the Constant protocol also produced a behavioural deficit, it was significantly smaller than that produced by the other sleep restriction protocols. The lesser impact of the Constant protocol on functional capacity may have been a result of its relatively weaker effect during the sleep restriction period, allowing the animals subjected to this protocol to maintain task engagement for longer than the other sleep restriction protocols did.

Limitations of the study

This study demonstrated that sleep restriction can readily be accomplished using non-invasive enforced activity methods in rats and with the appropriate choice of protocol can achieve similar functional deficits to that of an invasive protocol driven by EEG Biofeedback. The Weibull protocol may be the most widely applicable if extended periods of restriction are required that reflect the saturation of homeostatic sleep pressure. However, when used repeatedly on the same cohort of rats, rats appear to begin to acclimatise to the protocol and functional effects are only apparent on single use. This implies that whilst a Weibull protocol maybe a means by which to avoid surgery this appears an unsuitable method for repeated testing on subsequent weeks (i.e., chronic sleep restriction).

Ultimately, the aim of this work is to further facilitate translational sleep studies and determine a more applicable single method of sleep restriction in rodents. However, it may be more appropriate to conclude that different models may potentially have different utility in different translational contexts, depending on the clinical presentation of the sleep deficit. For instance, shift workers remain exposed to constant external environmental stimuli during performance of their night shift and have a greater internal imperative to stay awake. In this case, the Constant sleep restriction used in rodents would have greater pre-clinical validity. Whereas, sleep apnea patients experience sleep fragmentation loaded towards the end of the night when muscle atonia during REM sleep enhances the likelihood of upper airway collapse, and in this scenario the Weibull model would be more appropriate (Mokhlesi and Punjabi, 2012). Further work detailing potential mechanistic differences between human conditions of sleep deficit and how they relate to animal models of sleep restriction may therefore be of value.

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Comparisons with human literature

In humans, effects on PVT performance can be observed after experimental total sleep deprivation consisting of either one night without sleep (Adam et al., 2006) (McMahon et al., 2018), 3 nights without sleep or chronic sleep fragmentation (4, 6 or 8hr time in bed for 14 days) (Van Dongen et al., 2003). Equally, it may be observed as a consequence of clinically presented excessive daytime sleepiness (EDS) (Czeisler et al., 2005) (Dinges and Weaver, 2003). Our findings on omissions are consistent with the human literature (McCoy and Strecker, 2011) (Banks and Dinges, 2007), with 11-h sleep restriction in rats leading to an increase in omissions, referred to as lapses in humans. In contrast a slowing of reaction times in human PVT appears to be a robust measure of sleep deprivation with performance lapses (i.e., reaction times >500ms) being a defining hallmark of sleep restriction (Rakitin et al., 2012, Lim and Dinges, 2008). This gradual deterioration in reaction time across subsequent days of sleep restriction however is quickly ameliorated when returned to normal sleeping patterns (Balkin, 2011). Using a 5-choice serial reaction time task, which measures reaction times similar to SRLT, Cordova showed reaction time increases after just 4 hours, unfortunately our rodent studies showed inconsistent effects with regard to reaction time (Cordova et al., 2006).

Poor PVT performance following sleep restriction may also result from task disengagement as a consequence of lack of motivation to perform the task, or an inability to maintain attention to the imperative cues, or a combination of both effects. PVT studies in humans report reaction time latency following each trial, providing subjects with an ongoing motivation to maintain performance during the test period. For rodents, motivation is more routinely achieved through food or water deprivation. Chapter 4 will utilise the non-invasive Weibull sleep restriction validated in the current Chapter to determine whether deficits in functional behaviour following sleep restriction are associated with motivational factors.

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CHAPTER FOUR

Chapter 4 - Motivation for Food as a Confounding Factor in the

Assessment of Sustained Attention following Insufficient Sleep in the

Rat

4.1 Introduction

Electrophysiological indices of sleep and wake behaviour have been studied in both rodents and humans with striking similarities found between species (Dijk, 2009), however less is known about the influence of factors, such as motivation, when measuring functional capacity of rodents in a translationally meaningful manner. Unmet medical need remains high for treatments that can ameliorate the effect of sleep loss (Bonnet et al., 2005), and in this context discovery of novel pharmacological treatments critically depends upon the translational validity of the animal models employed.

As described in Chapter 3, the PVT in humans and the SRLT in rodents are validated tasks to assess consequences of sleep restriction. However, the precise psychological factors contributing to performance decrement in each species is likely to be complex, including primary cognitive factors, but potentially also motivational factors (i.e., disinterest in test completion despite cognitive ability to do so (Hanlon et al., 2010)). Failure in signal detection that characterise performance decrements in vigilance tasks can reflect the subject’s attentiveness. To supplement outcomes from these behavioural tasks, an objective measure of brain activity that can be obtained from EEG recordings are event related potentials (ERP’s). These ERP’s provide a potential translational biomarker that can be recorded in both humans and animals to measure the course of fluctuations in attention (Saliasi et al., 2013) (Harper et al., 2014) (Ehlers et al., 2014) (Drinkenburg et al., 2015). ERP’s are postsynaptic potentials generated from cortical pyramidal cells close to the cortical surface that can be extracted from the EEG readout by time locking to a stimulus. The resulting ERP

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consists of a waveform that is made up of P, “positive” or N, “negative” peaks or components. These positive and negative peaks are thought to be related to specific attentional processes. Early components such as P1 and N1 occurring within 100ms relate more to sensory and perceptual processing, the physical properties of the stimulus whereas later components such as P3 (300ms), are more associated with evaluation of the stimulus and are deemed more “cognitive” (Woodman, 2010, Sur and Sinha, 2009). A response to a stimulus produces a distinct waveform whereby the amplitude and latency of the waveform is relative to the processing of the signal and hence decreases in attention and response to a stimulus corresponds to changes in the waveform (Luck et al., 2000). An advantage of ERP’s over imaging studies especially in aspects of attention that operate on the scale of milliseconds is the temporal resolution that can be achieved. A disadvantage of the technique is the low signal to noise ratio and consequently a high number of trials are required to achieve a clear interpretable waveform.

In humans and rodents, the nature of the stimulus used to motivate task performance is different. In most human studies, subjects are intrinsically motivated based on adherence to verbal instruction and receipt of feedback related to performance upon test completion. In rodent studies, food or water is used to initiate and reward performance in food or water-restricted subjects. The impact of this water or food deprivation on translational validity of results between species is often overlooked. Results from rodent studies focus on interpretation of data from an attentional perspective, whilst only a few have addressed the potential interaction of the drives of sleep and other motivational factors such as the drive for food or water intake (Davis et al., 2016) (Walker et al., 2011).

Motivation

Motivation is described as a goal-orientated behaviour, guided by a need or desire to obtain a reward. When used within the field of neuroscience motivation is broken down into two categories: “wanting” and “liking”. Wanting is directed towards a reward if positive or conversely away from a negative stimulus, whereas liking relates to seeking out a reward and subsequently enjoying the reward (Robinson et al., 2015).

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Motivation is linked to the dopaminergic pathway, also known to be associated with reward (Salamone et al., 2018). The nucleus accumbens is an area of the brain linked to reward prediction and anticipation (Haber, 2011), and receives dopaminergic inputs from the ventral tegmental area (VTA),via the mesolimbic pathway. The nucleus accumbens is thus implicated in motivation or ‘wanting’ during an effort-based task. Furthermore, instrumental behaviour in rodents with impaired DA transmission is characterised by a move towards low reinforcement/low cost options away from high cost rewards (Salamone et al., 2016). Importantly lesions in the nucleus accumbens in rats do not impair the appetitive taste for sucrose in rats (Berridge and Kringelbach, 2008); therefore, it appears that interference with dopamine transmission does not remove the primary reward value of food i.e., ’liking’.

Further evidence for the role of dopamine in motivation is shown by the effect of amphetamine known to promote dopamine release that increases the break point in a progressive ratio self-reinforcement schedule (Hailwood et al., 2018). Subjects were willing to go to greater lengths (e.g., press a lever more times) to obtain a reward. These findings support the view that the influence of the dopaminergic system on motivation is specifically linked to the willingness to exert effort for a particular reward, and the influence of dopamine increases with the level of exertion required (Hamid et al., 2016). These findings are an important consideration when using rodent operant assays that utilise food as a reward and may influence motivation.

Assays for measuring motivation in rodents

Three types of assays are routinely used to assess motivation in laboratory animals, variable interval (VI) schedules, progressive ratio (PR) and concurrent fixed ratio (CFR5). A VI schedule is an operant conditioning schedule of reinforcement where the response is rewarded after an unpredictable amount of time has passed. It reflects both motor and motivational behaviour towards a food reward (Brackney et al., 2011). Some studies have investigated motivation using a variable interval assay in conjunction with a 96-h disk-over-water sleep restriction protocol in rodents. A VI30 (30-sec variable interval between response and reward) and a VI15 (15-sec) schedules showed that effects of sleep restriction were only seen with the longer interval schedules (VI30) and therefore concluded that reinforcer’s availability moderates the effects of sleep restriction

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(Kirby and Kennedy, 2003). This suggests an interaction between motivation for food reward and the performance effects of insufficient sleep.

In PR schedules, the number of responses that the subject needs to make in order to obtain one reward pellet increases after each reinforcement. The main measure of performance is termed ‘break point’, i.e., the number of lever presses the rat made (final ratio) for a reward before giving up. Break point is presumed to reflect a situation where the “anticipated” effort required to obtain the next reinforcer is perceived to be too great and thereby the animal stops responding (Der-Avakian and Markou, 2012). Using the disk-over-water sleep restriction protocol for 120-h, the effects on “break-point” were not apparent until a minimum of 48-h sleep restriction (Hanlon et al., 2005). This implies that motivation for a food reward can override the effect of sleep restriction to a certain degree. The advantage of the progressive ratio (PR) task to study motivation is task reproducibility and the ease to perform and implement this task. However, it remains unclear why animals stop pressing for pellets and it is unclear whether this is due to exhaustion or decreased motivation.

A third and more specific task with regard to motivation in rodents is the Concurrent Fixed Ratio 5 task (CFR5) originally developed by Salamone in 1991. In this task, rats are presented with a choice between a readily available chow reward (i.e., low-value/low-effort) and a sugar pellet reward, which is only available after five lever presses (i.e., high-value/high-effort). Healthy rats generally prefer the high effort option, eating less of the low value reward even though it is readily available (Salamone, 1991). This task assesses willingness to exert effort for a reward based on the value of a stimulus relative to the cost or effort of obtaining it. In the context of sleep restriction, one might hypothesise that energy resources may be comprised and hence there would be deficit in effort-based tasks.

Motivation in rat sleep restriction studies

Feeding has been recognised as an important cue in rat studies, however few studies investigate the specific interaction between feeding and motivation on performance. In the context of entrainment, it has been shown that food restricted rats anticipated the cue to food as indexed by increased body temperature and locomotor activity approximately 1 hour prior to food was

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made available (Mendoza et al., 2005) and before regular feedings of midday sucrose solution presented 8 hours before daily feeding (Pecoraro et al., 2002). Studies investigating the effect of food motivation in the 5 choice serial reaction task (5CSRT) showed that anticipation of daily feeding in food-restricted rats can reduce behavioural motivation in instrumental tasks and shift circadian rhythms (Cordova et al., 2006).Using a VI schedule of VI30 and a VI15, as a motivational assay sleep deprivation, (a 96-h exposure to the pedestal over-water REM sleep deprivation method), only showed an effect in the longer interval schedules (VI30). This finding suggested that reinforcer availability moderates the effects of sleep deprivation which implies that food deprivation and sleep deprivation do interact (Kirby and Kennedy, 2003). This is in line with the hypothesis of a shift in resource allocation, whereby the need to sleep overrides the desire for food rather than an effect on the reward value of the food. Using the same REM sleep deprivation method for 120 hours in ad libitum-fed rats, no effect was observed on “break-point” in the progressive ratio task until 48 hours of sleep deprivation. Initial rates were comparable between the controls and sleep-deprived rats indicating a decreased motivation rather than aptitude or memory for the task (Hanlon et al., 2010). Whilst this study suggests a decrease in motivation to complete the task for a food reward following sleep deprivation, the length of sleep deprivation required to see an effect raises the question at which level of sleep deprivation does the animal change its resource allocation allowing sleep deprivation to out compete the desire for the food reward (Kennedy, 2002).

Effect of motivators in human sleep deprivation studies

In human subjects, additional incentives are often unnecessary to perform tasks such as PVT. In sleep deprivation studies , feedback of rated results from previous trials have been shown to enhance performance in a choice reaction task (Steyvers and Gaillard, 1993). This suggests that feedback on performance is sufficient to mobilise effort. In some experiments increased motivators such as monetary incentives have been used.

Studies including incentives have been used to assess performance using the PVT. For example, the effects of additional incentives have been investigated in an adapted motivated PVT task, in which after a baseline trial, participants are

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given two further trials with a monetary reward to assess whether performance can be increased beyond baseline levels. Using both a low and high reward value lapses, latencies and time on task parameters were all improved (Kurzban et al., 2013) (Massar et al., 2016). One theory that has been proposed is that performance is related to an allocation of resources (Thomson et al., 2015), where the cost-benefit is determined by the subject and the cost of performance is analysed with respect to the value of the reward. In an auditory vigilance task using monetary incentives, there is a point when sleep requirement becomes so great that the incentive is no longer sufficient for the subject to reallocate resource to the task (Horne and Pettitt, 1985).

Sleep deprivation has been linked to increased risk-taking thought to be due to an increase in the salience of the reward stimuli. Following one night of acute sleep deprivation, sleep-deprived individuals were willing to make riskier decisions to optimize gains on gambling tasks (Mckenna et al., 2007). Prefrontal cortical networks are most affected by sleep loss, and these networks are also integral to decision making and emotion regulation (Goel et al., 2009). In addition, the ventral striatum is most activated when individuals anticipate receiving money (Ernst et al., 2004), (Rademacher et al., 2013), or rewards such as praise (Kirsch et al., 2003). In a gambling task, reward-related activity in the striatum can be influenced by sleep loss (Venkatraman et al., 2007) and following only one night of sleep deprivation, subjects showed significantly elevated ventral striatum activity to monetary reward (Mullin et al., 2013).

These effects in human studies highlight the importance of motivation during periods of sleep deprivation. They demonstrate that the cognitive consequences of sleep loss can indeed be overridden by the introduction of motivators. This could prove an important consideration within the context of pre-clinical rodent studies that routinely use either food or water as a motivator to perform in operant tasks.

Chapter aims

To gain further insights into the competition between sleep need and other motivational drives, the overall aim of this chapter was to assess the effects of food regimen/hunger (i.e., ad libitum-fed vs. restricted food condition) on performance alterations induced by sleep restriction.

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We first assessed the impact of time of day on SRLT performance in the absence of sleep restriction with both feeding regimens to assess whether time of day has an effect on performance in rat. Then, using the SRLT to assess simple reaction times as an index of attention and the PR task using the number of responses made to obtain a palatable food reward as an index of motivation, we assessed the effects of feeding regimen on performance following an 11-h period of sleep restriction. In addition, brain activity during such behavioural tasks may provide an insight into sensory, motor and cognitive processes (Haubert et al., 2018). Thus, EEG/EMG recordings, as well as event related potentials (ERPs) to the stimulus light (imperative cue), were used to investigate behavioural outcomes from the SRLT following sleep restriction in ad-libitum fed and restricted food rats. We reasoned that ERPs may serve to bridge the gap between physiology and behaviour with an expected slowing of cognitive processes in response to sleep restriction. Sleep restriction in this chapter was achieved using the Weibull sleep restriction protocol as described in Chapter 3. We also made use of the CFR5 task to assess any changes in value of the sugar pellet food reward used in operant tasks, with and without sleep restriction.

Part of this Chapter was included in the following publication:

Sally Loomis*, Andrew McCarthy*, Derk-Jan Dijk, Gary Gilmour, Raphaelle

Winsky-Sommerer.

Food restriction induces functional resilience to sleep restriction in rats.

Submitted to Sleep October 2019. * Contributed equally to this work.

4.2 Methods

4.2.1 Subjects and housing conditions

Adult male Wistar rats (Charles River Laboratories, UK) were singly housed during the sleep

restriction studies and group housed at all other times, with a stable ambient temperature (21°C)

and a 12-hour light/dark cycle. Food restricted rats were maintained at 85% of their free-feeding

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body weight and weights were monitored weekly throughout the study. All rats were given ad

libitum water access.

4.2.2 Surgical procedures

Rats (approximately 270-300 g at time of surgery, Charles River Laboratories, Margate, UK) were implanted with electrodes for long-term EEG, ERP and EMG recordings (see Chapter 2 section 2.2).

4.2.3 Sleep restriction protocols

Sleep restriction, used for SRLT studies, was induced as described in Chapter 2, section 2.3 using the Weibull sleep restriction protocol but with EEG implanted animals to maintain consistency with the non-biofeedback sleep restriction used during the other behavioural tests within this chapter. Seven days prior to study start, all subjects were sleep restricted for 5-h using the designated sleep restriction protocol to habituate animals to the procedure. Subsequently, a crossover study design carried out over 2 weeks was conducted where each animal received both treatments (ad libitum and food restricted). Sleep-wake variables were recorded during the 11-h sleep restriction period (ZT0-ZT11), performance of the Simple Response Latency task and also during the subsequent 5-h recovery period. Non-biofeedback sleep restriction used in the PR and CFR5 studies was induced as described in Chapter 2, section 2.3, using the same Weibull method.

4.2.4 Behavioural tasks

Simple Response Latency task

EEG/EMG-implanted rats were used as subjects (n=32), were used. SRLT studies were run as per standard protocol described in Chapter 2, section 2.7. During training, all rats were food restricted. When rats were fully trained on the task, they were assigned to a feeding treatment (i.e., ad libitum or food restricted).

A separate cohort of rats (n = 32) were subjected to 30-min standard SRLT testing as described in Chapter 2, section 2.7., at 6 time points across the 24-h light-dark cycle, i.e., ZT0, ZT3, ZT8, ZT11, ZT16, ZT20. They were tested

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pseudo-randomly 3 times at each time point over a two-week period (Table 4.1). Food-restricted rats were assigned random feeding times following testing to account for entrainment of feeding times. At the start of testing animals weighed: 460±7.5 g (mean ± SEM).

Test Time 7 AM 10AM 3PM 6PM 11PM 3AM

ZT Time ZT0 ZT3 ZT8 ZT11 ZT16 ZT20

Day 0

Day 1 X X

Day 2 X

Day 3 X

Day 4 X X X

Day 5 X X

Day 6

Day 7

Day 8 X

Day 9 X X X

Day 10 X

Day 11 X X

Day 12 X X

Table 4.1 Time of day testing schedule. Animals were tested in the SRLT at the time points shown above (denoted by an “X”). Boxes highlighted in blue denote when animals

in the food restricted condition were fed (these were randomised across the days to account for feeding entrainment). Feeding schedules complied with Home Office

regulations of one feeding episode per day. Weekend days are highlighted in grey – animals were not tested on these days.

Progressive Ratio (PR) study

Rats (n = 32) used for PR studies were not implanted for EEG/EMG measurements. They were subjected to 30-min standard PR testing as described

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in Chapter 2, section 2.7. Once a stable behavioural baseline was achieved, animals were randomly divided into two groups and ascribed a designated feeding regimen. On test days, rats were transferred to sleep restriction chambers where they were individually housed for the duration of the 11-h sleep restriction period. The assigned feeding regimen was maintained during this period. The sleep restriction protocol used was a non-biofeedback, Weibull sleep restriction. Rats were tested immediately after the sleep restriction period at ZT-11 in the PR task. At the time of testing animals weighed 442±5g (mean ± SEM).

Concurrent Fixed Ratio 5 studies

A separate cohort of 64 rats weighing 250-370g were food restricted at the beginning of the study. Food restriction was maintained with supplemental chow feeding and ad libitum water access.

To test the effect of satiety in the CFR5 assay, rats were randomly assigned to food restriction or ad libitum feeding (n=16 per group). The fed ad libitum group were given access to unlimited chow the night before testing. The food-restricted group were used as controls and performed the task as trained. After 24 hours of free-feeding, rats underwent the CFR5 task and were returned to food restriction after task completion.

To test the effects of reward availability, rats were subjected to standard CFR5 testing as described in Chapter 2, section 2.7. Once a stable behavioural baseline was achieved, animals were randomly divided into 2 groups. Group 1 (n=15) was randomly assigned to receive sugar pellets (15-20 g) instead of chow pellets, freely available in the operant box. Group 2 (n=15) performed the task as normal and were used as a control group. The CFR5 task was run as normal and the remaining chow/sugar pellets (including spillage) were collected and weighed.

To test the effect of sleep restriction, rats trained in the CFR5 task were randomly assigned to a no sleep restriction or an 11-h sleep restriction conducted between ZT0 and ZT11 (n=16 per group). Immediately following sleep restriction, rats performed the CFR5 task. Animals were tested on the same procedure 24 hours later to allow recovery from sleep restriction.

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4.2.5 Data and statistical analyses

Statistical analyses for SRLT, sleep-wake variables and ERPs were performed using the SAS (version 9.4, SAS Institute, Inc., Cary, NC) software package. SRLT outcome variables were analysed using the mixed effect procedure whereby a repeated measures analysis of covariance (rm ANCOVA) was performed. Food restriction status and trial Day (baseline, test day, recovery) were fixed effects and the corresponding measures in the 24-h baseline acted as a covariate. As the study was a crossover design and treatments were administered at different dates, date was also included in the model. Planned comparisons were conducted between treatment groups separately on baseline, test day and recovery days with Bonferonni adjustments made for multiple comparisons. For ERP analysis throughout the SRLT, stimulus cues were time-locked to the EEG recording with the use of Transistor-Transistor Logic (TTL) signals supplied from the MedPC hardware (Med Associates, UK) into the EEG data acquisition hardware (DNR-12 RACKtangle. United Electronic Industries, USA). Grand average event related potentials were calculated offline using python 3.6. Data were recorded from 50ms immediately prior to the stimulus to 1000 milliseconds after the stimulus. ERPs were grouped according to sleep/wake staging and only ERPs where the animal was confirmed to be awake by EEG measure were included in the analysis. The amplitude and latency of the ERP components were identified manually as local maxima/minima occurring between 50-150, 110-210 and 160-360 ms post tone stimulus for P1, N1 and P2 respectively.

Correlations between variables were examined by linear regression, with the percentage of time spent awake during the SRLT as a predictor variable with trials, premature responses, omissions and latency as dependent variables. To account for the impact of group variability, correlation data are presented as residual values to partial out the effect of treatment group.

PR data were analysed using Statistica version 13.2 (Statsoft Ltd, Bedford, UK). PR parameters were analysed using a one-way repeated measure ANOVA to compare fed ad libitum and restricted food groups. Effect sizes for the SRLT and PR data were calculated using Cohen’s d. CFR5 results were analysed using a one-way repeated measure ANOVA, comparing the test day results to the previous and following day’s performance. For satiety experiment, the between

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factor was the feeding regimen (i.e., food restriction or ad libitum) and repeated factor was day on which task was performed i.e., (pre-test, test and post-test). For reward availability, the between factor was low-effort reward (i.e., chow, sugar pellets) and the repeated factor was day (i.e., pre-test and test). For sleep restriction study, the between factor was group (i.e., 10-h sleep restriction vs. no sleep restriction) and the repeated factor was day on which task was performed (i.e., pre-test day, test day, post-test day). Following a significant main effect a Fisher’s Least Significant Difference (LSD) post-hoc analysis was conducted.

4.3 Results

4.3.1 Effect of time of day and feeding status on SRLT performance

Rodent behavioural studies may be confounded in two simple ways. Firstly, studies in rodents routinely use a food restriction protocol to enhance performance particularly in operant tasks, secondly the majority of rodent testing occurs during the light phase which corresponds to the animal’s inactive phase. Prior to assessing the impact of sleep restriction on the SRLT we tested 2 cohorts of rats, one cohort ad libitum fed and one cohort food restricted across a 24-h day to assess the impact of time of day and feeding status (i.e. condition) on performance. Rats were tested every 4 hours throughout the 24-h light-dark cycle (ZT0-ZT24), with three time points during the light phase and three in the dark phase (please refer to Table 4.1 above in Methods section 4.2.4). The data showed significant effects of feeding status for premature responding only such that food restricted rats show increased premature responses particularly during the dark (active) phase (‘Feeding condition’ p = 0.031; interaction ‘feeding condition’ x ‘time’:

p=0.001; Figure 4.1c). A significant interaction between feeding regime and time was only seen in the SRLT parameters, trials and premature responding (Figure 4.1a & c). This interaction reflected feeding status may impact outcomes from the SRLT depending on when during the circadian cycle behavioural testing occurs. There was a tendency to decrease the number of omissions (interaction ‘feeding

condition’ x ‘time’: p=0.062: Figure 4.1b) and increase median response latency (interaction ‘feeding condition’ x ‘time’: p=0.090: Figure 4.1d) such that ad libitum fed rats reaction times are slower at specific points across the 24-h circadian time course indicating time of day of testing should be considered can play a role in behavioural outcomes that should be considered. The effect of time of day only

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reached significance for median response latency in the ad libitum fed group (time = p = 0.002).

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Condition p = 0.891Time p =0.474Condition * Time p = 0.001

Condition p = 0.06Time p = 0.073Condition * Time p = 0.062

Condition p = 0.031Time p = 0.057Condition * Time p = 0.026

Condition p = 0.303 Time p = 0.002Condition * Time p = 0.09

Figure 4.1. Effect of time of day on SRLT performance in food restricted and ad libitum fed rats. (a) Number of trials completed, (b) number of omissions, (c)

Number of premature responses, (d) median response latency assessed a 6 time points across the 24-h light-dark cycle (data are double plotted). The x-axis depicts Zeitgeber time (ZT) at which SRLT was performed, with n = 16 rats tested 3 times for each time

point (ad libitum fed rats – red circles; food restricted rats – blue circles). Data are shown as an overall average taken of the 3 test times. Dark bars along the x-axis indicate the

12-h dark (“lights off”) period. Variables were analysed using a repeated-measures mixed-effect model with feeding condition, time of day and time by feeding condition as

fixed effects and animal as a random effect.

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4.3.2 Effect of feeding regimen on sleep, wake, physiological parameters and SRLT

following an 11-h “Weibull” Biofeedback sleep restriction

Effect of feeding status on sleep-wake parameters and the EEG

On baseline day, during the light (inactive) period ad libitum fed rats achieved 354 ± 10 mins of sleep. This was similar to the restricted food rats which slept for 335 ± 6 mins. Analyses of the EEG recordings on test day (not shown) confirm that rats fed ad libitum and the food-restricted group underwent a similar amount of sleep loss during the 11-h weibull induced sleep restriction period (p=0.1016). Total amount of sleep obtained during the 11-h sleep restriction period did not vary significantly between food restricted rats, sleeping for 19% of the time during the 11-h sleep restriction, and ad libitum fed rats achieving 21% sleep (Figure 4.2a). The majority of sleep obtained was in NREM sleep, while REM sleep represented 0.5% and 0.2% of total sleep time, in food-restricted and ad libitum fed rats respectively (Figure 4.2 b & c). With regard to sleep bouts, no significant differences were found in their number or duration between the two groups (Figure 4.2 d-e).

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Figure 4.2: Effects of feeding regimen on sleep during an 11-h bio-feedback sleep restriction protocol. (a) Total sleep time; (b) NREM sleep duration (c) REM sleep

duration (d) Total number of sleep bouts; (e) Average sleep bout length. Data are represented as mean ± SEM. Food restricted rats (blue bars, n= 13), ad libitum fed rats

(red bars, n= 10)

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The time course of sleep was analysed for 24-h prior to sleep restriction, during the 11-h sleep restriction and subsequently for a 13-h recovery period. As expected, both groups showed an increase in wakefulness during the sleep restriction period and a subsequent increase in sleep during the 13-h recovery phase (Figure 4.3). During these periods, no significant differences were observed between the groups. During the SRLT and for the first 5 hours following sleep restriction food restricted rats maintained significantly more wakefulness than ad libitum fed rats (Figure 4.4). Sleep during the recovery period was also significantly different between the groups, whereby food restricted rats obtained less sleep between 75 and 120min of returning to home chambers, with a significant group (P < 0.001), time (RM ANOVA, F51,1026 = 4.73, P < 0.001) effects and a significant group*time interaction (RM ANOVA, F51,19 = 1.48, P = 0.018).

0 12 24 36 480

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Figure 4.3: Time-course of time spent awake before, during and after the 11-h sleep restriction. The grey box illustrates the 11-sleep restriction period followed by the 40-min Serial Response Latency Task. ad libitum fed group (red circles, n = 10) and

restricted food group (blue circles, n = 13). Baseline data shown within the cross hatching box were not included in the statistical analysis and are shown for illustrative

purposes only. Variables were analysed using a repeated measures ANOVA with feeding condition as fixed effect and animal as random effect. Time spent awake is shown as

percentage per 1 hour time bin.

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Figure 4.4: Time course of wakefulness following the 11-h EEG Biofeedback induced sleep restriction. Main Graph: 5-h time course, encompassing the 40-min Simple Response Latency task, immediately following the 11-h sleep restriction for the

ad libitum fed (red circles, n = 10) and food restricted (blue circles, n = 13) groups. Variables were analysed using a repeated measures ANOVA with feeding condition as fixed effect and animal as random effect. Inset Graph - Wakefulness during the SRLT

task. Wakefulness is shown as percentage per 5 min time bin. Despite the higher time spent in wakefulness in the food-restricted group, quantitative EEG power analyses during the 40-min SRLT showed no differences in the Waking EEG for delta, theta, alpha and beta power between the two feeding regimens (Figure 4.5)

(a)

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Figure 4.5: EEG power density during the 40-min Serial Response Latency Task SRLT. . (a) Waking EEG spectral power analysis in Food Restricted (blue; n=13) and ad libitum fed rats (red; n = 10) . Spectral power data was normalized relative to total EEG

power. Frequency bands are labelled and separated by vertical dotted lines. (b) EEG delta power (0.1-3.9 Hz); (c). Theta power (4.0-8.9 Hz); (d) Alpha power (9.0-11.9 Hz); (e)

Beta power (12.0-20.0 Hz). Graphs (b-d) present data as mean ± SEM. Effect of feeding status on body weight, locomotor activity (LMA) and body temperature

Measurements in locomotor activity and body temperature showed no significant differences between food restricted and ad libitum fed rats during the 11-h sleep restriction (Figure 4.6a-b). These findings support the data shown in section 4.1, where no significant differences were shown in the sleep parameters measured. By contrast, and as expected, ad libitum fed rats showed significantly higher weights compared to food restricted rats on test day (p = <0.001) (Figure 4.6c).

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Figure 4.6: Physiological parameters. Locomotor activity and body temperature

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were measured during the 11-h sleep restriction period. Body Weights were taken the day prior to the sleep restriction test day. Data represented as mean ± SEM.

Effect of feeding manipulations on SRLT parameters

In terms of SRLT performance, food-restricted and ad libitum rats completed a similar number of trials on baseline (Table 4.2; Figure 4.7a). However, food restricted animals were significantly faster to respond, made more premature responses and less omissions compared to ad libitum animals (Table 4.2; Figure 4.7b-d). On test day, after an 11-h sleep restriction protocol, the trial completion rate of ad libitum rats significantly decreased, and the omission rate significantly increased, both relative to baseline performance and to food-restricted group performance on test day (Table 4.2; Figure 4.7a & b). Response latencies and premature response rates remained unchanged relative to baseline day for food-restricted animals on test day. In contrast, SRLT performance on all four measured parameters was not significantly different to performance on baseline day for food-restricted animals. On recovery day, performance of both groups of animals was not significantly different to their relative baseline performances (Figure 4.7a-d). Analyses across the course of the task showed that ad libitum-fed rats performed significantly fewer trials compared to food restricted rats at time points 20 and 30 minutes (Figure 4.7e). Response latencies were not significantly different between the food restricted and ad libitum fed rats across the course of the task (Figure 4.7f). Empirical distribution of response times during the SRLT on test day following the 11-h sleep restriction showed that food-restricted rats have a higher percentage of faster reaction times compared to ad libitum animals (Figure 4.7g).

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Figure 4.7: Effects of feeding status on SRLT performance following 11-h of Biofeedback sleep restriction. (a) Number of trials completed; (b) number of omissions; (c) number of premature responses, (d) median response latencies, on

baseline day, ‘test’ day (i.e., following 11-h sleep restriction) and recovery “post” day after sleep restriction. Blue and red lines refer respectively to food restricted (n = 13)

and ad libitum fed rats (n = 10). Asterisks refer to planned comparisons of feeding regimen conditions within a test session, where *p < 0.05; **p < 0.01; ***p < 0.001.

Carots (^) refer to planned comparisons between baseline day and test day within test sessions, where ^ p < 0.05; ^^ p < 0.01; ^^^ p < 0.001. Hash (#) refer to planned

comparisons between feeding regime on baseline day within test sessions, where # p < 0.05; ## p < 0.01; ### p < 0.001, (e) Time on Task for number of trials; (f) Reaction

time latency on across the 40-min SRLT time course. Asterisks refer to planned comparisons of feeding regimen conditions by 10-min time intervals, where *p < 0.05;

**p < 0.01; ***p < 0.001. Graphs (a-f) show data presented as mean ± SEM. (g) Distribution of response latencies during the SRLT following 11-h of sleep-restriction.

Data are represented as a percentage of total response latencies per 250-ms time bin, ranging from 250-ms to 10,000-ms during 40-min SRLT for ad libitum food (ALF) rats (red

circles, n = 10) and restricted food (RF) rats (blue circles, n = 13). Data during 40-min SRLT on baseline day are shown as dotted lines for ad libitum (ALF) fed rats (red open

circles, n = 10) and restricted food (RF) rats (blue open circles, n = 13).

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Food Restriction

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Trials 53.6, 1.55, p = 0.218 50.6, 15.05, p 0.001 50.6, 4.46, p = 0.016

Omissions 55.8, 26.86, p 0.001 51.1, 15.60, p 0.001 51.1, 6.98, p = 0.002

Prematures 52.0, 78.73, p 0.001 50.2, 0.58, p = 0.562 50.2, 1.31, p = 0.279

Mag Latency 52.1, 60.43, p 0.001 50.5, 5.08, p = 0.010 50.5, 0.61, p = 0.548

Table 4.2: Effects of feeding condition and 11-h sleep restriction on SRLT parameters in adult male Wistar rats. Variables were analysed using an ANCOVA,

DF, F and p-values are shown for each parameter for each condition, food restriction and their interaction.

Effect sizes of SRLT alterations induced by feeding regimen and sleep restriction

Effect sizes were calculated using Cohen’s d for each of the SRLT parameters shown above. In the absence of sleep restriction, the largest effect of the feeding regimen was observed for premature responding and medium effects sizes were seen for omissions and response latency (Figure 4.8a). The number of trials completed were less affected by the feeding status when rats slept ad libitum (i.e., without sleep restriction). However, when sleep restriction was applied effect sizes for feeding regimen were large for all SLRT parameters (Figure 4.8b). With regard to the effect of sleep restriction, ad libitum fed rats show large effect sizes for numbers of completed trials, omissions, and median latency (Figure 4.8c), whereas for food restricted rats’ number of trials, omissions, median latency and premature responses were less impacted by 11-h sleep restriction, displaying small effect sizes (Figure. 4.8d).

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Figure 4.8: Cohen’s d effect sizes of SRLT parameters. (a) Food regimen effect without sleep restriction (i.e., during baseline). (b) Food regimen effect following sleep

restriction; (c). Sleep restriction effect in ad libitum fed rats. (d). Sleep restriction effect in food restricted rats (n = 10-13). Cohen’s d effect size were computed for numbers of trials, number of omissions, number of premature responses and response latencies.

Dashed lines show small (≥ 0.2), medium (≥ 0.5) or large (≥ 0.8) effect sizes (Maher et al., 2013). ALF: ad libitum-fed; RF: restricted food; SR: sleep restriction.

Event Related Potentials during the SRLT To further explore the effect of food restriction and sleep deprivation, we analysed the event-related responses to the imperative cue, for all successfully completed trials, during the SRLT. The visual evoked response to the imperative cue was altered by the food restriction (Figure 4.9a-d). At baseline, significant differences were seen in the amplitude of ERPs of ad libitum and food restricted groups at time points between 270-292.5 ms and 302.5-310 ms (Figure 4.9a). In both groups, the waveform exhibited two positive components (P1 and P2) and a single negative component (N1). In addition, the ad libitum group displayed a N2 component (Fig. 4.9a). After sleep restriction, food restricted rats displayed ERPs that were more similar to their baseline compared to the ad libitum-fed group, with higher amplitude in the N1 and P2 components in the food restricted group (Fig. 7b-d). A repeated measures ANOVA showed significant differences between

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feeding status between 162-171ms and 325-347ms. The negative peak between 162-171ms was greater in the food restricted animals whilst the increase between 325-347ms was suggestive of a more sustained increase of the second positive wave component. However, manual peak detection analysis showed no differences between the two groups on baseline day but a significant decrease in the latency of the P2 component and a significant increase in amplitude of the P1 component (Figure 4.9b-c) in the ad libitum rats compared to food restricted.

(a) Baseline – No Sleep RestrictionFoodRestrictedvs.AdLibitum

(b) Ad Libitum Fed RatsTestDay(SleepRestriction)vs.Baseline(NoSleepRestriction)

(d) Test Day– Sleep RestrictionFoodRestrictedvs.AdLibitum

(c) Food RestrictedRatsTestDay(SleepRestriction)vs.Baseline(NoSleepRestriction)

Figure 4.9. Effect of Food Restricted and Ad Libitum Feeding on Evoked Response Potential to the magazine light (imperative cue) during the SRLT.

Graph (a) shows the effect of feeding status in 11-h sleep deprived rats on event related potentials in response to the magazine light on baseline day, Graph (b) shows the effect of 11-h sleep restriction on event related potentials in response to the magazine light on

test day in ad libitum fed rats, Graph (c) shows the effect of 11-h sleep restriction on event related potentials in response to the magazine light on test day in food restricted

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fed rats, Graph (d) shows the effect of feeding status in 11-h sleep deprived rats on event related potentials in response to the magazine light on test day, Graph (e & f) represents

the latency and amplitude results following peak detection analysis on baseline day (mean ± SEM), Graphs (g & h) represents the latency and amplitude results following

peak detection analysis on test day (mean ± SEM). Ad libitum fed rats (red; n = 10) and food restricted rats (blue; n=13).

Correlation of time awake with performance assessed in the SRLT

Figure 4.10 depicts regression slopes for each SRLT parameter as a function of time awake during the 40-minute task. Number of trials completed exhibited a significant relationship with time awake, with a linear increase in trial number as a function of time awake. The number of omissions displayed a significant linear decrease with time awake (r2 = 0.6211). For the median reaction time, the more time spent awake during the task correlated with the slowest responses (r2 = 0.3409). Premature responses did not show any linear relationship between time spent awake during the SRLT and the number of premature responses made.

P<0.0001 P<0.0001

P<0.0034P<0.2336

Figure 4.10: Correlations between time spent awake during the SRLT and performance parameters. (a). Number of trials completed; (b). Number of omissions; (c). Number of premature responses; (d). Response latencies. ad libitum food rats (red

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circles, n=10) and restricted food rats (blue circles, n = 13). Correlation data are presented as residual values to partial out the effect of treatment group.

4.3.3 Effect of feeding regimen on motivation assessed by the progressive ratio task

following 11-h sleep restriction

During the Progressive Ratio task, no significant change in breakpoint was seen following 11-h sleep restriction, for either ad libitum or food restricted rats. However, breakpoint was significantly higher in the food-restricted group regardless of sleep condition (baseline F= 1,15 = 27.37, p =<0.001), (sleep restriction F= 1,20 = 17.62, p =<0.001), (Figure 4.11a). As for SRLT, effect sizes (Cohen’s d) were computed for breakpoint (Figure 4.11b). The effect of feeding regimen (ad libitum vs. food restricted) on breakpoint were large irrespective of the sleep condition, i.e., when the PR task was performed on baseline day or following sleep restriction. By contrast, the effect of sleep restriction on breakpoint was small, both in the ad libitum-fed group and food restricted rats. Food restricted rats had an overall faster press rate than ad libitum fed rats, and this was more significant during the earlier components. In addition, food restricted rats also continued to press at components beyond where ad libitum rats had ceased pressing (Figure4.10c&d). Despite no effect of sleep restriction on breakpoint shown in the PR task, press rate was significantly decreased at components 3, 5 and 8 in the food restricted group with sleep restriction but there were no significant effects of sleep restriction in press rate in the ad libitum group. A SRLT ad libitum fed group (n =12) was included in the progressive ratio experiment to represent a positive control for the sleep restricted group on test day. Sleep restriction showed a significant decrease in trials (p<0.01), a trend in decrease in omissions (p = 0.056) and a small but significant increase in premature responding (p<0.05). Median latency was not significantly different between sleep restricted and non-sleep restricted groups (Data not shown).

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food-restricted rats (blue) and rats fed ad libitum (red) under baseline condition or following an 11-h sleep restriction (“test”). Data represented as mean ± SEM. Asterisks

refer to planned comparisons of food restricted vs. ad Libitum treatment within a test session or for individual components within each study, where *p < 0.05; **p < 0.01; ***p < 0.001. (b) Effect size of feeding regimen (AL ad libitum; FR food restricted) for

breakpoint during PR task performed during baseline or after sleep restriction and effect size of sleep restriction in AL and FR rats ( n = 7-11). Effect sizes were computed as

Cohen’s d and highlighted as small (≥ 0.2), medium (≥ 0.5) or large (≥ 0.8) (Maher et al., 2013). (c) and (d) Press rate for food-restricted rats and rats fed ad libitum during

baseline (NSR: no sleep restriction – blue lines) and following 11-h sleep restriction (SR – red lines). Data represented as mean ± SEM.

4.3.4 Effect of feeding regimen and sleep restriction in the CFR5 assay.

Effect of feeding regimen on satiety Rats were tested once they had reached stable levels of lever presses and chow consumption whilst on food restriction. During baseline assessments, rats pressed for pellets on average 895 ±100 times over 30 minutes and consumed

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5.6 ± 0.5 g of freely available chow. On test day, i.e., after 24-h of free feeding with chow, the number of lever presses in ad libitum fed rats was significantly decreased compared to the food-deprived control group (Fig. 4.12a). Chow consumption also significantly decreased in the ad libitum fed group compared to food-deprived controls (Fig. 4.12b). On the post-test day, 24-h after the ad libitum-fed group were returned to a food restriction regimen, rats were retested on the CFR5 task. While the number of lever presses were not significantly different to their pre-test day performance, the chow consumption remained significantly lower than pre-test day, and significantly different compared to the food-deprived group. Rats that were maintained under the food restriction regimen did not show significant changes in either parameter on the test and post-test days compared to their pre-test performance.

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Figure 4.12. Effect of feeding regimen to satiety assessed during the CFR5 task. Rats were either kept on food restriction (n=15) or fed ab libitum (n=15) for 24-h prior to testing. Behavioural performance in the CFR5 task was measured during a 24-h

baseline (pre-test), on test day and 24-h after the food regimen was changed (post-test). (a) Number of lever presses during the CFR5 task (Mean ± SEM). (b) Chow consumed during the CFR5 task (Mean ± SEM). ***p<0.001 compared to food restricted group,

### p<0.001 compared to the baseline ‘pre-test’ day.

Effect of sugar availability in the CFR5 assay On the pre-test day, both treatment groups (chow-fed and pellet-fed) were presented, as usual with readily available chow pellets during the CFR5 task.

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Both groups performed at a similar level on the pre-test day (Fig.4-13). On test day, the chow group were presented with readily available chow pellets and the pellet group were presented with readily available sugar pellets. The pellet-fed group demonstrated a significant 10-fold decrease in lever presses for sugar pellets compared to the chow control group, while significantly increasing their free pellet consumption by 228%. The chow-fed group served as controls and did not differ significantly from their performance on the previous (baseline) day.

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Figure 4.13. Effect of sugar pellet availability on performance in the CFR5 task. Performance was measured on both the pre (i.e., baseline) and test day, where the CFR5 task was run as usual on pre day. On test day, half of the rats had freely available chow

substituted for freely available pellets (n=15) and half had freely available chow as normal (n=15). (a) Number of lever presses during the CFR5 task. (b) Amount of chow

consumed during the CFR5 task (Mean ± SEM). ***p<0.001 compared to food restricted group, ### p<0.001 compared to the baseline ‘pre-test’ day.

Effect of sleep restriction in the CFR5 assay Prior to sleep restriction the non-sleep restricted (Pre NSR) control group and the 11-h sleep-restricted (Pre SR) group showed no significant difference in lever pressing or chow consumption in the CFR5 task (fig. 4.14). The performance of NSR rats did not significantly change from the baseline levels during the test (i.e. following sleep restriction?) and post-test days for either lever presses or chow consumption. By contrast, for the SR group, lever pressing for sugar pellets decreased during the 30minute CFR5 task, suggesting a reduced willingness to exert an effort for a higher value reward following 11-h of sleep restriction,

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compared to the control rats. Chow consumption did not significantly differ between the two groups. On post day, after the SR group had recovered from sleep restriction, a significant increase in lever pressing was observed compared to the test day with no effect on chow consumption (Fig. 4.14).

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Figure 4.14. Effect of sleep restriction on CFR5 task. Behavioural performance on the CFR5 task was measured 24h before (Pre) and 24 after (Post) test day. On test day,

food restricted rats (n=32) were sleep restricted for 11-hours (Test SR), or not sleep restricted (Test NSR). (a) Mean ± SEM number of lever presses during CFR5 task. (b) Mean ± SEM grams of chow consumed during the CFR5 task (*p<0.05 significantly different from NSR; ^ p<0.05 or ^^p<0.01 significantly different from test day).

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4.4 Discussion

The overall purpose of this chapter was to evaluate the interaction of motivators, such as food reward and sustained attention deficits following sleep restriction. We found that sleep pressure and hunger drive have different main effects on baseline performance assessed in a sustained attention task and in food-rewarded motivational tasks in rats. Importantly, sleep pressure and hunger drive significantly interact to influence observed cognitive performance. Food restricted rats displayed resilience to the detrimental effects of sleep restriction compared to ad libitum fed rats, which may have significant implications for how translational research should be interpreted in this context.

Considering feeding status alone on baseline day (i.e., a comparison of ad libitum versus food restricted rats in a non-sleep deprived state), showed that both groups completed the same number of trials. However, ad libitum fed animals had increased omissions and response latencies and a decreased number of premature responses, as well reduced incentive to perform a purely motivational task, as shown by decreased breakpoints in the PR task. Thus, before investigating the effects of sleep pressure, these preliminary findings suggest that less hungry rats are less motivated to perform in behavioural operant tasks using food reward. However, much larger effects of hunger/feeding status on these behavioural tasks emerged when sleep pressure was experimentally modulated.

Circadian testing times on sustained attention

Few rodent studies have examined how feeding regime and time of feeding may interact with outcomes in operant behaviour in rodents. In this context, we evaluated the effects of circadian rhythmicity (i.e., time of day testing) across a 24-h period on rats previously trained in the SRLT and fed either on an ad libitum or food restricted regime. Results suggested that feeding status impacted number of omissions and reaction time. Furthermore, the time of day at which sustained attention was tested impacted performance for premature responding, omissions and reaction time and hence feeding by time interaction was only present for omissions and response latency. Trial numbers were not affected by either feeding regime or time of testing. Overall these significant effects of time of day are in contrast to a study using a set-shifting paradigm which showed that

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whilst rats took longer to acquire the task during their night phase, once trained there was no difference in their cognitive flexibility whether tested during the day or the night. It should be noted that these rats were food restricted (Robertson et al., 2017). A link between cognition and circadian rhythm was also shown in hamsters subjected to circadian arrhythmia and tested in a T-maze (a spatial working memory task) and object recognition task (a working memory task). In this study, hamsters without the circadian rhythm did not perform as well as those hamsters with a normal circadian rhythm (Ruby et al., 2013).In humans, it is also well documented that sleepiness and cognitive functioning are affected by circadian rhythms and homeostatic pressure (Wright Jr et al., 2012) (Santhi et al., 2016) (Wyatt et al., 1999). This becomes accentuated under situations of high sleep pressure i.e. in the face of total sleep deprivation (Maire et al., 2018). This study highlighted the functional relevance of circadian and homeostatic regulation of neurobehavioral performance at the cerebral level and is in support of a further study reporting a local modulation of human brain responses by circadian rhythmicity and sleep debt (Muto et al., 2016). In a separate study looking at shift workers with known circadian misalignment, there was a decrement in PVT outcomes, suggesting circadian misalignment does lead to a dramatical deterioration of cognitive performance in chronic shift workers. This decrease in cognitive ability could have important safety implications, in occupations requiring chronic night shift work where cognitive resources are crucial in appropriate and critical decision making (Chellappa et al., 2019). Therefore, further understanding of the relationship between circadian misalignment and deficits in cognition, in rodents and humans, would help to elucidate those underlying mechanisms providing an insight into a means to overcome these deficits.

Sleep restriction and feeding regimen on sustained attention

To better understand how feeding interacts with sleep restriction in an attention task the previously validated Weibull sleep restriction protocol described in Chapter 3 was applied to EEG implanted SRLT trained rats. Both ad libitum and food restricted animals experienced the sleep restriction process in a similar manner, i.e. the amount of sleep lost during the 11-h restriction was not significantly different between the two groups. EEG analyses during task performance however confirmed that ad libitum fed rats were significantly sleepier relative to food-restricted rats and displayed more propensity for

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recovery sleep in the period subsequent to testing. Results from the SRLT showed sleep restricted ad libitum fed rats exhibited a decrease in the number of trials and an increased number of response omissions compared to baseline day. In contrast, food restricted animals completed the SRLT task on test day following sleep restriction in a manner that was not significantly different to their baseline performance. This might suggest a functional resilience to the sleep restriction protocol in this test context at least. All effects of sleep restriction on SRLT performance had disappeared by the next day. It is accepted that during both acquisition and maintenance of most operant tasks training is impaired in sleep-restricted rats (Hanlon et al., 2005). The increase in response latency and number of omissions seen in our results are consistent with a similar study in ad libitum fed rats using 24-h acute sleep restriction (Christie et al., 2008) and a further study using a chronic sleep restriction paradigm where increases in latency and omissions were evident (Deurveilher et al., 2015). Interestingly this study used a 100-h and a 148-h protocol but deficits in the rat PVT were only apparent at 28-h and by 52-h PVT outcomes had returned to baseline levels. This indicates a possible adaptation to sleep restriction. Moreover, this particular study had employed a 3-h on/1-h off schedule to provide a means of chronic sleep restriction that accounts for the polyphasic sleep patterns in a rat. Important to note, however, in both studies rats were water deprived for task motivation not food deprived. Early studies have suggested that water availability in rats is not a potent entraining cue for circadian rhythms unlike food, suggesting that water deprivation is a weaker ‘motivator than food deprivation (Mistlberger, 1992). A previous study reported that 24 h or 5 days of total sleep deprivation affected neither the amount of water intake nor motivation to perform for water reward, indicating that water restriction is well suited for motivating rats to perform the rPVT (Christie 2010). A possible caveat to this is that despite dry food (chow) availability the lack of water to supplement the food may have caused rats to consume most of their food around the time of daily water availability. This paradigm could therefore have shifted food-entrainable circadian mechanisms (Deurveilher et al., 2015). Equally, in line with our results, EEG analyses showed more sleepiness during recovery (Christie et al., 2008). Another study using water restriction to motivate performance in a PVT-like task following sleep restriction, found less pronounced effects of sleep restriction on time-on-task measures than would normally be expected using food reward (Oonk et al., 2015)

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In addressing the resilience to sleep restriction as exhibited by the food restricted animals our results are inconsistent with a study using the 5CSRT in food restricted rats sleep restricted for 4, 7 and 10 hours by gentle handing technique. The results of that study showed a pattern similar to those of ad libitum fed rats whereby lapses and response latencies increased (Cordova et al., 2006). A further study used an acute and chronic sleep restriction paradigm induced by means of a slowly rotating drum. Food restricted rats were then tested in a differential reinforcement of low rate responding (DRL) task, an operant task in which rats must withhold lever presses until an imposed delay has passed to receive the reward. When rats were sleep restricted for 20-h per day for 7 days and tested daily it was shown that after the first day (acute sleep restriction) rats had already lost the ability to time their responses correctly (Kamphuis et al., 2017).Event Related Potentials (ERPs) were assessed through EEG measurements and represent EEG at a defined epoch that relates to the onset of sensory stimuli. Early deflection at N100 and P200, reflect activity of primary and secondary sensory cortices. Later deflections at N200 and P300 are linked to cortical association networks signalling information processing and stimulus evaluation. Shorter latencies in the P300 are thought to be indicative of increased cognitive performance, whereas a decrease of P300 amplitude across time on task has been identified as representing a decrease in vigilance (Schmidt 2009). Our study only analysed ERPs related to imperative cues of successfully completed trials, thus by definition all animals were successfully attending to these stimuli and it was clear that food restriction and sleep restriction had qualitatively different effects on ERP waveforms. Following sleep restriction, our ERP measurements, in the presence of adequate food, showed a reduction in the amplitude and increase in latency of the evoked response potential to the imperative cue (magazine light) reflecting the SRLT outcomes. This effect was not present in sleep-deprived food-restricted rats. This is consistent with human ERP data showing subjects, after 40 hours total sleep deprivation, also showed a decrease in amplitude and an increase in latency (Corsi-Cabrera et al., 1996). Given that PR performance was not affected by sleep restriction in ad libitum fed rats, it seems reasonable to suggest that the changes in P2 peak amplitude observed in these animals are likely to be minimally influenced by motivational factors and perhaps results from an increase in attention to the signal. Interestingly, food restriction in sleep restricted animals at least partly restored

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the amplitude of the P2 waveform, and these animals performed the SRLT test at unimpaired levels. These findings are consistent with the concept of hunger arousal, and resonates with human findings showing heightened attention to food cues in hungry subjects (Stockburger et al., 2009). Furthermore, we showed that time spent awake during the task correlated with increased performance for trial number, omissions and reaction latency, and thereby the ability to maintain attention. The ad libitum fed rat dataset is in concordance and extends findings from our previously published work and data presented in Chapter 3 of this thesis showing robust behavioural deficits in the Simple Response Latency task following a non-invasive enforced activity sleep restriction protocol (McCarthy et al., 2017), although as discussed above is in contrast to some published reported data in food restricted animals.

Sleep restriction and feeding regimen on progressive ratio

Decreases in responding during SRLT, especially those seen within time on task effects, may however be due to a motivational decrement rather than an attentional deficit. To date little preclinical work has focussed on the effect of either food restriction or food reward value on the interaction with sleep loss. We used the PR task to further assess these interactions. In our PR studies described above, whilst an overall lowering of baseline breakpoint was observed with ad libitum fed rats compared to food restricted animals, no further effect on breakpoint was observed after 11-h sleep restriction in either group. These results correspond to a study that combined sleep restriction with water deprivation. The rationale to use water, rather than food, was that water (thirst) is not known to be affected by energy homeostasis following sleep restriction (Christie et al., 2010). Similarly, 24-h sleep restriction had no effect on breakpoint in the PR task for water consumption, suggesting that alterations in motivation are unlikely underlying any effects of sleep disruption previously described in water-rewarded rats in an attention task

Reward value and sleep restriction using a CFR5 task

As mentioned above another consideration when utilising food deprivation as motivation for performance in an operant task, is that motivational effects may change depending on the perceived value of the reward available with regard the effort required to obtain that reward. The CFR5 task concurrently offers the rats a low-effort/low-value option, allowing for a more direct investigation into

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effort-based decision making. In the CFR5 task, it is possible to discern why rats stop pressing for sugar pellets, for example, a reduction in willingness to exert effort would present as decreased lever pressing without negatively impacting chow consumption, whereas a reduction in appetite would negatively impact both lever pressing and chow consumption. Furthermore, impairments in motor function would also be discernible, seen as a cessation in both lever pressing and free-feeding. This confers an advantage over the PR task in which it is difficult to determine whether rats stop pressing due to changes in motivation, appetite, or other causes. Therefore, presentation of concurrently available chow makes it possible to single out reasons for altered performance following sleep restriction. In the progressive ratio task following sleep restriction the rats appear to continue to be willing to make the effort of lever pressing required to obtain a reward, however it is not evident whether the rats are pressing for pellets because they are motivated by hunger or by the prospect of a high-value reward. The fact that both ad libitum and food restricted rats have equivalent breakpoints following sleep restriction compared to their baseline days implies this is more due to the high value sugar pellet reward than hunger itself. By providing readily-available chow in the CFR5 task, it is possible to unmask differences in reward value. In experiments using the CFR5 task to assess effort-related choice behaviour, when rats were presented with a choice between a high-value/high-effort reward and a low-value/low-effort reward, they preferred to press for the high value sugar pellets, while consuming relatively less of the freely available chow. Furthermore, feeding to satiety with sugar pellets caused a global decrease in task performance. This suggests the sugar pellets often used in rodent studies are indeed a motivator for completion of rewarded tasks. Following sleep restriction, rats showed decreased willingness to work for a higher value reward, with a significant reduction in lever pressing, but no change in chow consumption. This behavioural shift suggests that sleep restriction does affect feeding behaviour and the motivation to press for a food reward is primarily driven by hunger rather than the value of the reward. Overall the combined results obtained in the PR and CFR5 tasks demonstrate the value of providing a low-effort option during a task assessing motivation.

To place the results of this study in a translational context, a human equivalent of the CFR5 task is the Effort-Expenditure for Rewards Task (EEfRT), in which subjects must choose between completing a high-effort task versus a low-effort task, with the possibility of obtaining monetary rewards of varying value. These

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trials have varying levels of probability, meaning subjects are not guaranteed to win a reward (Treadway and Zald, 2013). In these studies, a significant inverse relationship between anhedonia and willingness to exert effort for rewards has been demonstrated. These studies highlight the importance of the nature of the reward presented i.e., monetary rewards used in clinical studies do not compare to food rewards in preclinical studies.

Limitations of the study

One major plausible confound of operant rodent assays is the utilisation of either water or food deprivation to provide a motivational aspect to complete the task. We have shown that food depriving rats mitigates the effect of 11-h sleep restriction in a rodent attention task, suggesting restricting food in rodents can mask attention deficits. Thus, the nature of the motivator must be taken into consideration when comparing human and rodent data. The datasets in this Chapter focused solely on a sustained attention task, hence it would be of interest to understand if instrumental tasks that focus on assessing other cognitive domains are equally affected in rodent assays utilising food rewards. Furthermore, the ERPs assessment performed concomitantly in this study offers a translational insight into the mechanisms by which hunger may modulate and sustain attentional performance. A difficulty with these measurements is that high trial numbers of correct responses are required to produce sufficient epochs for averaging to eliminate noise to signal ratio. The purpose of our studies was to investigate the motivational confound in our appetitive tasks and therefore we applied only an 11-h sleep restriction protocol, known to produce a robust reduction in SRLT. Consequently, a decrease in breakpoint as an index of motivation, as measured in the progressive ratio task, may have become apparent if a longer sleep restriction protocol had been applied. The lack of effect in the progressive ratio task indicates that rats are willing to maintain responding following sleep restriction regardless of their hunger state. A caveat of this study was that ad libitum rats had a greatly reduced breakpoint at baseline whereby any additional reduction following sleep restriction maybe be difficult to achieve. Comparison of the results obtained during the CFR5 demonstrate the value of providing a low-effort option concurrently with a high-effort task, in order to elucidate the reasons behind lever pressing and to more accurately ascertain the animals’ motivation level. In addition, based on the influence of dopamine in motivation, as mentioned in this Chapter introduction,

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obtaining real-time dopamine measurements in conjunction behaviour would complement this dataset.

We have established that SRLT can be routinely used to index the deleterious effect of sleep restriction on sustaining attention in ad libitum fed rats. In considering the translational value of pre-clinical studies the following chapter will examine whether pharmacological and non-pharmacological countermeasures, already shown to be effective in restoring sustained attention in human sleep deprived subjects, translate to equivalently attenuate these SRLT deficits in sleep restricted rats.

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CHAPTER FIVE

Chapter 5 - Assessing Pharmacological and Non-Pharmacological Countermeasures for Sleep Loss in Rats

5.1 Introduction

Insufficient sleep due to sleep deprivation or disturbed sleep and the cognitive sequelae of excessive daytime sleepiness (EDS) are prevalent in today’s society (Slater and Steier, 2012). Disturbances in sleep may affect either the quantity or quality of sleep. Insufficient restorative sleep is present in many diseases such as insomnia, sleep apnea and narcolepsy (Roth and Roehrs, 1996). EDS may also be associated with many other disorders, such as Parkinson’s disease, multiple sclerosis, chronic pain and depression (Boulos and Murray, 2010) (Chellappa et al., 2009) (Knie et al., 2011). Whilst recovery sleep would be the best remedy for EDS, this is often unattainable, especially in patients exhibiting co-morbidities. Hence, a clinical need remains for pharmacotherapies to address EDS and its associated cognitive deficits, ideally without causing debilitating rebound hypersomnolence. Little preclinical work so far has focused on behavioural pharmacology in sleep-restricted rodents. Serial reaction latency testing in the rat as a translational homologue of the human psychomotor vigilance task (PVT) may be useful for specifically assessing potential pro-vigilant pharmacological effects.

Pharmacological countermeasures of excessive daytime sleepiness

Wake-promoting compounds known to increase wakefulness in humans and rodents following lack of sleep are already available. Stimulants such as caffeine, amphetamine and modafinil are routinely used (Banerjee et al., 2004) (Killgore et al., 2008). Whilst these compounds do indeed maintain wakefulness in rodents, they demonstrate varying effects on waking functionality which we will present in the current Chapter (Loomis et al., 2015).

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Caffeine, a widely used psychoactive compound, promotes wakefulness via antagonism of the

adenosine A2A receptors (Lazarus et al., 2011). The effects of caffeine on the sleep EEG and

performance have also been shown to be mediated by polymorphisms of the adenosine A2A

receptors in humans (Bodenmann et al., 2012). A more recent study in Drosophila showed that the

effects of caffeine recruit the dopaminergic pathway (Nall et al., 2016) and further studies have

demonstrated that caffeine has an effect on the circadian clock (Burke et al., 2015). Conventional stimulants, such as amphetamine, are associated with increased levels of norepinephrine, serotonin and dopamine by inhibiting their reuptake and increasing presynaptic release (Wisor et al., 2001) (Hoffman and Lefkowitz, 1996).

By contrast, how modafinil works as a wake-promoting compound remains unclear and seems to be associated with numerous sites of action (Young and Geyer, 2010) (Gerrard and Malcolm, 2007) (Scammell and Saper, 2007). Both direct and indirect effects on the GABAergic system have been identified, as well as activation of noradrenergic receptors, orexin-producing neurons, enhancement of serotonin release and increased dopamine, norepinephrine and histamine levels (Figure 5.1). Given the involvement of the monoaminergic, GABAergic and orexinergic systems in the sleep-wake cycle this may explain in part modafinil’s wake promoting properties (España and Scammell, 2011).

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Figure 5.1 – Figure showing a simplified mechanism of action for modafinil.

Modafinil has been recognised for its eugeroic properties since the late 1990’s. Rodent sleep-wake EEG experiments using modafinil by Edgar and Seidel showed less locomotor effects and more consolidated bouts of wakefulness compared with the stimulant methamphetamine. More interestingly, modafinil did not show the typical rebound hypersomnolence usually seen with other stimulants. They concluded that modafinil was effective in inhibiting the sleep drive with fewer repercussions on recovery sleep patterns (Edgar and Seidel, 1997). EEG measures from human subjects given modafinil have also shown a reduction in recovery sleep for both time in bed and actual sleep time following 64-h sleep deprivation (Buguet et al., 1995). Detailed analysis of EEG power during one night’s sleep deprivation with prophylactic modafinil administration showed a specific decrease in delta and theta power, but little effect on alpha and beta bands during wakefulness (Caldwell et al., 2000).(James et al., 2011). These alterations in delta and theta power were replicated in studies comparing effects of modafinil and amphetamine administration on the wake EEG (Chapotot et al., 2003). However, while a decrease in alpha power was seen following amphetamine administration, an increase in alpha power was observed when using modafinil. These concurrent increases in alpha and decreases in theta power are more consistent with the wake promotion and increased vigilance shown with modafinil following sleep deprivation and may relate to modafinil’s action on dopaminergic transmission (Bodenmann et al., 2009). In addition, the effects on the EEG and gene expression in the brain induced by modafinil and amphetamine were compared in mice. This study extended findings to show increases in waking EEG activity in gamma and the lesser reported higher frequency (60-80Hz) bands. These high frequency bands are associated with attentional processes (Canolty et al., 2006) and hence an increase in these bands during wakefulness could account for any cognitive enhancement (Hasan et al., 2009).

Non-Pharmacological countermeasures

Taking naps are a second and seemingly simple countermeasure that may be viable in ameliorating

EDS (Bonnet, 1991), (Takahashi et al., 1998), but clinical data shows mixed effects as to the true

benefits of taking a nap. Prior sleep/wake history, as well as length and timing of naps, play a

significant role in their efficacy (Lovato and Lack, 2010) (Hilditch et al., 2017). One downside to using napping as a countermeasure for sleep loss is the associated

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sleep inertia encountered after a sleep episode. Sleep inertia is a brief period (15-30mins) described as confusion, disorientation, decrease in alertness and a desire to return to sleep that occurs immediately after waking. Brief nap times are more achievable, with the added advantage of minimal interference from sleep inertia. Short duration naps of 20-30 minutes have been shown to be immediately beneficial in restoring vigilance, reaction times and alertness (Brooks and Lack, 2006) (Tietzel and Lack, 2002). These brief naps work well as a short-term restorative remedy during the immediate period following the nap and more generally following relatively normal nocturnal sleep durations. However, one human study showed no effect of a 30-min nap on reaction time compared to a no nap condition in a simulated night shift (Tremaine et al., 2010). Extended naps however, beyond 30 minutes, have been shown to be more beneficial in situations of sleep deprivation and more usually whilst these benefits are longer lasting, onset can often be delayed for at least a couple of hours post nap. In a separate study using human participants tested the day after one night’s sleep deprivation, there was an increase in both objective measures of alertness and subjective performance in PVT following a 2-hour mid afternoon nap. Despite some restoration, post-nap performance did not equate to pre sleep deprivation levels (Vgontzas et al., 2007). Conversely, recent data suggest however that napping may not always be beneficial (Dijk, 2015). Napping and in particular for extended lengths is associated with increased sleepiness and higher body mass index and consequently may be associated with increased risk of mortality, poor health and cognitive decline (Tsapanou et al., 2016) (Yamada et al., 2015). Using EEG and ERPs measurements to assess post-nap performance and sleep inertia, the P300 amplitude is reduced but latencies are relatively unaffected in control non sleep deprived subjects (Bastuji et al., 2003). However, in sleep deprived subjects that took a nap the latency was prolonged, but amplitude was unchanged. An increase in EEG delta power during the nap was associated with the slower P300 latency immediately post nap indicating that achieving deeper sleep during the nap may affect information processing ability immediately post nap such that this is improved 2-3 hours after waking. This lag time in restoration could in part to be due to the proceeding sleep inertia and may explain why shorter naps appear to give more immediate benefits (Takahashi et al., 1998). Studies on the effect of sleep inertia post nap on cognitive load and time of day of nap demonstrated that afternoon naps compared to morning naps caused a greater deterioration in

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cognitive tasks, but this deterioration was limited to higher executive load tasks and did not affect tasks assessing simple cognitive functions (Groeger et al., 2011). Napping is a non-pharmacological countermeasure often assessed with respect to shift work sleep disorder (SWSD). SWSD is becoming increasingly more popular due to the move towards a 24 hour society (Schwartz and Roth, 2006). It becomes problematic over time as the sleep/wake pattern becomes misaligned with the endogenous circadian rhythm. It has long been recognised in human studies that have evaluated the benefits of napping of around 1 hour during a night shift that it has a positive effect in sleep disturbances, alertness, reaction time and subjective sleepiness (Härmä et al., 1989) (Bonnefond et al., 2001) (Sallinen et al., 1998). Even prophylactic napping prior to a night shift are shown to significantly increase the ability to maintain alertness and reduces risk of sleep loss related accidents (Ficca et al., 2010). Despite this seemingly simply remedy to combat sleepiness during night shifts there is a reluctance to introduce napping as a therapy as it is often deemed napping during working hours reflects laziness and or poor work ethic.

and in particular long naps, areassociated in general with poor health,

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mortality and cognitivedecline (Tsapanou et al., 2015; Yamada et al., 2015To date there appears to be no specific literature relating to rodent studies looking at the impact of napping from a pre-clinical perspective. This is most likely due to the complexity of design regarding the polyphasic nature of rodent sleep patterns and the cost benefit of such studies to determine whether the sleep state of polyphasic rodents constitute a nap equivalent to humans.

Aims of chapter

This chapter characterises the effects of pharmacological and non-pharmacological interventions for counteracting impairments in functioning due to sleep loss. For the purpose of this evaluation, three pharmacological countermeasures (i.e., modafinil, amphetamine and caffeine) were included and their effects on performance compared following sleep restriction. We also studied how these three pharmacological agents interact with the sleep-wake pattern during both sleep restriction and the recovery phase. For a non-

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pharmacological countermeasure, we attempted to recapitulate human napping by allowing rats a brief period of sleep following 10.5-h sleep restriction before being subjected to the SRLT.

Part of this Chapter was included in the following publication:

Sally Loomis*, Andrew McCarthy*, Christopher Baxter, Daniel O. Kellett, Dale M. Edgar, Mark Tricklebank, and Gary Gilmour. Distinct pro-vigilant profile induced in rats by the mGluR5 potentiator LSN2814617 Psychopharmacology (Berl). 2015; 232(21-22): 3977–3989

*Contributed equally to this work.

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5.2 Methods

5.2.1 Animals and housing conditions

Adult, male Wistar rats (Charles River Laboratories) were used. Ambient temperature was maintained at 23 ± 1C and relative humidity at 50% approximately. A 24-hr light-dark cycle (LD 12:12) was maintained throughout the study with a light intensity of 35-40 lux at mid-level inside the cage. For EEG-implanted rats, after recovery from surgery and during behavioural training, rats were food restricted to 85% of their ad libitum feeding weight. Following surgery, animals were singly housed in standard home cages, and when a stable behavioural baseline in the Simple Response Latency task was achieved, animals were transferred to sleep restriction chambers, as described in Chapter 2, section 2.3 and returned to an ad libitum food regime. For non-implanted rats, animals were trained in the behavioural task on a food restricted regime after which they were randomly assigned to either a food restricted group or ad libitum fed group.

5.2.2 Surgical proceduresEEG/EMGAdult, male Wistar rats were implanted with electrodes for long-term electroencephalogram (EEG) and electromyogram (EMG) recordings as per Chapter 2, section 2.2. All animals were surgically prepared at Charles River Laboratories, Margate, UK prior to arrival and weighed approximately 270-300 g at time of surgery.

5.2.3 Sleep restriction methodologyBiofeedback sleep restriction protocolEEG-Biofeedback was carried out as described in Chapter 2, section 2.3. Sleep restriction protocols were run in a within-subjects crossover study design, where rats were pseudo-randomly assigned to one drug treatment condition per week. Subjects were allowed at least 7 days “washout” preceding and following treatments with no duplication. Prior to any study, all subjects were sleep deprived for 5 hours to eliminate unpredictable changes due to novel exposure to the procedure.All drugs were administered at ZT 10.5, 30 min prior to SRLT testing to ensure compounds were on-board at time of testing. Sleep-wake variables (i.e., time spent in wake, NREM sleep and REM sleep) were recorded during a 24-h baseline

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period, the 11-h sleep restriction period (ZT0-ZT11), performance of the Simple Response Latency task and also during the subsequent 72-h recovery period.Non-Biofeedback sleep restriction protocolNon-invasive sleep restriction using the Weibull protocol was carried out as described in Chapter 2, section 2.3. Subjects were randomly assigned to each treatment sequence. In keeping with pre-test conditions used in Chapter 3 for Biofeedback sleep restriction, prior to any study all subjects were sleep deprived for 5-h to eliminate unpredictable changes due to novel exposure to the procedure.

5.2.4 Simple Response Latency task (SRLT)

SRLT tests were conducted as described in Chapter 2, section 2.7. For 3 consecutive days (Pre, Test and Post) animals were removed from sleep restriction chambers and placed immediately into operant boxes. Rats were subjected to 40 minutes of SLRT. On test day this was immediately following 11-h sleep restriction protocol (ZT 11); on pre and post days rats were tested at ZT 2-5.

Rats subjected to nap times were removed from the sleep restriction wheels following 10.5-h sleep restriction protocol (ZT 10.5) and placed in the operant boxes whereby they were they were left to nap for 15-min (short nap) or 30-min. Short nap and control rats (no nap) were kept awake by gentle procedures or tapping of the boxes during the remaining 15 or 30 minutes until ZT 11, when the SLRT was initiated. Rats were subjected to 30 minutes of SLRT, where the number of trials completed, and response errors (premature responses and omissions) were counted. Reaction times were also collected in addition to time on task effects.

5.2.5 Drugs

Modafinil (Apin Chemicals UK; milled to a modal particle size of 7 microns) was dissolved in 0.25% methylcellulose (15 centipoise) and administered intraperitoneally at a volume of 2ml/kg, at a dose of 300 mg/kg. D-amphetamine sulphate (Sigma Aldrich, UK) was dissolved in 5% (w/v) glucose solution and administered subcutaneously at a volume of 1 ml/kg at a dose of 1 mg/kg. Caffeine (Lilly Research Labs) was dissolved int0 0.25% methylcellulose (15 centipoise) and administered intraperitoneally at a volume of 2 ml/kg at a dose of 12 mg/kg. Control groups were administered vehicles that corresponded to

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drug formulation i.e. 0.25% methylcellulose for modafinil and caffeine and 5% glucose for amphetamine studies. All doses refer to free base or acid weights of compounds. To administer a drug treatment, rats were removed from their cage for about 60-90 seconds to be dosed. All drugs were administered 30-min prior to SRLT testing.

5.2.6 Data analyses

Drug studies

All data were analysed using Statistica v9.0 (Statsoft Ltd, Bedford, UK). For sleep/wake state parameter assessments, the first 5 hours following drug administration were analysed with Repeated Measures ANOVA, with “Treatment” and “Time” as within-subjects factors. Planned comparisons of each treatment group compared to the vehicle group were also conducted at each time point for each drug. SRLT task parameters were analysed with Repeated Measures ANOVA, with “Treatment” and “Day” as within-subjects factors. Planned comparisons were then conducted, where respective Vehicle and Drug-treated groups were compared between Pre Day - Test Day and Pre Day - Post Day, and also for each separate day a planned comparison was made between the Vehicle group and the Drug-treated group. For REM and non-REM sleep measures, four differential measures were calculated between two 12h blocks of data: “Sleep Restriction” = Test Day Light Phase – Pre Day Light Phase; “Drug Treatment” = Test Day Dark Phase – Pre Day Dark Phase; “Recovery – Light” = Post Day Light Phase – Pre Day Light Phase; “Recovery – Dark” – Post Day Dark Phase – Pre Day Dark Phase. ANOVA, with “Treatment” as a within-subject factor, was conducted on each of these measures for each drug.Nap studies

SRLT data were analysed using Statistica version 13.2 (Statsoft Ltd, Bedford, UK). SRLT parameters were analysed using a one-way repeated measure ANOVA to compare treated groups planned comparisons of each Treatment group compared to the Controls (either sleep restricted or non-sleep restricted group) were also conducted.

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5.3 Results

5.3.1 Effects of Modafinil, Amphetamine and Caffeine on sleep-wake parameters

following 11-h sleep restriction

A baseline period of 24 hours was used to evaluate the sleep history of individual animals prior to treatment. During this time, all rats displayed a circadian rhythm in sleep, locomotor activity and body temperature that was consistent between treatment groups. As depicted in Figure 5.2 (a, c, e), following 11 hours of sleep restriction, all three compounds significantly increased wakefulness for several hours following dosing (main effects of drug: all p < 0.0001). Figure 5.2 (b, d, f) shows a zoomed in view of the 5 hours following the end of the sleep restriction period. Wake-promoting effects of modafinil, amphetamine and caffeine had effectively dissipated by 4 hours post-dose. By comparison to the same time period the day before, the biofeedback method of sleep restriction caused a loss of approximately 6-h non-REM sleep (Figures 5.3, 5.4 & 5.5). In the “Drug Treatment” period after sleep restriction, vehicle-treated animals recovered 90 – 120 min non-REM sleep and 38 – 47 min REM sleep. Both amphetamine and caffeine significantly decreased the amount of non-REM sleep recovered during this period (amphetamine: F1,8 = 15.7, p = 0.004; caffeine: F1,7 = 36.4, p = 0.001), while modafinil showed a non-significant trend towards the same effect (modafinil: F1,13 = 3.5, p = 0.084). All drug treatment groups showed some level of non-REM recovery during the subsequent day (Table 5.2). Amphetamine or caffeine treated animals displayed a significant increase in non-REM sleep during the subsequent light phase (amphetamine: F1,8 = 14.4, p = 0.005; caffeine: F1,7 = 11.2, p = 0.012), while for modafinil-treated animals it was delayed to the subsequent dark phase (modafinil: F1,13 = 15.5, p = 0.002).

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Figure 5.2: The effect of pro-vigilant compounds on wakefulness in rats. (a, c, e) 72-h plot of wakefulness including modafinil, amphetamine and caffeine treatment (▼) (Mean ± SEM). Solid bars above the bottom x-axis indicate the 12-h dark phase. Vertical

dotted lines depict the sleep restriction period. (b,d,f), first five hours of wakefulness following drug administration (Mean ± SEM). Asterisks refer to planned comparisons of drug treatment groups against the respective Veh group for each study. *p < 0.05; **p

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above the bottom x-axis indicate when dark phases occurred. Vertical dotted lines at time depict the beginning and end of the sleep restriction period. The black triangle on the bottom of the x-axis indicates when drugs were administered. (b, c, d & e) show the differential amounts of non-REM sleep achieved by rats between different periods of the

study and were calculated as follows: Sleep Dep = “Test” Light Phase – “Pre” Light Phase; Drug Treatment = “Test” Dark Phase – “Pre” Dark Phase; “Recovery” Light = “Post” Light Phase – “Pre” Light Phase; “Recovery” Dark = “Post” Dark Phase – “Pre” Dark Phase. Asterisks refer to planned comparisons of drug treatment groups against

the respective Vehicle (Veh) group for each study. *p < 0.05; **p < 0.01; ***p < 0.001. All data represented as mean ± SEM.

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treatment (n = 13). Solid bars above the bottom x-axis indicate when dark phases occurred. Vertical dotted lines at time depict the sleep restriction period. The black

triangle on the bottom of the x-axis indicates when drugs were administered. (b, c, d & e) show differential amounts of non-REM sleep achieved by rats and were calculated as follows: Sleep Dep = “Test” Light Phase – “Pre” Light Phase; Drug Treatment = “Test”

Dark Phase – “Pre” Dark Phase; “Recovery” Light = “Post” Light Phase – “Pre” Light Phase; “Recovery” Dark = “Post” Dark Phase – “Pre” Dark Phase. Asterisks refer to planned comparisons of drug treatment groups against the respective Veh group for

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Figure 5.5: The effect of caffeine on non-REM sleep in 11 h sleep-restricted rats. (a) 72-h time course in non-REM sleep in the caffeine group (n = 11). Solid bars above the bottom x-axis indicate when dark phases occurred. Vertical dotted lines at time depict the sleep restriction period. The black triangle on the bottom of the x-axis indicates when drug was administered. (b, c, d & e) show the differential amounts of non-REM sleep achieved by rats and were calculated as follows: Sleep Dep = “Test” Light Phase – “Pre” Light Phase; Drug Treatment = “Test” Dark Phase – “Pre” Dark

Phase; “Recovery” Light = “Post” Light Phase – “Pre” Light Phase; “Recovery” Dark = “Post” Dark Phase – “Pre” Dark Phase. Asterisks refer to planned comparisons of drug treatment groups against the respective Veh group for each study. *p < 0.05; **p <

0.01; ***p < 0.001. All data represented as mean ± SEM.

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Vigilance State Deficit (min) Recovery (min)

Recovery (%)

Vehicle Non-REM 253±4 145±5 57REM 47±2 39±3 82

Modafinil (300mg/kg) Non-REM 35±19 21±7 60REM 15±6 12±5 84

Amphetamine(1mg/kg) Non-REM 45±11 40±8 89REM 21±8 11±4 53

Caffeine (12mg/kg) Non-REM 83±14 49±15 59REM 19±8 9±6 46

Table 5.1: Effects of modafinil, amphetamine and caffeine on sleep parameters during recovery in sleep-restricted rats. The deficits relative to vehicle were

calculated during the 12-hour “drug” treatment period, whilst recovery was calculated from the remaining 24 h recording time (i.e. “Recovery” Light + “Recovery” Dark

phases). Values presented are means and standard errors of the mean.

Drug effects on REM sleep following sleep restriction are shown in Figures 5.6, 5.7 & 5.8. REM sleep was completely abolished during the sleep restriction period, which in vehicle treated animals was almost completely recovered during the “Drug Treatment” period. All three compounds significantly decreased the amount of REM sleep recovered during this period (modafinil: F1,13 = 5.6, p = 0.035; amphetamine: F1,8 = 7.4, p = 0.026; caffeine: F1,7 = 6.2, p = 0.042). During the subsequent day, amphetamine and caffeine did not show a significant compensatory increase in REM sleep, although there was a non-significant trend for caffeine to increase REM sleep during the subsequent light phase (caffeine: F1,7 = 4.1, p = 0.083). Modafinil displayed a modest but significant increase in REM sleep during the subsequent light period (modafinil: F1,13 = 5.8, p = 0.031).

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bottom x-axis indicate dark phases. Vertical dotted lines depict the sleep restriction period. The black triangle on the bottom of the x-axis indicates when drugs were

administered. (b, c, d & e) Amounts of REM sleep achieved by rats between different periods of the study. Sleep Dep = “Test” Light Phase – “Pre” Light Phase; Drug

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triangle on the bottom of the x-axis indicates when drugs were administered. (b, c, d & e). Amount of REM sleep achieved by rats were calculated as follows: Sleep Dep =

“Test” Light Phase – “Pre” Light Phase; Drug Treatment = “Test” Dark Phase – “Pre” Dark Phase; “Recovery” Light = “Post” Light Phase – “Pre” Light Phase; “Recovery” Dark = “Post” Dark Phase – “Pre” Dark Phase. Asterisks refer to planned comparisons of drug treatment groups against the respective Veh group for each study. *p < 0.05; **p <

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black triangle on the bottom of the x-axis indicates when drugs were administered. (b, c, d & e) Amounts of REM sleep achieved by rats during different periods of the study. Sleep Dep = “Test” Light Phase – “Pre” Light Phase; Drug Treatment = “Test” Dark

Phase – “Pre” Dark Phase; “Recovery” Light = “Post” Light Phase – “Pre” Light Phase; “Recovery” Dark = “Post” Dark Phase – “Pre” Dark Phase. Asterisks refer to planned

comparisons of drug treatment groups against the respective Veh group for each study. *p < 0.05; **p < 0.01; ***p < 0.001. All data represented as mean ± SEM.

5.3.2 Effects of pharmacological compounds on PVT performance following 11-h sleep

restriction

Effects of sleep restriction and drug administration on the Simple Response Latency task are depicted in Figure 5.9. Considering sleep restriction alone, a consistent effect of sleep restriction was present across the vehicle groups of each individual drug study. Compared to “pre” day all vehicle groups on test day showed a marked, significant decrease in completed trials (Vehicle group, planned comparisons of “Pre” day to “Test” day performance; all compounds p <0.05) and an increase in omissions (Vehicle group, planned comparisons of

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“Pre” day to “Test” day performance; modafinil: p < 0.0001; amphetamine: p = 0.01; caffeine: p = 0.001) were observed. Premature responding was significantly decreased in the vehicle groups of the modafinil and amphetamine studies, (Vehicle group, planned comparisons of “Pre” day to “Test” day – modafinil: p = 0.001; amphetamine: p = 0.012), while showing no effect in the caffeine study. Response latencies were significantly increased in the modafinil group by sleep restriction (Vehicle group, planned comparisons of “Pre” day to “Test” day; modafinil and caffeine: p = <0.05), but not in the amphetamine condition. No significant carry-over effects of sleep restriction could be detected on any SRLT parameter during the “Post” test session. Next a direct comparison of the effects of sleep restriction on each individual compound was made. Drug doses were chosen on the basis of inducing similar pro-vigilant effects as assessed by EEG parameters (300 mg/kg modafinil, 1 mg/kg amphetamine, 12 mg/kg caffeine). However, their effects on sleep restriction-impaired task performance were markedly different. Caffeine normalized trials completed i.e. trial number was similar to “pre” day and significantly higher than vehicle rats (“Test” day, planned comparisons of “Veh” to “Drug” group performance– caffeine: p = 0.005). This was the same for omissions (“Test” day, planned comparisons of “Veh” to “Drug” group – caffeine: p = 0.001). Modafinil was unable to restore trials completely to “pre” day levels but the rats still completed significantly more trials than the vehicle group. Like caffeine, modifinil was able to restore omissions to baseline levels in light of sleep restriction (“Test” day, planned comparisons of “Veh” to “Drug” group – modafinil: p = 0.002). Both caffeine and modafinil significantly increased premature responding (“Test” day, planned comparisons of “Veh” to “Drug” group – modafinil: p = 0.005; caffeine: p = 0.034) following dosing after sleep restriction. The effect of modafinil was particularly marked for this parameter, where premature response rate increased 4-fold compared to the vehicle group during the “Test” session. With regard response latency, both caffeine and modafinil displayed a non-significant trend towards decreasing this measure on test day compared to the vehicle group (“Test” day, planned comparisons of “Veh” to “Drug” group – modafinil: p = 0.058; caffeine: p = 0.057). While modafinil and caffeine showed some evidence of improving trial and omissions performance following sleep restriction compared to vehicle dosed rats, animals dosed with amphetamine did not complete the SRLT task properly. On “Test” day, relative to vehicle control animals, amphetamine treated animals displayed

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a further marked and significant decrease in completed trials (“Test” day, planned comparisons of “Veh” to “Drug” group– amphetamine: p = 0.008), increase in omissions (“Test” day, planned comparisons of “Veh” to “Drug” group– amphetamine: p = 0.008), and a dramatic increase in response latencies (“Test” day, planned comparisons of “Veh” to “Drug” group– amphetamine: p = 0.008). Finally, in terms of a “Post” session testing impairment, only modafinil displayed a small but significant decrease in trials completed (“Test” day, planned comparisons of “Veh” to “Drug” group performance– modafinil: p = 0.027) and increase in omissions (“Test” day, planned comparisons of “Veh” to “Drug” group performance– modafinil: p = 0.041) and response latency on the day following dosing.

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Figure 5.9. Pro-vigilant drug effects on SRLT response parameters in 11 h sleep-restricted ad libitum fed rats. The four main SRLT response parameters are

(a) number of trials completed, (b) number of omissions, (c) number of premature responses and (d) response latencies. For individual graphs, the x-axis depicts the summary of each measure for the “Pre”, “Test” and “Post” drug administration test session. Asterisks refer to planned comparisons of Vehicle (Veh) vs. Drug treatment

within a test session for each study, where *p < 0.05; **p < 0.01; ***p < 0.001. Carets refer to planned comparisons for either Veh (light grey) or Drug (black) treatments

between test sessions, where ^ p < 0.05; ^^ p < 0.01; ^^^ p < 0.001, in comparison to the “Pre” session. All data represented as mean ± SEM.

5.3.3 Use of Naps as a non-pharmacological countermeasure to potentially improve SRLT performance following 11-h non-biofeedback sleep restrictionEffect of napping in food restricted and ad-libitum fed rats

Figure 5.10 shows data from food restricted rats subjected to an 11-h non-biofeedback Weibull sleep restriction protocol. At ZT10. 5, i.e. 30 minutes before the end of the 11-h sleep restriction period rats were allowed either no nap, 15-min or a 30-min nap compared to a non-sleep restricted control group before testing in the SRLT. No significant differences between any of the groups were found in these animals for any of the 4 SRLT parameters tested (i.e., number of completed trials, number of omissions, premature responding and median magazine latency) (trials: F7,59 = 1.07, p = 0.394; omissions: F7,59 = 0.48, p = 0.846; prematures: F7,59 = 0.90, p = 0.509; median latency: F7,59 = 1.31, p = 0.262). No time on task effects were evident in this study (events: F16,118 = 1.19, p = 0.285; latency: F16, 118 = 1.30, p = 0.206). Figure 5.11 shows data from ad libitum fed rats following 11-h Weibull sleep restriction subjected to the same no nap, 15-min or 30-min nap conditions immediately prior to SRLT. In contrast to food restricted rats, considering the effect of sleep restriction alone ad libitum fed rats showed a significant decrease in the number of trials and a significant increase in omissions compared to non-sleep restricted controls (trials: F3,51 = 24.08, p = <0.001; omissions: F3,51 = 21.25, p = <0.001). Both nap groups (15 min and 30 min) also showed a decrease in trials and an increase in omissions compared to the control group. Compared to the sleep restricted group the 30-min nap group also showed a significant further decrease in completed trials and a significant increase in omissions, compared to the sleep restriction group. There were no effects of either sleep restriction or sleep restriction with napping

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for either premature responding or the median magazine latency parameters (prematures: F3,51 = 1.47, p = 0.235; median latency: F3,51 = 1.48, p = 0.231) compared to the non-sleep restricted control group. Showed. Unfortunately, no time on task data were available due to a technical error with the acquisition software.

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5.4 Discussion

By using an EEG biofeedback-induced sleep restriction methodology as described in Chapter 3, reliable and quantifiable levels of sleep restriction were induced in rats and the behavioural effects were indexed by the serial reaction latency test (SRLT). Administration of pro-vigilant compounds, at doses with broadly comparable effects on sleep-wake parameters and the EEG showed differential effects of the drugs on task engagement. Instrumentation of the rats allowed quantification of EEG parameters throughout, providing exact definition of the sleep-wake state of animals during both restriction and recovery periods. To date, very few behavioural pharmacological studies in sleep restricted rats have collected such information to guide interpretation. In addition, allowing a 30-minute nap time post sleep restriction showed a worsening of the deficit in the SRLT rather than an improvement, suggestive of sleep inertia in rodents, highlighting the difficulty in examining non-pharmacological countermeasures pre-clinically.

Comparison of pharmacological countermeasures following sleep restriction

When an 11-h sleep restriction was applied prior to performance of a SRLT, rats consistently lost around 6 h of non-REM sleep and 40 min of REM sleep. This magnitude of sleep loss was sufficient to cause behavioural impairment in the SRLT task in rats fed ad libitum. Impairment was reflected in an increase in errors of omission, accompanied by a decrease in premature response rate and lengthening of reaction time. In humans, such effects on PVT performance can be observed after total sleep deprivation or chronic sleep fragmentation (Van Dongen and Dinges, 2003), but also as a consequence of clinically presented EDS (Czeisler et al., 2005) (Dinges and Weaver, 2003). The most important finding from the present study was that different pro-vigilant compounds had differential effects on the ability to restore SRLT performance in rats, despite all having significant effects on sleep-wake parameters. Of the three compounds tested, amphetamine (1 mg/kg) was the only drug to have marked negative effect on performance in sleep-restricted animals, such that they completely disengaged from the task. Omissions increased drastically, and when animals did complete trials they occurred with very long response latencies. This profile suggests that this dose of amphetamine, while wake-promoting, results in the expression of stimulant hyperactivity in sleep-restricted rats that competes

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with task engagement. Amphetamine is known to have a broad effect across brain structures, involved in the regulation of sleep and wakefulness, as well as motor activity and other functions (Nishino et al., 1998). Conversely the effects of modafinil are more selective, acting on distinct brain structures involved in the regulation of sleep and wakefulness (Engber et al., 1998). Therefore, it is unsurprising that these results imply that modafinil and amphetamine promote wakefulness by distinctly different mechanisms This represents a potentially important disconnect to existing clinical data, whereby most human studies report positive effects of standard 10 or 20 mg amphetamine doses on attentional and other cognitive tasks following sleep deprivation. The dose of amphetamine in our studies was chosen on the basis that it showed equivalent wake promoting effects to modafinil and caffeine. Previous studies have also indicated that amphetamine at 0.1mg/kg in rats induces cognitive effects up to 1mg/kg that produces moderate hyperactivity effects (Grilly and Loveland, 2001). However a similar dose used in Sprague Dawley rats in a signal detection task showed that at 1.25mg/kg behaviour was also impaired, although lower doses 0.1 and 0.5mg/kg improved accuracy (Turner and Burne, 2016). This could mean that whilst wake promoting, the doses used in our study were too high to see the improvements in performance reported in the human literature. By comparison, caffeine (12 mg/kg) and modafinil (300 mg/kg) had beneficial effects on SRLT performance in sleep-restricted rats. Compared to vehicle these two compounds significantly decreased omissions and increased the number of trials completed, allowing sleep-restricted animals to engage in the task more effectively. Caffeine and modafinil also displayed a trend-level tendency towards normalizing response latencies, although both compounds also had concomitant negative effects on premature response rates. Increases in premature responses were especially observed following modafinil administration, almost trebling in rate during the test session immediately following sleep restriction. Again, this finding is somewhat at odds with clinical data, which describes modafinil to be well tolerated with largely positive effects on human performance capacity and response inhibition parameters (Minzenberg and Carter, 2008). The effects of modafinil reported in preclinical studies have been much more mixed. Some studies report positive effects on stop signal (Eagle et al., 2007) and 5-choice serial reaction time (5CSRT) performance (Morgan et al., 2007) in control animals, while negative effects on accuracy and impulse control in 5CSRT in

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control (Waters et al., 2005) and REM sleep deprived animals (Liu et al., 2011) have been observed. A homeostatic process tracks the loss of sleep as “sleep pressure” that will result in a dose-dependent compensation proportional to the debt accrued (Daan et al., 1984) (Dijk et al., 1990). All treatments compared in the present study resulted in immediate attenuation of the compensatory sleep response elicited by 11 hours of sleep restriction. However, the lack of sleep recovery immediately following pro-vigilant treatment was later recovered to a degree during the subsequent recording period, suggesting that requirement for homeostatic sleep was not completely alleviated by any compound. The most rapid and complete compensatory non-REM sleep response was observed for amphetamine, whilst both caffeine and modafinil produced similar non-REM sleep recovery relative to the additional wakefulness gained. REM sleep recovery however was greatest for modafinil with amphetamine and caffeine regaining only about half of their lost REM sleep back during the recovery period. Human studies have suggested compensatory sleep responses are more apparent in situations of chronic sleep restriction. Doty 2017 tested caffeine across a 5-day sleep restriction protocol of 5-hr time in bed. During recovery nights subjects that had taken caffeine showed an increase in NREM (stage N3) sleep indicating an increased homeostatic sleep need (Doty et al., 2017). Acute studies however show lesser effects on recovery sleep mechanisms however it is easy to surmise that due to the wake-promoting nature of stimulants, recovery sleep will be disrupted whilst the stimulant remains on board. In a human study testing dextroamphetamine, modafinil and caffeine, in an acute sleep deprivation protocol of 64-88-h, recovery sleep was only equivalent to placebo groups if attempted 20-h following drug administration (Wesensten et al., 2005). A later study testing the same compounds but with a shorter 44-h sleep deprivation protocol allowed recovery sleep to commence after 17-h of final administration. Dextroamphetamine was found to have the most disruptive effect on recovery sleep with less total sleep time achieved during the recovery phase compared to caffeine (Killgore et al., 2008).

Non-pharmacological countermeasures following sleep restriction

An 11-h non-biofeedback sleep restriction in food restricted rats showed no effect on SRLT measures, as predicted from data presented in Chapter 4. Therefore, unsurprisingly, allowing a 15-min or 30-min nap at the end of the

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sleep restriction period did not have any effect on SRLT parameters measured. However, this was in contrast to rats fed ad libitum. We have previously shown (Chapter 3 and 4) that behavioural deficits in the SRLT are apparent in ad libitum fed rats following an 11-h non-biofeedback Weibull sleep restriction. These data support previous work showing a marked reduction in trials and an increase in omissions in the presence of sleep restriction with no effect evident for premature responding and reaction latencies. Focussing on the significantly different parameters (i.e., trials and omissions), both nap groups also demonstrated deficits in trial number and omissions caused by sleep restriction. However, for rats given a 30-min nap opportunity prior to SRLT testing, a further decrease was observed in the number of trials completed and a further increase in the number of omissions made. An explanation as to this worsening of sleep restriction effects may be sleep inertia. Sleep inertia is evident after any sleep period of more than about 30 minutes (Muzet et al., 1995). This is supported from data showing benefits of brief naps (defined as 5-15 min) in humans. These benefits are observed immediately after the nap but last a limited period (1-3h). Unfortunately, we were unable to recapitulate these results in our experiments in rats. Longer naps (> 30 min) show impairment most likely due to sleep inertia for a short period after waking but then are able to improve cognitive performance for a longer period (i.e., several hours) (Lovato and Lack, 2010). In a human study comparing cognitive testing following an 8-hour night sleep to one night of sleep deprivation, the impairment from sleep inertia was greater than impairment from sleep deprivation (Wertz et al., 2006). The magnitude and duration of sleep inertia and duration has been shown to depend on many factors including prior sleep duration, recent sleep history, timing of sleep and wake, and the sleep stage immediately prior to awakening (Hilditch et al., 2015). It has been shown that the effects of sleep inertia are more pronounced when the sleep episode is preceded by a period of sleep deprivation during the circadian nadir, or when the subject is aroused from slow-wave sleep (Lovato and Lack, 2010).Numerous studies have shown that naps are effective countermeasures that prevent performance decline in situations of sleep loss (Faraut et al., 2011) (Vgontzas et al., 2007). The alternative countermeasure of pharmacological interventions as reflected in our results shows variable results. Whilst caffeine is readily available in the form of coffee, energy drinks and tablets, there is a hidden risk of physiological side effects from taking unknown quantities and

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individual tolerance. So whilst prescribed pharmacological intervention such as caffeine or modafinil may be useful for short term or acute sleep loss they are not necessarily a healthy or economically viable option for extended or long term periods of sleep loss, such as associated with shift work, when actual recovery of sleep lost would be a better alternative (Sallinen et al., 1998). Pharmacological countermeasures used in conjunction with non-pharmacological approaches may provide a more effective countermeasure particularly in cases where slow wave (deep NREM, stage N3) sleep is disrupted (Schweitzer et al., 2006) Caffeine studies in humans report that sustained low-dose caffeine significantly attenuates decrements in reaction time and number of lapses in attention in the PVT. However, this effect becomes less effective with extended wakefulness (Spaeth et al., 2014). However, combined with a 2-hour nap every 12 hours during the 88-hour sleep deprivation, an improvement was observed in attention and memory throughout the whole sleep deprivation period. From a translational perspective, we could not find any papers showing the effects of napping in the context of sleep restriction on behaviour in rodents. However, it appears that rodents do experience sleep inertia. Vyazovskiy showed in rats that for approximately one minute post waking there is a decrease in overall population spiking associated with the occurrence of brief periods of neuronal silencing. Moreover, the incidence of neuronal silencing was increased if waking occurred during a REM period of sleep when compared to waking during a NREM sleep episode. The study concluded that sleep inertia may be accounted for by the intrusion of sleep-like cortical neuronal activity in the immediate wake period (Vyazovskiy et al., 2014). A similar outcome in humans was described as “the increased leakage of sleep-like activity”, with a lower prevalence of high-frequency EEG power following awakenings from REM sleep, compared to awakening from NREM awakening (Marzano et al., 2011).

Future Work and Limitations

The present study confirmed that the SRLT can be used in rats to detect behavioural impairments caused by a quantified loss of sleep. All three compounds tested showed distinct results regarding recovery from sleep restriction and SRLT outcomes. The methodology and pharmacological effects described may offer utility for future work directed at understanding the translational correspondence of pro-vigilant drug effects between species. However, considering the mixed effects observed with modafinil pre-clinically,

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several variables, including strain and age of animals, dose of modafinil, assay designs and protocol variants may need to be carefully considered here to understand this mixture of effects.

Unfortunately, nap data presented in this chapter did not show any conclusive results regarding the ameliorating effects of short naps following a period of sleep restriction in rodents. In this respect, a valuable addition to this study would have been EEG measures to assess the actual type and depth of sleep achieved both during the sleep restriction, during the napping phase and subsequently in the recovery phase. Post nap nocturnal sleep shows significant increases in sleep latency and a decrease in REM sleep latency in subsequent nocturnal sleep in humans. In this context it is suggested post-nap nocturnal sleep could be a useful model of sleep onset insomnia (Dijk, 2009). This would be of interest if this extended to pre-clinical studies. Furthermore, human studies suggest the length of the nap is key to its restorative properties, based on this it would have been worthwhile to include longer nap protocols of an hour or more. Our data did not support our hypothesis that napping would restore some of the SRLT deficits caused by an 11-h sleep restriction. Together with evidence that rodents do indeed experience sleep inertia (Vyazovskiy et al., 2014) and human PVT studies showing positive effects of extended napping after the sleep inertia has passed (Lovato and Lack, 2010), further work should consider investigating recovery time from sleep inertia prior to SRLT testing. More specific detailed nap studies in rodents may help to better understand and explain how naps contribute to attenuating the effects of sleep loss and whether the use of rodents to study the effects of naps pre-clinically are actually viable due to their distinctly different polyphasic sleep pattern compared to humans.

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CHAPTER SIX

Chapter 6 - Effects of Sleep Restriction on the Nucleus Accumbens using in vivo Amperometry

6.1 Introduction

One of the overarching aims of this thesis was to address how appropriately animal sleep restriction models translate to human studies. To assess the translational validity of the deficits in attention observed following sleep deprivation from rats to humans, an understanding of physiological fluctuations in brain regions involved in sustained attention and their similarities between species would be beneficial. In vivo oxygen amperometry in rodents enables to assess the levels of oxygen in discrete brain regions, providing a surrogate of brain activation during a behavioural task such as the Simple Response Latency task.

Oxygen amperometry

In humans, imaging, and in particular functional magnetic resonance imaging (fMRI), enables to assess activity in different brain regions during behavioural testing combined with sleep restriction protocols. To date several methods have been developed in rodents to study correlates of neural activation, such as 2-Deoxy-D-glucose (2-DG), c-fos labelling, brain microdialysis, fMRI or event related potentials (ERP) measures (Wree, 1990) (Chung, 2015) (Chefer et al., 2009) (Seiriki et al., 2017). Oxygen amperometry has been developed as a preclinical translational imaging biomarker, allowing a point location blood-oxygen-level-dependent (BOLD) surrogate but with the advantage of using on-line and freely moving behaving rats (Li et al., 2015b) (Kiyatkin, 2018). BOLD signals benefit from the ability to measure blood flow in discrete brain regions. Oxygen is an essential substrate for neurons, consumption of which is associated with increased blood flow to an active area. Thus, measuring the tissue oxygen

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concentration in a specific brain region will provide a surrogate of the level of neuronal activation (Lowry et al., 1997). Oxygen amperometry confers some advantages over animal fMRI studies, including measurements in unrestrained and non-anaesthetised animals, which can be conducted for up to several months. This reduces the number of invasive surgeries and this method also provides a good temporal resolution that more accurately reflects the timings of neuronal activity. Carbon paste electrodes (CPE’s) for amperometry presented in this Chapter are ideal for in vivo rodent experiments. They are more stable over several months compared to other metal-based O2 electrodes such as Clark electrodes (O'Neill et al., 1998). Their long-term functionality in vivo allows for longitudinal studies into behaviour and aging. Currently up to four separate sensors can be implanted simultaneously into different brain regions of a rat, allowing investigation of several regions simultaneously. However, a disadvantage of CPEs is their relatively large size (~200 μM). Overall, if rodent O2 amperometry techniques were shown to provide a suitable proxy for BOLD signals in human event-related fMRI it would present a significant advancement to better understand region-specific mechanisms in translational research.

Sleep restriction, performance and the Default Mode Network coherence

The effects of sleep deprivation have been primarily characterised by examining deficits in performance during cognitive testing. Fewer studies have examined the impact of sleep deprivation and recovery on cognitive performance by investigating alterations in off-task (i.e. when not engaged in any behaviour) resting-state brain activity. The Default Mode Network (DMN) includes brain regions that become more activated in the absence of any cognitive tasks and disengaged during goal directed behaviour. This deactivation of the DMN is crucial to achieve positive goal-driven behaviour (Greicius et al., 2003). The DMN comprises of the posterior cingulate cortex (PCC), precuneus and retrosplenial cortex, dorsal and ventral medial prefrontal cortex (MPFC), inferior parietal lobe (IPL), lateral temporal cortex (LTC) and hippocampal formation (Nair et al., 2018). Resting-state BOLD signals within the DMN have been shown to be temporally anti-

correlated with those from other cortical networks comprising regions that are typically activated in

tasks requiring cognitive control and attention (Schwarz et al., 2013). Studies using resting-state fMRI to determine resting brain functional connectivity responses to one night of partial sleep deprivation showed reduced functional connectivity within and between the default mode network and its anti-correlated network after

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sleep restriction (Raichle et al., 2001, Raichle, 2015). Using 72-h total sleep deprivation in healthy right-handed males, another study also showed intrinsic dysfunctional connectivity. Decreased resting state functional connectivity (rsFC) was apparent between the right inferior parietal lobe (IPL) and the left precuneus/posterior cingulate cortex (PrC/PCC) and increased rsFC between the right IPL

and the left fusiform gyrus and the left cluster of middle temporal gyrus and inferior temporal gyrus.

The left PrC/PCC did not show any connectivity differences (Dai et al., 2015). Evaluation of the default network has expanded to assess network activity during a resting state and during experimental task, i.e., during “task-induced deactivation”. In a study using both resting-state fMRI and conventional task related fMRI following one night of total sleep deprivation, reduced functional connectivity was shown within and between the default mode network (DMN) and its anti-correlated network both at rest and during task performance (De Havas et al., 2012). Further evidence showed that after one night of total sleep deprivation in humans using event-related fMRI, activity was altered between the anterior cingulate cortex and precuneus. This suggested a reduced task-induced deactivation in anterior areas, whilst observing a greater deactivation in posterior areas. This study was also able to discriminate sleep-deprived subjects from non-sleep deprived controls with over 90% accuracy (Gujar et al., 2010). This study suggests that a characteristic of the sleep-deprived brain may be the dysregulation not only of on-task brain activity, but also off-task, resting-state modes of brain activity

Investigation of the DMN in the context of performance in rats may therefore provide an insight into the brain regions involved in the loss of sustained attention following sleep restriction, and the restorative role of sleep.

The role of the nucleus accumbens in reward and motivational behaviour

The reward network has also been shown to play a critical role in many cognitive functions. Reward pathways are conserved across species (Öngür and Price, 2000) (Knutson and Cooper, 2005). The dopaminergic reward pathway, also referred to as the mesolimbic pathway, is part of the reward circuitry and the nucleus accumbens (NAc) is one of its major reward-related output (Figure 6.1). The NAc is located in the basal forebrain between the caudate and putamen. It is the main component in the ventral striatum and considered part of the basal ganglia. It is comprised of two areas, a core and a shell. The core and the shell whilst connected may contribute differentially to the function of the NAc. The

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nucleus accumbens receives outputs from dopaminergic neurons located in the ventral tegmental area (VTA). Dopamine is the key neurotransmitter of the VTA (~60 %), the remaining neurons are made up of glutamatergic and the inhibitory GABAergic neurons (Volman et al., 2013). Many studies investigated the role of the NAc dopaminergic activity, showing that dopamine levels in the NAc are elevated during rewarding and aversive stimuli in humans and animals (de Jong et al., 2019). Human imaging studies described the activation of the NAc during rewarding experiences such as monetary gain (Knutson et al., 2001), food stimuli (O’Doherty, 2004) and smiling faces (Spreckelmeyer et al., 2013). Beyond simple reward activation, dopamine levels can predict reward value, such as larger vs smaller rewards (Tobler et al., 2005) as well as the value of monetary reward (Zaghloul et al., 2009). Based on these rewarding properties the NAc has been implicated in addictive behaviour, with dysregulation of the mesolimbic pathway being associated with schizophrenia, and mood disorders (Grace, 2016). Dopamine may also be involved with memories associating pleasurable or aversive stimuli for future use and relating environmental cues that trigger reward seeking and motivational behaviours.

Ventral Tegmental Area(VTA)

Nucleus Accumbens (NAc)Sublenticular Extended Amygdala (SLEA)

Insula

Thalamus &Hypothalamus

Prefrontal Cortex &Cingulate Cortex

Limbic BrainstemAmygdala & Hippocampus

Dopaminergic & GABAergic

Dopaminergic

Glutamatergic

Figure 6.1: Simplified view of the mesolimbic reward pathway. Dopaminergic neurons in the ventral tegmental area project to a number of brain regions including the

nucleus accumbens, hippocampus and amygdala and the prefrontal cortex which are part of the limbic system The mesolimbic pathway is thought to be key in mediating

pleasure and rewarding experiences (adapted from (Makris et al., 2008)).

Aims of chapter

To date, it has not been possible to perform simultaneous EEG recordings with amperometry. Having previously established a non-invasive method of sleep

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restriction that induced performance deficits indexed by SRLT in Chapter 3, we decided to evaluate oxygen levels during SRLT performance following a non-invasive sleep restriction in rodents implanted with depth electrodes. The current chapter aimed to address how the nucleus accumbens responds during a reward driven attention task in the presence of sleep restriction in rats.

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6.2 Methods

6.2.1 Subjects and housing

Male Wistar rats (Charles River Laboratories, UK) were included in the analysis (n=35). Following recovery from surgery, animals were housed in standard animal holding cages. During SLRT training, rats were food restricted and once trained they were kept on an ad libitum feeding regime. At the start of testing animals weighed 495 ±12 g on Week #1 and 538 ± 14 go Week #2 (mean ± SEM).

6.2.2 Surgical procedures

Surgery for Amperometry recordings were carried out as described in Chapter 2, section 2.2

6.2.3 Amperometric technique

Changes in extracellular tissue oxygen concentration were measured using constant potential amperometry with Carbon Paste Electrodes (CPEs) implanted in the nucleus accumbens of the rat brain. A negative potential (−650 mV) was applied to the CPE to allow the electrochemical reduction of dissolved oxygen to occur at the tip of the electrode. Electrochemical reduction of O2 at carbon electrodes is a two-electron process producing H2O2:

O2 + 2H+ + 2e- → H2O2

H2O2 + 2H+ + 2e- → 2H2OThe direct reduction and oxidation of H2O2 is severely inhibited at carbon electrode surfaces hence the rate-limiting step is the initial one electron followed by protonation of the superoxide ion and further reduction. Therefore, changes in the measured current that are produced by the electrochemical reduction of O2 are directly proportional to the local extracellular tissue O2 concentration. Due to the large dimension of CPEs (typically 100–200 μm) being greater than the scale of a capillary zone (approx. 50-100 μm), an average extracellular tissue O2 level is detected regardless of the orientation of the electrode relative to the blood vessels and metabolically active sites, or the depth of penetration into the tissue (Francois et al., 2012).6.2.4 Sleep restriction methodology

Non-biofeedback sleep restriction was induced as described in Chapter 2, section 2.3 using the Weibull sleep restriction protocol. Seven days prior to study start, subjects were sleep restricted for 5 hours to habituate animals to the procedure.

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Subsequently, a randomised study design was conducted over 2 weeks where 16 animals received no sleep restriction and 16 animals received 11-h Weibull sleep restriction.

6.2.5 Behavioural tasks

Simple Response Latency task

SRLT studies were run as per the standard protocol as described in Chapter 2, section 2.7. The experimental protocol including sleep restriction is shown in Figure 6.2. From the SRLT task, the number of trials completed, and response errors (premature responses and omissions) were counted. Reaction times were recorded in addition to time on task effects.

ZT12

Sleep RestrictionZT0 – ZT11

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Behaviour“Test”

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Figure 6.2: Experimental protocol for sleep restriction. Male Wistar rats were housed under a 12-h:12-h Light (L) – Dark (D) cycle (lights ON at 08:00). On the baseline

day (i.e., pre-sleep restriction day, “Pre”), rats underwent the attention task (SRLT) approximately at Zeitgeber Time (ZT) 2-5. Following the 24-h baseline, sleep restriction commenced at the beginning of the light phase and lasted for 11 hours (ZT0-ZT11). At

ZT11, animals were placed immediately into operant boxes for a 30-min SRLT (“Test day”). Rats were then re-tested in the SRLT during the recovery period (“Post” day) ZT

2-5.

6.2.6 Histology

At the end of the study, animals were euthanised by a rising concentration of CO2 gas. Brains were removed and placed in 10% (w/v) buffered paraformaldehyde and shipped for histological processing (Neuroscience Associates Inc., Knoxville, TN).CPE placement was confirmed in 40 µm coronal sections and staining with thionin for Nissl bodies with reference to a standard rat brain atlas (George and Charles, 2007). Animals with inaccurate placements were excluded from subsequent analyses. Of the 35 brains, 6 could not be included due to missing data. CPE placement was checked in the remaining 29 brains (Figure 6.3). Only 12 rats showed placements that were deemed on target in the nucleus accumbens.

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+1.6 mm +1.7 mm

Figure 6.3: CPE placement confirmed by histology. Electrode placements in the nucleus accumbens taken from Paxinos and Watson rat brain atlas (George and Charles,

2007). The tip of each CPE is represented by a green triangle.

6.2.7 Data and statistical analyses

From the 12 rats with correct CPE placement, animals with poor signal quality or signal losses were further excluded from further analysis prior to data processing. Thus, after histology and signal quality exclusions, ten animals were included in the final analysis.Signal analysis was conducted using in-house Linux Data Analysis System (LDAS) (version 1) software. CPEs will vary in their sensitivity to O2 so it is not meaningful to compare signals between CPEs. For this reason, oxygen signals recorded per CPE was normalised according to their baseline (i.e., current averaged over a 1-sec period before beginning of trial). The basic measure was O2 levels measured in nA using the Chart software at a frequency of 40 Hz. These averaged signals were then reduced to 0.5-s time bins, and the O2

responses were compared using repeated-measures analysis of variance (ANOVA) followed by a univariate post-hoc analysis. The area under the curve (AUC) and the latency to peak amplitude (X-peak) of the signal were extracted

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from the average curve obtained for each behavioural outcome. General linear model factorial ANOVA was used for analysis. All statistics were conducted using Statistica (v13.2) software.

6.3 Results

6.3.1 Overall oxygen responses within the nucleus accumbens during the behavioural

SRLT task

We first analysed the amperometry recordings to assess the overall oxygen responses in the nucleus accumbens during a 10-min ‘baseline’ recording prior to the SRLT, both on the baseline (pre-test) day (Figure 6.4) and test day (Fig 6.5). On the baseline day, no significant changes in overall regional oxygen levels, Area Under the Curve (AUC) (F1,8 = 0.57, p = 0.471) and peak response (i.e., X-peak corresponding to the latency to oxygen peak response) (F1,8 = 0.07, p = 0.795) were observed during the SRLT (Fig. 6.4 a-c).

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Figure 6.4. Oxygen responses during SRLT on baseline (pre) day. (a) Time course of raw oxygen levels in the nucleus accumbens during a 10-min baseline recording and

subsequent 30-min SRLT, (b) Averaged AUC of O2 responses during the 40-min block (i.e., 10-min baseline + 30-min SRLT) (mean ± SEM), (c) Average X-Peak values of O2

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responses during the 40-min session (mean ± SEM). Control group (NSR; blue), sleep deprived (SR; red). Group numbers are indicated on panels.

On test day, amperometry recordings were performed following sleep restriction during a 10-min baseline recording followed recordings throughout SRLT testing. The behavioural task did not induce significant differences in oxygen levels and averaged AUC (F1,7 = 0.28, p = 0.616) across the course of the SRLT (Fig. 6.5a-b). However, peak values showed a tendency for a greater O2 peak response in the sleep restricted group compared to the control group (F1,7 = 5.2, p = 0.057; Fig. 6.5c).

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Figure 6.5. Oxygen responses during the SRLT tasks following 11-hr sleep restriction. (a) Overall oxygen change in the nucleus accumbens during 10 min baseline recordings followed by the 30 min SRLT, (b) AUC of regional response during the 40-min

recording session (i.e., 10-min baseline and 30-min SRLT) (mean ± SEM), (c) X-Peak responses during the 40-min recording session (mean ± SEM). Control group (NSR;

blue), sleep deprived (SR; red). Group numbers are indicated on panels.

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Based on these results, we decided to refine the analysis of task-induced changes in the oxygen response focusing on the 30-min SRLT and specifically for specific task parameters.

6.3.2 Oxygen response in the nucleus accumbens during specific SRLT events

On baseline (pre-) day, averaged oxygen levels during SRLT were analysed for correct responses, premature responses and omissions. The mean O2 responses showed an overall effect of time during correct responses and premature responses (Fig 6.6 a, d). The AUC of the NAc response showed no significant differences during the 30-min SRLT for correct responses and premature responses between the two assigned groups, as expected (i.e., sleep restriction will only take place during the subsequent “test” day. Fig. 6.6 b, e; Table 6.1). An overall decrease in oxygen levels was observed during omissions (Fig. 6.6h). However, in this analysis no animals were included in the assigned “sleep restriction” group due to exclusion. The peak response showed a similar magnitude for all SRLT parameters and there were no differences between the two assigned groups.

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Figure 6.6. Amperometry readouts within the nucleus accumbens during specific SRLT parameters on baseline (pre-test) day. (a) Time course of mean

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oxygen responses in the nucleus accumbens during Correct Responses, (b) Area Under the Curve (AUC) for correct responses, (c) X-Peak response for correct responses, (d)

Mean oxygen response during omissions, (e) AUC during omissions, (f) X Peak values for omissions, (g) Mean oxygen response during premature responses, (h) AUC for

premature responses, (i) X Peak values for premature responses. (a), (d) & (g): time course of O2 response are averaged per minute across the 30-min test session and

include a 5-min baseline. Control group (NSR; blue), group that will be sleep deprived on test day (SR; red). Group numbers are displayed on each panel. Asterisks refer to

significance of sleep restriction condition to the Control condition, * p <0.05. All data represented as mean ± SEM.

P Values Parameter PRE DAY TEST DAYCorrectResponses AUC F1,5 = 0.61, p = 0.470 F1,8 = 2.78, p = 0.134

X-Peak F1,5 = 2.24, p = 0.195 F1,8 = 6.4, p = 0.035

PrematureResponding AUC F1,8 = 1.34, p = 0.723 F1,8 = 1.78, p = 0.219

X-Peak F1,8 = 0.84, p = 0.386 F1,8 = 1.14, p = 0.718

Omissions AUC No Data F1,5 = 0.61, p = 0.470

X-Peak No Data F1,5 = 2.24, p = 0.195

Table 6.1. Statistical F and p-values for AUC and X-Peak for pre day and test day. Table shows statistics reported as FDFNUMERATOR, DFDENOMINATOR, p-value for AUC and X-

Peak shown in Figures 6.6 and 6.7.

On test day, the sleep restricted group showed an overall reduced oxygen response within the nucleus accumbens during correct trials/responses compared to the non-sleep restricted group (Fig. 6.7 a). Post-hoc analyses showed statistical significance between the two groups at time points between 16 and 20 minutes during the SRLT (Fig. 6.7 a). Whilst the corresponding AUC was not significantly different, the O2 peak response was significantly decreased in the sleep-restricted group (Fig. 6.7 b-c; Table 6.1). While O2 responses during premature responding were associated with an overall time and condition effect (Fig. 6.7 d), the AUC and peak response showed no significant differences

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between conditions (Fig. 6.7 e-f). Omissions were not associated with any significant differences in overall oxygen levels between the non-sleep deprived group and sleep restricted group (Fig. 6.7 g-i).

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Figure 6.7. Amperometry readouts during specific SRLT events following an 11-hr Weibull non-invasive sleep restriction. (a) Time course of oxygen responses

during Correct Responses, (b) AUC for correct responses, (c) X-Peak values for correct responses, (d) Time course of oxygen responses during omissions, (e) AUC during

omissions, (f) X Peak values for omissions, (g) Time course of oxygen responses during premature responses, (h) AUC for premature responses, (i) X Peak values for premature

responses. Control group (NSR; blue), sleep deprived (SR; red). Group numbers are shown on individual panels. Asterisks refer to significance of sleep restriction condition to the Control condition, * p <0.05; ** p < 0.01. All data represented as mean ± SEM.

6.3.3 SRLT outcomes following 11-h non-biofeedback sleep restriction in rats used for

amperometry recordings

We first confirmed that the SRLT performance showed a deficit following 11-h sleep restriction (Figure 6.8). The complete cohort of rats tested showed a

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significant decrease in number of correct trials (F1,28 = 13.15, p = <0.001; Figure 6.8a) and premature responses (F1,28 = 4.20, p = 0.050; Figure 6.8 c) in the rats subjected to an 11-h sleep restriction, while the number of omissions was increased (F1,28 = 17.88, p = <0.001; Figure 6.8b). In addition, a significant reduction in time-on-task effects for reaction time events was observed following sleep restriction compared to the control group at 20 mins and 30 minutes (Figure 6.8e). No significant effect was observed for reaction time latency (F1,28 = 1.98, p = 0.170; Figure 6.8d) or time on task reaction time (Figure 6.8f).

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Figure 6.8. Effect of a 11-h sleep restriction on SRLT in all ad libitum-fed rats instrumented for amperometry. (a) Number of Trials, (b) Number of Omissions, (c)

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Number of Premature Responses, (d) Median Magazine Latency, (e) Time on Task (Number of Reaction Time Events), (f) Time on Task (Reaction Time Latency ms). All

data represented as mean ± SEM. Control (NSR) group (blue; n=15), sleep restricted (SR) group (red; n=16). Condition relates to SR or NSR and Time refers to each 10 min time

block. Asterisks refer to significance of sleep restriction condition to the Control condition, * p <0.05; **p < 0.01, ***p < 0.001.

As a number of subjects had to be excluded from the amperometry analysis, we then re-analysed the performance data including only the rats that participated in the amperometry analysis (Figure 6.9). The number of trials completed showed no significant differences in rats included for amperometry analysis (F1,8

= 0.34, p = 0.575; Figure 6.9a). The number of omissions and premature responses only showed a tendency (defined as p<0.08) towards differences between the groups (omissions: F1,5 = 4.75, p = 0.081; prematures: F1,8 = 4.34, p = 0.071; Figure 6.9b-c).

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Figure 6.9. Effect of 11-h sleep restriction on SRLT in ad libitum fed rats included in the amperometry analysis. (a) Number of Trials, (b) Number of

Omissions, (c) Number of Premature Responses, (mean ± SEM). Control (NSR) group (blue bars), sleep restricted (SR) group (red bars).

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6.3.4 Correlations between SRLT outcomes and the oxygen responses within the

nucleus accumbens following 11-h sleep restriction.

We computed regression slopes for each SRLT parameter as a function of oxygen levels indexed by the AUC and X-peak of regional responses during the 30-minute behavioural task (Figures 6.10 & 6.11). None of the parameters exhibited a significant linear relationship.

Figure 6.10: Correlations between the AUC of oxygen response in the nucleus accumbens and SRLT performance parameters. (a). Correlation for the number of

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trials completed; (b). Correlation for the number of omissions; (c). Correlation for the number of premature responses. Non-sleep restricted rats (blue circles) and sleep restricted food rats (red circles). Group numbers are shown on individual panels.

Figure 6.11: Correlations between X-Peak in the nucleus accumbens and SRLT performance parameters. (a). Correlation for the number of trials completed; (b). Correlation for the number of omissions; (c). Correlation for the number of premature responses. Non-sleep restricted rats (blue circles) and sleep restricted food rats (red

circles). Group numbers are shown on individual panels.

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6.4 Discussion

The aim of this chapter was to assess how the nucleus accumbens (NAc) responds during a sustained attention task following sleep restriction and to which extent the in vivo amperometry technique provided translation outcomes that could be compared to studies using BOLD measurements in humans to characterise the effects of sleep loss. We observed a significant reduction in oxygen X-peak responses during correct responses in the sleep restricted group during the SRLT. Nonetheless, the task-induced changes in overall oxygen levels within the NAc during the SRLT did not show any differences between sleep restricted rats and the control group. A number of limitations is associated with the study presented in this Chapter and will be discussed below.

Oxygen amperometric effects in reward driven tasks.

Human and rat studies are congruent in reporting the involvement of the NAc in reward based tasks (Hikida et al., 2016) (Nunes et al., 2013). Any observed increase in oxygen levels in the NAc during performance could reflect activity in the mesolimbic dopamine neurons. BOLD signal increases in the accumbens have been shown in humans performing monetary rewarded tasks (Knutson et al., 2001) (Tobler et al., 2007) (Miyapuram et al., 2012). In rodents similar increases in oxygen have been measured in the accumbens in response to food rewarded tasks which can be modulated by both the magnitude and motivational incentive of the food (Francois et al., 2012). In a subsequent study by Francois, regionally specific differential responses were observed in the NAc and infralimbic cortex of rats tested in rewarded tasks (Francois et al. 2014). Together these results indicate oxygen amperometric responses to manipulation of reward based tasks can be observed in an equivalent manner in humans and rodents (Li et al., 2015b). Based on the literature from humans and rats, we therefore hypothesised that an increase in the oxygen levels in the NAc would be apparent during a food rewarded attention task in the rats and then diminished following sleep restriction. However, there are many limitations associated with this study and in particular the low number of animals compared to other studies. This may have contributed in part to the negative results shown here. The only significant difference observed was a significant decrease in latency to reach peak as indexed by X-peak values in the sleep restricted group. Previous findings in rodents reported a specific increase in NAc tissue oxygen levels in response to

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food rewarded lever presses on presentation of rewarded cues, whereas unrewarded cues did not produce a similar increase. These data suggested that operant responding itself does not play a role in driving these oxygen increases. This implies that either the delivery of the reward or the motivational value of the cue is pivotal in producing the rise in oxygen (Francois et al., 2012). Furthermore, previous studies in rats demonstrated that a stimulus can increase oxygen levels within the NAc. These studies also highlighted that the speed of the increase was dependent on the stimulus, such that exposure to weak (e.g., auditory tone) stimuli were accompanied by a slower increase and strong (e.g., tail pinch) arousing stimuli with a faster increase. The increases were shown to be rapid, occurring within several seconds from the stimulus onset (Solis Jr et al., 2017). A further study corroborated these findings showing overall increases in NAc oxygen levels in the presence of arousing stimuli, with oxygen responses occurring 8-12-s after stimulus onset (Kiyatkin, 2018). This could be important in reassessing the temporal resolution of our oxygen signals during stimulus presentation in the attention task.

Carbon paste electrodes measure extracellular oxygen levels and it is thus not feasible to distinguish between consumption and supply. Since during amperometry experiments readings are recorded over seconds, rather than longer time frames, the levels measured are more likely to reflect supply. Thus, signals are dependent on cerebral blood flow more akin to BOLD signal measures using fMRI. Based on the strong relationship between the haemodynamic response and neuronal activity, amperometric responses may be driven to a degree by the haemodynamic response following dopamine release. Pharmacological MRI studies have supported this concept by showing that dopamine releasing agents or agents that block dopamine reuptake increased the BOLD signal in the NAc (Knutson and Gibbs, 2007). In addition, studies using PET and fMRI in a human monetary incentive delay task showed correlations between BOLD activation during reward anticipation and dopamine release in the ventral striatum (Schott et al., 2008).

Oxygen amperometric effects on task performance following sleep restriction

It has been established that attention tasks are particularly sensitive to chronic and total sleep loss in humans (McHill et al., 2018) (Kusztor et al., 2019) and rats (Deurveilher et al., 2015) (Oonk et al., 2015). Evidence was obtained from studies using fMRI in human subjects tested in an attention task under different

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conditions of arousal (i.e., normal arousal, caffeine-induced arousal and low level arousal induced by 24-h sleep deprivation). Arousal was found to modulate activation of the thalamus, with the greatest activation correlating with the lowest arousal level induced by sleep deprivation. No modulation was observed in the prefrontal cortex, anterior cingulate cortex or the parietal regions (Portas et al., 1998). Human PET studies have tested subjects in a continuous performance task following 32-h sleep deprivation. Reduced metabolic rates (i.e., regional cerebral metabolic rate of glucose) were observed specifically in the thalamus and cerebellum (Hershey et al., 1991) (Wu et al., 2006). Evaluation of the cerebral haemodynamic response using BOLD fMRI combined with a divided attention and verbal learning task following one night’s sleep deprivation showed activation in the prefrontal cortex regions (precentral gyrus and precuneus) and the parietal lobes. By contrast, a decreased activation was see during an arithmetic task in these same regions (Drummond and Brown, 2001). These results indicate that activation and deactivation of specific brain regions during attention tasks following sleep deprivation may depend on the nature of the task used.

Limitations of in vivo amperometry

Rodent oxygen amperometry is still a relatively recent advancement and several shortcomings remain and may impact on its translational utility. A significant downside to electrode implantation is in the a priori approach required. Unlike fMRI, it is not feasible to monitor the whole brain simultaneously and hence discrete areas of interest have to be decided prior to the study. To date, a maximum of four working electrodes can be implanted (4 unilateral or 2 bilateral). Due to the size of the CPE electrodes, there is also a limitation as to which areas of the brain implantation can be achieved. Implantation can only be performed in relatively large areas such as the striatum, nucleus accumbens, cortex, pallidum or hippocampus (O'Neill et al., 1998). It is understood that neuronal activation dilates cerebral vessels and hence increases local cerebral blood flow oxygen. With respect to the BOLD signal, as mentioned above BOLD fMRI and O2 amperometry measure regional neuronal activity via the heamodynamic processes of neurovascular coupling and therefore any effects that alter cerebral blood flow or neurovascular coupling will affect the signal but will not necessarily reflect neural activity per se. From work with stimulus-related functional MRI (fMRI), it is known that neurovascular coupling is conserved across species, although specific features such as hemodynamic delay

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may differ (de Zwart et al., 2005). In addition, neurovascular coupling mechanisms in different brain regions may be differentially altered in various disease states. As shown in the current study, another consideration concerns the throughput that can be achieved to generate sufficiently powered studies and produce robust pre-clinical data. This is due to a number of factors. The requirement to tether animals can be a limitation both with respect to the equipment required and equally in the variety of behavioural tests available under tethered conditions. In addition, due to the inherent noise in the amperometric signals, behavioural tasks run in parallel to amperometry must have sufficient numbers of events per session to acquire enough data fit for analysis to increase the signal to noise ratio. From a purely behavioural aspect, our data showed that 11-h sleep restriction was effective in producing significant behavioural deficits in the SRLT in line with previous experiments presented in this thesis, namely an effect on trials, omissions and premature responses. Despite this overall effect on SRLT using the complete cohort of rats when we re-analysed these experiments excluding animals that had poor signal quality in amperometry recordings or inaccurate electrode placements, the performance data no longer showed significant differences. The reduced numbers included in the amperometry analysis, due to exclusions for histology placements and signal quality led to a limited statistical power. This is highlighted in our data set where the original cohort of 32 animals was reduced to less than a third of this number following exclusions.

Translational implications of in vivo amperometry

Human fMRI delivers a reliable platform to measure neuronal activity and has been key to gain further understanding into brain function and aiding in drug discovery. Oxygen amperometry provides a surrogate for the BOLD fMRI in rodents and has the potential to offer a preclinical translational biomarker that supercedes previously available anaesthetised rat fMRI. The rodent amperometry technique offers the advantage of real-time measurements in freely moving animals allowing the ability to couple behaviour and regional brain activity and back translate to human outcomes.

Whilst only one region was presented in this Chapter, the method provides in theory the opportunity to assess functional connectivity. Intrinsic functional brain connectivity between discrete brain regions when subjects are at rest have shown organised spatial-temporal patterns in low frequency (0.1 Hz) BOLD signal

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fluctuations leading to the identification of many resting networks (RSNs) (Marino et al., 2019). Abnormalities in these resting state networks may hold promise to determining the basis of neuropsychiatric disorders (Mohan et al., 2016) (McTeague et al., 2017), pharmacological agents and the impact of sleep deprivation. Resting state networks in rodents have been fundamentally difficult to assess as a rodent resting state is difficult to determine and often requires anaesthesia during recordings (Pan et al., 2018). This should be taken into account given that anaesthetic can cause metabolic inhibition, brain hypothermia and altered tone of cerebral vessels (Pan et al., 2015). Studies investigating connectivity in rodents using oxygen amperometry indicate dynamic differences in functional connectivity that equate to human studies. In freely moving rats, tasked with instrumental responding for a food reward, activity was measured between rodent brain regions known to be part of the default mode network (DMN) coupled with brain regions of the lateral cortical network (LCN) (Li et al., 2015b). This showed that baseline levels of functional connectivity were greater for those node pairs within the DMN and LCN compared to pairings measured between the networks, a finding consistent with the human rsfMRI results. The ability to simultaneously measure functional connectivity with behaviour using O2 amperometry revealed that the DMN, but not the LCN, was sensitive to behavioural state, where decreases in functional connectivity occurred as animals engaged in instrumental action compared to periods of spontaneous, unscheduled behaviour (Li et al., 2015b). The ability to determine intrinsic connectivity in rodent studies using BOLD fMRI has introduced the ability to compare of the action of pharmacological agents in humans and rodents on functional connectivity (Zhu et al., 2013) (Gass et al., 2014). Oxygen amperometry has already contributed to our understanding of antipsychotics, where distinct mechanistic differences between typical and atypical antipsychotics have been determined. Li and collaborators showed that haloperidol and clozapine have dissociable effects on ketamine-induced increases in O2 signal magnitude and coherence between the medial prefrontal cortex and ventral striatum in the rat (Li et al. 2014). These results show an equivalence with human pharmacological studies (De Simoni et al., 2013) (Driesen et al., 2013) (Joules et al., 2015).

Rodent amperometry holds thus potential to provide the connection in testing drug target engagement in rodents with outcomes in human clinical trials that would increase the success rate in drug discovery. However, there remains a

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requirement in refinement of the protocol to be able to produce the throughput that sufficiently powers studies for pre-clinical validation. Recent studies have also begun to investigate mapping of rsfMRI functional networks in the mouse (Shah et al., 2016) (Sforazzini et al., 2014). Using an anterograde tracer map of axonal projections across the mouse CNS (i.e., mesoscale structural connectome) it has been shown that a DMN in the mouse corresponds structurally and functionally to humans (Stafford et al., 2014). The extensive availability of mouse transgenic models opens up a vast resource with the capability of assessing brain function in more specific models of disease states (Reid and Finnerty, 2017) (Xu et al., 2015). If the limitations as set out above could be addressed, the prospect of using oxygen amperometric techniques would deliver exciting opportunities and complement human studies to bridge a translational gap.

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CHAPTER SEVEN

Chapter 7 - General Discussion

Sleep deprivation is a widespread problem associated with a number of negative health outcomes (Liu, 2016). In particular, deficit in performance associated with sleep loss has been well documented. Thus, there is a need for treatments to restore cognitive functioning in unavoidable situations of reduced sleep. Well designed, preclinical rodent sleep restriction studies have the capacity to facilitate translational sleep research. Effective preclinical studies need to deliver sleep restriction protocols that produce functional impairments equivalent to cognitive deficits in humans induced by sleep deprivation. These paradigms could then be utilised to test novel pharmacological compounds or alternative methods to combat sleep loss. This thesis aimed to assess the translational value of our in-house rat ‘model’ which combined newly developed non-invasive sleep restriction protocols with attentional tasks. We used this model to assess similarities and/or differences on the consequences of sleep loss in rodents and humans. We demonstrated that the non-invasive sleep restriction protocols produced a functional behavioural deficit in a simple attention task. It was further shown that rodent attentional tasks that utilise food for task reward affect motivational behaviour that attenuates sleep restriction effects. To further assess the translational value of our rodent sleep restriction set-up, we applied pharmacological manipulations using wake-promoting compounds. We were able to replicate results from the human literature providing a platform that can be utilised to assess the efficacy of novel pro-vigilant compounds. We also assessed to which extend oxygen amperometry, a surrogate of human neuroimaging, would provide a potential biomarker for brain function in the rodent. However, our results were inconclusive, likely due to technical limitations.

The majority of preclinical studies focus on the effects of total sleep restriction in simple attention tasks. This has a number of limitations which will be discussed in this Chapter, and in particular, it has not yet been fully determined whether

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total sleep restriction and chronic sleep restriction produce equivalent effects in terms of cognitive deficits, recovery, effects on homeostasis or circadian rhythms and physiological consequences in rodents that translate to human data. Another caveat of many studies is that simple attention tasks do not sufficiently addresses loss of cognitive function across multiple domains.

Comparison of sleep deprivation and induced performance deficits in rats and

humans

Nature of the sleep manipulation

First, to compare the effects of sleep loss in rodents and humans, it is important to consider the nature of the intervention used to curtail sleep. Enforced activity non-biofeedback sleep restriction studies often employ on/off cycles with a constant interval (Stephenson et al., 2015), or a linear decreasing inter stimulus interval that shortens over the sleep restriction period reflecting the increasing homeostatic drive to sleep with extended wakefulness (Leenaars et al., 2011). In that context, we developed three non-invasive sleep restriction protocols that were directly compared within one study with a “gold standard” sleep restriction protocol driven by EEG biofeedback. This study compared a constant interval protocol, a decreasing inter-stimulus protocol and a ‘Weibull’ protocol inducing a wheel turn pattern comparable to automated EEG-driven biofeedback sleep restriction (McCarthy et al., 2017). The Weibull model was developed in order to prevent entrainment to an arousal synchrony i.e. the animals could not predict when the wheel would turn. All protocols reduced total REM and NREM sleep, although the number of sleep attempts was increased in the three non-invasive methods, while the sleep bout length was shorter compared to the gold standard biofeedback method. The number of sleep bouts may have contributed to differences in functioning. With regard to the recovery period, all methods showed an increase in total sleep time, longer sleep bout lengths and increased NREM sleep delta power (SWA) compared to control non-sleep restricted rats. These data are in accordance with previous studies showing both in rats and mice that acute sleep deprivation ranging from 6 to 24-h duration, are associated with increased NREM sleep and delta power (Huber et al., 2000) (Curie et al., 2013). These findings in rodents replicate the effects of total sleep deprivation on sleep and the EEG in humans (Achermann and Borbély, 2017) (Dijk et al., 1987), with a significant rebound hypersomnolence proportional to the length of the sleep restriction (Borbély et al., 1981; Bonnet and Arand, 2003).

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Sleep restriction of between 4 and 6 hours per night showed that a sleep debt may be accumulated over time, causing gradual cognitive impairment and greater rebound hypersomnolence (Van Dongen et al., 2003). During recovery, there is evidence that chronic partial sleep deprivation is akin to total sleep deprivation (Philip et al., 2012). Limiting sleep opportunity to 6-h per 24-h for seven days produced, on recovery days an increase in total sleep time during recovery sleep that was similar to recovery sleep increases following TSD (Lo et al., 2012).

All experiments reported in this thesis have primarily used acute 11-h sleep restriction that would compare to standard 40-h total sleep deprivation in humans. However, to ultimately study the effects of sleep loss on performance, it could be argued that one week of sleep restriction (e.g., 4-h of sleep opportunity per 24-h) would better reflect sleep loss encountered in real life. In rodents, few studies have used chronic partial sleep restriction and these studies reported mixed results. Using 4-h sleep per 24-h during five days, Leemburg and collaborators showed an increase in SWA, reflecting a homeostatic regulation of NREM sleep during sleep restriction (Leemburg et al., 2010). By contrast, 5 days of chronic partial sleep restriction of 20-h with 4-h sleep opportunity in rats showed that on the first recovery day (i.e. an acute effect), there was enhanced NREM sleep, delta power and REM sleep indicative of a homeostatic response. On days 2-5 when the sleep restriction became chronic, there was no compensatory NREM delta power or REM sleep suggesting a more allostatic response (Kim et al., 2007). These results have been supported by Deurveilher using 3-h deprivation/1-h sleep protocol for four days whereby it was shown the initial increase in SWA then declined, suggesting an allostatic response to chronic sleep restriction (Deurveilher et al., 2012). These results indicate that certain chronic sleep restriction protocols are sufficient to induce allostasis (Stephenson et al., 2015). In humans, the effects of subchronic sleep restriction was studied for a large range of durations (i.e., 2 days to several weeks) and overall showed a homeostatic response indexed by SWA (Brunner et al., 1993)(Åkerstedt et al., 2009) (Plante et al., 2016). However, a recent study showed that during 1-week chronic sleep restriction in humans, reductions were mainly observed in REM sleep while SWA remains largely unchanged (Skorucak et al., 2018). This conservation of SWA suggests that NREM EEG slow waves may provide some protection against the effects of sleep loss, suggesting that

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enhance SWA enhancement could be an avenue for improving cognition (Wilckens et al., 2018).

Nature of the behavioural assessment

There is a requirement in pre-clinical research for translational cognitive assessment that rely on construct validity, i.e. the ability to test the same psychological construct across species. Some commercially available human cognitive test batteries have been back-translated for use in rodents (Horner et al. 2013, Mar et al. 2013, Oomen et al. 2013), one of which is the psychomotor vigilance task (Dinges and Powell, 1985). Vigilance tasks have been used extensively in human (Grandner et al., 2018) (Basner and Dinges, 2011) and rodent studies (Loomis et al., 2015) (Davis et al., 2016) to assess the consequences of insufficient sleep. Sustained attention in humans has been known to be highly sensitive to the impact of sleep loss (Lim and Dinges, 2008). The hallmark of sleep deprivation is a deterioration in task performance progressing in a dose-dependent manner with increasing time awake, due to mounting sleep pressure (Van Dongen et al., 2003). The SRLT is considered a rodent homologue of the human PVT (Davis et al., 2016) (Deurveilher et al., 2015), and is a validated translational assay (Christie et al., 2008) within the context of sleep restriction protocols. However, some fundamental differences between the human PVT and rat SRLT should be noted. The rat SRLT used in this thesis incorporates a house light to act as the preparatory cue, followed by a variable interval (range 4-6s), after which the magazine light (imperative cue) is illuminated. The rat then has a period of 10-s to perform a nose poke to receive a food reward. This is followed by a 20-s interval before the next trial commences. Minor deviations across laboratories in the rodent version include the variable interval (3-10-s), and time allowed to respond to the imperative cue (1.5 -3-s) (Davis et al., 2016) (Deurveilher et al., 2015) (Christie et al., 2008). The most fundamental difference between the rodent SRLT and human PVT is the exclusion of a preparatory cue and the time allowed to respond to the cue before it is recorded as an omission. As with rodent studies, human PVT protocols are not fully standardised across laboratories (Basner and Dinges, 2011). Despite these slight variations in protocol, the three main and most consistent outcomes from rodent SRLT and human PVT in the presence of sleep restriction are an increase in omissions, a slowing of reaction time and a

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deterioration of performance across the task. The number of trials completed and premature responding (a response before the stimulus is presented) are also reported in rats. The latter corresponds to an error of commission in humans. Human data unlike rodent data often report lapses (reaction times slower than 500ms) this outcome appears consistent between acute and partial sleep deprivation (Philip et al., 2012) and may relate to the increase in sleep related accident risk (Goel et al., 2009). In human PVT studies, impulsivity per se is reported to be differentially affected by both partial and acute sleep deprivation (Demos et al., 2016) (Cedernaes et al., 2014). How this impacts increased risk taking and affects informed decision making would need to be further studied to understand how it contributes to the increased accidental risk seen under conditions of sleep deprivation (Womack et al., 2013) (Lowe et al., 2017).

We showed that an acute 11-h sleep restriction using either the decreasing or Weibull sleep restriction protocols produce deficits in the standard measures of SRLT (i.e., an increase in trials and omissions), as well as time-on-task effects as per human data (Doran et al., 2001a) (Satterfield et al., 2017). By contrast, the constant interval turning protocol did not induce behavioural deficits measured using SRLT and we were unable to demonstrate consistent increases in reaction time. In addition, findings presented in this thesis show a full recovery of performance on post days i.e., the day after sleep restriction. A limitation of our experiments is that the effects of chronic partial sleep restriction were not addressed. However, our results are in accordance with the human literature. A rapid decline in PVT performance as indexed by PVT lapses has been well documented after one night of total sleep deprivation. The equivalent number of lapses induced by short-term chronic sleep deprivation do not occur until around 10 days with 6-h hours sleep opportunity (Short and Banks, 2014). A comprehensive meta-analysis on sleep restriction and a range of cognitive domains suggested that sustained attention and executive function domains are most affected by sleep deprivation and that these effects increase with accumulating days of sleep loss (Lowe et al., 2017). Subsequent recovery from sleep deprivation is also faster with acute sleep deprivation than chronic. Two studies one using 1 nights total sleep deprivation and the second using 2 nights showed the decline in PVT is rapid but returns to baseline after one nights recovery (Jewett et al., 1999b) (Drummond et al., 2006). Chronic sleep restriction limited to 3-6 hours per night for one week results in cognitive decline as indexed by vigilance tasks (Cohen et al., 2010) (McHill et al., 2018) and

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cognitive recovery requires 3-4 nights (Belenky et al., 2003). In more prolonged chronic studies, applying 6-h restricted sleep time for weeks rather than days, individuals begin to adapt and waking function is no longer affected (Van Dongen et al., 2003). The two-process model regulating sleep may provide an explanation to loss of vigilant attention under acute sleep deprivation conditions. McCauley and collaborators adapted the two-process model to investigate why attentional deficits are not maintained during chronic sleep loss and included an additional component whereby the homeostatic process was modulated across weeks (McCauley et al., 2009). It was postulated that a critical duration of 20.2h wakefulness per day would detrimental impact neurobehavioural performance. If this critical amount is not exceeded over time, functional deficits are stabilised, which supports an ability to functionally adapt to sleep loss.

Interaction of motivation in assessing the effect of sleep in cognitive tasks

An important consideration in the translational value of rodent assays is that most rodent operant task use food or liquid reward in order to achieve task performance. These rewards may potentially confound outcomes by eliciting increased motivation to complete the task. Our study assessing the effects of motivation on SRLT performance following sleep restriction revealed that rats in a food deprived state were able to overcome the effects of sleep deprivation. Their performance in the SRLT was similar to the one measured on baseline day. This was unlikely due to a motivational decrement as performance in the progressive ratio task did not show any effect of sleep deprivation. In addition, performance in the Concurrent Fixed Ratio 5 task (CFR5) task provide an index of effort-related choice behaviour. This study highlighted the importance of the food value of the sugar pellet rather than the reward value. When rats were sleep deprived, their total food consumption was not decreased but they were less willing to increase their effort for a higher reward. Thus, during SRLT it was more likely hunger that provided the motivation to overcome the impact of sleep deprivation rather than the sugar pellet. Human studies reached similar conclusions. Monetary incentives, and hence motivation are reported to overcome deficits caused by sleep deprivation in human studies (Mullin et al., 2013). From a translational viewpoint, monetary reward cannot be applied to rodent studies. However, human studies have also shown activation in the striatum and amygdala following sleep deprivation to neutral and smiling faces (Gujar et al., 2011) or high fat and regular food (Greer et al., 2013). This

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activation appears dependent on receiving reward and does not necessarily relate to the value of the reward.

Overall, the human PVT and rat SRLT can measure vigilance deficit in light of sleep restriction but how relevant this deficit is will be discussed later in this section, as it should be discussed to which extent vigilance may only measure a form of sleepiness or whether it may inform on other cognitive measures.

Translational value: pharmacology and napping in sleep studies

The translation of findings from rodent to human studies has been criticised as lacking cross-species measures to demonstrate the efficacy of drugs of interest in different species (Morgan et al. 2012). To date, very few pharmacological studies in sleep restricted rats have assessed the drug effect on behaviour and performance. We set out to assess if sleep restriction protocol in rats mirrored pharmacological outcomes in humans that would pave the way in preclinical research of novel pro-vigilant pharmacological agents. The effects of three compounds caffeine, amphetamine and modafinil were evaluated for their ability to ameliorate sleep restriction effects in the rat as indexed by SRLT. We showed that established wake-promoting compounds such as caffeine and modafinil counteracted the effects of sleep restriction on performance on SRLT, whereas amphetamine worsened performance in the SRLT. Our results parallel pro vigilant action of caffeine and modafinil as shown in human studies (Loomis et al., 2015). Our data provide a positive argument in favour of the translational value to assess sustained attention following acute sleep restriction. However, we have not addressed whether these pro-vigilant effects would be as effective in situations of chronic sleep loss or effective on other cognitive domains. In the context of translational value, particular attention should be paid to the doses tested and route of administration to ensure a comprehensive comparison between rodents and humans.

We also assessed the impact of napping on performance deficit induced by sleep restriction, as in human studies, napping has been shown to improve performance in human studies (McDevitt et al., 2018) (Oriyama and Miyakoshi, 2017). However, we showed no effect of napping in rats on SRLT outcomes. This could suggest either napping is simply less effective in rodent studies or the process of sleep inertia in rodents interfered with examining this non-

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pharmacological countermeasure in the attention task. Sleep inertia is known to affect processing on waking in humans (Vallat et al., 2019) and this process has also been identified in rodents (Vyazovskiy et al., 2014). However, we did not assess the effect of sleep inertia in the studies presented in this thesis.

Neuroimaging contributions to understanding cognitive function and sleep

Advances in neuroimaging studies in humans have accelerated our understanding of brain function in relation to vigilance, sleep/wake states, circadian processes and pharmacological interactions. If these techniques can be applied to rodents, they hold the potential to provide a translational tool to expedite the introduction of compounds into the clinic. Neuroimaging could help elucidate which networks are resilient or vulnerable to sleep deprivation by understanding regional or network activity, changes in functional connectivity and explain changes in behavioural responses in light of sleep deprivation (Krause et al., 2017). Neuroimaging in human studies have already reported effects that may help determine the nature of attentional deficits with sleep deprivation, showing reductions in fMRI signals in the dorsolateral prefrontal cortex and intraparietal sulcus during attention task performance following sleep deprivation (Chee et al., 2011). It has also been shown that the default mode network continues to engage during sleep (Horovitz et al., 2009). Human imaging during PVT assessment in sleep deprivation studies have shown that increments in EEG power in alpha, theta and beta bands in parieto-occipital, central medial and frontal components were associated with slower reaction times. These EEG power increments reflect the increased activity of the default mode network that would be indicative of attention lapses (Molina et al., 2017).If neuroimaging techniques could identify biomarkers and be used for early detection of the detrimental impact of sleep restriction, or even the detection of cognitive decline early on, this would provide a significant breakthrough in early diagnosis. In humans, deregulation of the default mode network during wake has been recognised in many neuropsychiatric and neurodegenerative disorders, including schizophrenia (Garrity et al., 2007), autism spectrum disorders (Cherkassky et al., 2006), anxiety disorders (Zhao et al., 2007), attention deficit disorder (Helps et al., 2010) and Alzheimer’s disease (Agosta et al., 2012). These disorders all show comorbidity with sleep abnormalities.

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Rodent oxygen amperometry is an exciting tool that has contributed to our understanding of brain functioning during periods of insufficient sleep and/or during task performance (McHugh et al., 2011) (Li et al., 2015a) (Li et al., 2015b). However, our study underscored some technical limitations that precluded making conclusions regarding the circuitry response in the nucleus accumbens in rats and also suggest that this technique is less likely to identify biomarkers than neuroimaging methods in humans may achieve.Taking into account circadian rhythms when assessing the impact of sleep restriction on

behaviour

Circadian rhythms influence performance, resulting in exponentially scaling attentional impairment with extended wakefulness (Silva et al., 2010). Extended time spent awake predicts lapses in attention during acute sleep deprivation and during chronic partial sleep restriction. Circadian rhythms have an important impact on performance during sleep deprivation such that the poorest performance coincides with the circadian trough of core body temperature (Van Dongen and Dinges, 2005) Dijk et al 1992. Lo and collaborators showed that repeated partial sleep deprivation led to a delay of the melatonin rhythm (Lo et al., 2012). Further control of the circadian wake promoting signal is highlighted in non-sleep deprived conditions, where prior to the onset of the melatonin increase there is a strong circadian arousal period called the wake-maintenance zone (inability to sleep at certain points during the circadian phase) (Dijk and Czeisler, 1994). In the presence of sleep deprivation sustained attention performance during this wake maintenance zone is preserved (de Zeeuw et al., 2018) Wyatt et al .

Neuroimaging studies have provided insights into brain regions associated with homeostatic and circadian regulation of attention and the functional connectivity between regions during sleep deprivation (Patanaik et al., 2018). Following sleep deprivation, thalamic activation was observed when PVT performance was at its highest during circadian timings that mirrored the time course of subjective sleepiness, whereas cortical activation was decreased as sleepiness increased, showing circadian modulation in brain correlates of vigilant attention (Maire et al., 2018). A study suggested that predictions of vigilant performance can be improved by determining an individual’s circadian phase, current wake duration, and cumulative sleep loss and that subjective alertness alone is a weak predictor of vigilant attnetion (Bermudez 2016 prediction of

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vigilant attention). In addition, association cortices in the fronto-parietal attention network appear particularly sensitive to sleep pressure whereas the subcortical thalamic and basal ganglia appear more associated with circadian rhythm (Muto 2016). In human studies, investigation of the role of circadian rhythmicity on performance have benefited from forced desynchrony protocols (Wu et al., 2015) (Santhi et al., 2016). Few preclinical studies are taking into account the time of day at which performance is measured. The forced desynchrony protocol has also been performed in rats (Strijkstra et al., 1999) (Schwartz et al., 2009). It has been used to assess behaviours that are associated with a depressive-like phenotype (Ben-Hamo et al., 2016) and it would be of interest to apply it to study the effects of circadian rhythms on vigilance.

Effects of sleep deprivation on cognitive function

Measuring more than attention

PVT is considered a gold standard in sleep studies for humans, however it really only addresses the likelihood to disengage with the task and this for only one cognitive domain. It does not assess deficits in cognitive function in a broader sense. Simple measures of sustained attention, although sensitive to sleep deprivation, do not predict how well individuals perform on other tasks (Frey et al., 2004). It is often argued that attention tasks are simpler and monotonous in nature when compared to more complex and engaging tasks of working memory or other executive functions (Lim and Dinges, 2010a) (Pilcher and Huffcutt, 1996) (Aidman et al., 2019), and thereby attention tasks are more responsive to sleep deprivation showing a larger effect size (Lo et al., 2012). Measuring outcomes over a range of cognitive functions would provide a more comprehensive characterisation of cognitive deficits relating to sleep loss. These broader functional domains have been shown to be inconsistent in their vulnerability to disruption, including aspects of executive attention, working memory, and various other higher-level functions (e.g. perception and response flexibility). Studies suggested that sleep deprivation will affect cognitive tasks involving decision making, innovative thinking, revising plans and effective communication (Harrison and Horne, 2000). A careful consideration of task components is required to isolate the processes of interest. This relates to a concept referred to as ‘task impurity’, where for example the sleep deprivation effects on working

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memory are more likely due to non-working memory processes (Whitney 2010). In animals, the novel and/or place object recognition tasks have been used extensively to characterise the role of sleep in learning and memory (Havekes et al., 2015) and the vulnerability of the hippocampus to sleep deprivation (Kreutzmann et al., 2015). While these tasks are relatively easy to implement, and were suggested to have translational value and be comparable to human episode-like memory (Dere et al., 2004), they are associated with some limitations, such as differences in protocols used between laboratories and discrepancies between strains as well as differences between rodent species (Şık et al., 2003).Other aspects to consider are the sleep deprivation methods used to assess the impact of sleep loss on cognitive functions and how pharmacological compounds are shown to counteract these deficits. It could be argued that using longer sleep restriction protocols would be more aligned with real-life experience. A comprehensive study assessing the effects of repeated partial and acute total sleep deprivation on several cognitive domains showed that the effects of sleep deprivation were greater, i.e., larger effect sizes, on sustained attention than working memory and were present for both total and partial sleep deprivation (Lo et al., 2012). A meta-analysis of the effects of total sleep deprivation across cognitive domains also indicated that the largest effects are on simple attention tasks (Lim and Dinges, 2010b). In one of our studies, we reported effect sizes to compare effects of sleep restriction on a motivational and an attentional assay. For the attention task, the effects of sleep restriction were large for trials, omissions and reaction time, effects known to be particularly sensitive to sleep deprivation on PVT in humans.

For future studies, longer sleep restriction protocols as well as sleep fragmentation or night-shift protocols would provide valuable insights to further characterise cognitive domains relevant to real-life situations such as shift-work (Grønli et al., 2017). In addition, it would also be interesting to explore sex differences, individual differences and adaptation and coping mechanisms in sleep restriction protocols.

Sleep disruption in the laboratory can be achieved in a multitude of ways, from total acute periods, to chronic partial reduction, sleep fragmentation or selective REM deprivation. In terms of cognition, researchers can then ascertain how lack of sleep affects behavioural outcomes. The Weibull model used in this thesis

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was successful in producing attentional deficits in rats over the course of an 11-hour sleep restriction. However, in the real world sleep may be disrupted in a variety of patterns which require different protocols to mimic them in the laboratory settings. The constant protocol for example may be more applicable to reproduce sleep fragmentation commonly observed in disorders such as obstructive sleep apnoea, restless leg syndrome and periodic limb movement, and psychiatric disorders. Sleep disruption in these disorders are associated with daytime sleepiness (Hombali et al., 2018) (Venkateshiah and Ioachimescu, 2015). Schizophrenia and mood disorders such as major depression often present with deficits in attention and memory that may be a result of altered sleep (Manoach et al., 2010). Obstructive sleep apnoea is also associated with impaired vigilance and impaired memory and executive function (Daurat et al., 2016). The decreasing sleep restriction method presented in Chapter 3 may provide a valid experimental sleep tool to investigate the effects of obstructive sleep apnoea on cognition in rodents.

In addition, as the aging population begins to rise (https://www.who.int/ageing/en/), there is an increased focus on sleep alterations during aging and how they may predict changes in cognition (della Monica et al., 2018) (Lo et al., 2016) (Groeger et al., 2014). In this respect it is important to understand how sleep architecture changes naturally with aging in order to diagnose any further abnormal sleep problems that may indicate the onset of neurological diseases such as Alzheimer’s disease. In Alzheimer’s disease, these changes in sleep are exacerbated (Winsky-Sommerer et al., 2018) (Vitiello and Borson, 2001) and associated with neuropsychiatric symptoms (Winsky-Sommerer et al., 2018). As current therapies do not provide the ability to reverse neurodegenerative disease, any pharmacological compound that could halt the development of these disorders may improve quality of life for these patients.

It is also important to consider the effect of biological sex combined with age when studying sustained attention and other cognitive domains in the context of sleep restriction. Sex differences and age-related changes in sleep are well documented in humans and rodents (Carrier et al., 2017). A recent study showed that women displayed greater night-time impairment in cognitive performance which became more apparent when out of phase with their intrinsic circadian rhythm (Santhi et al., 2016).

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It is worth noting that while this thesis did not address the concept of individual phenotypic differences in the rats in their response to sleep restriction, individual differences in vulnerability to sleep deprivation particularly in human studies have been reported (Rupp et al., 2012). Individual variability accounted for 83% of the variance in progressive changes of the PVT performance across 14 days with sleep being restricted to 4-, 6-, or 8-hour per day (Van Dongen et al., 2004). Another study reported that in a human cohort, a third showed minimal impairments in sustained attention tested every 2-h during an acute 88-h sleep deprivation, while a third displayed severe impairments (Basner et al., 2013). Evidence of intra-individual differences has also been shown in sleep. Whilst acute sleep deprivation is shown to induce a greater increase in SWA than chronic sleep restriction, individuals who showed the greater increase during acute sleep deprivation also show the larger increases in SWA in the context of chronic sleep restriction. Importantly, individuals with the highest increase in SWA also showed greater impairments in PVT lapses (Maric et al., 2017). Overall, sex, age and individual differences are important and will inform the development of adequate drug therapies for the treatment of cognitive deficits associated with sleep disorders.

Summary of findings and further work

This thesis has demonstrated the utility of a novel non-invasive acute sleep restriction protocol in rodents to produce behavioural responses in an attention task resulting in a deficit which was to a large extent comparable to human studies. We have identified that motivation should be considered as a confounding factor and hope this will inform future studies using tasks associated with a reward to study the effects of sleep restriction on cognitive domains in rodents. We have also shown that pre-clinical models are useful to assess novel pharmacological treatments. The human PVT and the rat homologue SRLT are useful tasks to assess the effects of sleep loss on sustained attention, however they primarily assess vigilance rather than cognition. Furthermore, while acute sleep deprivation studies are very valuable to study memory consolidation and underlying mechanisms such as synaptic plasticity, they are not necessarily ideal to assess cognitive function. Individuals are more likely to experience repeated inadequate sleep rather than an acute episode of sleep deprivation (Krueger and Friedman, 2009). Our 24-h modern society

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shows a shift towards reduced sleep per day (Liu, 2016) and understanding long-term chronic sleep loss effects would be more indicative and relevant to real life. To date, chronic partial sleep deprivation has been applied for relatively short durations. To characterise the implications of sleep loss on cognitive functioning, longer sleep restriction, using sleep fragmentation or chronic reduced sleep time protocols could be used for several weeks or even months. It would also be useful study the recovery from such chronic sleep restriction. Assessing changes of sleep-wake cycles combined with behavioural and cognitive outcomes would be very valuable and could then be compared to acute studies. While vigilance is more sensitive to both acute and chronic sleep deprivation than other cognitive domains, using comprehensive behavioural assessment within a battery of tasks that encompasses several cognitive domains (e.g., vigilance, memory and executive functions) would be useful. In addition, a wider range of cognitive testing would provide further insights into the interactions between sleep and cognitive function in specific situations (e.g., ability to decision making during night shifts) or diseases (e.g., short-term memory loss in mild cognitive impairment and neurodegenerative diseases; emotional processing in psychiatric disorders). Human studies have begun to address these questions (Lo et al., 2012) (McMahon et al., 2018) although to date no such comprehensive study has been attempted in a preclinical setting. The sleep restriction methods described in this thesis may provide useful protocols in a preclinical setting to assess long-term partial chronic studies in rodents with behavioural outcomes ranging across all cognitive domains.

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References

ACHERMANN, P. & BORBÉLY, A. A. 2017. Sleep homeostasis and models of sleep regulation. Principles and practice of sleep medicine. Elsevier.ADAM, M., RÉTEY, J. V., KHATAMI, R. & LANDOLT, H.-P. 2006. Age-related changes in the time course of vigilant attention during 40 hours without sleep in men. Sleep, 29, 55-57.AGOSTA, F., PIEVANI, M., GEROLDI, C., COPETTI, M., FRISONI, G. B. & FILIPPI, M. 2012. Resting state fMRI in Alzheimer's disease: beyond the default mode network. Neurobiology of aging, 33, 1564-1578.AIDMAN, E., JACKSON, S. A. & KLEITMAN, S. 2019. Effects of sleep deprivation on executive functioning, cognitive abilities, metacognitive confidence, and decision making. Applied Cognitive Psychology, 33, 188-200.ÅKERSTEDT, T., KECKLUND, G., INGRE, M., LEKANDER, M. & AXELSSON, J. 2009. Sleep homeostasis during repeated sleep restriction and recovery: support from EEG dynamics. Sleep, 32, 217-222.ÅKERSTEDT, T., PHILIP, P., CAPELLI, A. & KECKLUND, G. 2011. Sleep loss and accidents—Work hours, life style, and sleep pathology. Progress in brain research. Elsevier.ÅKERSTEDT, T. & WRIGHT, K. P. 2009. Sleep Loss and Fatigue in Shift Work and Shift Work Disorder. Sleep Medicine Clinics, 4, 257-271.ALDABAL, L. & BAHAMMAM, A. S. 2011. Metabolic, endocrine, and immune consequences of sleep deprivation. The open respiratory medicine journal, 5, 31.ALHOLA, P. & POLO-KANTOLA, P. 2007. Sleep deprivation: Impact on cognitive performance. Neuropsychiatric disease and treatment, 3, 553-567.ANDERSEN, M. L., MARTINS, P. J. F., D'ALMEIDA, V., BIGNOTTO, M. & TUFIK, S. 2005. Endocrinological and catecholaminergic alterations during sleep deprivation and recovery in male rats. Journal of sleep research, 14, 83-90.ANDERSEN, M. L. & TUFIK, S. 2015. Sleep and the modern society. Journal of Sleep Disorders & Therapy, 4, 2167-0277.1000.ARNAL, P. J., SAUVET, F., LEGER, D., VAN BEERS, P., BAYON, V., BOUGARD, C., RABAT, A., MILLET, G. Y. & CHENNAOUI, M. 2015. Benefits of sleep extension on sustained attention and sleep pressure before and during total sleep deprivation and recovery. Sleep, 38, 1935-1943.ASSEFA, S. Z., DIAZ-ABAD, M., WICKWIRE, E. M. & SCHARF, S. M. 2015. The functions of sleep. AIMS Neuroscience, 2, 155-171.

173

BADR, M. S., BELENKY, G., BLIWISE, D. L., BUXTON, O. M., BUYSSE, D., DINGES, D. F., GANGWISCH, J., GRANDNER, M. A., KUSHIDA, C. & MALHOTRA, R. K. 2015. Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. Journal of Clinical Sleep Medicine, 11, 591-592.BALKIN, T. J. 2011. Behavioral biomarkers of sleepiness. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 7, S12-5.BALKIN, T. J., BLIESE, P. D., BELENKY, G., SING, H., THORNE, D. R., THOMAS, M., REDMOND, D. P., RUSSO, M. & WESENSTEN, N. J. 2004. Comparative utility of instruments for monitoring sleepiness-related performance decrements in the operational environment. Journal of sleep research, 13, 219-227.BALKIN, T. J., RUPP, T., PICCHIONI, D. & WESENSTEN, N. J. 2008. Sleep loss and sleepiness: current issues. Chest, 134, 653-660.BALLINGER, E. C., ANANTH, M., TALMAGE, D. A. & ROLE, L. W. 2016. Basal forebrain cholinergic circuits and signaling in cognition and cognitive decline. Neuron, 91, 1199-1218.BANERJEE, D., VITIELLO, M. V. & GRUNSTEIN, R. R. 2004. Pharmacotherapy for excessive daytime sleepiness. Sleep medicine reviews, 8, 339-354.BANKS, S. & DINGES, D. F. 2007. Behavioral and physiological consequences of sleep restriction. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 3, 519-528.BANKS, S., VAN DONGEN, H. P., MAISLIN, G. & DINGES, D. F. 2010. Neurobehavioral dynamics following chronic sleep restriction: dose-response effects of one night for recovery. Sleep, 33, 1013-1026.BASNER, M. & DINGES, D. F. 2009. Dubious bargain: trading sleep for Leno and Letterman. Sleep, 32, 747-752.BASNER, M. & DINGES, D. F. 2011. Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss. Sleep, 34, 581-591.BASNER, M., HERMOSILLO, E., NASRINI, J., MCGUIRE, S., SAXENA, S., MOORE, T. M., GUR, R. C. & DINGES, D. F. 2017. Repeated Administration Effects on Psychomotor Vigilance Test Performance. Sleep, 41, zsx187.BASNER, M., RAO, H., GOEL, N. & DINGES, D. F. 2013. Sleep deprivation and neurobehavioral dynamics. Current opinion in neurobiology, 23, 854-863.

174

BASTUJI, H., PERRIN, F. & GARCIA‐LARREA, L. 2003. Event‐related potentials during forced awakening: a tool for the study of acute sleep inertia. Journal of sleep research, 12, 189-206.BAUD, M. O., MAGISTRETTI, P. J. & PETIT, J. 2013. Sustained sleep fragmentation affects brain temperature, food intake and glucose tolerance in mice. Journal of sleep research, 22, 3-12.BEEBE, D. W., ROSE, D. & AMIN, R. 2010. Attention, learning, and arousal of experimentally sleep-restricted adolescents in a simulated classroom. The Journal of adolescent health : official publication of the Society for Adolescent Medicine, 47, 523-525.BELENKY, G., WESENSTEN, N. J., THORNE, D. R., THOMAS, M. L., SING, H. C., REDMOND, D. P., RUSSO, M. B. & BALKIN, T. J. 2003. Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: a sleep dose-response study. Journal of sleep research, 12, 1-12.BEN-HAMO, M., LARSON, T. A., DUGE, L. S., SIKKEMA, C., WILKINSON, C. W., DE LA IGLESIA, H. O. & GONZÁLEZ, M. M. 2016. Circadian forced desynchrony of the master clock leads to phenotypic manifestation of depression in rats. Eneuro, 3.BENINGTON, J. H. & HELLER, H. C. 1995. Restoration of brain energy metabolism as the function of sleep. Progress in neurobiology, 45, 347-360.BERGER, H. 1929. Über das elektrenkephalogramm des menschen. European archives of psychiatry and clinical neuroscience, 87, 527-570.BERRIDGE, K. C. & KRINGELBACH, M. L. 2008. Affective neuroscience of pleasure: reward in humans and animals. Psychopharmacology, 199, 457-480.BLOUIN, A. M., FRIED, I., WILSON, C. L., STABA, R. J., BEHNKE, E. J., LAM, H. A., MAIDMENT, N. T., LAPIERRE, J. L. & SIEGEL, J. M. 2013. Human hypocretin and melanin-concentrating hormone levels are linked to emotion and social interaction. Nature communications, 4, 1547.BODENMANN, S., HOHOFF, C., FREITAG, C., DECKERT, J., RÉTEY, J., BACHMANN, V. & LANDOLT, H. P. 2012. Polymorphisms of ADORA2A modulate psychomotor vigilance and the effects of caffeine on neurobehavioural performance and sleep EEG after sleep deprivation. British journal of pharmacology, 165, 1904-1913.BODENMANN, S., XU, S., LUHMANN, U., ARAND, M., BERGER, W., JUNG, H. & LANDOLT, H. 2009. Pharmacogenetics of modafinil after sleep loss: catechol‐O‐methyltransferase genotype modulates waking functions but not recovery sleep. Clinical Pharmacology & Therapeutics, 85, 296-304.

175

BONNEFOND, A., MUZET, A., WINTER-DILL, A.-S., BAILLOEUIL, C., BITOUZE, F. & BONNEAU, A. 2001. Innovative working schedule: introducing one short nap during the night shift. Ergonomics, 44, 937-945.BONNET, M. H. 1991. The effect of varying prophylactic naps on performance, alertness and mood throughout a 52-hour continuous operation. Sleep, 14, 307-315.BONNET, M. H. & ARAND, D. L. 2003. Clinical effects of sleep fragmentation versus sleep deprivation. Sleep medicine reviews, 7, 297-310.BONNET, M. H., BALKIN, T. J., DINGES, D. F., ROEHRS, T., ROGERS, N. L., WESENSTEN, N. J., SLEEP, D. & STIMULANT TASK FORCE OF THE AMERICAN ACADEMY OF SLEEP, M. 2005. The use of stimulants to modify performance during sleep loss: a review by the sleep deprivation and Stimulant Task Force of the American Academy of Sleep Medicine. Sleep, 28, 1163-1187.BORBÉLY, A. A. 1982. A two process model of sleep regulation. Human neurobiology.BORBÉLY, A. A., DAAN, S., WIRZ‐JUSTICE, A. & DEBOER, T. 2016. The two‐process model of sleep regulation: a reappraisal. Journal of sleep research, 25, 131-143.BOULOS, M. I. & MURRAY, B. J. 2010. Current evaluation and management of excessive daytime sleepiness. Canadian Journal of Neurological Sciences, 37, 167-176.BRACKNEY, R. J., CHEUNG, T. H., NEISEWANDER, J. L. & SANABRIA, F. 2011. The isolation of motivational, motoric, and schedule effects on operant performance: a modeling approach. Journal of the experimental analysis of behavior, 96, 17-38.BROOKS, A. & LACK, L. 2006. A brief afternoon nap following nocturnal sleep restriction: which nap duration is most recuperative? Sleep, 29, 831-840.BROOKS, P. L. & PEEVER, J. H. 2008. Unraveling the Mechanisms of REM Sleep Atonia: A Response to Kubin LK, Berger AJ, Funk GD, Soja P, and Chase MH. Critical Topics Forum. Sleep 2008; 31: 1473-91. Sleep, 31, 1492.BRUNNER, D. P., DIJK, D.-J. & BORBÉLY, A. A. 1993. Repeated partial sleep deprivation progressively changes the EEG during sleep and wakefulness. Sleep, 16, 100-113.BUCKLEY, R. J., HELTON, W. S., INNES, C. R., DALRYMPLE-ALFORD, J. C. & JONES, R. D. 2016. Attention lapses and behavioural microsleeps during tracking, psychomotor vigilance, and dual tasks. Consciousness and cognition, 45, 174-183.

176

BUGUET, A., MONTMAYEUR, A., PIGEAU, R. & NAITOH, P. 1995. Modafinil, d‐amphetamine and placebo during 64 hours of sustained mental work. II. Effects on two nights of recovery sleep. Journal of sleep research, 4, 229-241.BURKE, T. M., MARKWALD, R. R., MCHILL, A. W., CHINOY, E. D., SNIDER, J. A., BESSMAN, S. C., JUNG, C. M., O’NEILL, J. S. & WRIGHT, K. P. 2015. Effects of caffeine on the human circadian clock in vivo and in vitro. Science translational medicine, 7, 305ra146-305ra146.BUSCHMAN, T. J. & MILLER, E. K. 2007. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science (New York, N.Y.), 315, 1860-1862.CALDWELL, J. A., CALDWELL, J. L., SMYTH, N. K. & HALL, K. K. 2000. A double-blind, placebo-controlled investigation of the efficacy of modafinil for sustaining the alertness and performance of aviators: a helicopter simulator study. Psychopharmacology, 150, 272-282.CANOLTY, R. T., EDWARDS, E., DALAL, S. S., SOLTANI, M., NAGARAJAN, S. S., KIRSCH, H. E., BERGER, M. S., BARBARO, N. M. & KNIGHT, R. T. 2006. High gamma power is phase-locked to theta oscillations in human neocortex. science, 313, 1626-1628.CARRIER, J., SEMBA, K., DEURVEILHER, S., DROGOS, L., CYR-CRONIER, J., LORD, C. & SEKEROVICK, Z. 2017. Sex differences in age-related changes in the sleep-wake cycle. Frontiers in neuroendocrinology, 47, 66-85.CARSKADON, M. A. & DEMENT, W. C. 2005. Normal human sleep: an overview. Principles and practice of sleep medicine, 4, 13-23.CATON, R. 1875. Electrical Currents of the Brain. The Journal of nervous and mental disease, 2, 610.CEDERNAES, J., BRANDELL, J., ROS, O., BROMAN, J. E., HOGENKAMP, P. S., SCHIÖTH, H. B. & BENEDICT, C. 2014. Increased impulsivity in response to food cues after sleep loss in healthy young men. Obesity, 22, 1786-1791.CHAPOTOT, F., PIGEAU, R., CANINI, F., BOURDON, L. & BUGUET, A. 2003. Distinctive effects of modafinil and d-amphetamine on the homeostatic and circadian modulation of the human waking EEG. Psychopharmacology, 166, 127-138.CHATZITHEOCHARI, S. & ARBER, S. 2009. Lack of sleep, work and the long hours culture: evidence from the UK Time Use Survey. Work, Employment and Society, 23, 30-48.

177

CHEE, M. W., GOH, C. S., NAMBURI, P., PARIMAL, S., SEIDL, K. N. & KASTNER, S. 2011. Effects of sleep deprivation on cortical activation during directed attention in the absence and presence of visual stimuli. Neuroimage, 58, 595-604.CHEE, M. W. L. & TAN, J. C. 2010. Lapsing when sleep deprived: neural activation characteristics of resistant and vulnerable individuals. NeuroImage, 51, 835-843.CHEFER, V. I., THOMPSON, A. C., ZAPATA, A. & SHIPPENBERG, T. S. 2009. Overview of brain microdialysis. Current protocols in neuroscience, 47, 7.1. 1-7.1. 28.CHELLAPPA, S. L., MORRIS, C. J. & SCHEER, F. A. 2019. Effects of circadian misalignment on cognition in chronic shift workers. Scientific reports, 9, 699.CHELLAPPA, S. L., SCHRÖDER, C. & CAJOCHEN, C. 2009. Chronobiology, excessive daytime sleepiness and depression: Is there a link? Sleep medicine, 10, 505-514.CHERKASSKY, V. L., KANA, R. K., KELLER, T. A. & JUST, M. A. 2006. Functional connectivity in a baseline resting-state network in autism. Neuroreport, 17, 1687-1690.CHIEFFI, S., CAROTENUTO, M., MONDA, V., VALENZANO, A., VILLANO, I., PRECENZANO, F., TAFURI, D., SALERNO, M., FILIPPI, N. & NUCCIO, F. 2017. Orexin system: the key for a healthy life. Frontiers in physiology, 8, 357.CHRISTIE, M. A., MCCARLEY, R. W. & STRECKER, R. E. 2010. Twenty-four hours, or five days, of continuous sleep deprivation or experimental sleep fragmentation do not alter thirst or motivation for water reward in rats. Behavioural brain research, 214, 180-186.CHRISTIE, M. A., MCKENNA, J. T., CONNOLLY, N. P., MCCARLEY, R. W. & STRECKER, R. E. 2008. 24 Hours of Sleep Deprivation in the Rat Increases Sleepiness and Decreases Vigilance: Introduction of the Rat-Psychomotor Vigilance Task. Journal of sleep research, 17, 376-384.CHUNG, L. 2015. A brief introduction to the transduction of neural activity into Fos signal. Development & reproduction, 19, 61.CLINTON, J. M., DAVIS, C. J., ZIELINSKI, M. R., JEWETT, K. A. & KRUEGER, J. M. 2011. Biochemical regulation of sleep and sleep biomarkers. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 7, S38-42.COHEN, D. A., WANG, W., WYATT, J. K., KRONAUER, R. E., DIJK, D. J., CZEISLER, C. A. & KLERMAN, E. B. 2010. Uncovering residual effects of chronic sleep loss on human performance. Science translational medicine, 2, 14ra3.

178

COLAVITO, V., FABENE, P. F., GRASSI ZUCCONI, G., PIFFERI, F., LAMBERTY, Y., BENTIVOGLIO, M. & BERTINI, G. 2013. Experimental sleep deprivation as a tool to test memory deficits in rodents. Frontiers in systems neuroscience, 7, 106.CORBETTA, M. 1998. Frontoparietal cortical networks for directing attention and the eye to visual locations: identical, independent, or overlapping neural systems? Proceedings of the National Academy of Sciences, 95, 831-838.CORDOVA, C. A., SAID, B. O., MCCARLEY, R. W., BAXTER, M. G., CHIBA, A. A. & STRECKER, R. E. 2006. Sleep deprivation in rats produces attentional impairments on a 5-choice serial reaction time task. Sleep, 29, 69-76.CORSI-CABRERA, M., ARCE, C., RAMOS, J. & LORENZO, I. 1996. Time course of reaction time and EEG while performing a vigilance task during total sleep deprivation. Sleep: Journal of Sleep Research & Sleep Medicine.CURIE, T., MONGRAIN, V., DORSAZ, S., MANG, G. M., EMMENEGGER, Y. & FRANKEN, P. 2013. Homeostatic and circadian contribution to EEG and molecular state variables of sleep regulation. Sleep, 36, 311-323.CZEISLER, C. A., WALSH, J. K., ROTH, T., HUGHES, R. J., WRIGHT, K. P., KINGSBURY, L., ARORA, S., SCHWARTZ, J. R., NIEBLER, G. E., DINGES, D. F. & GROUP, U. S. M. I. S. W. S. D. S. 2005. Modafinil for excessive sleepiness associated with shift-work sleep disorder. The New England journal of medicine, 353, 476-486.DAAN, S., BEERSMA, D. & BORBÉLY, A. A. 1984. Timing of human sleep: recovery process gated by a circadian pacemaker. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 246, R161-R183.DAI, X.-J., LIU, C.-L., ZHOU, R.-L., GONG, H.-H., WU, B., GAO, L. & WANG, Y.-X. J. 2015. Long-term total sleep deprivation decreases the default spontaneous activity and connectivity pattern in healthy male subjects: a resting-state fMRI study. Neuropsychiatric disease and treatment, 11, 761.DAURAT, A., SARHANE, M. & TIBERGE, M. 2016. Obstructive sleep apnea syndrome and cognition: a review. Neurophysiologie clinique= Clinical neurophysiology, 46, 201-215.DAVIES, D. R. & PARASURAMAN, R. 1982. The psychology of vigilance, Academic Pr.DAVIS, C. M., ROMA, P. G. & HIENZ, R. D. 2015. A rodent model of the human psychomotor vigilance test: Performance comparisons. Journal of neuroscience methods, 259, 57-71.

179

DAVIS, C. M., ROMA, P. G. & HIENZ, R. D. 2016. The rodent Psychomotor Vigilance Test (rPVT): a method for assessing neurobehavioral performance in rats and mice. JoVE (Journal of Visualized Experiments), e54629.DE HAVAS, J. A., PARIMAL, S., SOON, C. S. & CHEE, M. W. 2012. Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance. Neuroimage, 59, 1745-1751.DE JONG, J. W., AFJEI, S. A., DOROCIC, I. P., PECK, J. R., LIU, C., KIM, C. K., TIAN, L., DEISSEROTH, K. & LAMMEL, S. 2019. A Neural Circuit Mechanism for Encoding Aversive Stimuli in the Mesolimbic Dopamine System. Neuron, 101, 133-151. e7.DE SIMONI, S., SCHWARZ, A. J., O'DALY, O. G., MARQUAND, A. F., BRITTAIN, C., GONZALES, C., STEPHENSON, S., WILLIAMS, S. C. & MEHTA, M. A. 2013. Test–retest reliability of the BOLD pharmacological MRI response to ketamine in healthy volunteers. Neuroimage, 64, 75-90.DE ZEEUW, J., WISNIEWSKI, S., PAPAKONSTANTINOU, A., BES, F., WAHNSCHAFFE, A., ZALESKA, M., KUNZ, D. & MÜNCH, M. 2018. The alerting effect of the wake maintenance zone during 40 hours of sleep deprivation. Scientific reports, 8, 11012.DE ZWART, J. A., SILVA, A. C., VAN GELDEREN, P., KELLMAN, P., FUKUNAGA, M., CHU, R., KORETSKY, A. P., FRANK, J. A. & DUYN, J. H. 2005. Temporal dynamics of the BOLD fMRI impulse response. Neuroimage, 24, 667-677.DEBOER, T. 2018. Sleep homeostasis and the circadian clock: Do the circadian pacemaker and the sleep homeostat influence each other’s functioning? Neurobiology of Sleep and Circadian Rhythms.DELLA MONICA, C., JOHNSEN, S., ATZORI, G., GROEGER, J. A. & DIJK, D.-J. 2018. Rapid eye movement sleep, sleep continuity and slow wave sleep as predictors of cognition, mood, and subjective sleep quality in healthy men and women, aged 20–84 years. Frontiers in psychiatry, 9.DEMOS, K., HART, C., SWEET, L., MAILLOUX, K., TRAUTVETTER, J., WILLIAMS, S., WING, R. & MCCAFFERY, J. 2016. Partial sleep deprivation impacts impulsive action but not impulsive decision-making. Physiology & behavior, 164, 214-219.DER-AVAKIAN, A. & MARKOU, A. 2012. The neurobiology of anhedonia and other reward-related deficits. Trends in neurosciences, 35, 68-77.DERE, E., SILVA, M. D. S. & HUSTON, J. P. 2004. Higher order memories for objects encountered in different spatio-temporal contexts in mice: evidence for episodic memory. Reviews in the Neurosciences, 15, 231-240.

180

DEURVEILHER, S., BUSH, J. E., RUSAK, B., ESKES, G. A. & SEMBA, K. 2015. Psychomotor vigilance task performance during and following chronic sleep restriction in rats. Sleep, 38, 515-528.DEURVEILHER, S., RUSAK, B. & SEMBA, K. 2012. Time-of-day modulation of homeostatic and allostatic sleep responses to chronic sleep restriction in rats. American journal of physiology.Regulatory, integrative and comparative physiology, 302, R1411-25.DIEKELMANN, S. 2014. Sleep for cognitive enhancement. Frontiers in systems neuroscience, 8, 46.DIJK, D.-J., BRUNNER, D. P., BEERSMA, D. G. & BORBÉLY, A. A. 1990. Electroencephalogram power density and slow wave sleep as a function of prior waking and circadian phase. Sleep, 13, 430-440.DIJK, D.-J. & CZEISLER, C. A. 1994. Paradoxical timing of the circadian rhythm of sleep propensity serves to consolidate sleep and wakefulness in humans. Neuroscience letters, 166, 63-68.DIJK, D.-J. & CZEISLER, C. A. 1995. Contribution of the circadian pacemaker and the sleep homeostat to sleep propensity, sleep structure, electroencephalographic slow waves, and sleep spindle activity in humans. Journal of Neuroscience, 15, 3526-3538.DIJK, D. J. 2009. Regulation and functional correlates of slow wave sleep. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 5, S6-15.DIJK, D. J. 2015. Napping: when sleep is bad for you? Journal of sleep research, 24, 475-475.DIJK, D. J., BEERSMA, D. G., DAAN, S., BLOEM, G. M. & VAN DEN HOOFDAKKER, R. H. 1987. Quantitative analysis of the effects of slow wave sleep deprivation during the first 3 h of sleep on subsequent EEG power density. European archives of psychiatry and neurological sciences, 236, 323-328.DINGES, D. F. 1992. Probing the limits of functional capability: the effects of sleep loss on short-duration tasks. Sleep, arousal, and performance, 176-188.DINGES, D. F. 1995. An overview of sleepiness and accidents. Journal of sleep research, 4, 4-14.DINGES, D. F., PACK, F., WILLIAMS, K., GILLEN, K. A., POWELL, J. W., OTT, G. E., APTOWICZ, C. & PACK, A. I. 1997. Cumulative sleepiness, mood disturbance and psychomotor vigilance performance decrements during aweek of sleep restricted to 4-5 hours per night. Sleep: Journal of Sleep Research & Sleep Medicine.

181

DINGES, D. F. & POWELL, J. W. 1985. Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Behavior research methods, instruments, & computers, 17, 652-655.DINGES, D. F. & WEAVER, T. E. 2003. Effects of modafinil on sustained attention performance and quality of life in OSA patients with residual sleepiness while being treated with nCPAP. Sleep medicine, 4, 393-402.DORAN, S. M., VAN DONGEN, H. P. & DINGES, D. F. 2001a. Sustained attention performance during sleep deprivation: evidence of state instability. Archives Italiennes de Biologie, 139, 253-267.DORAN, S. M., VAN DONGEN, H. P. A. & DINGES, D. F. 2001b. Sustained attention performance during sleep deprivation: evidence of state instability. Archives Italiennes de Biologie, 139, 253-267.DORRIAN, J., ROGERS, N. L. & DINGES, D. F. 2005. Psychomotor vigilance performance: Neurocognitive assay sensitive to sleep loss.DOTY, T. J., SO, C. J., BERGMAN, E. M., TRACH, S. K., RATCLIFFE, R. H., YARNELL, A. M., CAPALDI, V. F., MOON, J. E., BALKIN, T. J. & QUARTANA, P. J. 2017. Limited Efficacy of Caffeine and Recovery Costs During and Following 5 Days of Chronic Sleep Restriction. Sleep, 40, zsx171.DRIESEN, N. R., MCCARTHY, G., BHAGWAGAR, Z., BLOCH, M., CALHOUN, V., D'SOUZA, D. C., GUEORGUIEVA, R., HE, G., RAMACHANDRAN, R. & SUCKOW, R. F. 2013. Relationship of resting brain hyperconnectivity and schizophrenia-like symptoms produced by the NMDA receptor antagonist ketamine in humans. Molecular psychiatry, 18, 1199.DRINKENBURG, W. H., RUIGT, G. S. & AHNAOU, A. 2015. Pharmaco-EEG studies in animals: an overview of contemporary translational applications. Neuropsychobiology, 72, 151-164.DRUMMOND, S., PAULUS, M. P. & TAPERT, S. F. 2006. Effects of two nights sleep deprivation and two nights recovery sleep on response inhibition. Journal of sleep research, 15, 261-265.DRUMMOND, S. P. A. & BROWN, G. G. 2001. The effects of total sleep deprivation on cerebral responses to cognitive performance. Neuropsychopharmacology, 25, S68-S73.DURMER, J. S. & DINGES, D. F. 2005. Neurocognitive consequences of sleep deprivation. Seminars in neurology, 25, 117-129.EAGLE, D. M., TUFFT, M. R., GOODCHILD, H. L. & ROBBINS, T. W. 2007. Differential effects of modafinil and methylphenidate on stop-signal reaction time

182

task performance in the rat, and interactions with the dopamine receptor antagonist cis-flupenthixol. Psychopharmacology, 192, 193-206.EDGAR, D. M. & SEIDEL, W. F. 1997. Modafinil induces wakefulness without intensifying motor activity or subsequent rebound hypersomnolence in the rat. The Journal of pharmacology and experimental therapeutics, 283, 757-769.EHLERS, C. L., DESIKAN, A. & WILLS, D. N. 2014. Event‐Related Potential Responses to the Acute and Chronic Effects of Alcohol in Adolescent and Adult W istar Rats. Alcoholism: Clinical and Experimental Research, 38, 749-759.ENGBER, T., DENNIS, S., JONES, B., MILLER, M. & CONTRERAS, P. 1998. Brain regional substrates for the actions of the novel wake-promoting agent modafinil in the rat: comparison with amphetamine. Neuroscience, 87, 905-911.ERNST, M., NELSON, E. E., MCCLURE, E. B., MONK, C. S., MUNSON, S., ESHEL, N., ZARAHN, E., LEIBENLUFT, E., ZAMETKIN, A. & TOWBIN, K. 2004. Choice selection and reward anticipation: an fMRI study. Neuropsychologia, 42, 1585-1597.ESPAÑA, R. A. & SCAMMELL, T. E. 2011. Sleep neurobiology from a clinical perspective. Sleep, 34, 845-858.FARAUT, B., BOUDJELTIA, K. Z., DYZMA, M., ROUSSEAU, A., DAVID, E., STENUIT, P., FRANCK, T., VAN ANTWERPEN, P., VANHAEVERBEEK, M. & KERKHOFS, M. 2011. Benefits of napping and an extended duration of recovery sleep on alertness and immune cells after acute sleep restriction. Brain, behavior, and immunity, 25, 16-24.FERNANDES, C., ROCHA, N. B. F., ROCHA, S., HERRERA-SOLÍS, A., SALAS-PACHECO, J., GARCÍA-GARCÍA, F., MURILLO-RODRÍGUEZ, E., YUAN, T.-F., MACHADO, S. & ARIAS-CARRIÓN, O. 2015. Detrimental role of prolonged sleep deprivation on adult neurogenesis. Frontiers in cellular neuroscience, 9, 140.FICCA, G., AXELSSON, J., MOLLICONE, D. J., MUTO, V. & VITIELLO, M. V. 2010. Naps, cognition and performance. Sleep medicine reviews, 14, 249-258.FOLKARD, S. & ÅKERSTEDT, T. 1992. A three-process model of the regulation of alertness-sleepiness. Sleep, arousal and performance, 11-26.FRANCOIS, J., CONWAY, M. W., LOWRY, J. P., TRICKLEBANK, M. D. & GILMOUR, G. 2012. Changes in reward-related signals in the rat nucleus accumbens measured by in vivo oxygen amperometry are consistent with fMRI BOLD responses in man. Neuroimage, 60, 2169-2181.FRANKEN, P., DIJK, D., TOBLER, I. & BORBELY, A. A. 1991. Sleep deprivation in rats: effects on EEG power spectra, vigilance states, and cortical temperature.

183

American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 261, R198-R208.FREY, D. J., BADIA, P. & WRIGHT JR, K. P. 2004. Inter‐and intra‐individual variability in performance near the circadian nadir during sleep deprivation. Journal of sleep research, 13, 305-315.FREY, D. J., FLESHNER, M. & WRIGHT, K. P. 2007. The effects of 40 hours of total sleep deprivation on inflammatory markers in healthy young adults. Brain, behavior, and immunity, 21, 1050-1057.FUJITA, A., BONNAVION, P., WILSON, M. H., MICKELSEN, L. E., BLOIT, J., DE LECEA, L. & JACKSON, A. C. 2017. Hypothalamic tuberomammillary nucleus neurons: electrophysiological diversity and essential role in arousal stability. Journal of Neuroscience, 37, 9574-9592.GAO, C., TERLIZZESE, T. & SCULLIN, M. K. 2018. Short sleep and late bedtimes are detrimental to educational learning and knowledge transfer: An investigation of individual differences in susceptibility. Chronobiology international, 1-12.GARRITY, A. G., PEARLSON, G. D., MCKIERNAN, K., LLOYD, D., KIEHL, K. A. & CALHOUN, V. D. 2007. Aberrant “default mode” functional connectivity in schizophrenia. American journal of psychiatry, 164, 450-457.GASS, N., SCHWARZ, A. J., SARTORIUS, A., SCHENKER, E., RISTERUCCI, C., SPEDDING, M., ZHENG, L., MEYER-LINDENBERG, A. & WEBER-FAHR, W. 2014. Sub-anesthetic ketamine modulates intrinsic BOLD connectivity within the hippocampal-prefrontal circuit in the rat. Neuropsychopharmacology, 39, 895.GEORGE, P. & CHARLES, W. 2007. The rat brain in stereotaxic coordinates. Qingchuan Zhuge translate, 32.GERRARD, P. & MALCOLM, R. 2007. Mechanisms of modafinil: a review of current research. Neuropsychiatric disease and treatment, 3, 349.GOEL, N., RAO, H., DURMER, J. S. & DINGES, D. F. 2009. Neurocognitive consequences of sleep deprivation. Seminars in neurology, 29, 320-339.GOLDSTEIN, A. N. & WALKER, M. P. 2014. The role of sleep in emotional brain function. Annual review of clinical psychology, 10, 679-708.GRACE, A. A. 2016. Dysregulation of the dopamine system in the pathophysiology of schizophrenia and depression. Nature Reviews Neuroscience, 17, 524.GRANDNER, M. A., WATSON, N. F., KAY, M., OCAÑO, D. & KIENTZ, J. A. 2018. Addressing the need for validation of a touchscreen psychomotor vigilance task:

184

important considerations for sleep health research. Sleep Health: Journal of the National Sleep Foundation, 4, 387-389.GRAW, P., KRAUCHI, K., KNOBLAUCH, V., WIRZ-JUSTICE, A. & CAJOCHEN, C. 2004. Circadian and wake-dependent modulation of fastest and slowest reaction times during the psychomotor vigilance task. Physiology & Behavior, 80, 695-701.GREER, S. M., GOLDSTEIN, A. N. & WALKER, M. P. 2013. The impact of sleep deprivation on food desire in the human brain. Nature communications, 4.GREICIUS, M. D., KRASNOW, B., REISS, A. L. & MENON, V. 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 100, 253-258.GRILLY, D. M. & LOVELAND, A. 2001. What is a" low dose" of d-amphetamine for inducing behavioral effects in laboratory rats? Psychopharmacology, 153, 155-169.GROEGER, J. A., LO, J. C., BURNS, C. G. & DIJK, D. J. 2011. Effects of sleep inertia after daytime naps vary with executive load and time of day. Behavioral neuroscience, 125, 252-260.GROEGER, J. A., STANLEY, N., DEACON, S. & DIJK, D.-J. 2014. Dissociating effects of global SWS disruption and healthy aging on waking performance and daytime sleepiness. Sleep, 37, 1127-1142.GRØNLI, J., MEERLO, P., PEDERSEN, T. T., PALLESEN, S., SKREDE, S., MARTI, A. R., WISOR, J. P., MURISON, R., HENRIKSEN, T. E. & REMPE, M. J. 2017. A rodent model of night-shift work induces short-term and enduring sleep and electroencephalographic disturbances. Journal of biological rhythms, 32, 48-63.GUJAR, N., YOO, S.-S., HU, P. & WALKER, M. P. 2011. Sleep deprivation amplifies reactivity of brain reward networks, biasing the appraisal of positive emotional experiences. The Journal of neuroscience, 31, 4466-4474.GUJAR, N., YOO, S. S., HU, P. & WALKER, M. P. 2010. The unrested resting brain: sleep deprivation alters activity within the default-mode network. Journal of cognitive neuroscience, 22, 1637-1648.HABER, S. N. 2011. 11 Neuroanatomy of Reward: A View from the Ventral Striatum. Neurobiology of sensation and reward, 235.HAFNER, M., STEPANEK, M., TAYLOR, J., TROXEL, W. M. & VAN STOLK, C. 2017. Why sleep matters—the economic costs of insufficient sleep: a cross-country comparative analysis. Rand health quarterly, 6.

185

HAILWOOD, J. M., HEATH, C. J., ROBBINS, T. W., SAKSIDA, L. M. & BUSSEY, T. J. 2018. Validation and optimisation of a touchscreen progressive ratio test of motivation in male rats. Psychopharmacology, 235, 2739-2753.HAMID, A. A., PETTIBONE, J. R., MABROUK, O. S., HETRICK, V. L., SCHMIDT, R., VANDER WEELE, C. M., KENNEDY, R. T., ARAGONA, B. J. & BERKE, J. D. 2016. Mesolimbic dopamine signals the value of work. Nature neuroscience, 19, 117.HANLON, E. C., ANDRZEJEWSKI, M. E., HARDER, B. K., KELLEY, A. E. & BENCA, R. M. 2005. The effect of REM sleep deprivation on motivation for food reward. Behavioural brain research, 163, 58-69.HANLON, E. C., BENCA, R. M., BALDO, B. A. & KELLEY, A. E. 2010. REM sleep deprivation produces a motivational deficit for food reward that is reversed by intra-accumbens amphetamine in rats. Brain research bulletin, 83, 245-254.HÄRMÄ, M., KNAUTH, P. & ILMARINEN, J. 1989. Daytime napping and its effects on alertness and short-term memory performance in shiftworkers. International archives of occupational and environmental health, 61, 341-345.HARPER, J., MALONE, S. M. & BERNAT, E. M. 2014. Theta and delta band activity explain N2 and P3 ERP component activity in a go/no-go task. Clinical Neurophysiology, 125, 124-132.HARRISON, Y. & HORNE, J. A. 2000. The impact of sleep deprivation on decision making: a review. Journal of Experimental Psychology: Applied, 6, 236.HASAN, S., PRADERVAND, S., AHNAOU, A., DRINKENBURG, W., TAFTI, M. & FRANKEN, P. 2009. How to keep the brain awake? The complex molecular pharmacogenetics of wake promotion. Neuropsychopharmacology, 34, 1625-1640.HASLER, G., BUYSSE, D. J., GAMMA, A., AJDACIC, V., EICH, D., ROSSLER, W. & ANGST, J. 2005. Excessive daytime sleepiness in young adults: a 20-year prospective community study. The Journal of clinical psychiatry, 66, 521-529.HAUBERT, A., WALSH, M., BOYD, R., MORRIS, M., WIEDBUSCH, M., KRUSMARK, M. & GUNZELMANN, G. 2018. Relationship of event-related potentials to the vigilance decrement. Frontiers in psychology, 9, 237.HAVEKES, R., MEERLO, P. & ABEL, T. 2015. Animal studies on the role of sleep in memory: from behavioral performance to molecular mechanisms. Sleep, Neuronal Plasticity and Brain Function. Springer.HELPS, S. K., BROYD, S. J., JAMES, C. J., KARL, A., CHEN, W. & SONUGA-BARKE, E. J. 2010. Altered spontaneous low frequency brain activity in attention deficit/hyperactivity disorder. Brain research, 1322, 134-143.

186

HERSHEY, T., HAZLETT, E., SICOTTE, N. & BUNNEY JR, W. E. 1991. The effect of sleep deprivation on cerebral glucose metabolic rate in normal humans assessed with positron emission tomography. Sleep, 14, 155-162.HIKIDA, T., MORITA, M. & MACPHERSON, T. 2016. Neural mechanisms of the nucleus accumbens circuit in reward and aversive learning. Neuroscience research, 108, 1-5.HILDITCH, C. J., CENTOFANTI, S. A., DORRIAN, J. & BANKS, S. 2015. A 30-Minute, But Not a 10-Minute Nighttime Nap is Associated with Sleep Inertia. Sleep.HILDITCH, C. J., DORRIAN, J. & BANKS, S. 2017. A review of short naps and sleep inertia: do naps of 30 min or less really avoid sleep inertia and slow-wave sleep? Sleep medicine, 32, 176-190.HOFFMAN, B. & LEFKOWITZ, R. 1996. Catecolaminas, drogas simpatomiméticas e antagonistas dos receptores adrenérgicos. HARDMAN, JG, LIMBIRD, JE, Goodman & Gilman: as bases farmacológicas da terapêutica, 9, 146-182.HOLST, S. C. & LANDOLT, H.-P. 2018. Sleep-wake neurochemistry. Sleep medicine clinics, 13, 137-146.HOMBALI, A., SEOW, E., YUAN, Q., CHANG, S. H. S., SATGHARE, P., KUMAR, S., VERMA, S. K., MOK, Y. M., CHONG, S. A. & SUBRAMANIAM, M. 2018. Prevalence and correlates of sleep disorder symptoms in psychiatric disorders. Psychiatry research.HORNE, J. A. 1988. Why we sleep? The functions of sleep in humans and other animals.HORNE, J. A. & PETTITT, A. N. 1985. High incentive effects on vigilance performance during 72 hours of total sleep deprivation. Acta Psychologica, 58, 123-139.HOROVITZ, S. G., BRAUN, A. R., CARR, W. S., PICCHIONI, D., BALKIN, T. J., FUKUNAGA, M. & DUYN, J. H. 2009. Decoupling of the brain's default mode network during deep sleep. Proceedings of the National Academy of Sciences of the United States of America, 106, 11376-11381.HUBER, R., DEBOER, T. & TOBLER, I. 2000. Effects of sleep deprivation on sleep and sleep EEG in three mouse strains: empirical data and simulations. Brain research, 857, 8-19.IMERI, L. & OPP, M. R. 2009. How (and why) the immune system makes us sleep. Nature Reviews Neuroscience, 10, 199-210.

187

INNES, C. R., POUDEL, G. R. & JONES, R. D. 2013. Efficient and regular patterns of nighttime sleep are related to increased vulnerability to microsleeps following a single night of sleep restriction. Chronobiology international, 30, 1187-1196.IRWIN, M. R., WANG, M., CAMPOMAYOR, C. O., COLLADO-HIDALGO, A. & COLE, S. 2006. Sleep deprivation and activation of morning levels of cellular and genomic markers of inflammation. Archives of Internal Medicine, 166, 1756-1762.JACKSON, M. L., GUNZELMANN, G., WHITNEY, P., HINSON, J. M., BELENKY, G., RABAT, A. & VAN DONGEN, H. P. 2013. Deconstructing and reconstructing cognitive performance in sleep deprivation. Sleep medicine reviews, 17, 215-225.JAMES, L. M., IANNONE, R., PALCZA, J., RENGER, J. J., CALDER, N., CERCHIO, K., GOTTESDIENER, K., HARGREAVES, R., MURPHY, M. G. & BOYLE, J. 2011. Effect of a novel histamine subtype-3 receptor inverse agonist and modafinil on EEG power spectra during sleep deprivation and recovery sleep in male volunteers. Psychopharmacology, 215, 643-653.JASPER, H. H. 1958. The ten twenty electrode system of the international federation. Electroencephalography and clinical neurophysiology, 10, 371-375.JEWETT, M. E., DIJK, D.-J., KRONAUER, R. E. & DINGES, D. F. 1999a. Dose-response relationship between sleep duration and human psychomotor vigilance and subjective alertness. Sleep, 22, 171-179.JEWETT, M. E., DIJK, D. J., KRONAUER, R. E. & DINGES, D. F. 1999b. Dose-response relationship between sleep duration and human psychomotor vigilance and subjective alertness. Sleep, 22, 171-179.JOULES, R., DOYLE, O., SCHWARZ, A., O’DALY, O., BRAMMER, M., WILLIAMS, S. & MEHTA, M. 2015. Ketamine induces a robust whole-brain connectivity pattern that can be differentially modulated by drugs of different mechanism and clinical profile. Psychopharmacology, 232, 4205-4218.KAMPHUIS, J., BAICHEL, S., LANCEL, M., DE BOER, S. F., KOOLHAAS, J. M. & MEERLO, P. 2017. Sleep restriction in rats leads to changes in operant behaviour indicative of reduced prefrontal cortex function. Journal of sleep research, 26, 5-13.KANE, M. J. & ENGLE, R. W. 2002. The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic bulletin & review, 9, 637-671.KATSUKI, F. & CONSTANTINIDIS, C. 2012. Early involvement of prefrontal cortex in visual bottom-up attention. Nature neuroscience, 15, 1160-1166.

188

KENNEDY, C. H. 2002. Effects of REM sleep deprivation on a multiple schedule of appetitive reinforcement. Behavioural brain research, 128, 205-214.KILLGORE, W. D., RUPP, T. L., GRUGLE, N. L., REICHARDT, R. M., LIPIZZI, E. L. & BALKIN, T. J. 2008. Effects of dextroamphetamine, caffeine and modafinil on psychomotor vigilance test performance after 44 h of continuous wakefulness. Journal of sleep research, 17, 309-321.KIM, Y., LAPOSKY, A. D., BERGMANN, B. M. & TUREK, F. W. 2007. Repeated sleep restriction in rats leads to homeostatic and allostatic responses during recovery sleep. Proceedings of the National Academy of Sciences, 104, 10697-10702.KIRBY, M. & KENNEDY, C. H. 2003. VARIABLE‐INTERVAL REINFORCEMENT SCHEDULE VALUE INFLUENCES RESPONDING FOLLOWING REM SLEEP DEPRIVATION. Journal of the experimental analysis of behavior, 80, 253-260.KIRSCH, P., SCHIENLE, A., STARK, R., SAMMER, G., BLECKER, C., WALTER, B., OTT, U., BURKART, J. & VAITL, D. 2003. Anticipation of reward in a nonaversive differential conditioning paradigm and the brain reward system:: an event-related fMRI study. Neuroimage, 20, 1086-1095.KIYATKIN, E. A. 2018. Central and peripheral mechanisms underlying physiological and drug-induced fluctuations in brain oxygen in freely-moving rats. Frontiers in integrative neuroscience, 12.KNIE, B., MITRA, M. T., LOGISHETTY, K. & CHAUDHURI, K. R. 2011. Excessive daytime sleepiness in patients with Parkinson’s disease. CNS drugs, 25, 203-212.KNUTSON, B., ADAMS, C. M., FONG, G. W. & HOMMER, D. 2001. Anticipation of increasing monetary reward selectively recruits nucleus accumbens. Journal of Neuroscience, 21, RC159-RC159.KNUTSON, B. & COOPER, J. C. 2005. Functional magnetic resonance imaging of reward prediction. Current opinion in neurology, 18, 411-417.KNUTSON, B. & GIBBS, S. E. 2007. Linking nucleus accumbens dopamine and blood oxygenation. Psychopharmacology, 191, 813-822.KRAUSE, A. J., SIMON, E. B., MANDER, B. A., GREER, S. M., SALETIN, J. M., GOLDSTEIN-PIEKARSKI, A. N. & WALKER, M. P. 2017. The sleep-deprived human brain. Nature Reviews Neuroscience, 18, 404.KREUTZMANN, J., HAVEKES, R., ABEL, T. & MEERLO, P. 2015. Sleep deprivation and hippocampal vulnerability: changes in neuronal plasticity, neurogenesis and cognitive function. Neuroscience, 309, 173-190.

189

KRUEGER, P. M. & FRIEDMAN, E. M. 2009. Sleep duration in the United States: a cross-sectional population-based study. American journal of epidemiology, 169, 1052-1063.KURZBAN, R., DUCKWORTH, A., KABLE, J. W. & MYERS, J. 2013. An opportunity cost model of subjective effort and task performance. Behavioral and Brain Sciences, 36, 661-679.KUSZTOR, A., RAUD, L., JUEL, B. E., NILSEN, A. S., STORM, J. F. & HUSTER, R. J. 2019. Sleep deprivation differentially affects subcomponents of cognitive control. Sleep.LAZARUS, M., SHEN, H.-Y., CHERASSE, Y., QU, W.-M., HUANG, Z.-L., BASS, C. E., WINSKY-SOMMERER, R., SEMBA, K., FREDHOLM, B. B. & BOISON, D. 2011. Arousal effect of caffeine depends on adenosine A2A receptors in the shell of the nucleus accumbens. Journal of Neuroscience, 31, 10067-10075.LEE, I.-S., BARDWELL, W. A., ANCOLI-ISRAEL, S. & DIMSDALE, J. E. 2010. Number of lapses during the psychomotor vigilance task as an objective measure of fatigue. Journal of clinical sleep medicine, 6, 163-168.LEEMBURG, S., VYAZOVSKIY, V. V., OLCESE, U., BASSETTI, C. L., TONONI, G. & CIRELLI, C. 2010. Sleep homeostasis in the rat is preserved during chronic sleep restriction. Proceedings of the National Academy of Sciences of the United States of America, 107, 15939-15944.LEENAARS, C. H., DEMATTEIS, M., JOOSTEN, R. N., EGGELS, L., SANDBERG, H., SCHIRRIS, M., FEENSTRA, M. G. & VAN SOMEREN, E. J. 2011. A new automated method for rat sleep deprivation with minimal confounding effects on corticosterone and locomotor activity. Journal of neuroscience methods, 196, 107-117.LEGER, D. 1994. The cost of sleep-related accidents: a report for the National Commission on Sleep Disorders Research. Sleep, 17, 84-93.LI, J., MARTIN, S., TRICKLEBANK, M. D., SCHWARZ, A. J. & GILMOUR, G. 2015a. Task-induced modulation of intrinsic functional connectivity networks in the behaving rat. Journal of Neuroscience, 35, 658-665.LI, J., SCHWARZ, A. J. & GILMOUR, G. 2015b. Relating translational neuroimaging and amperometric endpoints: Utility for neuropsychiatric drug discovery. Translational Neuropsychopharmacology. Springer.LIM, J. & DINGES, D. F. 2008. Sleep deprivation and vigilant attention. Annals of the New York Academy of Sciences, 1129, 305-322.

190

LIM, J. & DINGES, D. F. 2010a. A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychological bulletin, 136, 375-389.LIM, J. & DINGES, D. F. 2010b. A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychological bulletin, 136, 375.LIM, J., WU, W. C., WANG, J., DETRE, J. A., DINGES, D. F. & RAO, H. 2010. Imaging brain fatigue from sustained mental workload: an ASL perfusion study of the time-on-task effect. NeuroImage, 49, 3426-3435.LIU, Y.-P., TUNG, C.-S., LIN, Y.-L. & CHUANG, C.-H. 2011. Wake-promoting agent modafinil worsened attentional performance following REM sleep deprivation in a young-adult rat model of 5-choice serial reaction time task. Psychopharmacology, 213, 155-166.LIU, Y. 2016. Prevalence of healthy sleep duration among adults—United States, 2014. MMWR. Morbidity and mortality weekly report, 65.LO, J. C., DIJK, D. J. & GROEGER, J. A. 2014. Comparing the effects of nocturnal sleep and daytime napping on declarative memory consolidation. PloS one, 9, e108100.LO, J. C., GROEGER, J. A., CHENG, G. H., DIJK, D.-J. & CHEE, M. W. 2016. Self-reported sleep duration and cognitive performance in older adults: a systematic review and meta-analysis. Sleep medicine, 17, 87-98.LO, J. C., GROEGER, J. A., SANTHI, N., ARBON, E. L., LAZAR, A. S., HASAN, S., VON SCHANTZ, M., ARCHER, S. N. & DIJK, D.-J. 2012. Effects of partial and acute total sleep deprivation on performance across cognitive domains, individuals and circadian phase. PloS one, 7, e45987.LOOMIS, A. L., HARVEY, E. N. & HOBART, G. A. 1937. Cerebral states during sleep, as studied by human brain potentials. Journal of experimental psychology, 21, 127.LOOMIS, S. & GILMOUR, G. 2010. Corticosterone urinalysis and nicotinic receptor modulation in rats. Journal of neuroscience methods, 188, 243-249.LOOMIS, S., MCCARTHY, A., BAXTER, C., KELLETT, D. O., EDGAR, D. M., TRICKLEBANK, M. & GILMOUR, G. 2015. Distinct pro-vigilant profile induced in rats by the mGluR5 potentiator LSN2814617. Psychopharmacology, 232, 3977-3989.LOUCA, M. & SHORT, M. A. 2014. The effect of one night's sleep deprivation on adolescent neurobehavioral performance. Sleep, 37, 1799-1807.LOVATO, N. & LACK, L. 2010. The effects of napping on cognitive functioning. Progress in brain research. Elsevier.

191

LOWE, C. J., SAFATI, A. & HALL, P. A. 2017. The neurocognitive consequences of sleep restriction: a meta-analytic review. Neuroscience & Biobehavioral Reviews, 80, 586-604.LOWRY, J. P., BOUTELLE, M. G. & FILLENZ, M. 1997. Measurement of brain tissue oxygen at a carbon paste electrode can serve as an index of increases in regional cerebral blood flow. Journal of neuroscience methods, 71, 177-182.LUCK, S. J., WOODMAN, G. F. & VOGEL, E. K. 2000. Event-related potential studies of attention. Trends in cognitive sciences, 4, 432-440.LUPPI, P.-H. & FORT, P. 2011. Neurochemistry of sleep: an overview of animal experimental work. Handbook of clinical neurology. Elsevier.LUPPI, P.-H., PEYRON, C. & FORT, P. 2017. Not a single but multiple populations of GABAergic neurons control sleep. Sleep medicine reviews, 32, 85-94.LUYSTER, F. S., STROLLO, P. J., ZEE, P. C. & WALSH, J. K. 2012. Sleep: a health imperative. Sleep, 35, 727-734.M KRUEGER, J. & TONONI, G. 2011. Local use-dependent sleep; synthesis of the new paradigm. Current topics in medicinal chemistry, 11, 2490-2492.MA, N., DINGES, D. F., BASNER, M. & RAO, H. 2015. How acute total sleep loss affects the attending brain: a meta-analysis of neuroimaging studies. Sleep, 38, 233-240.MACHADO, R. B., HIPÓLIDE, D. C., BENEDITO-SILVA, A. A. & TUFIK, S. 2004. Sleep deprivation induced by the modified multiple platform technique: quantification of sleep loss and recovery. Brain research, 1004, 45-51.MACKWORTH, N. H. 1948. The breakdown of vigilance during prolonged visual search. Quarterly Journal of Experimental Psychology, 1, 6-21.MAHER, J. M., MARKEY, J. C. & EBERT-MAY, D. 2013. The other half of the story: effect size analysis in quantitative research. CBE—Life Sciences Education, 12, 345-351.MAIRE, M., REICHERT, C. F., GABEL, V., VIOLA, A., PHILLIPS, C., BERTHOMIER, C., BORGWARDT, S., CAJOCHEN, C. & SCHMIDT, C. 2018. Human brain patterns underlying vigilant attention: impact of sleep debt, circadian phase and attentional engagement. Scientific reports, 8, 970.MAKRIS, N., OSCAR-BERMAN, M., JAFFIN, S. K., HODGE, S. M., KENNEDY, D. N., CAVINESS, V. S., MARINKOVIC, K., BREITER, H. C., GASIC, G. P. & HARRIS, G. J. 2008. Decreased volume of the brain reward system in alcoholism. Biological psychiatry, 64, 192-202.

192

MARIC, A., LUSTENBERGER, C., WERTH, E., BAUMANN, C. R., PORYAZOVA, R. & HUBER, R. 2017. Intraindividual Increase of Homeostatic Sleep Pressure Across Acute and Chronic Sleep Loss: A High-Density EEG Study. Sleep, 40.MARINO, M., LIU, Q., SAMOGIN, J., TECCHIO, F., COTTONE, C., MANTINI, D. & PORCARO, C. 2019. Neuronal dynamics enable the functional differentiation of resting state networks in the human brain. Human brain mapping, 40, 1445-1457.MARTIN, S. E., BRANDER, P. E., DEARY, I. J. & DOUGLAS, N. J. 1999. The effect of clustered versus regular sleep fragmentation on daytime function. Journal of sleep research, 8, 305-311.MARZANO, C., FERRARA, M., MORONI, F. & DE GENNARO, L. 2011. Electroencephalographic sleep inertia of the awakening brain. Neuroscience, 176, 308-317.MASSAR, S. A., LIM, J., SASMITA, K. & CHEE, M. W. 2016. Rewards boost sustained attention through higher effort: A value-based decision making approach. Biological psychology, 120, 21-27.MCCARTHY, A., LOOMIS, S., EASTWOOD, B., WAFFORD, K. A., WINSKY‐SOMMERER, R. & GILMOUR, G. 2017. Modelling maintenance of wakefulness in rats: comparing potential non‐invasive sleep‐restriction methods and their effects on sleep and attentional performance. Journal of sleep research, 26, 179-187.MCCAULEY, P., KALACHEV, L. V., SMITH, A. D., BELENKY, G., DINGES, D. F. & VAN DONGEN, H. P. 2009. A new mathematical model for the homeostatic effects of sleep loss on neurobehavioral performance. Journal of theoretical biology, 256, 227-239.MCCOY, J. G., CHRISTIE, M. A., KIM, Y., BRENNAN, R., POETA, D. L., MCCARLEY, R. W. & STRECKER, R. E. 2013. Chronic sleep restriction impairs spatial memory in rats. Neuroreport, 24, 91.MCCOY, J. G. & STRECKER, R. E. 2011. The cognitive cost of sleep lost. Neurobiology of learning and memory, 96, 564-582.MCDEVITT, E. A., SATTARI, N., DUGGAN, K. A., CELLINI, N., WHITEHURST, L. N., PERERA, C., REIHANABAD, N., GRANADOS, S., HERNANDEZ, L. & MEDNICK, S. C. 2018. The impact of frequent napping and nap practice on sleep-dependent memory in humans. Scientific reports, 8, 15053.MCHILL, A. W., HULL, J. T., WANG, W., CZEISLER, C. A. & KLERMAN, E. B. 2018. Chronic sleep curtailment, even without extended (> 16-h) wakefulness,

193

degrades human vigilance performance. Proceedings of the National Academy of Sciences, 115, 6070-6075.MCHUGH, S. B., FILLENZ, M., LOWRY, J. P., RAWLINS, J. N. P. & BANNERMAN, D. M. 2011. Brain tissue oxygen amperometry in behaving rats demonstrates functional dissociation of dorsal and ventral hippocampus during spatial processing and anxiety. European Journal of Neuroscience, 33, 322-337.MCKENNA, B. S., DICKINSON, D. L., ORFF, H. J. & DRUMMOND, S. P. 2007. The effects of one night of sleep deprivation on known‐risk and ambiguous‐risk decisions. Journal of sleep research, 16, 245-252.MCMAHON, W. R., FTOUNI, S., DRUMMOND, S. P., MARUFF, P., LOCKLEY, S. W., RAJARATNAM, S. M. & ANDERSON, C. 2018. The wake maintenance zone shows task dependent changes in cognitive function following one night without sleep. Sleep, 41, zsy148.MCTEAGUE, L. M., HUEMER, J., CARREON, D. M., JIANG, Y., EICKHOFF, S. B. & ETKIN, A. 2017. Identification of common neural circuit disruptions in cognitive control across psychiatric disorders. American Journal of Psychiatry, 174, 676-685.MEERLO, P., MISTLBERGER, R. E., JACOBS, B. L., HELLER, H. C. & MCGINTY, D. 2009. New neurons in the adult brain: the role of sleep and consequences of sleep loss. Sleep medicine reviews, 13, 187-194.MENDELSON, W. B., GUTHRIE, R. D., FREDERICK, G. & WYATT, R. J. 1974. The flower pot technique of rapid eye movement (REM) sleep deprivation. Pharmacology Biochemistry and Behavior, 2, 553-556.MENDOZA, J., ANGELES-CASTELLANOS, M. & ESCOBAR, C. 2005. Entrainment by a palatable meal induces food-anticipatory activity and c-Fos expression in reward-related areas of the brain. Neuroscience, 133, 293-303.MINZENBERG, M. J. & CARTER, C. S. 2008. Modafinil: a review of neurochemical actions and effects on cognition. Neuropsychopharmacology, 33, 1477.MISTLBERGER, R. E. 1992. Anticipatory activity rhythms under daily schedules of water access in the rat. Journal of biological rhythms, 7, 149-160.MIYAPURAM, K. P., TOBLER, P. N., GREGORIOS-PIPPAS, L. & SCHULTZ, W. 2012. BOLD responses in reward regions to hypothetical and imaginary monetary rewards. Neuroimage, 59, 1692-1699.MOHAN, A., ROBERTO, A. J., MOHAN, A., LORENZO, A., JONES, K., CARNEY, M. J., LIOGIER-WEYBACK, L., HWANG, S. & LAPIDUS, K. A. 2016. Focus: the aging brain: the significance of the default mode network (DMN) in neurological and

194

neuropsychiatric disorders: a review. The Yale journal of biology and medicine, 89, 49.MOKHLESI, B. & PUNJABI, N. M. 2012. “REM-related” obstructive sleep apnea: an epiphenomenon or a clinically important entity? Sleep, 35, 5-7.MOLINA, E., SANABRIA, D., JUNG, T.-P. & CORREA, A. 2017. Electroencephalographic and peripheral temperature dynamics during a prolonged psychomotor vigilance task. Accident Analysis & Prevention.MONTI, J. M. 2011. Serotonin control of sleep-wake behavior. Sleep medicine reviews, 15, 269-281.MORGAN, R. E., CROWLEY, J. M., SMITH, R. H., LAROCHE, R. B. & DOPHEIDE, M. M. 2007. Modafinil improves attention, inhibitory control, and reaction time in healthy, middle-aged rats. Pharmacology Biochemistry and Behavior, 86, 531-541.MULLIN, B. C., PHILLIPS, M. L., SIEGLE, G. J., BUYSSE, D. J., FORBES, E. E. & FRANZEN, P. L. 2013. Sleep deprivation amplifies striatal activation to monetary reward. Psychological medicine, 43, 2215-2225.MULLINGTON, J. M., HAACK, M., TOTH, M., SERRADOR, J. M. & MEIER-EWERT, H. K. 2009. Cardiovascular, inflammatory, and metabolic consequences of sleep deprivation. Progress in cardiovascular diseases, 51, 294-302.MUTO, V., JASPAR, M., MEYER, C., KUSSÉ, C., CHELLAPPA, S. L., DEGUELDRE, C., BALTEAU, E., SHAFFII-LE BOURDIEC, A., LUXEN, A. & MIDDLETON, B. 2016. Local modulation of human brain responses by circadian rhythmicity and sleep debt. Science, 353, 687-690.MUZET, A., NICOLAS, A., TASSI, P., DEWASMES, G. & BONNEAU, A. 1995. Implementation of napping in industry and the problem of sleep inertia. Journal of sleep research, 4, 67-69.NAIR, J., KLAASSEN, A.-L., ARATO, J., VYSSOTSKI, A. L., HARVEY, M. & RAINER, G. 2018. Basal forebrain contributes to default mode network regulation. Proceedings of the National Academy of Sciences, 115, 1352-1357.NALL, A. H., SHAKHMANTSIR, I., CICHEWICZ, K., BIRMAN, S., HIRSH, J. & SEHGAL, A. 2016. Caffeine promotes wakefulness via dopamine signaling in Drosophila. Scientific reports, 6, 20938.NETO, J. C. R., LIRA, F. S., VENANCIO, D. P., CUNHA, C. A., OYAMA, L. M., PIMENTEL, G. D., TUFIK, S., DO NASCIMENTO, C. M. O., SANTOS, R. V. & DE MELLO, M. T. 2010. Sleep deprivation affects inflammatory marker expression in adipose tissue. Lipids in health and disease, 9, 125.

195

NEVÁREZ, N. & DE LECEA, L. 2018. Recent advances in understanding the roles of hypocretin/orexin in arousal, affect, and motivation. F1000Research, 7.NIR, Y., ANDRILLON, T., MARMELSHTEIN, A., SUTHANA, N., CIRELLI, C., TONONI, G. & FRIED, I. 2017. Selective neuronal lapses precede human cognitive lapses following sleep deprivation. Nature medicine, 23, 1474.NISHINO, S., MAO, J., SAMPATHKUMARAN, R., SHELTON, J. & MIGNOT, E. 1998. Increased dopaminergic transmission mediates the wake-promoting effects of CNS stimulants. Sleep Res Online, 1, 49-61.NORMAN, R. G., SCOTT, M. A., AYAPPA, I., WALSLEBEN, J. A. & RAPOPORT, D. M. 2006. Sleep continuity measured by survival curve analysis. Sleep, 29, 1625-1631.NUNES, E. J., RANDALL, P. A., PODURGIEL, S., CORREA, M. & SALAMONE, J. D. 2013. Nucleus accumbens neurotransmission and effort-related choice behavior in food motivation: effects of drugs acting on dopamine, adenosine, and muscarinic acetylcholine receptors. Neuroscience & Biobehavioral Reviews, 37, 2015-2025.O'NEILL, R. D., LOWRY, J. P. & MAS, M. 1998. Monitoring brain chemistry in vivo: voltammetric techniques, sensors, and behavioral applications. Critical Reviews™ in Neurobiology, 12.O’DOHERTY, J. P. 2004. Reward representations and reward-related learning in the human brain: insights from neuroimaging. Current opinion in neurobiology, 14, 769-776.OCAMPO‐GARCÉS, A. & VIVALDI, E. A. 2002. Short‐term homeostasis of REM sleep assessed in an intermittent REM sleep deprivation protocol in the rat. Journal of sleep research, 11, 81-89.OKEN, B. S., SALINSKY, M. C. & ELSAS, S. 2006. Vigilance, alertness, or sustained attention: physiological basis and measurement. Clinical neurophysiology, 117, 1885-1901.ÖNGÜR, D. & PRICE, J. 2000. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral cortex, 10, 206-219.OONK, M., DAVIS, C. J., KRUEGER, J. M., WISOR, J. P. & VAN DONGEN, H. P. 2015. Sleep deprivation and time-on-task performance decrement in the rat psychomotor vigilance task. Sleep, 38, 445-451.

196

ORIYAMA, S. & MIYAKOSHI, Y. 2017. The effects of nighttime napping on sleep, sleep inertia, and performance during simulated 16 h night work: a pilot study. Journal of occupational health, 17-0070-OA.PAN, W.-J., BILLINGS, J. C., GROOMS, J. K., SHAKIL, S. & KEILHOLZ, S. D. 2015. Considerations for resting state functional MRI and functional connectivity studies in rodents. Frontiers in neuroscience, 9, 269.PAN, W. J., BILLINGS, J., NEZAFATI, M., ABBAS, A. & KEILHOLZ, S. 2018. Resting State fMRI in Rodents. Current protocols in neuroscience, 83, e45.PANEL, C. C., WATSON, N. F., BADR, M. S., BELENKY, G., BLIWISE, D. L., BUXTON, O. M., BUYSSE, D., DINGES, D. F., GANGWISCH, J. & GRANDNER, M. A. 2015. Joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society on the recommended amount of sleep for a healthy adult: methodology and discussion. Sleep, 38, 1161-1183.PATANAIK, A., TANDI, J., ONG, J. L., WANG, C., ZHOU, J. & CHEE, M. W. 2018. Dynamic functional connectivity and its behavioral correlates beyond vigilance. NeuroImage, 177, 1-10.PECORARO, N., GOMEZ, F., LAUGERO, K. & DALLMAN, M. F. 2002. Brief access to sucrose engages food-entrainable rhythms in food-deprived rats. Behavioral neuroscience, 116, 757.PETER-DEREX, L., YAMMINE, P., BASTUJI, H. & CROISILE, B. 2015. Sleep and Alzheimer's disease. Sleep medicine reviews, 19, 29-38.PETERSEN, S. E. & POSNER, M. I. 2012. The attention system of the human brain: 20 years after. Annual review of neuroscience, 35, 73-89.PETIT, J.-M., BURLET-GODINOT, S., MAGISTRETTI, P. J. & ALLAMAN, I. 2015. Glycogen metabolism and the homeostatic regulation of sleep. Metabolic brain disease, 30, 263-279.PHILIP, P., SAGASPE, P., PRAGUE, M., TASSI, P., CAPELLI, A., BIOULAC, B., COMMENGES, D. & TAILLARD, J. 2012. Acute versus chronic partial sleep deprivation in middle-aged people: differential effect on performance and sleepiness. Sleep, 35, 997-1002.PHILLIPS, A. J. K., ROBINSON, P. A., KEDZIORA, D. J. & ABEYSURIYA, R. G. 2010. Mammalian sleep dynamics: how diverse features arise from a common physiological framework. PLoS computational biology, 6, e1000826.PILCHER, J. J., BAND, D., ODLE-DUSSEAU, H. N. & MUTH, E. R. 2007. Human performance under sustained operations and acute sleep deprivation conditions:

197

toward a model of controlled attention. Aviation, Space, and Environmental Medicine, 78, B15-24.PILCHER, J. J. & HUFFCUTT, A. I. 1996. Effects of sleep deprivation on performance: a meta-analysis. Sleep, 19, 318-326.PLANTE, D. T., GOLDSTEIN, M. R., COOK, J. D., SMITH, R., RIEDNER, B. A., RUMBLE, M. E., JELENCHICK, L., ROTH, A., TONONI, G. & BENCA, R. M. 2016. Effects of partial sleep deprivation on slow waves during non-rapid eye movement sleep: a high density EEG investigation. Clinical Neurophysiology, 127, 1436-1444.PORTAS, C. M., REES, G., HOWSEMAN, A., JOSEPHS, O., TURNER, R. & FRITH, C. D. 1998. A specific role for the thalamus in mediating the interaction of attention and arousal in humans. Journal of Neuroscience, 18, 8979-8989.POSNER, M. I. & DEHAENE, S. 1994. Attentional networks. Trends in neurosciences, 17, 75-79.POSNER, M. I. & PETERSEN, S. E. 1990. The attention system of the human brain. Annual Review of Neuroscience, 13, 25-42.POUDEL, G. R., INNES, C. R. & JONES, R. D. 2012. Cerebral perfusion differences between drowsy and nondrowsy individuals after acute sleep restriction. Sleep, 35, 1085-1096.RADEMACHER, L., SALAMA, A., GRÜNDER, G. & SPRECKELMEYER, K. N. 2013. Differential patterns of nucleus accumbens activation during anticipation of monetary and social reward in young and older adults. Social cognitive and affective neuroscience, 9, 825-831.RAICHLE, M. E. 2015. The brain's default mode network. Annual review of neuroscience, 38, 433-447.RAICHLE, M. E., MACLEOD, A. M., SNYDER, A. Z., POWERS, W. J., GUSNARD, D. A. & SHULMAN, G. L. 2001. A default mode of brain function. Proceedings of the National Academy of Sciences, 98, 676-682.RAKITIN, B. C., TUCKER, A. M., BASNER, R. C. & STERN, Y. 2012. The effects of stimulus degradation after 48 hours of total sleep deprivation. Sleep, 35, 113-121.RAO, H., LIM, J., ZHU, S., WU, W., HU, S., DETRE, J. A. & DINGES, D. F. Differential effects of total sleep deprivation on regional cortical activity at rest and during Psychomotor Vigilance Test performance. Sleep, 2011. AMER ACAD SLEEP MEDICINE ONE WESTBROOK CORPORATE CTR, STE 920, WESTCHESTER, IL 60154 USA, A97-A97.

198

RAVEN, F., VAN DER ZEE, E. A., MEERLO, P. & HAVEKES, R. 2018. The role of sleep in regulating structural plasticity and synaptic strength: Implications for memory and cognitive function. Sleep Medicine Reviews, 39, 3-11.RECHTSCHAFFEN, A. & BERGMANN, B. M. 1995. Sleep deprivation in the rat by the disk-over-water method. Behavioural brain research, 69, 55-63.RECHTSCHAFFEN, A., BERGMANN, B. M., EVERSON, C. A., KUSHIDA, C. A. & GILLILAND, M. A. 1989. Sleep deprivation in the rat: X. Integration and discussion of the findings. Sleep, 12, 68-87.RECHTSCHAFFEN, A. & KALES, A. 1968. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects.REID, C. & FINNERTY, N. 2017. Long term amperometric recordings in the brain extracellular fluid of freely moving immunocompromised NOD SCID mice. Sensors, 17, 419.REINER, P. & KAMONDI, A. 1994. Mechanisms of antihistamine-induced sedation in the human brain: H1 receptor activation reduces a background leakage potassium current. Neuroscience, 59, 579-588.REVEL, F. G., GOTTOWIK, J., GATTI, S., WETTSTEIN, J. G. & MOREAU, J.-L. 2009. Rodent models of insomnia: a review of experimental procedures that induce sleep disturbances. Neuroscience & Biobehavioral Reviews, 33, 874-899.RICHARDSON, N. R. & ROBERTS, D. C. 1996. Progressive ratio schedules in drug self-administration studies in rats: a method to evaluate reinforcing efficacy. Journal of neuroscience methods, 66, 1-11.ROBERTSON, A. L., BALACHANDRAN, R. C., MAHONEY, M. M. & EUBIG, P. A. 2017. Circadian disruption affects initial learning but not cognitive flexibility in an automated set-shifting task in adult Long-Evans rats. Physiology & behavior, 179, 226-234.ROBINSON, M., FISCHER, A., AHUJA, A., LESSER, E. & MANIATES, H. 2015. Roles of “wanting” and “liking” in motivating behavior: gambling, food, and drug addictions. Behavioral neuroscience of motivation. Springer.ROTH, T. & ROEHRS, T. A. 1996. Etiologies and sequelae of excessive daytime sleepiness. Clinical therapeutics, 18, 562-576.RUBY, N. F., FERNANDEZ, F., GARRETT, A., KLIMA, J., ZHANG, P., SAPOLSKY, R. & HELLER, H. C. 2013. Spatial memory and long-term object recognition are impaired by circadian arrhythmia and restored by the GABAAAntagonist pentylenetetrazole. PloS one, 8, e72433.

199

RUPP, T. L., WESENSTEN, N. J. & BALKIN, T. J. 2012. Trait-like vulnerability to total and partial sleep loss. Sleep, 35, 1163-1172.SALAMONE, J. D. 1991. Behavioral pharmacology of dopamine systems: a new synthesis. The mesolimbic dopamine system: From motivation to action, 599-613.SALAMONE, J. D., CORREA, M., YANG, J.-H., ROTOLO, R. & PRESBY, R. 2018. Dopamine, Effort-Based Choice, and Behavioral Economics: Basic and Translational Research. Frontiers in behavioral neuroscience, 12, 52.SALAMONE, J. D., CORREA, M., YOHN, S., CRUZ, L. L., SAN MIGUEL, N. & ALATORRE, L. 2016. The pharmacology of effort-related choice behavior: dopamine, depression, and individual differences. Behavioural processes, 127, 3-17.SALIASI, E., GEERLIGS, L., LORIST, M. M. & MAURITS, N. M. 2013. The relationship between P3 amplitude and working memory performance differs in young and older adults. PLoS One, 8, e63701.SALLINEN, M., HÄRMÄ, M., ÅKERSTEDT, T., ROSA, R. & LILLQVIST, O. 1998. Promoting alertness with a short nap during a night shift. Journal of Sleep Research, 7, 240-247.SANTHI, N., LAZAR, A. S., MCCABE, P. J., LO, J. C., GROEGER, J. A. & DIJK, D.-J. 2016. Sex differences in the circadian regulation of sleep and waking cognition in humans. Proceedings of the National Academy of Sciences, 113, E2730-E2739.SARTER, M., GIVENS, B. & BRUNO, J. P. 2001. The cognitive neuroscience of sustained attention: where top-down meets bottom-up. Brain Research Reviews, 35, 146-160.SARTER, M., LUSTIG, C., BLAKELY, R. D. & CHERIAN, A. K. 2016. Cholinergic genetics of visual attention: Human and mouse choline transporter capacity variants influence distractibility. Journal of Physiology-Paris, 110, 10-18.SATTERFIELD, B. C., WISOR, J. P., SCHMIDT, M. A. & VAN DONGEN, H. P. 2017. Time-on-task effect during sleep deprivation in healthy young adults is modulated by dopamine transporter genotype. Sleep, 40, zsx167.SCAMMELL, T. E., ARRIGONI, E. & LIPTON, J. O. 2017. Neural circuitry of wakefulness and sleep. Neuron, 93, 747-765.SCAMMELL, T. E. & SAPER, C. B. 2007. Orexins: looking forward to sleep, back at addiction. Nature medicine, 13, 126.

200

SCHMIDT, M. H. 2014. The energy allocation function of sleep: a unifying theory of sleep, torpor, and continuous wakefulness. Neuroscience & Biobehavioral Reviews, 47, 122-153.SCHOTT, B. H., MINUZZI, L., KREBS, R. M., ELMENHORST, D., LANG, M., WINZ, O. H., SEIDENBECHER, C. I., COENEN, H. H., HEINZE, H.-J. & ZILLES, K. 2008. Mesolimbic functional magnetic resonance imaging activations during reward anticipation correlate with reward-related ventral striatal dopamine release. Journal of Neuroscience, 28, 14311-14319.SCHREINER, S. J., IMBACH, L. L., WERTH, E., PORYAZOVA, R., BAUMANN‐VOGEL, H., VALKO, P. O., MURER, T., NOAIN, D. & BAUMANN, C. R. 2019. Slow‐wave sleep and motor progression in Parkinson disease. Annals of neurology.SCHWARTZ, J. R. & ROTH, T. 2006. Shift work sleep disorder: burden of illness and approaches to management. Drugs, 66, 2357-2370.SCHWARTZ, M. D., WOTUS, C., LIU, T., FRIESEN, W. O., BORJIGIN, J., ODA, G. A. & HORACIO, O. 2009. Dissociation of circadian and light inhibition of melatonin release through forced desynchronization in the rat. Proceedings of the National Academy of Sciences, 106, 17540-17545.SCHWARZ, A. J., GASS, N., SARTORIUS, A., RISTERUCCI, C., SPEDDING, M., SCHENKER, E., MEYER-LINDENBERG, A. & WEBER-FAHR, W. 2013. Anti-correlated cortical networks of intrinsic connectivity in the rat brain. Brain connectivity, 3, 503-511.SCHWEITZER, P. K., RANDAZZO, A. C., STONE, K., ERMAN, M. & WALSH, J. K. 2006. Laboratory and field studies of naps and caffeine as practical countermeasures for sleep-wake problems associated with night work. Sleep, 29, 39-50.SEIRIKI, K., KASAI, A., HASHIMOTO, T., SCHULZE, W., NIU, M., YAMAGUCHI, S., NAKAZAWA, T., INOUE, K.-I., UEZONO, S. & TAKADA, M. 2017. High-speed and scalable whole-brain imaging in rodents and primates. Neuron, 94, 1085-1100. e6.SFORAZZINI, F., SCHWARZ, A. J., GALBUSERA, A., BIFONE, A. & GOZZI, A. 2014. Distributed BOLD and CBV-weighted resting-state networks in the mouse brain. Neuroimage, 87, 403-415.SHAH, D., BLOCKX, I., KELIRIS, G. A., KARA, F., JONCKERS, E., VERHOYE, M. & VAN DER LINDEN, A. 2016. Cholinergic and serotonergic modulations differentially affect large-scale functional networks in the mouse brain. Brain Structure and Function, 221, 3067-3079.

201

SHEA, J., MOCHIZUKI, T., SAGVAAG, V., ASPEVIK, T., BJORKUM, A. & DATTA, S. 2008. Rapid eye movement (REM) sleep homeostatic regulatory processes in the rat: Changes in the sleep–wake stages and electroencephalographic power spectra. Brain research, 1213, 48-56.SHORT, M. A. & BANKS, S. 2014. The functional impact of sleep deprivation, sleep restriction, and sleep fragmentation. Sleep deprivation and disease. Springer.ŞıK, A., VAN NIEUWEHUYZEN, P., PRICKAERTS, J. & BLOKLAND, A. 2003. Performance of different mouse strains in an object recognition task. Behavioural brain research, 147, 49-54.SILVA, E. J., WANG, W., RONDA, J. M., WYATT, J. K. & DUFFY, J. F. 2010. Circadian and wake-dependent influences on subjective sleepiness, cognitive throughput, and reaction time performance in older and young adults. Sleep, 33, 481-490.SKORUCAK, J., ARBON, E., DIJK, D.-J. & ACHERMANN, P. 2018. Response to chronic sleep restriction, extension, and total sleep deprivation in humans: adaptation or preserved sleep homeostasis? Sleep.SLAMA, H., CHYLINSKI, D. O., DELIENS, G., LEPROULT, R., SCHMITZ, R. & PEIGNEUX, P. 2017. Sleep deprivation triggers cognitive control impairments in task-goal switching. Sleep, 41, zsx200.SLATER, G. & STEIER, J. 2012. Excessive daytime sleepiness in sleep disorders. Journal of thoracic disease, 4, 608.SOLIS JR, E., CAMERON-BURR, K. T. & KIYATKIN, E. A. 2017. Rapid physiological fluctuations in nucleus accumbens oxygen levels induced by arousing stimuli: relationships with changes in brain glucose and metabolic neural activation. Frontiers in integrative neuroscience, 11, 9.SPAETH, A. M., GOEL, N. & DINGES, D. F. 2014. Cumulative neurobehavioral and physiological effects of chronic caffeine intake: individual differences and implications for the use of caffeinated energy products. Nutrition reviews, 72, 34-47.SPIEGEL, K., LEPROULT, R. & VAN CAUTER, E. 1999. Impact of sleep debt on metabolic and endocrine function. The lancet, 354, 1435-1439.SPRECKELMEYER, K. N., RADEMACHER, L., PAULUS, F. M. & GRÜNDER, G. 2013. Neural activation during anticipation of opposite-sex and same-sex faces in heterosexual men and women. Neuroimage, 66, 223-231.

202

ST HILAIRE, M. A., RÜGER, M., FRATELLI, F., HULL, J. T., PHILLIPS, A. J. & LOCKLEY, S. W. 2017. Modeling neurocognitive decline and recovery during repeated cycles of extended sleep and chronic sleep deficiency. Sleep, 40.STAFFORD, J. M., JARRETT, B. R., MIRANDA-DOMINGUEZ, O., MILLS, B. D., CAIN, N., MIHALAS, S., LAHVIS, G. P., LATTAL, K. M., MITCHELL, S. H. & DAVID, S. V. 2014. Large-scale topology and the default mode network in the mouse connectome. Proceedings of the National Academy of Sciences, 111, 18745-18750.STEPHENSON, R., CARON, A. M. & FAMINA, S. 2015. Behavioral sleep-wake homeostasis and EEG delta power are decoupled by chronic sleep restriction in the rat. Sleep, 38, 685-697.STEYVERS, F. J. & GAILLARD, A. W. 1993. The effects of sleep deprivation and incentives on human performance. Psychological research, 55, 64-70.STOCKBURGER, J., SCHMÄLZLE, R., FLAISCH, T., BUBLATZKY, F. & SCHUPP, H. T. 2009. The impact of hunger on food cue processing: an event-related brain potential study. Neuroimage, 47, 1819-1829.STRIJKSTRA, A. M., MEERLO, P. & BEERSMA, D. G. 1999. Forced Desynchrony of Orcadian Rhythms of Body Temperature and Activity in Rats. Chronobiology international, 16, 431-440.SUCHECKI, D., LOBO, L., HIPÓLIDE, D. & TUFIK, S. 1998. Increased ACTH and corticosterone secretion induced by different methods of paradoxical sleep deprivation. Journal of sleep research, 7, 276-281.SUR, S. & SINHA, V. K. 2009. Event-related potential: An overview. Industrial psychiatry journal, 18, 70-73.TAKAHASHI, K., LIN, J.-S. & SAKAI, K. 2006. Neuronal activity of histaminergic tuberomammillary neurons during wake–sleep states in the mouse. Journal of Neuroscience, 26, 10292-10298.TAKAHASHI, M., FUKUDA, H. & ARITO, H. 1998. Brief naps during post-lunch rest: effects on alertness, performance, and autonomic balance. European journal of applied physiology and occupational physiology, 78, 93-98.TASSI, P. & MUZET, A. 2000. Sleep inertia. Sleep medicine reviews, 4, 341-353.THOMSON, D. R., BESNER, D. & SMILEK, D. 2015. A Resource-Control Account of Sustained Attention:Evidence From Mind-Wandering and Vigilance Paradigms. Perspectives on Psychological Science, 10, 82-96.

203

TIETZEL, A. J. & LACK, L. C. 2002. The recuperative value of brief and ultra‐brief naps on alertness and cognitive performance. Journal of Sleep Research, 11, 213-218.TOBLER, I., DEBOER, T. & FISCHER, M. 1997. Sleep and sleep regulation in normal and prion protein-deficient mice. Journal of Neuroscience, 17, 1869-1879.TOBLER, P. N., FIORILLO, C. D. & SCHULTZ, W. 2005. Adaptive coding of reward value by dopamine neurons. Science, 307, 1642-1645.TOBLER, P. N., FLETCHER, P. C., BULLMORE, E. T. & SCHULTZ, W. 2007. Learning-related human brain activations reflecting individual finances. Neuron, 54, 167-175.TONONI, G. & CIRELLI, C. 2006. Sleep function and synaptic homeostasis. Sleep medicine reviews, 10, 49-62.TREADWAY, M. T. & ZALD, D. H. 2013. Parsing anhedonia: translational models of reward-processing deficits in psychopathology. Current Directions in Psychological Science, 22, 244-249.TREMAINE, R., DORRIAN, J., LACK, L., LOVATO, N., FERGUSON, S., ZHOU, X. & ROACH, G. 2010. The relationship between subjective and objective sleepiness and performance during a simulated night-shift with a nap countermeasure. Applied Ergonomics, 42, 52-61.TSAPANOU, A., GU, Y., O'SHEA, D., EICH, T., TANG, M. X., SCHUPF, N., MANLY, J., ZIMMERMAN, M., SCARMEAS, N. & STERN, Y. 2016. Daytime somnolence as an early sign of cognitive decline in a community‐based study of older people. International journal of geriatric psychiatry, 31, 247-255.TURNER, K. M. & BURNE, T. H. 2016. Improvement of attention with amphetamine in low-and high-performing rats. Psychopharmacology, 233, 3383-3394.UNDERWOOD, E. 2013. Sleep: The Brain's Housekeeper? : American Association for the Advancement of Science.VALLAT, R., MEUNIER, D., NICOLAS, A. & RUBY, P. 2019. Hard to wake up? The cerebral correlates of sleep inertia assessed using combined behavioral, EEG and fMRI measures. NeuroImage, 184, 266-278.VAN DONGEN, H. P. & DINGES, D. F. 2003. Investigating the interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance. Journal of sleep research, 12, 181-187.

204

VAN DONGEN, H. P. & DINGES, D. F. 2005. Sleep, circadian rhythms, and psychomotor vigilance. Clinics in sports medicine, 24, 237-49, vii-viii.VAN DONGEN, H. P., MAISLIN, G., MULLINGTON, J. M. & DINGES, D. F. 2003. The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep, 26, 117-126.VAN DONGEN, H. P. A., BAYNARD, M. D., MAISLIN, G. & DINGES, D. F. 2004. Systematic interindividual differences in neurobehavioral impairment from sleep loss: evidence of trait-like differential vulnerability. SLEEP-NEW YORK THEN WESTCHESTER-, 27, 423-433.VAN DORT, C. J., ZACHS, D. P., KENNY, J. D., ZHENG, S., GOLDBLUM, R. R., GELWAN, N. A., RAMOS, D. M., NOLAN, M. A., WANG, K. & WENG, F.-J. 2015. Optogenetic activation of cholinergic neurons in the PPT or LDT induces REM sleep. Proceedings of the National Academy of Sciences, 112, 584-589.VAN GELDER, R. N., EDGAR, D. M. & DEMENT, W. C. 1991. Real-time automated sleep scoring: validation of a microcomputer-based system for mice. Sleep, 14, 48-55.VENKATESHIAH, S. B. & IOACHIMESCU, O. C. 2015. Restless legs syndrome. Critical care clinics, 31, 459-472.VENKATRAMAN, V., CHUAH, Y. L., HUETTEL, S. A. & CHEE, M. W. 2007. Sleep deprivation elevates expectation of gains and attenuates response to losses following risky decisions. Sleep, 30, 603-609.VGONTZAS, A. N., PEJOVIC, S., ZOUMAKIS, E., LIN, H.-M., BIXLER, E. O., BASTA, M., FANG, J., SARRIGIANNIDIS, A. & CHROUSOS, G. P. 2007. Daytime napping after a night of sleep loss decreases sleepiness, improves performance, and causes beneficial changes in cortisol and interleukin-6 secretion. American Journal of Physiology-Endocrinology and Metabolism, 292, E253-E261.VGONTZAS, A. N., ZOUMAKIS, E., BIXLER, E. O., LIN, H.-M., FOLLETT, H., KALES, A. & CHROUSOS, G. P. 2004. Adverse effects of modest sleep restriction on sleepiness, performance, and inflammatory cytokines. The Journal of Clinical Endocrinology & Metabolism, 89, 2119-2126.VITIELLO, M. V. & BORSON, S. 2001. Sleep disturbances in patients with Alzheimer’s disease. CNS drugs, 15, 777-796.VOLMAN, S. F., LAMMEL, S., MARGOLIS, E. B., KIM, Y., RICHARD, J. M., ROITMAN, M. F. & LOBO, M. K. 2013. New insights into the specificity and plasticity of

205

reward and aversion encoding in the mesolimbic system. Journal of Neuroscience, 33, 17569-17576.VYAZOVSKIY, V., BORBELY, A. A. & TOBLER, I. 2000. Fast track: Unilateral vibrissae stimulation during waking induces interhemispheric EEG asymmetry during subsequent sleep in the rat. Journal of sleep research, 9, 367-371.VYAZOVSKIY, V. V., CUI, N., RODRIGUEZ, A. V., FUNK, C., CIRELLI, C. & TONONI, G. 2014. The dynamics of cortical neuronal activity in the first minutes after spontaneous awakening in rats and mice. Sleep, 37, 1337-1347.WALKER, J. L., WALKER, B. M., FUENTES, F. M. & RECTOR, D. M. 2011. Rat psychomotor vigilance task with fast response times using a conditioned lick behavior. Behavioural brain research, 216, 229-237.WALKER, M. P. 2004. Issues surrounding sleep-dependent memory consolidation and plasticity. Cellular and Molecular Life Sciences CMLS, 61, 3009-3015.WATERS, K. A., BURNHAM, K. E., O’CONNOR, D., DAWSON, G. R. & DIAS, R. 2005. Assessment of modafinil on attentional processes in a five-choice serial reaction time test in the rat. Journal of Psychopharmacology, 19, 149-158.WEBER, F. & DAN, Y. 2016. Circuit-based interrogation of sleep control. Nature, 538, 51.WEISSMAN, D. H., GOPALAKRISHNAN, A., HAZLETT, C. & WOLDORFF, M. 2004. Dorsal anterior cingulate cortex resolves conflict from distracting stimuli by boosting attention toward relevant events. Cerebral cortex, 15, 229-237.WERTZ, A. T., RONDA, J. M., CZEISLER, C. A. & WRIGHT, K. P. 2006. Effects of sleep inertia on cognition. Jama, 295, 159-164.WESENSTEN, N. J., BELENKY, G., THORNE, D. R., KAUTZ, M. A. & BALKIN, T. J. 2004. Modafinil vs. caffeine: effects on fatigue during sleep deprivation. Aviation, Space, and Environmental Medicine, 75, 520-525.WESENSTEN, N. J., KILLGORE, W. D. & BALKIN, T. J. 2005. Performance and alertness effects of caffeine, dextroamphetamine, and modafinil during sleep deprivation. Journal of sleep research, 14, 255-266.WILCKENS, K. A., FERRARELLI, F., WALKER, M. P. & BUYSSE, D. J. 2018. Slow-wave activity enhancement to improve cognition. Trends in neurosciences.WINSKY-SOMMERER, R., DE OLIVEIRA, P., LOOMIS, S., WAFFORD, K., DIJK, D.-J. & GILMOUR, G. 2018. Disturbances of sleep quality, timing and structure and their relationship with other neuropsychiatric symptoms in Alzheimer’s disease and schizophrenia: insights from studies in patient populations and animal models. Neuroscience & Biobehavioral Reviews.

206

WISOR, J. P., NISHINO, S., SORA, I., UHL, G. H., MIGNOT, E. & EDGAR, D. M. 2001. Dopaminergic role in stimulant-induced wakefulness. Journal of Neuroscience, 21, 1787-1794.WOMACK, S. D., HOOK, J. N., REYNA, S. H. & RAMOS, M. 2013. Sleep loss and risk-taking behavior: a review of the literature. Behavioral sleep medicine, 11, 343-359.WOODMAN, G. F. 2010. A brief introduction to the use of event-related potentials in studies of perception and attention. Attention, Perception, & Psychophysics, 72, 2031-2046.WREE, A. 1990. Principles of the 2-deoxyglucose method for the determination of the local cerebral glucose utilization. European journal of morphology, 28, 132-138.WRIGHT JR, K. P., LOWRY, C. A. & LEBOURGEOIS, M. K. 2012. Circadian and wakefulness-sleep modulation of cognition in humans. Frontiers in molecular neuroscience, 5, 50.WU, J. C., GILLIN, J. C., BUCHSBAUM, M. S., CHEN, P., KEATOR, D. B., WU, N. K., DARNALL, L. A., FALLON, J. H. & BUNNEY, W. E. 2006. Frontal lobe metabolic decreases with sleep deprivation not totally reversed by recovery sleep. Neuropsychopharmacology, 31, 2783.WU, L. J., ACEBO, C., SEIFER, R. & CARSKADON, M. A. 2015. Sleepiness and cognitive performance among younger and older adolescents across a 28-hour forced desynchrony protocol. Sleep, 38, 1965-1972.WURTS, S. W. & EDGAR, D. M. 2000. Circadian and homeostatic control of rapid eye movement (REM) sleep: promotion of REM tendency by the suprachiasmatic nucleus. The Journal of neuroscience : the official journal of the Society for Neuroscience, 20, 4300-4310.WYATT, J. K., CECCO, A. R.-D., CZEISLER, C. A. & DIJK, D.-J. 1999. Circadian temperature and melatonin rhythms, sleep, and neurobehavioral function in humans living on a 20-h day. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 277, R1152-R1163.XIE, L., KANG, H., XU, Q., CHEN, M. J., LIAO, Y., THIYAGARAJAN, M., O'DONNELL, J., CHRISTENSEN, D. J., NICHOLSON, C., ILIFF, J. J., TAKANO, T., DEANE, R. & NEDERGAARD, M. 2013. Sleep drives metabolite clearance from the adult brain. Science (New York, N.Y.), 342, 373-377.

207

XU, H., ZUO, P., WANG, S., ZHOU, L., SUN, X., HU, M., LIU, B., WU, Q., DOU, H. & LIU, B. 2015. Striatal dopamine release in a schizophrenia mouse model measured by electrochemical amperometry in vivo. Analyst, 140, 3840-3845.YAMADA, T., HARA, K., SHOJIMA, N., YAMAUCHI, T. & KADOWAKI, T. 2015. Daytime napping and the risk of cardiovascular disease and all-cause mortality: a prospective study and dose-response meta-analysis. Sleep, 38, 1945-1953.YASENKOV, R. & DEBOER, T. 2010. Circadian regulation of sleep and the sleep EEG under constant sleep pressure in the rat. Sleep, 33, 631-641.YEHUDA, S., SREDNI, B., CARASSO, R. L. & KENIGSBUCH-SREDNI, D. 2009. REM sleep deprivation in rats results in inflammation and interleukin-17 elevation. Journal of Interferon and Cytokine Research, 29, 393-398.YOUNG, J. W. & GEYER, M. A. 2010. Action of modafinil—increased motivation via the dopamine transporter inhibition and D1 receptors? Biological psychiatry, 67, 784-787.ZAGHLOUL, K. A., BLANCO, J. A., WEIDEMANN, C. T., MCGILL, K., JAGGI, J. L., BALTUCH, G. H. & KAHANA, M. J. 2009. Human substantia nigra neurons encode unexpected financial rewards. Science, 323, 1496-1499.ZANT, J. C., KIM, T., PROKAI, L., SZARKA, S., MCNALLY, J., MCKENNA, J. T., SHUKLA, C., YANG, C., KALINCHUK, A. V. & MCCARLEY, R. W. 2016. Cholinergic neurons in the basal forebrain promote wakefulness by actions on neighboring non-cholinergic neurons: an opto-dialysis study. Journal of Neuroscience, 36, 2057-2067.ZHAO, X.-H., WANG, P.-J., LI, C.-B., HU, Z.-H., XI, Q., WU, W.-Y. & TANG, X.-W. 2007. Altered default mode network activity in patient with anxiety disorders: an fMRI study. European Journal of Radiology, 63, 373-378.ZHU, Y., GAO, B., HUA, J., LIU, W., DENG, Y., ZHANG, L., JIANG, B. & ZANG, Y. 2013. Effects of methylphenidate on resting-state brain activity in normal adults: an fMRI study. Neuroscience bulletin, 29, 16-27.

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Appendix

Information supplied by Dr. Andrew McCarthy

Kaplan-Meier Estimates

The Kaplan-Meier estimate is a non-parametric method of computing the probability of the occurrence of an event at a certain point in time. Equation 1 is used to calculate the survival probability of an event at any particular time:

Equation 1: S_t = (N_wake_bouts - N_sleep_attempts) / N_wake_bouts.

where, S_t = the probability estimate; N_wake_bouts = the starting population of wake bouts; N_sleep_attempts = the number of failed wake bouts

Each successive probability is multiplied by earlier probabilities to give the final estimate, as shown in Appendix Table A1.

Time in wake

bout (min)

Sleep

attempts

(n)

Wake bouts in

remaining

population (n)

Probabilities (p) Kaplan- Meier probability

estimate

Sleep Continuity

0.5 0 25 0 1 1.0

1 1 24 0.04 0.96 1.0 * 0.96 = 0.96

10 5 20 0.17 0.83 0.96 * 0.83 = 0.80

30 15 10 0.5 0.5 0.96 * 0.83 * 0.5 = 0.40

90 25 0 1 0 0.96 * 0.83 * 0.5 * 0 = 0

Appendix Table A1. Kaplan Meier probability estimates of sleep attempts as a function of time spent in a wake bout.

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Weibull Distribution

A Weibull distribution is a fully parametric model of survival analysis, which can be used to describe the underlying hazard of interest in a biological context. In this context the hazard rate represents the probability of a sleep attempt at a specific time within a wake bout. In the present study, the Weibull distribution was used to model changes in wake bout hazards over the time course of experimental sleep restriction. The probability density function of the Weibull distribution has three key parameters, the scale, shape and location (fixed at 10 seconds in this instance), which can be used to derive metrics (e.g. reliability function, failure rate, mean and median bout lengths) to describe biofeedback sleep restriction:

Equation 2: f(T) = β/α *(T - γ/ α)β-1 *e –( T -γ / α)^β

where, t= time in minutes; α = the scale parameter; β= the shape parameter and γ = the location parameter.

When a Weibull distribution model was fitted to a Biofeedback sleep restriction protocol dataset (Appendix Figure A1), the scale parameter (α) decreased and shape parameter (β) increased over time. The decrease in α acts to contract the probability density function and increase its peak, which means that the distribution becomes less disperse as the total probability density (area under the curve) remains equal to 1. The increase in β directly reflects the increasing failure rates, such that maintenance of a wake bout becomes increasingly less likely as time spent in sleep restriction increases.

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Appendix Figure A1. Fitting a Weibull distribution model to Biofeedback sleep restriction wake bout data (n = 42). Weibull distribution modelling showed that as sleep restriction progressed from the first to the eleventh hour, the scale parameter (a) decreased and the shape parameter (b) increased. Individual data points and penalized

b-spline are presented with 95% confidence limits and 95% prediction limits. (C) Probability density function of the distribution of wake bouts during the 1st (red), 3rd (blue), 5th (green) and 11th (grey) hour of sleep restriction. The probability density function shows relative likelihood (f(t)) of a sleep attempt for a given length of wake

bout. As the area under the curve is fixed, shifts to the left represent increased likelihood of a sleep attempt early in a wake bout.

Survival analysis was performed on calculated wake bout lengths, where each bout was assigned to the hour in which it began and pooled within-subjects for each given hour under sleep restriction. No data was censored in the study. Kaplan-Meier curves were generated using SAS 9.2 (SAS Institute Inc., Cary, NC) using the “Lifereg” procedure. The survival function for each hour of sleep restriction was calculated using the Weibull distribution in the “survreg” package in R (https://www.r-project.org). To account for the potential correlation between

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observations within subjects, an additional term in the model, ‘the frailty’, was included (Norman et al., 2006).

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